Climate Change Impact on Watershed Sustainability Index Assessment

Abstract

The Watershed Sustainability Index (WSI) is a widely used parameter that provides an integrated assessment of the baseline state of watershed management, considering hydrology, environment, life, and policy. The impacts of climate change on sustainability are becoming increasingly evident. These impacts are discussed in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This study refines the Watershed Sustainability Index (WSI) by embedding climate discontinuities from the IPCC AR6, applying dual climate scenarios (RCP4.5 and RCP8.5), and incorporating comprehensive sensitivity and uncertainty analyses. The approach provides a transferable basis for basin-scale management tools that integrate climate stressors, explore alternative futures, and support adaptive water governance. The impacts of climate change on watershed sustainability have been developed from hydrological, environmental, life, and policy perspectives with an innovative approach. The new WSI assessment methodology is implemented for the Central North Aegean Basin, Türkiye. The WSI was applied to two periods, including five years of baseline condition (2016–2020) and ten years of projected future condition (2021–2030). The future condition was assessed with climate change impacts. The study shows how WSI assessment under climate change considerations may support coordination among all relevant institutions and stakeholders responsible for natural resource management. This approach can be a valuable resource for decision-makers and provide an effective management tool for the basin, considering future conditions.

1. Introduction

The IPCC 6th Assessment Report, Chapter 4 [1], analyzes the current and future impacts of climate change on sustainable development and water security, as well as the potential societal repercussions. According to this report, climate change could seriously affect the hydrological cycle and deepen problems such as reduced water availability, deterioration in water quality, floods, and droughts. Uncertainties about future water security lead to water management policy and planning challenges, deepening socioeconomic vulnerabilities, and increasing governance gaps. Therefore, adaptation and mitigation efforts to meet sustainable development goals and the Paris Agreement are critical to build a climate-resilient future [1]. Achieving integrated basin management in line with sustainable development goals is challenging as it involves managing the basin’s environmental, social, and economic components and hydrology [2].

A water sustainability assessment has been conducted using various indicators [3,4], including the Falkenmark Indicator [5], which evaluates water sustainability, and the Water Resources Vulnerability Index [6], which refers to the ratio of water withdrawals to basin water resources. The Water Poverty Index relates the distribution of water resources to the socio-economic level of the society. It points out the injustice in distributing water resources for low-income groups [7]. The Watershed Sustainability Index (WSI) provides an integrated assessment of the baseline state of watershed management and is widely used in the literature [2,8,9,10]. The main reason for the widespread use of the WSI in the literature and its preference in this study is that it offers a more comprehensive and multidimensional framework compared to the other indices mentioned above. While other indices generally focus on a single dimension (per capita water availability, water withdrawal rate, access to water, etc.), the WSI allows for the simultaneous assessment of hydrological, ecological, socio-economic, and managerial components. This enables a comprehensive analysis of basin conditions. The WSI considers hydrology (H), environment (E), life (L), and politics (P) issues when calculating the sustainability of the watershed [11]. In addition, life and policy issues include the society’s income, economic, and human development index [12,13]. The WSI assesses cause-and-effect relationships, environmental factors, and policy implications. This approach makes it possible to measure the impact of policy implementation on the environment and natural resources. WSI’s advantages are that it is easy to implement, measurable, revision-friendly, and valuable for basin planning activities [8,11].

An integrated watershed management approach where WSI is a valuable index has been adopted in recent years to manage the complexities of the basins by international funding agencies such as the World Bank (2022) [14], as noted in case studies such as the Integrated Watershed Management of the Putumayo-I river basin, the tenth-longest tributary of the Amazon River [15]. Using the WSI allows decision-makers to understand the state of river basins and the changes that have occurred over the study period [13]. The WSI has the potential to guide decision-makers on critical issues such as developing climate crisis adaptation strategies and preparing development projects.

The impact of climate change on watershed vulnerability is becoming more critical, as noted in policymakers’ work. The U.S. Environmental Protection Agency has conducted watershed assessment studies to evaluate the vulnerability of watershed health to future degradation. A single vulnerability index is developed from sub-indices characterizing potential risks (including future climate, land use, water use change, and wildfire risk). Although there are some studies in the literature that examine the effects of climate change on the environment or watershed hydrology [16,17], the literature focusing on the direct effects of climate change on WSI is limited. However, the increasing number of studies investigating the effects of climate change on basin vulnerability [18,19] using data from the IPCC 6th Assessment Report [1] supports the approach of the present study. The IPCC 6th Assessment Report [1] emphasizes the importance of improving water management practices, promoting efficient and economical water use, and taking urgent measures within the scope of climate change adaptation and mitigation. It also points out the need for an assessment approach that includes economic, social, and management aspects of water-related adaptations.

This study advances the classical WSI by embedding climate discontinuities from the IPCC AR6, extending assessment across dual climate scenarios (RCP4.5 and RCP8.5), and explicitly incorporating sensitivity and uncertainty analyses, thereby providing a transferable foundation for developing basin-scale management tools that can integrate climate stressors, explore alternative futures, and transparently account for uncertainty in support of adaptive water governance. In classical WSI applications, each of the hydrology, environment, life, and policy indicators is typically symbolized by a single parameter and describes the current state or a past period. The methodology presented in this study, unlike the classical WSI approach, integrates the climate change impacts highlighted in IPCC AR6 directly into the WSI assessment, thereby providing a new and comprehensive framework.

The study enhances the contextual depth of the WSI assessment. Hence, the impacts of climate change on watershed sustainability are assessed not only through physical modelling from a hydrological perspective, but also with a new and detailed approach from environmental, life, and policy perspectives. A detailed comparison of the classic WSI characteristics and the proposed new WSI characteristics is presented in

Table 1. Comparison of classic WSI and proposed AR6-associated WSI characteristics.

Feature	Classic WSI Adaptations	WSI Associated with AR6 (This Study)
Focus dimensions	Hydrology-quantity (water availability per capita (Wa), water efficiency), hydrology-quality (water quality), environment (Environmental Pressure Index (EPI), natural vegetation cover, protected areas), life (human development index (HDI, HDI -income)), policy (HDI-education, integrated water resources management (IWRM))	Hydrology-quantity (Wa, water efficiency, precipitation-flow patterns, groundwater storage), hydrology-quality (water quality, sediment yield, flood events, flow pattern), environment (EPI, natural vegetation cover, protected areas, hydrological and agricultural drought, flood events), life (HDI, HDI-income, sectoral impacts, changes in energy production, wildfires), policy (HDI-education, IWRM, climate change awareness, participatory water resources management, inter-institutional cooperation and coordinated actions)
Time horizon	Assessment of past or current conditions. Reactive reporting.	AR6-compliant, forward-looking projections. Proactive planning and adaptation.
Climate integration	Limited. Direct climate change projections are not used; indirect or qualitative references to climate variability are.	High. Including AR6 refractions such as precipitation- flow patterns, drought, floods, and energy production. The effects of climate change could be assessed directly and quantitatively.
Methodological strength	Basic, deterministic.	More complex. Indicators supported by climate change refraction.
Policy relevance	It provides a basic indicator for decision-makers.	It provides direct input on climate change risks and adaptation strategies for policymakers.
Decision-support value	It provides general information about watershed sustainability.	It provides forward-looking strategic guidance to decision-makers under climate change scenarios. It supports climate-resilient watershed management.

. With the new approach presented, the WSI has been transformed into a methodologically stronger, more policy-relevant, and more comprehensive index. The present study is the first initiative to systematically incorporate AR6 potential climate impacts directly into the WSI assessment, thereby filling the significant gap between climate projections and basin sustainability criteria. This approach has the potential to convert WSI from a static assessment tool into a forward-looking index that captures the dynamic pressures of climate change.

This framework enables decision-makers to assess basin sustainability under future climate scenarios with greater confidence, prioritise climate adaptation measures, and align basin-level strategies with global climate mitigation and adaptation pathways. In this sense, the integration of climate impacts fundamentally strengthens the role of the WSI as a decision support tool, transforming it from a descriptive index of current conditions into a predictive guide for sustainable water management in a changing climate. The added value of incorporating AR6 into WSI could be summarised as follows:

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AR6 is global in scope, while WSI is local and basin-specific. This integration makes global climate science applicable at the basin scale.

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As AR6 uses standardised climate scenarios (RCPs), applying this to WSI makes future sustainability scores comparable across basins.

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The implementation of AR6 strengthens the index’s policy relevance by creating a direct link between basin-scale management and international climate adaptation and sustainability commitments.

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The integration of AR6 projections transforms the WSI from a retrospective tool into a proactive risk-signaling tool, enabling policymakers to identify and address sustainability challenges before they become acute.

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Linking the WSI to AR6 transforms it from a descriptive, basin-specific tool into a forward-looking, risk-sensitive, and policy-relevant decision-support system.

The new WSI assessment methodology is implemented in the Central North Aegean Basin (2112 km²) as a case study, examining the impact of climate change on WSI assessment. This illustrates how the process can be implemented and how the results can be interpreted. The WSI value calculated for the baseline condition was compared to the WSI values estimated for the future condition to evaluate the basin’s sustainability. The 2021–2030 future conditions have been assessed with RCP4.5 and RCP8.5 projections.

2. Methodology

The WSI is based on four main sub-indices in the basin: hydrology, environment, life, and policy [8]. Each sub-index is characterized by a pressure–state–response (P-S-R) model involving a cause-and-effect relationship. This model is a tool for managers, decision-makers, and stakeholders to understand the relationships between sub-indices [20]. Pressure refers to the effects of human activities on natural resources. In contrast, the state refers to the general condition of natural resources during the study period. The response evaluates the impact of new practices and changes in the basin [2]. When calculating the watershed sustainability index, each sub-index is weighted equally and takes a value between 0 and 1 [8]:

Watershed Sustainability Index = (Hydrology + Environment + Life + Policy)/4

(1)

Equal weighting of the sub-indices reduces information bias by assigning equal weights to all possible events in line with the Principle of Maximum Entropy [21,22], has a simple formalism, and makes it easier for users to specify parameter values. To assess the robustness of the equal-weighting approach in the WSI, a sensitivity analysis was conducted in this study. The weights of the four indices were varied in 10% increments, resulting in 286 alternative weighting schemes. For each combination, WSI values were computed, and the resulting distributions were summarized using the median, minimum, maximum, and percentile values (P10–P90)—this enabled evaluation of whether equal weighting significantly affects index outcomes across different periods and climate scenarios.

Appropriate tables are used to calculate each sub-index in the WSI [8]. The WSI’s sub-indicators include numerical and non-numerical (interpretative) data types. These data have various uncertainties and require research and interpretation [3]. In this study, each sub-index in the WSI was linked to the IPCC 6th Assessment Report, Chapter 4 [1], and new refinements were added to the parameters of the WSI sub-indices as presented in the tables in Chaves and Alipaz (2007) [8]. To determine the level and score, the newly added refractions have been classified according to different data characteristics and methods consistent with the literature (

Table 2. Pressure parameters, levels, and scores of the proposed WSI approach.

Indicator	Pressure		Criteria	Level	Score
Hydrology-Quantity	Main parameter	Variation in the basin’s per capita water availability during the studied period, relative to the long-term average (m3/person/year) [8]	The amount of water per capita in the basin is determined for the long term and for the study period, and the change (%) is calculated.	Δ1 < −20%	0.00
				−20% < Δ1 < −10%	0.25
				−10% < Δ1 < 0%	0.50
				0 < Δ1 < +10%	0.75
				Δ1 > +10%	1.00
	Supporting parameters	Change in precipitation patterns (see Sections 4.4.1.1 and 4.6.1 of [1])	The annual mean precipitation pattern in the basin during the study period is compared with the long-term mean precipitation.	100% < |Δ|	0.00
				80% < |Δ| < 100%	0.25
				40% < |Δ| < 80%	0.50
				20% < |Δ| < 40%	0.75
				|Δ| < 20%	1.00
		Change in streamflow patterns (see Sections 4.4.3 and 4.6.1 of [1])	The change in streamflow patterns in the basin during the study period compared to the long-term average can be examined.	100% < |Δ|	0.00
				75% < |Δ| < 100%	0.25
				25% < |Δ| < 75%	0.50
				8% < |Δ| < 25%	0.75
				|Δ| < 8%	1.00
		Change in groundwater storage (see Sections 4.4.6 and 4.6.1 of [1])	The groundwater withdrawal/recharge ratio changes are analysed. The score is assigned from 0.00 to 1.00, ranging from very high pressure to very low pressure.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				20% < Δ < 40%	0.75
				Δ < 20%	1.00
Hydrology-Quality	Main parameter	Variation in the basin water quality in the period studied, relative to the long-term average (see Section 4.4.7 of [1] and in [8])	Water quality parameters can be selected based on the characteristics of the basin. The average value of the water quality parameter is determined for the long term and for the study period, and the change (%) is calculated.	Δ2 > 20%	0.00
				20% > Δ2 > 10%	0.25
				0 < Δ2 < 10%	0.50
				−10% < Δ2 < 0%	0.75
				Δ2 < −10%	1.00
	Supporting parameters	Change in the mean annual sediment yield (kg/ha/yr) (see Sections 4.4.8 and 4.6.1 of [1])	Mean annual sediment yield (kg/ha/yr) value is determined for the long term and for the study period, and the change (%) is calculated.	100% < |Δ|	0.00
				70% < |Δ| < 100%	0.25
				20% < |Δ| < 70%	0.50
				10% < |Δ| < 20%	0.75
				|Δ| < 10%	1.00
		Variation in streamflow patterns (see Sections 4.4.3 and 4.6.1 of [1])	The average flow in the period analysed is compared with the long-term average flow data. The score is assigned according to the amount of variation.	100% < |Δ|	0.00
				75% < |Δ| < 100%	0.25
				25% < |Δ| < 75%	0.50
				8% < |Δ| < 25%	0.75
				|Δ| < 8%	1.00
		Changes in the frequency of flood events (see Sections 4.4.4 and 4.6.1 of [1])	The score is assigned based on changes in the frequency of flood events or the intensity of their impact. As the number of events increases, the pressure increases.	Event num. ≥ 6	0.00
				4 < event num. < 6	0.25
				2 < event num. ≤ 4	0.50
				0 < event num. ≤ 2	0.75
				Event num. = 0	1.00
Environment	Main parameter	Basin Environmental Pressure Index (EPI) (rural and urban) in the period studied [8]	The change in the basin agricultural areas and the change in the basin population in the study period are determined. The average change in the basin agricultural areas and population is calculated (EPI = (% change of the basin agricultural areas + % change of the basin population)/2).	EPI > 20%	0.00
				10% < EPI < 20%	0.25
				5% < EPI < 10%	0.50
				0% < EPI < 5%	0.75
				EPI < 0%	1.00
	Supporting parameters	Changes in the frequency and severity of hydrological drought events (see Sections 4.4.5 and 4.6.1 of [1])	An appropriate hydrological drought index for the basin, such as the Palmer Hydrological Drought Severity Index (PHDI), can be used for the assessment, whose input data are precipitation, temperature, and soil water holding capacity. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high change to very low change.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				10% < Δ < 40%	0.75
				Δ < 10%	1.00
		Changes in the frequency of agricultural drought events in the basin during the studied period (see Sections 4.4.5 and 4.6.1 of [1])	An agricultural drought index, such as the Vegetation Condition Index (VCI), whose input is satellite data, can be used in the assessment. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high change to very low change.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				20% < Δ < 40%	0.75
				Δ < 20%	1.00
		Changes in the frequency of flood events (see Sections 4.4.4 and 4.6.1 of [1])	The score is assigned based on changes in the frequency of flood events or the intensity of their impact. As the number of events increases, the pressure increases.	Event num. ≥ 6	0.00
				4 < event num. < 6	0.25
				2 < event num. ≤ 4	0.50
				0 < event num. ≤ 2	0.75
				Event num. = 0	1.00
Life	Main parameter	Variation in the basin per capita HDI-Income in the period studied, relative to the previous period [8]	The average value of the HDI-Income parameter is determined for the long term and for the study period, and the change (%) is calculated.	Δ < −20%	0.00
				−20% < Δ < −10%	0.25
				−10% < Δ < 0%	0.50
				0 < Δ < +10%	0.75
				Δ > +10%	1.00
	Supporting parameters	Change in the vulnerability of the tourism sector to drought events (to assess the impact of drought events on the economy) (see Section 4.2.7 of [1])	Changes in the region’s ‘sensitivity of tourism activities to drought’ and changes in meteorological and hydrological drought can be evaluated together. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high change to very low change.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				20% < Δ < 40%	0.75
				Δ < 20%	1.00
		Change in the vulnerability of the industrial sector to drought events (to assess the impacts of drought events on the economy) (see Sections 4.5.2 and 4.5.6 of [1])	Changes in ‘industrial water consumption’, ‘number of workers employed’, and ‘export values’ can be assessed. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high change to very low change.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				20% < Δ < 40%	0.75
				Δ < 20%	1.00
		Change in hydroelectricity generation due to changes in river flow (see Sections 4.5.2 and 4.6.1 of [1])	Changes in ‘water consumption for the energy sector’ and ‘installed power value of the energy sector’ during the study period can be compared with the long-term. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high change to very low change.	Δ > 80%	0.00
				60% < Δ < 80%	0.25
				40% < Δ < 60%	0.50
				20% < Δ < 40%	0.75
				Δ < 20%	1.00
		Changes in the frequency of wildfire events in the basin (see Section 4.2.5 of [1])	The score is assigned based on changes in the frequency of wildfire events or the intensity of their impact. As the number of events increases, the pressure increases. The score is assigned based on the change amount, from 0.00 to 1.00, ranging from very high pressure to very low pressure.	Event num. ≥ 22	0.00
				17 < event num. ≤ 21	0.25
				12 < event num. ≤ 17	0.50
				8 < event num. ≤ 12	0.75
				Event num. ≤ 8	1.00
Policy	Main parameter	Variation in the basin HDI-Education in the period studied, relative to the previous period [8]	The average value of the HDI-Education parameter is determined for the long term and for the study period, and the change (%) is calculated.	Δ < −20%	0.00
				−20% < Δ <−10%	0.25
				−10% < Δ < 0%	0.50
				0 < Δ < +10%	0.75
				Δ > +10%	1.00
	Supporting parameter	Variation in the basin climate change awareness in the public and government domains in the period studied, relative to the previous period (see Sections 4.5.2 and 4.6.1 of [1])	The proactive approach of local authorities, the effective participation of non-governmental organisations (NGOs), studies conducted with academic support, and education and public awareness campaigns could be evaluated. The score is assigned a value from 0.00 to 1.00, ranging from very low awareness to very high awareness.	Very low	0.00
				Low	0.25
				Medium	0.50
				High	0.75
				Very high	1.00

,

Table 3. State parameters, levels, and scores of the new WSI approach.

Indicator	State		Criteria	Level	Score
Hydrology-Quantity	Main parameter	Basin per capita water availability (m3/person year), considering both surface and groundwater sources [8]	Annual water availability per capita is calculated by proportioning the long-term annual average amount of surface water and groundwater to the total population of the basin during the study period.	Wa < 1700	0.00
				1700 < Wa < 3400	0.25
				3400 < Wa < 5100	0.50
				5100 < Wa < 6800	0.75
				Wa > 6800	1.00
	Supporting parameters	The annual mean precipitation amount (mm) (see Sections 4.4.1.1 and 4.6.1 of [1])	The annual mean precipitation amount (mm) in the basin during the study period is determined.	precip. < 235	0.00
				235 < precip. < 478	0.25
				478 < precip. < 964	0.50
				964 < precip. < 1208	0.75
				precip. > 1208	1.00
		Average annual streamflow of the basin during the study period (see Section 4.2.3 of [1])	Annual average flow (m3/s) data in the study area are considered.	flow < 1	0.00
				1 < flow < 3	0.25
				3 < flow < 9	0.50
				9 < flow < 12	0.75
				flow > 12	1.00
		Groundwater withdrawal/recharge status of the basin during the study period (see Section 4.2.6 of [1])	The groundwater withdrawal/recharge ratio is analysed. If it is small, it can be considered low pressure.	rate > 0.8	0.00
				0.7 < rate < 0.8	0.25
				0.6 < rate < 0.7	0.50
				0.5 < rate < 0.6	0.75
				rate < 0.5	1.00
Hydrology-Quality	Main parameter	Basin averaged long-term TN and TP (mg/L) (see Section 4.2.7 of [1] and in [8])	The long-term average value of the water quality parameter selected according to the basin characteristics is determined.	TN > 25	0.00
				11.5 < TN < 25	0.25
				7.5 < TN < 11.5	0.50
				3.5 < TN < 7.5	0.75
				TN < 3.5	1.00
				TP > 0.8	0.00
				0.2 < TP < 0.8	0.25
				0.14 < TP < 0.2	0.50
				0.08 < TP < 0.14	0.75
				TP < 0.08	1.00
	Supporting parameters	Sediment yield (kg/ha/yr) (see Section 4.2.8 of [1])	The mean annual sediment yield (kg/ha/yr) is determined.	sediment > 940	0.00
				680 < sediment < 940	0.25
				180 < sediment < 680	0.50
				100 < sediment < 180	0.75
				sediment < 100	1.00
		Average annual streamflow of the basin during the study period (see Section 4.2.3 of [1])	Annual average flow (m3/s) data in the study area are considered.	flow < 1	0.00
				1 < flow < 3	0.25
				3 < flow < 9	0.50
				9 < flow < 12	0.75
				flow > 12	1.00
		Severity of flood events in the basin during the study period (see Section 4.2.4 of [1])	The number of residential areas and workplaces (property (pcs)) affected by flood events during the period under review is taken into account.	5001 ≤ pcs	0.00
				25001 ≤ pcs ≤ 5000	0.25
				1751 ≤ pcs ≤ 2500	0.50
				751 ≤ pcs ≤ 1750	0.75
				pcs ≤ 750	1.00
Environment	Main parameter	The percent of basin area under natural vegetation (Av) [8]	It can be determined using a land use map or remote sensing and satellite data.	Av < 5	0.00
				5 < Av < 10	0.25
				10 < Av < 25	0.50
				25 < Av < 40	0.75
				Av > 40	1.00
	Supporting parameters	Frequency and severity of hydrological drought events in the basin during the study period (see Section 4.2.5 of [1])	An appropriate hydrological drought index for the basin, such as PHDI, can be used in the assessment. The score is given from 0.00 to 1.00, ranging from severe drought to normal conditions.	PHDI < −4	0.00
				−4 < PHDI < −3	0.25
				−3 < PHDI < −2	0.50
				−2 < PHDI < −0.5	0.75
				PHDI > −0.5	1.00
		Frequency or severity of agricultural drought in the basin during the period studied (see Sections 4.2.5 and 4.3.1 of [1])	An agricultural drought index such as VCI can be used in the assessment. The score is given from 0.00 to 1.00, ranging from severe drought to normal conditions.	0 < VCI < 12.5	0.00
				12.5 < VCI < 25	0.25
				25 < VCI < 37.5	0.50
				37.5 < VCI < 50	0.75
				VCI > 50	1.00
		Severity of flood events in the basin during the study period (see Section 4.2.4 of [1])	The number of residential areas and workplaces (property (pcs)) affected by flood events during the period under review is taken into account.	5001 ≤ pcs	0.00
				25001 ≤ pcs ≤ 5000	0.25
				1751 ≤ pcs ≤ 2500	0.50
				751 ≤ pcs ≤ 1750	0.75
				pcs ≤ 750	1.00
Life	Main parameter	Basin Human Development Index (HDI) [8].	The average value of the HDI is determined for the year preceding the study period.	HDI < 0.5	0.00
				0.5 < HDI < 0.6	0.25
				0.6 < HDI < 0.75	0.50
				0.75 < HDI < 0.9	0.75
				HDI > 0.9	1.00
	Supporting parameters	Vulnerability of the tourism sector to drought events (see Section 4.2.7 of [1])	The region’s sensitivity of tourism activities to drought and meteorological and hydrological drought events can be considered together. Score is assigned from 0.00 to 1.00, ranging from high vulnerability (5) to low vulnerability (1).	5	0.00
				4	0.25
				3	0.50
				2	0.75
				1	1.00
		Vulnerability of the industrial sector to drought events	To assess the vulnerability of the industrial sector to drought events, ‘industrial water consumption’, ‘number of workers employed’ and ‘export values’ can be used. Score is assigned from 0.00 to 1.00, ranging from high vulnerability (5) to low vulnerability (1).	5	0.00
				4	0.25
				3	0.50
				2	0.75
				1	1.00
		Efficiency of hydroelectric power plants (see Sections 4.2.3 and 4.3.2 of [1])	The parameters ‘water consumption in the energy sector’ and ‘installed power value of the energy sector in economic terms’ can be considered. The vulnerability of efficiency is scored from 0.00 to 1.00, ranging from high vulnerability (5) to low vulnerability (1).	5	0.00
				4	0.25
				3	0.50
				2	0.75
				1	1.00
		Severity of wildfire events in the basin during the study period (see Section 4.2.5 of [1])	Burned area (ha) due to wildfires can be considered. The score is given from 0.00 to 1.00, ranging from very low exposure to very high exposure.	1–0.8	0.00
				0.8–0.6	0.25
				0.6–0.4	0.50
				0.4–0.2	0.75
				0.2–0	1.00
Policy	Main parameter	Basin institutional capacity in Integrated Water Resources Management (IWRM) (legal and organizational) [8]	The institutional capacity of the basin in integrated water resources management can be assessed as follows: - Institutions, committees, or associations responsible for water management at the basin level. - Adequacy of water laws and institutional frameworks during the period under review. - Laws in force.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
	Supporting Parameter	Efforts to develop participatory water resources management in the public and government domains.	Strategies and practices for involving the public and NGOs in the water resources management process are evaluated. Score is assigned from 0.00 to 1.00, ranging from very poor participation to very high participation.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00

and

Table 4. Response parameters, levels, and scores of the proposed WSI approach.

Indicator	Response		Criteria	Level	Score
Hydrology-Quantity	Main parameter	Improvement in water-use efficiency in the basin, in the period studied [8] and activities carried out to meet water demands sustainably (see Sections 4.6.4 and 4.6.5 of [1])	Efforts/projects for the economical use of water, social projects that will direct the public to conscious consumption of water, economic/financial incentives, etc. (see Section 4.6.3 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
	Supporting parameters	Measures taken against changing precipitation patterns and extreme rainfall events	Discrete stormwater and sewerage systems, early warning systems, adequate infrastructure and urban planning, construction and maintenance of embankments, landslide prevention works, emergency management and coordination works, sustainable urban drainage systems, etc. (see Sections 4.6.4 and 4.6.5 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures taken to prevent streamflow decreases	Increasing water use efficiency, water recovery, reducing water losses, ensuring water saving and conservation, sustainable use of water resources, use of appropriate irrigation techniques, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Actions for the protection and improvement of groundwater storage (see Section 4.6.4 of [1])	Establishment of groundwater monitoring system, detection and prevention of illegal wells, use of metered measurement systems, improvement of water supply systems and reduction in water losses, efficient water use and recovery, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
Hydrology-Quality	Main parameter	Improvement of inadequate sewage treatment/disposal in the basin during the period studied [8]	Actions taken to protect and improve water quality are evaluated, such as fertilizer and pesticide management, wastewater treatment, public awareness-raising activities, etc. (see Section 4.6.2 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
	Supporting parameters	Measures to prevent soil erosion (see Section 4.6.2 of [1])	Adaptation studies to increase soil fertility, measures to protect waterways, ecosystems and infrastructure, preparation of erosion risk map, afforestation activities, erosion control in all basins, especially in dam and pond basins, etc. (see Sections 4.6.2 and 4.6.4 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures taken to prevent streamflow decreases	Increasing water use efficiency, water recovery, reducing water losses, ensuring water saving and conservation, sustainable use of water resources, use of appropriate irrigation techniques, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures against flood events/flood risk minimization measures (see Section 4.6.5 and Box 4.7 of [1])	Early warning systems for floods, flood-resistant housing, floodplain management, wetland restoration, structural measures (dykes, flood control gates, weirs, dams), flood management and inter-institutional cooperation, participation in training and awareness raising on flood mitigation, strengthening the capacities of local organisations, increasing the amount of wooded areas, creating rain ditches and rain gardens, using permeable materials in road construction works, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
Environment	Main parameter	Evolution in basin conservation areas (Protected areas and Best Management Practices (BMPs)) in the basin during the period studied [8]	The development of protected areas in the basin, the availability of BMPs, and the development in the area where BMPs have been or are planned to be implemented are evaluated.	Δ < −10%	0.00
				−10% < Δ < 0%	0.25
				0 < Δ < +10%	0.50
				+10% < Δ < +20%	0.75
				Δ > 20%	1.00
	Supporting parameters	Measures against hydrological drought	Conservation of water resources and soil moisture, efforts related to water management, preparation of drought strategic plan, establishment of drought monitoring system, regular training and awareness raising activities for local governments and citizens, etc. (see Sections 4.6.4, 4.6.5 and 4.7.1.1 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures taken to mitigate agricultural drought	Improved varieties and agronomic practices within the scope of combating agricultural drought, changes in crop patterns and crop systems, diversification of livelihoods, identification of valuable plants and promotion of their cultivation, improvement of irrigation systems, agricultural drought disaster analysis, provision of innovative water management measures, etc. (see Sections 4.6.2 and 4.7.1.1 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures against flood events/flood risk minimisation measures (see Section 4.6.5 and Box 4.7 of [1])	Early warning systems for floods, flood-resistant housing, floodplain management, wet-land restoration, structural measures (dykes, flood control gates, weirs, dams), flood management and inter-institutional cooperation, participation in training and awareness raising on flood mitigation, strengthening the capacities of local organisations, increasing the amount of wooded areas, creating rain ditches and rain gardens, using permeable materials in road construction works, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
Life	Main parameter	Evolution in the basin HDI [8]	The score is assigned according to the amount of variation in the basin HDI in the analysed period.	Δ < −10%	0.00
				−10% < Δ < 0%	0.25
				0 < Δ < +10%	0.50
				+10% < Δ < +20%	0.75
				Δ > 20%	1.00
	Supporting parameters	Measures taken to protect the tourism sector from the effects of drought	For example, creating sustainable, planned, and developed tourism areas, identifying historical sites under drought risk, increasing institutional capacity and inter-institutional cooperation, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Measures taken to protect the industrial sector from the effects of drought (see Section 4.6.3 of [1])	Recycling of wastewater, treatment of industrial wastewater, and reuse in industry, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Adaptation studies related to energy production (see Sections 4.6.3 and 4.7.1.1 of [1])	Adaptation efforts on renewable energy, change in hydroelectric operation protocol, or change in plant design, change in cooling water source for thermoelectric power plants, or use of alternative cooling technologies, diversification of energy portfolio to reduce water-related impacts, efforts to contribute to increasing energy and water use efficiency, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
		Actions taken to combat wildfire risks	Using early warning systems, increasing the effectiveness of the fire-fighting department, establishing an equipped and technological fire-fighting department, training and exercise activities for civilian citizens, etc.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
Policy	Main parameter	Evolution in the basin’s WRM expenditures in the basin, in the period studied (see Section 4.6.5 of [1] and in [8])	Drinking water and wastewater treatment plant, responses of stakeholders and decision makers to overcome water resources problems, policies and projects in the basin on water resources management, integrated urban water management projects, efforts towards sustainable and climate resilient management objectives, etc. (see Sections 4.6.2, 4.6.4 and 4.6.5 of [1]).	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00
	Supporting parameter	Efforts to improve water security, cooperation, and coordinated actions among various institutions in the public and government domains (see Sections 4.6.2, 4.6.4 and 4.6.5 of [1])	Strategies and practices for enhancing water security, developing cooperation, and promoting coordinated action among various institutions are evaluated. Score is assigned from 0.00 to 1.00, ranging from very poor effort to excellent effort.	Very poor	0.00
				Poor	0.25
				Medium	0.50
				Good	0.75
				Excellent	1.00

). Flow and precipitation data were evaluated using the Normal Distribution Approach, taking into account their distribution characteristics. They were divided into five classes using the mean and standard deviation values of the data. Since flood and wildfire events are countable variables, these indicators were classified based on the Poisson Distribution. Due to extreme values and high variance, burned area data were normalized to the 0–1 range using the Min-Max Normalization method and then divided into five classes. For hydrological drought, agricultural drought, sectoral vulnerability, and groundwater extraction/recharge parameters, threshold values defined in official reports were used to ensure methodological consistency [23,24]. Non-numerical sub-refractions were divided into five equal classes (very poor, poor, medium, good, excellent) in the 0.00–1.00 range, in accordance with the natural structure of the WSI. Since the classification approach for the sub-refractions used in this study was developed according to the characteristics of the investigated basin, it is possible to adapt these ranges to the climatic, hydrogeological, and socio-economic conditions of each basin when applying this methodology to different basins. Therefore, the proposed methodology offers a flexible structure, allowing for similar applications of the WSI in different basins. The changes introduced to the WSI assessment are as follows:

i.

Climate change: The WSI was linked to key climate change discontinuities highlighted in the IPCC 6th Assessment Report [1].

ii.

Study periods: To assess the impact of climate change on the selected subindices, the WSI needs to be implemented over two consecutive periods rather than a single period. The difference between the baseline WSI and the near-future WSIs (using the RCP4.5 and RCP8.5 scenarios) can be assessed to determine the sensitivity of the basin to climate change. It can be used to analyse the modification of responses to ensure that WSI levels are maintained or improved. Furthermore, the actions policymakers can formulate to maintain watershed sustainability conditions can be determined.

iii.

Water quality parameter: The BOD5 water quality parameter considered in the WSI calculation can be replaced by other meaningful water quality parameters for the basin [8]. Since agricultural production is a significant source of pollution in the studied basin, the Total Nitrogen (TN) and Total Phosphorus (TP) parameters were used in assessing water quality.

iv.

The proposed WSI approach includes numerical and non-numerical data, as in the classical WSI approach. Numerical data can be obtained by modelling, projection, remote sensing, satellite data, official reports, or statistical data. To evaluate the data based on interpretation and assign scores, studies, implementations, the scope of implementations, and the strength of the implementer, as well as reports, strategic plans, action plans, and projects carried out in the basin, need to be analyzed. The resolution, uncertainty, and validation method of the data used in this study are provided in the Appendix A at the end of the study.

v.

Vulnerability to the impacts of climate change is expected to intensify in various sectors (especially in agriculture, energy, and industry) [1]. For example, new challenges can arise in renewable energy generation; changes in water inputs to hydroelectric power plants may occur, decreasing water availability and increasing water temperatures may affect the cooling of thermoelectric power plants.

vi.

Increased wildfires, environmental pollution, and the invasion of the seas by harmful species could negatively affect tourism. Weather patterns affect vegetation cover, influencing soil erosion and triggering land degradation. This can lead to reduced soil fertility, reduced water quality, degradation of aquatic ecosystems, increased eutrophication, and damage to infrastructure. In addition, deterioration in land conditions, water availability, and quality can also affect livestock activities. The increasing demand for food with a growing population can lead to higher agricultural water use. In areas with intensive agricultural activities, subsistence farmers are more likely to be exposed to water-related stress and have a lower adaptive capacity, thereby posing a high risk of food insecurity [1].

vii.

Climate change has significant impacts on the economy and thus on the quality of life [25,26]. The impact of the climate crisis on economically valuable crops for the region can increase regional economic risks. It is predicted that the negative impacts of climate change on water resources will reduce gross domestic product (GDP) in various sectors of the economy, and economic losses will be higher in low- and middle-income countries [1]. Furthermore, climate change can seriously impact employment, since the most critical sectors that provide employment, such as agriculture and tourism, are affected by climate change [1].

Based on the above discussions, criteria were identified and presented in Table 2, Table 3 and Table 4 to facilitate the understanding and interpretation of all parameters in the hydrology, environment, life, and policy indicators. The parameters expressed as main parameters in these tables are the ones used in the classical WSI assessment [8]. The parameters expressed as supporting parameters have been included in this study for the first time as a result of linking WSI with AR6 and have been added to the scoring table. It should be noted that the supporting parameters are flexible and could be modified by the practitioner depending on the case study being evaluated.

Pressure, state, and response scores are identified for each main and supporting parameter using the tables provided above. The final score for the relevant parameter is calculated by taking the average of these scores. Then, the final score for each parameter is multiplied by its assigned weight and added up. In the environment, life, and policy indicators, this process directly produces the resulting score, while in the hydrology indicator, it creates the scores for the quantity and quality sub-indicators. The scores of these sub-indicators are multiplied by their weights to create the hydrology score. In the last step, the hydrology, environment, life, and policy indicators are multiplied by their weights and summed to obtain the WSI. The weights of the parameters are presented in Figure 1, and the values assigned to the main and supporting parameters were identified based on the characteristics of the studied watershed. However, it is possible to rearrange these weights for more meaningful or effective parameters in different watersheds.

3. Case Study

3.1. Study Area

The Central North Aegean Basin of Türkiye was chosen as the case study area due to its intensive agricultural activities and high tourism potential. The basin with an area of 2224 km² covers five districts of Balıkesir province (Ayvalık, Burhaniye, Edremit, Gömeç, and Havran) (Figure 2). Since climatic conditions and soil characteristics are favorable in the basin, it is possible to grow various crops. Edremit, Havran, and Burhaniye plains especially have high agricultural value. The basin is one of the regions with the highest olive production in Türkiye [27]. Livestock activities are quite common in the high parts of the province, such as pastures and meadows. Although a Mediterranean climate is observed throughout the province, the effect of a continental climate is felt towards the interior [28].

3.2. WSI Assessment

3.2.1. Hydrology Indicator for 2016–2020 Period (Baseline Condition)

The hydrology indicator is the average of the quantity and quality components of water resources. Per capita water availability (Wa) is used as the main parameter in the quantitative assessment of water resources. Changes in precipitation patterns, streamflow, and groundwater reserves can affect this parameter. Therefore, the potential impacts of climate change on water resources have been assessed by analysing these hydrological parameters for both baseline and future periods.

Water quality is the leading indicator in evaluating water resources in terms of quality. However, sediment yield, changes in streamflow patterns, and flood events can also directly or indirectly impact water quality [29,30,31]. In this context, these parameters that can affect quality were analysed for both periods, and an integrated assessment approach was adopted.

Water availability per capita (Wa)

Water availability per capita (Wa) is obtained by dividing the total groundwater and surface water potential by the basin population. The Central North Aegean Basin has 2.73 m³/s of groundwater, 6.31 m³/s of long-term average surface water, and 9.04 m³/s of total water potential [28,32]. In 2020, the total population of the basin was 337,871. The ratio of total water potential to population is 843.8 m³/person year, and the state score becomes 0.

In the case of pressure, the change in Wa is −4.0%, compared to the long-term average. Therefore, the pressure score is 0.5. While evaluating the response parameter, basin water use efficiency improvement is considered. Zero Loss in Water and Water Footprint projects have been carried out in recent years to efficiently use water resources in the basin [33]. Within the scope of these projects, the Water Efficiency Mobilisation Movement was launched, aerators were installed in mosques and public toilets to use water efficiently, and online water footprint calculations were made by introducing the project to the public to direct them to the conscious use of water [34,35]. Therefore, the overall improvement in water use efficiency in the basin was considered “good,” and the response score becomes 0.75. The average pressure, state, and response parameters (0.0 + 0.5 + 0.75)/3 = 0.42 is calculated as 0.42. The sub-criteria of precipitation, streamflow, and groundwater patterns that can affect per capita water availability, as expressed in Table 2, Table 3 and Table 4, are discussed below for the baseline condition.

Precipitation Patterns

The change in the mean precipitation amount in this period compared to the long-term average is +2.8% [36]. Therefore, the pressure score is 1.00. The annual mean precipitation in the basin during this period is 836 mm [36], and the state score is 0.5. The recent increase in the mean precipitation compared to the long-term average is a small but significant increase, reflecting the regional impacts of climate change. Although this situation shows that the current conditions have not yet experienced a radical climate change, it indicates that it is necessary to be prepared for possible future risks. Indeed, efforts were made to establish and disseminate early warning systems against changing precipitation patterns and extreme weather events [37]. It is planned to eliminate infrastructure deficiencies, improve road standards, and modify and renew the network [38]. In this context, since the measures taken can be effective in mitigating potential risks and enhancing the community’s adaptive capacity, the response level was determined as ‘medium’ and the score as “0.5”. The average of pressure, state, and response parameters ((1.00 + 0.50 + 0.50)/3 = 0.67) is calculated as 0.67.

Streamflow Patterns

The change in the average flow in this period compared to the long-term average flow is −0.004% [28,32,39], and the pressure score is 1.00. The annual average flow during this period is 6.31 m³/s, and the state score is 0.5. The fact that there is no significant change in surface water resources suggests that the hydrometeorological conditions in the region are relatively stable and there is no sudden deterioration in the water regime. However, this does not eliminate the necessity of long-term water management policies. As a matter of fact, since the last years of this period, it has been aimed to reduce water losses, ensure the protection and sustainability of water resources, and develop irrigation techniques by soil and water conditions [40,41]. In this context, the strategies developed are essential because climate change can severely pressure water resources in the medium and long term. Thus, the level is selected as ‘medium’ and the response score is set to 0.5. The average of the pressure, state, and response parameters ((1.00 + 0.50 + 0.50)/3 = 0.67) is calculated as 0.67.

Groundwater Patterns

The pressure on the region’s groundwater withdrawal/recharge ratio has increased due to increasing industrial water, potable water, and agricultural irrigation water needs [23]. Due to increasing pressures, the level is selected as ‘high’ and the pressure score is set as 0.25. In this period, it is stated that the groundwater withdrawal/recharge ratio in the region is 2.72, and the quantity situation is poor [23]. Therefore, the state score is 0.00. The withdrawal/recharge ratio indicates that the amount of water withdrawn is approximately three times the amount of water recharged and indicates an unsustainable situation in hydrogeological terms. In this context, the poor groundwater quantity status emphasises the need for holistic and preventive water management approaches at the basin scale. Studies have been conducted to establish groundwater monitoring systems and achieve groundwater recharge-discharge balance [41]. The studies carried out can be considered essential steps in terms of the sustainable management of resources. The level is selected as ‘medium’ and the response score is set as 0.5. The average of pressure, state, and response parameters ((0.25 + 0.00 + 0.50)/3 = 0.25) is calculated as 0.25.

Thus, the hydrology-quantity indicator for 2016–2020 ((0.42 + 0.67 + 0.67 + 0.25) × 0.25 = 0.50) is calculated as 0.50.

Water Quality (TN and TP)

The basin’s average TN and TP values are 0.98 mg/L and 0.03 mg/L [17]. Therefore, the state score is 1. The average TN and TP changes during the study period are +18.4% and +17.9%, respectively [17], and the pressure score for both is 0.25. In this period, both the ratio of the population served by wastewater treatment plants to the municipal population in the basin and the ratio of the population served by sewerage services to the municipal population in the basin increased compared to the previous periods [42]. Thus, considering the sewage treatment and disposal improvements, the response score is 0.75. The average pressure, state, and response parameters (1.0 + 0.25 + 0.75)/3 = 0.67) are calculated as 0.67. The sub-criteria of sediment yield, streamflow patterns, and flood events that can affect water quality, as expressed in Table 2, Table 3 and Table 4, are discussed below for the baseline condition.

Sediment Yield

The mean annual sediment yield during the relevant period is 386 kg/ha/year [43]. Hence, the state score is 0.50. The change in mean annual sediment yield compared to the long-term average is −9% [43]. Thus, the pressure score is 1.00. This decrease in sediment yield could be due to the effect of watershed management practices. During this period, interventions such as the preparation of erosion risk maps, carrying out basin-based rehabilitation works with a participatory approach, and revising afforestation activities were carried out [37]. Considering the availability of these studies, the response score is selected as 0.5 (medium). The average of pressure, state, and response parameters (0.50 + 1.00 + 0.50)/3 = 0.67) is calculated as 0.67.

Streamflow Patterns

It is known that the risk of water pollution increases during low-flow periods. During these periods, the reduction in river flow volume makes it difficult to dilute the pollution load and can lead to an increase in the concentration of dissolved pollutants [29]. As explained in the “streamflow patterns” section above, the average of the pressure, state, and response parameters ((1.00 + 0.50 + 0.50)/3 = 0.67) during this period is calculated as 0.67.

Flood Events

Floods can cause a sudden increase in pollutant concentrations in water resources, worsen water quality, and pose challenges for water management by accelerating runoff in urban areas [31,44]. The average number of flood events during this period is 3.2 [45]. Thus, the pressure score is set as 0.5. The number of residential areas and workplaces (property (number)) affected by flood events during the period is 496 [45]. Therefore, the state score is set as 1.00. Efforts have been made to combat flooding in the basin, prevent potential loss of life and property, minimise soil loss, manage floods, promote inter-institutional cooperation, ensure participation in training and awareness-raising on flood mitigation, identify flood risks, and strengthen the capacities of local organisations working on natural disasters [37,40,41]. Therefore, the response score is assigned as 0.75 (good). The average of the pressure, state, and response parameters (0.50 + 1.00 + 0.75)/3 = 0.75) is calculated as 0.75.

Thus, the hydrology-quality indicator for 2016–2020 (0.67 × 0.4 + 0.67 × 0.1 + 0.67 × 0.25 + 0.75 × 0.25 = 0.69) is calculated as 0.69. Hydrology indicator (0.50 × 0.50 + 0.69 × 0.5 = 0.59) becomes 0.59.

3.2.2. Hydrology Indicator for 2021–2030 Period (Future Conditions)

Water availability per capita (Wa)

This period’s average surface runoff value is 5.45 and 6.06 m³/s, based on climate change RCP4.5 and RCP8.5 scenarios, respectively [17]. The groundwater potential in the basin is 2.73 m³/s [28,32]. Thus, the total surface and groundwater potential for the RCP4.5 and RCP8.5 scenarios is projected to be 8.18 and 8.79 m³/s, respectively. In 2030, the total population of the Central North Aegean Basin is estimated to be 419669. The ratio of total water potential to population Wa is calculated as 614.7 m³/person-year for the RCP4.5 projection and 660.5 m³/person-year for the RCP8.5 projection. The state scores are 0 for both projections.

The change in Wa is −17% for RCP4.5 and −8.6% for RCP8.5 compared to the long-term average [17,28,32]. The pressure scores are 0.25 and 0.50, respectively. Targets and strategies have been determined to promote and increase water efficiency in all sectors, especially urban, agricultural, and industrial water use efficiency [46]. Therefore, the response parameter is selected as 0.75 for both projections. The average of the pressure, state, and response parameters for RCP4.5 is calculated as (0.0 + 0.25 + 0.75)/3 = 0.33) 0.33. For RCP8.5, it is calculated as 0.42 (0.0 + 0.5 + 0.75)/3 = 0.42). The potential sub-criteria previously described above are discussed below for the future condition as expressed in Table 2, Table 3 and Table 4.

Precipitation Patterns

The change in the mean precipitation amount relative to the long-term average is −3.75% for RCP4.5 and +5% for RCP8.5 [47]. The pressure score is 1.00 for both scenarios. The annual mean precipitation amount in the basin is 782 mm for the RCP4.5 scenario and 854 mm for the RCP8.5 scenario. The state score corresponds to 0.50 for both projections. These changes in precipitation patterns are noteworthy in terms of indicating regional-level impacts. In this period, studies have started to be carried out for adaptation and mitigation measures that should be taken primarily against the adverse effects of climate change [48]. In this context, it is aimed to develop early warning systems, to carry out necessary infrastructure works in settlements to prevent damage that may occur as a result of excessive precipitation, to encourage rainwater harvesting, to build dams on rivers with flood hazard, and to include climate scenarios in emergency planning, evacuation training, and drills [48,49]. These measures are important as they are concrete steps towards reducing disaster risk and making water management more resilient. Thus, for both projections, the response level is assessed as ‘good’ and the score as 0.75. The average of the pressure, state, and response parameters is calculated as 0.75 for both projections (1.00 + 0.50 + 0.75)/3 = 0.75).

Streamflow Patterns

It is foreseen that the change in the average flow amount in this period compared to the long-term average flow amount could be −13.5% for the RCP4.5 projection and −4% for the RCP8.5 projection [17]. The pressure scores are 0.75 and 1.00, respectively. During this period, the average annual flow for the RCP4.5 and RCP8.5 projections is 5.45 and 6.06 m³/s, respectively. The state score is 0.5 for both projections. The decrease in flow could be associated with the changes in precipitation patterns caused by climate change, an increase in evaporation, and an increase in water demand. This decrease puts pressure on aquatic ecosystems and threatens the sustainability of water-dependent sectors, especially agriculture. In this context, the objectives are to reduce water losses, recover and conserve water, and promote efficient irrigation techniques [41,48]. Therefore, for the future period (for both projections), the level is selected as “good” and the response score is set as 0.75. The average of the pressure, state, and response parameters for RCP4.5 is calculated as 0.67 (0.75 + 0.50 + 0.75)/3 = 0.67). For RCP8.5, it is calculated as 0.75 (1.00 + 0.50 + 0.75)/3 = 0.75).

Groundwater Patterns

Climate change projections reveal that the region’s groundwater resources are at risk [23,24]. The groundwater potential trend is −1.21% for the RCP8.5 scenario, whereas it is −0.77% for the RCP4.5 scenario [24]. These findings indicate that the groundwater potential trend in the RCP8.5 scenario, which represents pessimistic climate conditions, is approximately twice as high as that in the RCP4.5 scenario. Accordingly, the pressure level for the RCP4.5 scenario is classified as ‘high’ with a pressure score of 0.25. While for the RCP8.5 scenario, it is classified as ‘very high’ with a pressure score of 0.00.

It is planned to implement basic and complementary measures such as detection and prevention of illegal wells, use of metered measurement systems, and reduction in water losses in the region. It is foreseen that the groundwater withdrawal/recharge ratio can be reduced from 2.72 to around 0.7 following the measures to be implemented [23]. Assuming that the groundwater withdrawal/recharge ratio could be 0.7 in the future period with the help of measures, the level is selected as ‘medium’ for both projections, and the state score is set as 0.5. In addition to the basic and complementary measures planned to be implemented, studies such as determining the protection areas of groundwater resources, conducting studies on groundwater bodies, preparing annual groundwater withdrawal monitoring and control reports in groundwater operation areas, improving drinking water supply systems, rehabilitation of irrigation areas, and ensuring efficient use and recovery of used water are planned [50]. These studies can be an important step towards reducing the risk of groundwater stress and increasing resilience to climate change on the regional scale. Thus, for both climate scenarios, the level is selected as ‘medium’ and the response score is set as 0.5. The average of the pressure, state, and response parameters for RCP4.5 is calculated as 0.42 (0.25 + 0.50 + 0.50)/3 = 0.42). For RCP8.5, it is calculated as 0.33 (0.00 + 0.50 + 0.50)/3 = 0.33).

Thus, the hydrology-quantity indicator for the 2021–2030 period under the RCP4.5 scenario (0.33 + 0.75 + 0.67 + 0.42) × 0.25 = 0.54) is calculated as 0.54. For the RCP8.5 scenario ((0.42 + 0.75 + 0.75 + 0.33) × 0.25 = 0.56), it is calculated as 0.56.

Water Quality (TN and TP)

The average TN and TP values in the basin for the RCP4.5 scenario are 4.67 mg/L and 0.18 mg/L, respectively [17]. For the RCP8.5 scenario, these values are 4.55 mg/L and 0.16 mg/L, respectively [17]. Therefore, the state score for both scenarios is 0.63 (0.75 for TN and 0.50 for TP, i.e., (0.75 + 0.50)/2 = 0.63). The average TN and TP changes during this period are +0.45% and +0.75% for the RCP4.5 scenario, respectively, and the pressure score for both is 0.5. For the RCP8.5 scenario, the TN and TP changes are 1.04% and +11.3%, respectively. The state score is 0.50 (0.75 for TN and 0.25 for TP, i.e., (0.75 + 0.25)/2 = 0.50).

It is aimed to improve the infrastructure of wastewater treatment plants, to ensure their integration with renewable energy projects, and to disseminate appropriate technologies for the reuse of water treated in wastewater treatment plants for different purposes [46]. Since the objectives related to wastewater management within the framework of adaptation to climate change are comprehensively addressed, the response score for TN and TP parameters is selected as 0.75 for both climate projections. The average of the pressure, state, and response parameters ((0.63 + 0.5 + 0.75)/3 = 0.63) is calculated as 0.63 for RCP4.5 and RCP8.5. The potential sub-criteria that can affect water quality, previously described above, are discussed below for the future condition as expressed in Table 2, Table 3 and Table 4.

Sediment Yield

During this period, the mean annual sediment yield for RCP4.5 and RCP8.5 scenarios is 220 and 239 kg/ha/year, respectively [43]. Thus, the state score is 0.5 (medium) for both scenarios. The change in the mean annual sediment yield relative to the long-term mean is −48% for RCP4.5 and −43% for RCP8.5 [43]. Pressure score is 0.5 (medium) for both scenarios. This decrease in sediment yield could be due to the decrease in surface runoff or the effect of watershed management practices. Within this period, it aims to establish integrated approaches related to desertification, erosion, and sedimentation at the basin scale [49]. Therefore, the response score is selected as 0.5 (medium) for both climate scenarios. The average of the pressure, state, and response parameters ((0.50 + 0.50 + 0.50)/3 = 0.50) is calculated as 0.5 for both RCP4.5 and RCP8.5.

Streamflow Patterns

A significant decrease in flow can affect nutrient dynamics, leading to a decrease in dissolved oxygen and an increase in pollutant concentrations [30]. As explained in the “streamflow patterns” section above, climate projections suggest that the average flow during this period could change significantly compared to the long-term average. Furthermore, in the relevant section, the average of the pressure, state, and response parameters is calculated as 0.67 (0.75 + 0.50 + 0.75)/3 = 0.67 for RCP4.5, and 0.75 ((1.00 + 0.50 + 0.75)/3 = 0.75) for RCP8.5.

Flood Events

Historical data indicate that the number and severity of floods have increased in recent years [45]. However, it is envisaged that flood risk can be reduced with the help of various adaptation and mitigation measures to be implemented in this period [51]. Reducing flood risk can contribute to the reduction in pressure on water quality by reducing the transport of pollutants to water resources and sudden concentration changes. Thanks to the relevant mitigation and adaptation measures, it is assumed that the risk of flood events could be reduced for the RCP4.5 and RCP8.5 projections in the future period, and therefore, a pressure score of 0.50 is selected. Furthermore, it is estimated that the number of residential areas and workplaces that could be affected under both climate change projections during this period would be less than 750 [51]. The state score is selected as 1.00 for both scenarios.

Within the scope of adaptation and mitigation measures to be taken against the adverse effects of climate change, it is planned to prevent intervention in stream beds and not to allow construction in these sections, to transform storm water and sewage water into separate systems, to minimise flows and leaks in sewage systems, to increase the amount of wooded areas in urban settlements to control flood water, to create rain ditches and rain gardens, to use permeable materials in road construction, to develop prediction and early warning systems [48,49]. Hence, the response score is chosen as 0.75 (good) for climate scenarios. The average of pressure, state, and response parameters ((0.50 + 1.00 + 0.75)/3 = 0.75) is calculated as 0.75 for both scenarios.

Thus, the hydrology-quality indicator for the 2021–2030 period under the RCP4.5 scenario (0.63 × 0.4 + 0.50 × 0.1 + 0.67 × 0.25 + 0.75 × 0.25 = 0.65) is calculated as 0.65. For the RCP8.5 scenario (0.63 × 0.4 + 0.50 × 0.1 + 0.75 × 0.25 + 0.75 × 0.25 = 0.68), it is calculated as 0.68.

Hydrology indicator (0.54 × 0.50 + 0.65 × 0.5 = 0.60) becomes 0.60 for the RCP4.5 scenario, and (0.56 × 0.50 + 0.68 × 0.5 = 0.62) becomes 0.62 for the RCP8.5 scenario.

3.2.3. Environment Indicator for 2016–2020 Period (Baseline Condition)

The Environmental Pressure Index (EPI), the percentage of the basin area under natural vegetation, and changes in protected areas are the main parameters of the environmental assessment [8]. The EPI is the average of the percentage change in the agricultural area in the basin and the percentage change in the basin population during the period analysed. The environmental indicator is affected by climate-based events such as hydrological drought, agricultural drought, and flood events [1,52]. Therefore, the potential impacts of climate change on the environment were assessed with a holistic approach by analysing these parameters for both periods.

Environmental Pressure Index (EPI)

CORINE Land Cover 2012 [53] and 2018 [54] data were analysed, and it was determined that the change in agricultural areas in the basin was 0.3%. The change in population during the period analyzed is 8.41%. The EPI value is calculated as (0.3% + 8.41%)/2 = 4%. The pressure parameter score is 0.75.

48.9% of the basin has natural vegetation cover [54], corresponding to a 1.00 score in the state parameter. According to the data of Balikesir Provincial Directorate of Culture and Tourism (2024) [55] and Balıkesir Protected Areas Report (2021) [56], the change in protected areas in the study area is approximately 0.7%, and the response parameter is 0.5. The average of the pressure, state, and response parameters ((0.75 + 1.00 + 0.50)/3 = 0.75) is calculated as 0.75. The sub-criteria of hydrological drought, agricultural drought, and flood events that can affect the environmental indicator, as expressed in Table 2, Table 3 and Table 4, are discussed below for the baseline condition.

Hydrological Drought Events

Historical drought events indicate that, according to the Palmer Hydrological Drought Severity Index (PHDI), mostly mild to moderate drought events occurred between 1984 and 2014 [24]. All drought events occurred before 2016 [24]. Therefore, it can be argued that drought events decreased in this period compared to the long term. Thus, the pressure score is selected as 0.75 (low risk). Furthermore, based on historical drought events, as there was no hydrological drought event during this period according to the PHDI, the state score is set as 1.00 (very low risk). Efforts have been made to establish a drought monitoring system, ensure cooperation between the institutions to be included in the system, and develop a hydrological drought assessment system [37,41]. Therefore, the response score is defined as 0.50 (medium). The average of the pressure, state, and response parameters (0.75 + 1.00 + 0.50)/3 = 0.75 is calculated as 0.75.

Agricultural Drought Events

Historical drought events indicate that mild to moderate drought events have occurred since the 2000s, according to the Vegetation Condition Index (VCI) [24]. In the 2016–2020 period, mild drought events occurred according to the VCI [24]. Therefore, it could be argued that the change in drought risk during this period is low. Thus, the level is selected as “low” and the pressure score is set as 0.75. Furthermore, since historical drought events according to the VCI showed that mild agricultural drought events occurred in the basin during this period [24], the level is selected as “low” and the state score is set as 0.75. Within the scope of combating agricultural drought, it is aimed to create an inventory of alternative livelihoods, identify plants with economic value and encourage their cultivation, introduce new production techniques, encourage water-saving irrigation systems, conduct and monitor disaster analysis for agricultural droughts, and prioritise economic, social and environmental impacts in regions that will be more affected by agricultural drought [37,38]. Hence, the level is chosen as “good” and the response score is set as 0.75. The average of the pressure, state, and response parameters (0.75 + 0.75 + 0.75)/3 = 0.75 is calculated as 0.75.

Flood Events

Changes in the frequency and severity of flood events could have adverse effects on the environment and pose a significant threat to environmental sustainability. As explained in the section on “flood events” above, the average of the pressure, state, and response parameters for this period ((0.50 + 1.00 + 0.75)/3 = 0.75) is calculated as 0.75.

Thus, the environment indicator for 2016–2020 (0.75 × 0.5 + 0.75 × 0.1 + 0.75 × 0.2 + 0.75 × 0.2 = 0.75) is calculated as 0.75.

3.2.4. Environment Indicator for 2021–2030 Period (Future Conditions)

Environmental Pressure Index (EPI)

The change in agricultural areas due to climate change in the region has been assessed through olive habitat areas, as the main agricultural product in the region is olives. Özdel et al. [57] analysed the future olive habitat areas under the RCP4.5 and RCP8.5 scenarios using MaxEnt 3.4.4 software. In their study, they noted that the Aegean region is highly sensitive to climate change and emphasised that a continuous decline could be expected for olive habitats. The results of their study showed that, under the RCP4.5 and RCP8.5 scenarios, the extent of olive habitat areas could decrease by 0.9% and 1.1%, respectively, in areas with highly suitable potential for olive habitat. Also, the population change in the basin during this period is estimated to be 21.8%. Considering the percentage changes in agricultural areas and population, the EPI value is calculated as 10.5% for the RCP4.5 scenario and 10.4% for the RCP8.5 scenario. The pressure score is 0.25 for both scenarios.

Considering that 48.9% of the basin has natural vegetation [54], and assuming that the change in agricultural areas could be approximately −0.9% for the RCP4.5 scenario and approximately −1.1% for the RCP8.5 scenario, it could be considered that the basin would have 48.5% and 48.4% natural vegetation cover in 2030, respectively. Thus, according to Table 3, the state score is 1.00 for both scenarios.

Since the ratio of pollution sensitive areas in the basin to the basin area is approximately 20% [58,59] and since Best Management Practices (BMP) will be applied to these areas to combat the effects of climate change within the framework of MSAP (2024) [49] and ASAP (2024) [50], it is expected that the protected areas (BMP applied areas) in the basin can increase by approximately 20% by 2030. Therefore, the response score is 1.00 for both scenarios. The average of the pressure, state, and response parameters (0.25 + 1.00 + 1.00)/3 = 0.75 is calculated as 0.75 for both climate scenarios. The potential sub-criteria that can affect the environment indicator previously described above are discussed below for the future condition, as expressed in Table 2, Table 3 and Table 4.

Hydrological Drought Events

Historical drought events indicate that the basin experienced mostly mild to moderate hydrogeological drought events according to the PHDI between 1984 and 2014 [24]. It is predicted that under climate change scenarios, the risk of hydrogeological drought would be moderate during this period [24]. Therefore, it can be said that the change in hydrological drought risk compared to the long term is small, and the risk continues at a medium level. For this reason, the level “medium” is selected for the RCP4.5 and RCP8.5 scenarios, and the pressure score is set as 0.50. Furthermore, as it is predicted that the risk of hydrological drought would be at a medium level according to the PHDI under climate change scenarios [24], the level “medium” is selected, and the state score is set as 0.50 for both climate change scenarios. Within the scope of measures to be taken against the adverse effects of climate change, it is planned to develop and implement early warning systems against drought events and to carry out regular training and awareness-raising activities for local authorities and citizens [48,49]. Therefore, the level “medium” is selected for both climate scenarios, and the response score is set as 0.50. The average of the pressure, state, and response parameters for RCP4.5 and RCP8.5 is calculated as 0.50 ((0.50 + 0.50 + 0.50)/3 = 0.50).

Agricultural Drought Events

Historical data indicate that drought events occurred in the basin during the periods 1989–1992, 2007–2008, and 2013–2014 [24]. Within the scope of climate projections, it is predicted that soil moisture, which is an important factor for agricultural drought, will decrease by 0.9% in the RCP4.5 scenario and 1.97% in the RCP8.5 scenario [24]. Indeed, Kurnaz (2014) [60] stated that soil moisture levels in the region could decrease by approximately −0.9 kg m⁻² (slightly) under climate scenarios. Therefore, it can be assumed that the risk of agricultural drought will continue at a mild level for the RCP4.5 scenario and at a medium level for the RCP8.5 scenario. In this regard, pressure scores of 0.75 and 0.50 are selected for the climate scenarios, respectively. Furthermore, considering that the trend of decrease in soil moisture is approximately twice as high in the RCP8.5 scenario, the state parameter is selected as 0.5 (medium) for RCP4.5 and 0.25 (poor) for RCP8.5.

Within the scope of combating agricultural drought in this period, adaptation and mitigation measures to be taken primarily against the negative effects of climate change were determined, and it was aimed to use low-loss irrigation systems in agricultural areas, to develop early warning systems, to identify alternative agricultural products within the scope of adaptation policies, to expand the sharing networks of climate-related agricultural research with local farmers and agricultural industry, to provide rainwater storage and smart water management measures to meet the water demand in agricultural production, and to encourage urban agriculture practices such as vertical farming and roof farming [48,49]. Considering these measures, the response score is set as 0.75 (good) for both scenarios. The average of the pressure, state, and response parameters is calculated as 0.67 for the RCP4.5 scenario ((0.75 + 0.50 + 0.75)/3 = 0.67), and ((0.50 + 0.25 + 0.75)/3 = 0.50) 0.50 for the RCP8.5 scenario.

Flood Events

The frequency and severity of floods have increased in recent years, as explained in the section on ‘flood events’ above [45]. However, it is anticipated that flood risk can be reduced through various adaptation and mitigation measures [51]. As stated in the relevant section, the average of the pressure, state, and response scores for both climate change scenarios ((0.50 + 1.00 + 0.75)/3 = 0.75) is calculated as 0.75.

Thus, the environment indicator for the 2021–2030 period under the RCP4.5 scenario (0.75 × 0.5 + 0.50 × 0.1 + 0.67 × 0.2 + 0.75 × 0.2 = 0.71) is calculated as 0.71. For the RCP8.5 scenario (0.75 × 0.5 + 0.50 × 0.1 + 0.50 × 0.2 + 0.75 × 0.2 = 0.68), it is calculated as 0.68.

3.2.5. Life Indicator for 2016–2020 Period (Baseline Condition)

Life indicator provides a holistic assessment of environmental and socioeconomic impacts. The Human Development Index (HDI) is used as the main determinant of this indicator in the classic WSI approach. Since HDI reflects the welfare level of society by measuring developments in areas such as health, education, and standard of living, it is directly affected by changes in environmental conditions. It is known that drought events have the potential to disrupt economic activities such as tourism and industry; affect energy supply security by reducing hydroelectricity generation capacity; increase the frequency and severity of wildfires, and cause major losses in both natural ecosystems and the economy [1]. For these reasons, physical environment, social, and economic dimensions should be taken into account when analysing the impacts of climate change on the life indicator. Comparative analyses for the baseline and future periods will enable decision makers to both anticipate risks and develop strategies to increase social resilience. Such multidimensional analyses are important for better understanding the impacts of climate change adaptation policies on quality of life.

Human Development Index (HDI)

The change in the HDI-Income sub-index in the period 2016–2020 compared to the previous period (2010–2015) is 3.6% [61,62]. Therefore, the pressure parameter becomes 0.75. In this period, the human development index in the basin is about 0.82 [61,62]. The state parameter is 0.75. Also, the HDI change in the basin during the study period is 1.34% [61,62]. The response parameter is 0.5. The average pressure, state, and response parameters ((0.75 + 0.75 + 0.50)/3 = 0.67) are calculated as 0.67. The sub-criteria of the vulnerability of the tourism and industrial sectors to drought events, hydroelectricity generation, and wildfire that can affect the life indicator are discussed below for the baseline condition, as expressed in Table 2, Table 3 and Table 4.

Vulnerability of the Tourism Sector to Drought Events

The sensitivity and vulnerability of tourism activities in the basin to drought events are very high, as water-sensitive tourism types such as marine, cultural, and nature tourism are prevalent in the basin [24]. However, no meteorological or hydrological drought events occurred in the region during this period [24]. Therefore, the level is selected as “low” and the state score is set to 0.75. The sensitivity and vulnerability of tourism activities in the region to drought events during this period are very high, as in previous years, meaning there is no change [24]. However, meteorological and hydrological drought events have decreased during this period compared to previous periods [24]. Thus, the level is selected as “low” and the pressure score is set as 0.75. Efforts are being made to protect the tourism sector and create a sustainable, developed, and modern tourism region [38]. Therefore, the level is selected as “medium” and the response score is set as 0.50. The average of the pressure, condition, and response parameters ((0.75 + 0.75 + 0.50)/3 = 0.67) was calculated as 0.67.

Vulnerability of the Industrial Sector to Drought Events

The vulnerability of the industrial sector in the basin to drought events is limited due to the region’s low industrial water consumption and sectoral economic indicators (e.g., number of employees and export volume) [24]. This indicates that the pressure on the industrial sector’s water resources is relatively low, and its vulnerability to climatic stress factors, such as drought, is also low. Therefore, the level “very low” is selected, and the state score is set as 1.00. In addition, the fact that there has not been a significant change in these indicators compared to previous periods confirms that the level of sectoral vulnerability in the region is continuous and that drought risk does not significantly threaten industrial activities [24]. Thus, the level “very low” is chosen, and the pressure score is set as 1.00. Although the vulnerability of the industrial sector is low, steps taken towards sustainable resource management are important. The adoption of circular economy approaches, such as the treatment and reuse of industrial wastewater, is considered a positive development in terms of both environmental sustainability and water resource conservation [40]. Hence, the level is chosen as “medium” and the response score is set as 0.50. The average of the pressure, state, and response parameters ((1.00 + 1.00 + 0.50)/3 = 0.83) is calculated as 0.83.

Hydroelectricity Generation

There is no hydroelectric power plant in the study area during the period under review, and no significant change is observed in the water consumption or installed capacity value of the energy sector since there is no significant infrastructure in terms of energy production in the previous periods [24]. Therefore, the level is selected as “very low” and the pressure score is set as 1.00. There is only one wind power plant in the region, characterized by very low water consumption and a limited economic contribution [24]. For this reason, the level is chosen as “very low” and the state score is set as 1.00. Within the framework of sustainability principles, some positive developments have been recorded in the fields of energy and environment. Studies on waste recovery, waste-to-energy, and environmental education in schools have been carried out [38]. Thus, the level is selected as “medium” and the response score is set as 0.50. The average of the pressure, state, and response parameters ((1.00 + 1.00 + 0.50)/3 = 0.83) is calculated as 0.83.

Wildfires

The mean number of wildfire events during the 2016–2020 period is 19.6 [63]. The pressure score is 0.25. The average burned area during this period is 37.4 ha [63]. The normalised average burned area value corresponds to 0.32, and the state score is 0.75. Within the scope of combating wildfires, the objectives are to develop early warning systems, increase the effectiveness of the fire brigade, raise awareness about fire prevention and preparedness by developing cooperation with national and international organisations, produce joint projects in cooperation with public institutions and NGOs, and organise educational activities for citizens [37,38]. Therefore, the level “good” is chosen, and the response score is set as 0.75. The average of the pressure, state, and response parameters ((0.25 + 0.75 + 0.75)/3 = 0.58) is calculated to be 0.58.

Thus, the life indicator for 2016–2020 (0.67 × 0.4 + 0.67 × 0.1 + 0.83 × 0.1 + 0.83 × 0.1 + 0.58 × 0.3 = 0.68) is calculated as 0.68.

3.2.6. Life Indicator for 2021–2030 Period (Future Conditions)

It is assumed that, due to data limitations, the life indicator (HDI, Vulnerability of the Tourism Sector to Drought Events, Vulnerability of the Industrial Sector to Drought Events, Hydroelectricity Generation, and Wildfires) for the 2021–2030 period would yield similar results for both the RCP4.5 and RCP8.5 scenarios. Therefore, a single value has been calculated for each parameter for the relevant scenarios.

Human Development Index (HDI)

The agricultural value of the study area is very important for the regional economy. Balıkesir province is one of the five provinces with the highest grain production in Türkiye [64]. Moreover, agricultural practices in the region provide 22.3% of employment [65]. Changing precipitation and temperatures due to the climate crisis could adversely affect agricultural production and productivity in the region, leading to increased food prices. It can cause a decrease in the welfare levels, labour productivity, and thus socio-economic structures of the people living in the region. In countries where the adverse effects of climate change are felt most, a 1 °C increase in temperatures causes changes of approximately −0.13% in economic growth, and approximately −0.67% in gross domestic product [26]. Considering that Türkiye is a country located in the Mediterranean climate zone where the negative effects of climate change are felt more and more each day, it is estimated that there will be a decrease of approximately 0.6% in income, especially considering the water scarcity problems and difficulties in agricultural production. This is a qualitative estimate based on the reasons mentioned above and reflects the authors’ contextual assessment. Therefore, the pressure parameter becomes 0.5.

The basin HDI value for the year one year before the study period is 0.83 [61,62]. The state parameter is 0.75. There is a negative relationship between temperature increases and economic growth in Türkiye [26]. Considering the economic difficulties experienced due to climate change, water scarcity, employment problems, and a decrease in production and productivity, the HDI is predicted to decrease by approximately 1%. This is a qualitative estimate based on the reasons mentioned above. The response parameter is 0.25. The average of the pressure, state, and response parameters (0.50 + 0.75 + 0.25)/3 = 0.50 is calculated as 0.50. The potential sub-criteria that can affect the life indicator previously described above are discussed below for the future condition as expressed in Table 2, Table 3 and Table 4.

Vulnerability of the Tourism Sector to Drought Events

The sensitivity and vulnerability of the tourism sector in the basin to drought events are quite high in future projections, as in previous periods [24]. This situation reveals that tourism activities in the region continue to depend on water resources and that the sector is structurally vulnerable to climatic variability. Meteorological and hydrological drought risk maps indicate that the basin has a moderate drought risk [24]. Therefore, the level “medium” is chosen, and the state score is set as 0.50. The vulnerability of the tourism sector to drought events remains very high, as in previous periods, and there is no change in this regard [24]. Furthermore, projections for the near future indicate that meteorological and hydrological drought risk would continue at a medium level, meaning there would be minimal change in risk compared to the long term [24]. Therefore, the level is selected as “low” and the pressure score is set as 0.75. Within the scope of adaptation and mitigation measures to be taken against climate change, it is planned to map historical and archaeological resources at risk, examine the vulnerability of national parks in the region to climate change, and increase institutional capacity and inter-institutional cooperation in the tourism sector regarding adaptation to climate change [48]. Therefore, the level is selected as “good” and the response score is set at 0.75. The average of the pressure, state, and response parameters ((0.75 + 0.50 + 0.75)/3 = 0.67) is calculated as 0.67.

Vulnerability of the Industrial Sector to Drought Events

Projections of the basin’s vulnerability to drought events indicate that the industrial sector is relatively resilient to drought events [24]. Accordingly, the level is chosen as “low” and the state score is set as 0.75. It is predicted that limited increases in industrial water consumption may occur in the region due to climate change, which does not significantly affect the sensitivity of the industrial sector to drought risk [24]. Therefore, it is foreseen that the drought pressure on the industrial sector may change slightly. Thus, the level is selected as “low” and the pressure score is set as 0.75. Within the scope of protection and sustainable use of water resources, measures such as treatment and reuse of industrial wastewater implemented in previous years are planned to be continued in this period [40,48]. Thus, the level is selected as “medium” and the response score is set as 0.50. The average of the pressure, state, and response parameters ((0.75 + 0.75 + 0.50)/3 = 0.67) is calculated to be 0.67.

Hydroelectricity Generation

There is no hydroelectric power plant in the study area, and the construction of such a facility is not planned in the period under review. Since there is no change in water consumption and installed capacity in the energy sector, it is predicted that the vulnerability of the energy sector in the basin to climate change may be quite low [24]. For this reason, the level is selected as “very low” and the pressure score is set as 1.00. Currently, there is only one wind power plant, and both the water consumption and installed power capacity of this plant are quite limited. Therefore, the vulnerability of the energy sector in the region due to climate change is expected to remain at a low level [24]. The level is chosen as “very low,” and the state score is set as 1.00. In this framework, it is planned to implement measures such as implementing incentive and credit programmes to increase energy efficiency, prioritising low-income households in energy efficiency programmes, encouraging local governments to implement efficient lighting practices, promoting sustainable structures such as green buildings, roofs, and walls, focusing on renewable energy policies, and strengthening energy transmission and distribution infrastructure [48]. Hence, the level is selected as “good,” and the response score is assigned a value of 0.75. The average of the pressure, state, and response parameters ((1.00 + 1.00 + 0.75)/3 = 0.92) is calculated as 0.92.

Wildfires

According to historical data [63], there has been a significant increase in the number of wildfires in the region in recent years. This increase is associated with the local effects of climate change, and it is predicted that the risk of wildfires could increase further in the future [66]. Therefore, the level is selected as “high” and the pressure score is set as 0.25. In addition, vulnerability to wildfire is considerable in the study area [24]. Accordingly, the level is chosen as “medium” and the state score is set as 0.50. To reduce fire risks, adaptation and mitigation measures have been determined. It aims to disseminate measures that reduce wildfire risk, strengthen close-to-nature management focused on adaptation to climate change, and take measures to mitigate risks in the supply of forest products and services affected by climate change [48,49]. Hence, the level is selected as “good” and the response score is set as 0.75. The average of the pressure, state, and response parameters ((0.25 + 0.50 + 0.75)/3 = 0.50) is calculated as 0.50.

Thus, the life indicator (0.50 × 0.4 + 0.67 × 0.1 + 0.67 × 0.1 + 0.92 × 0.1 + 0.50 × 0.3 = 0.58) becomes 0.58 for the RCP4.5 and RCP8.5 scenarios.

3.2.7. Policy Indicator for 2016–2020 Period (Baseline Condition)

The pressure parameter represents the change in the WSI-Education sub-index during the study period compared to the previous period, the state parameter represents the institutional capacity of the basin in IWRM, and the response parameter represents the development in IWRM investments/expenditures in the basin in classic WSI applications. As climate change awareness among the public and government, efforts to improve participatory water resources management, and efforts to improve water security, cooperation, and coordinated actions among various institutions are key factors affecting the success of IWRM, they have been included as sub-criteria of the policy indicator in this study. Evaluating these parameters together allows integrated water management practices to be addressed not only through physical investments but also through social awareness and institutional co-operation. Thus, sustainability in water management could be understood not only as the efficient use of resources, but also as a multi-actor governance model supported by participation in decision-making processes and long-term strategic planning.

HDI-Education and IWRM

The change in the HDI-Education sub-index during this period, compared to the previous period (2010–2015), was 9.8% [61,62]. The pressure parameter becomes 0.75. The basin has been evaluated in terms of institutional capacity, and it has been observed that it is sufficient in terms of strategic planning, management, and efficient use of physical resources. In the basin, various legal regulations are in force regarding water and the environment, as well as the institutions responsible for management. Studies are carried out on the protection and efficient use of water resources and the development of participatory water resources management [33]. Therefore, the condition of the basin was assessed as ‘good’ and the state parameter is set as 0.75.

There are drinking water treatment plants and wastewater treatment plants in the basin [67]. In addition, it was observed that the ratio of the population served by wastewater treatment plants in the basin to the municipal population increased in this period compared to the previous periods [42]. There are studies on the efficient use, protection, and effective management of water resources [24,33]. The presence of treatment plants and water resources management activities was evaluated together, and the level of development in water resources management expenditures in the basin during this period is considered good. In this case, the response parameter is 0.75. The average of pressure, state, and response parameters ((0.75 + 0.75 + 0.75)/3 = 0.75) is calculated as 0.75. The potential sub-criteria that can affect the policy indicator are discussed below for the baseline condition, as expressed in Table 2, Table 3 and Table 4.

Climate change awareness, participatory water resources management, and cooperation and coordinated actions

In this period, there are municipalities’ activities such as recycling and afforestation, and environmental trainings organised by NGOs; however, programmes on the climate crisis are not widespread, the climate agenda has found limited coverage in public opinion, and awareness has been shaped primarily through disasters [68]. Following the Paris Agreement, to which Türkiye is a signatory, public institutions in Balıkesir started to focus on environmentally sensitive projects. There is a limited but increasing awareness of climate change. Climate change has been addressed at a limited level within general environmental pollution topics, and measures have been developed through risk analyses on drought and water resource management [28,39]. However, in this period, awareness did not translate into significant and holistic climate policies at the implementation level. Due to the initial level of awareness and its slow progress, the pressure score is assigned a value of 0.25.

Water resources management in the region during the period under review was based mainly on technical planning and infrastructure investments of the central administration. In this process, direct participation of the public in decision-making mechanisms could not be ensured, and tools and practices for public participation remained limited [28,39]. In this context, it can be said that participatory water resources management did not develop in public opinion, and a governance-based approach could not be adopted sufficiently. Since the participatory water management approach is limited, the state score is set as 0.25.

Various activities have been carried out in the field of institutional coordination and governance within the scope of integrated water management. The General Directorate of Balıkesir Water and Sewerage Administration was established to ensure institutional coordination in water management. Strategic plans were prepared, in which institutional targets, performance criteria, and resource allocations were determined in the fields of water management and environmental protection [38]. Considering the efforts above, a response score of 0.50 is assigned. The average of the pressure, state, and response parameters (0.25 + 0.25 + 0.50)/3 = 0.33 is calculated as 0.33.

Thus, the policy indicator for 2016–2020 (0.75 × 0.7 + 0.33 × 0.3 = 0.63) is calculated as 0.63.

3.2.8. Policy Indicator for 2021–2030 Period (Future Conditions)

The policies, strategies, and measures prepared for the future period generally focus on raising awareness of the climate crisis, enhancing water security against its potential impacts, strengthening participatory water resource management, and promoting inter-institutional cooperation and coordinated action. Therefore, the plans and efforts are not based on a specific emissions scenario but are shaped within the framework of the potential risks and adaptation requirements of climate change.

Since there is no clear distinction between the RCP4.5 and RCP8.5 projections in planning, assessment, and IWRM investments, and due to data limitations for the future HDI-Education parameter, a single value is calculated for each parameter for both the RCP4.5 and RCP8.5 scenarios.

HDI-Education and IWRM

According to statistical data, the HDI-Education sub-index showed a 9.8% change in the 2016–2020 period compared to the previous period (2010–2015) [61,62]. Based on historical data, assuming that a similar change will continue, a similar development trend is qualitatively predicted for the 2021–2030 period, with an expected change of approximately 9%. In this case, the pressure parameter becomes 0.75, according to Table 2.

In the basin, there are studies for the protection and efficient use of the quantity and quality of water resources [46,67]. In the basin, there are institutions responsible for management, legal regulations, and laws related to water and the environment in force. There are new investments and projects [48,69] in the basin within the framework of protection and adaptation efforts towards climate change. There is also a study prepared to take measures against drought within the scope of combating climate change [24]. Therefore, the overall condition of the basin is considered “good,” and the state parameter is chosen as 0.75.

Studies on water resources management and sustainable agriculture have been carried out in the basin [64]. Mitigation and adaptation action plans and regional action plans have been prepared against the impacts of climate change [48,49,50]. Water efficiency strategies have been identified [46]. Integrated urban water management strategies were identified for Balıkesir province [69]. There are efforts to increase the number and capacity of treatment plants [67]. Therefore, the level of development in water resources management expenditures is considered good. In this case, the response parameter becomes 0.75. The average of the pressure, state, and response parameters ((0.75 + 0.75 + 0.75)/3 = 0.75) is determined as 0.75. The potential sub-criteria that could affect the policy indicator described above are discussed below for the future condition as expressed in Table 2, Table 3 and Table 4.

Climate change awareness, participatory water resources management, and cooperation and coordinated actions

A local climate change action plan was developed during this period, surveys were conducted to measure the opinions of the public and municipal staff, and NGOs were informed about the climate crisis through various activities [45,69]. Within the scope of the climate change action plan, participatory workshops involving public institutions were organized, a climate change adaptation strategy was established, and strategies for mitigating greenhouse gas emissions, managing disaster risk, and enhancing resilience were determined [69]. Therefore, it could be said that there has been an increase in the awareness of climate change among the public and the government. Considering the increased awareness and corporate efforts, a pressure score of 0.50 is assigned.

It is observed that the participatory water resources management approach started to develop in the region during this period. For example, the reuse of treated wastewater (greywater) not only provided a technical solution but also contributed to raising public awareness of water conservation and sustainability. Moreover, climate change-themed workshops and training programmes have been an important step in disseminating a participatory water management approach [69]. In line with these developments, it can be said that public participation has increased, and local governments have started to involve the community more in water management processes. Therefore, this period can be considered a phase of development in terms of participatory water resources management. Considering the developments in question, the state score is selected as 0.50.

Strategic plans covering the periods 2020–2024 and 2025–2029 were prepared to set institutional targets and strategies in the areas of water management and environmental protection [67,70]. Efforts were made to strengthen integrated planning, including public communication and inter-institutional data sharing mechanisms [69]. Planning aimed at harmonised management of water demands of sectors such as municipal services, agriculture, and industry [71]. These studies are crucial in enhancing institutional coordination within the context of integrated water management and climate change adaptation. Based on the aforementioned progress and efforts, the response score is set as 0.75. The average of the pressure, state, and response parameters ((0.50 + 0.50 + 0.75)/3 = 0.58) is calculated to be 0.58.

Thus, the policy indicator (0.75 × 0.7 + 0.58 × 0.3 = 0.70) remains at 0.70 for both the RCP4.5 and RCP8.5 scenarios.

4. Overall Watershed Sustainability

Hydrology, environmental, life, and policy indicators are calculated, and WSI values are determined using an equal weighting approach. WSI is calculated as 0.66 for the 2016–2020 period. For the 2021–2030 period, the decrease was −2.4% to 0.65 under the RCP4.5 scenario and −2.8% to 0.64 under the RCP8.5 scenario.

The results of the sensitivity analysis conducted to test the robustness of the equal weighting approach indicate that WSI values fluctuate within a narrow range. For the 2016–2020 period, WSI values ranged from 0.59 to 0.75, with a median of 0.66. The P10–P90 range was 0.62–0.70, with an interval width of 0.08. This indicates that the WSI varies within a very narrow range, except for extreme combinations. For the 2021–2030 period, WSI values ranged from 0.58 to 0.71 under the RCP4.5 scenario, with a median of 0.65. In this scenario, the P10–P90 range was calculated as 0.60–0.69 (width 0.09). In the RCP8.5 scenario, WSI values ranged from 0.58 to 0.70, with a median of 0.64. The P10–P90 range was determined as 0.61–0.68 (width 0.07). These results demonstrate that the WSI is robust to changes in weighting assumptions and remains within a relatively narrow range. In other words, WSI results obtained with equal weighting are almost identical to WSI results obtained with alternative weights. Therefore, equal weighting does not critically affect the results. Also, there is a study in the literature that supports these findings, demonstrating that the results are not significantly affected even when the weights of WSI indicators are altered using different methods [72].

Table 5. Average values of the pressure, state, and response parameters.

Indicator	Baseline			Average of P-S-R	RCP4.5 Scenario			Average of P-S-R	RCP8.5 Scenario			Average of P-S-R
	P	S	R		P	S	R		P	S	R
Hydrology Quantity	0.69	0.25	0.56	0.50	0.56	0.38	0.69	0.54	0.63	0.38	0.69	0.56
Hydrology Quality	0.58	0.83	0.66	0.69	0.56	0.68	0.73	0.65	0.63	0.68	0.73	0.68
Hydrology	0.63	0.54	0.61	0.59	0.56	0.53	0.71	0.60	0.63	0.53	0.71	0.62
Environment	0.70	0.95	0.60	0.75	0.43	0.85	0.85	0.71	0.38	0.80	0.85	0.68
Life	0.65	0.80	0.58	0.68	0.53	0.68	0.53	0.58	0.53	0.68	0.53	0.58
Policy	0.60	0.60	0.68	0.63	0.68	0.68	0.75	0.70	0.68	0.68	0.75	0.70
Average of H-E-L-P	0.65	0.72	0.62	0.66	0.55	0.68	0.71	0.65	0.55	0.67	0.71	0.64

shows the average P-S-R scores for the basin’s hydrology, environment, life, and policy indicators. The average P-S-R value for the baseline condition of the hydrology indicator is 0.59. At the same time, it is seen to have slight increases in the RCP4.5 and RCP8.5 scenarios, with values of 0.60 and 0.62, respectively. Similarly, slight increases are predicted for the hydrology-quantity indicator in future scenarios. Despite the increasing pressure on water resources due to climate change and population growth, which leads to increased water demand, the final scores for the hydrology and hydrology-quantity indicators do not decrease. This can be attributed to the increase in response scores, i.e., the positive effect of the developed policies. Furthermore, the state component of the hydrology-quantity indicator has the lowest value in the two periods (baseline: 0.25, RCP4.5 & RCP8.5: 0.38). This shows that the hydrology-quantity indicator is in a highly fragile state in both periods.

The average P-S-R value for the baseline condition of the hydrology-quality indicator is 0.69, while it decreases to 0.65 in the RCP4.5 scenario and to 0.68 in the RCP8.5 scenario. It can be argued that the measures taken to protect water resources and the reduced sediment yield resulting from decreases in surface runoff help alleviate the pressures on water quality in the future. However, the measures taken are insufficient to maintain the current state of water quality in the future.

The average P-S-R value for the baseline condition of the environmental indicator is 0.75, while it declines to 0.71 and 0.68 in the RCP4.5 and RCP8.5 scenarios, respectively. Furthermore, while the state component of the environment indicator has the highest value of 0.95 in the baseline period, it shows a downward trend in future period scenarios (RCP4.5: 0.85, RCP8.5: 0.80). In addition, the pressure component of the environment indicator is 0.70 in the baseline period but declines to 0.43 and 0.38 in the RCP4.5 and RCP8.5 scenarios, respectively. There is rapid population growth, increasing pressure on agricultural areas and natural vegetation cover, and the risk of hydrological and agricultural drought events in the basin. It can be stated that the measures taken, particularly developments in the BMP area, play a significant role in the basin. Still, it will not be sufficient to maintain the current situation in the future. Therefore, strengthening adaptation strategies appears critical to maintain the situation of the basin in the future in the face of increasing pressures.

The average P-S-R value for the baseline condition of the life indicator is 0.68, while it drops to 0.58 for the future conditions. It is predicted that pressure on the HDI-income parameter will increase in the future, and this could lead to decreases in social welfare and quality of life. In addition, since it is foreseen that drought events could affect the tourism sector, increase the pressure on the industrial sector, and trigger wildfires in the future, it is necessary to carry out more studies and take precautions in these areas.

The general condition of the institutional capacity in the basin is good, and essential studies on IWRM have been carried out for the baseline and future periods. Indeed, while the average P-S-R value for the baseline condition of the policy indicator is 0.63, it has increased to 0.70 for the future period. Although the high number of projects and planning has caused the policy score to be high for both periods, it should be emphasised that implementation efficiency is important. Uncertainties in the implementation level and implementation efficiency make it difficult to estimate a reliable WSI.

During the 2016–2020 period, the average response (0.62) and pressure (0.65) scores are low, while the average state (0.72) score is relatively higher. This situation indicates that the general condition of the basin was good during the relevant period, but the pressure was high, and the measures taken were insufficient.

During the 2021–2030 period, under the RCP4.5 scenario, the average pressure (0.55) and state (0.68) values have decreased compared to the baseline period, while the average response (0.71) value has increased. This indicates that pressures on the basin have increased, the overall condition of the basin has deteriorated, and that the measures taken play a critical role in mitigating negative impacts. Under the RCP8.5 scenario, the pressure and response scores are the same as in the RCP4.5 scenario, while the state score declines to 0.67. These results reveal that, despite various adaptation and mitigation measures, climate change could increase pressures on the basin, deteriorate its overall condition, and lead to a decline in its sustainability.

The effects of climate change on hydrology, environment, life, and policy indicators could be detailed as follows:

• Hydrology: Decrease in the amount of water per capita, changes in precipitation patterns, decrease in streamflow, and increase in pollution in water resources.
• Environment: Increased pressure on agricultural areas and natural vegetation, and drought pressures.
• Life: Decline in social welfare and quality of life, risk of tourism and industrial sectors being affected by drought events, and risk of wildfires.
• Policy: The development of mitigation and adaptation policies for climate change and the preparation of necessary projects have positively affected the WSI calculation in terms of policy parameters. However, raising awareness of climate change, developing participatory water resources, and enhancing inter-institutional cooperation are of critical importance for the basin.

5. Discussion

The WSI has a universal character, offering the possibility to compare the sustainability of watersheds. Results obtained in this study are compared with those of other studies applying WSI (Figure 3). The authors generally adopted the classical WSI approach [8] to estimate watershed sustainability. The basic principles of the index have generally been preserved in the studies, although some authors [11,72,73] have modified the index when estimating the WSI for different river basins. WSI was calculated for watersheds in many parts of the world. Chaves and Alipaz (2007) [8] applied the WSI to the Verdadeiro River Basin and calculated sustainability as 0.65. In addition, the WSI was applied by Catano et al. (2009) [74] to Reventazon Basin, Cortés et al. (2012) [11] to Elqui River Basin, Firdaus et al. (2014) [75] to Batang Merao Basin, Chandniha et al. (2014) [2] to Chhattisgarh Basin, Vanle et al. (2015) [76] to Nhue-Day Basin, de Castro et al. (2017) [77] to Curitiba Basin, Maynard et al. (2017) [78] to Japaratuba River Basin, Ahuchaogu et al. (2019) [79] to Ikpa River Basin, Núñez-Razo et al. (2023) [13] to Santiago River Basin and WSI scores varied from 0.36 to 0.74. These results indicate the sustainability of watersheds in developing countries. It is expected that WSI values in developing countries are at a medium level because socioeconomic and political problems in such countries tend to affect sustainability values negatively [72].

The sustainability index for the Central North Aegean Basin is 0.66 for the period 2016–2020. For the 2021–2030 period, it is 0.65 under the RCP4.5 scenario and 0.64 under the RCP8.5 scenario. Considering the results in the literature, it is seen that the WSI values calculated for the Central North Aegean Basin are at the medium level. Moreover, although the WSI values calculated for two different periods are close to each other (baseline: 0.66, RCP4.5: 0.65, RCP8.5: 0.64), it is clearly seen that climate change has a negative impact on basin sustainability. The comparative assessment of WSI across baseline, RCP4.5, and RCP8.5 scenarios highlights how the aggregate index can mask significant internal shifts under increasing climate stress. Although overall WSI values remain relatively stable across scenarios, the AR6-based breakdown reveals substantial variation in the sub-indices. Under present-day conditions, the basin maintains a moderate sustainability level with hydrological, ecological, socio-economic, and governance components in relative balance. Under RCP4.5, modest declines appear in hydrological and ecological dimensions, though socio-economic and governance indicators partly buffer these losses, leaving the overall WSI only slightly changed. By contrast, under RCP8.5, pronounced declines in water availability, ecosystem integrity, and agricultural stability emerge, with governance and socio-economic resilience proving insufficient to offset environmental deterioration. This concealed divergence underscores the added value of the revised WSI framework: while aggregate values may suggest stability, the sub-index dynamics clearly demonstrate that moderate climate futures may be managed with incremental interventions, whereas high-emission trajectories will demand transformative changes in basin management to preserve long-term sustainability.

This study advances the classical WSI framework by explicitly embedding climate discontinuities derived from the IPCC AR6 into the sub-index structure, rather than relying solely on hydrological or socio-economic indicators as in previous applications. By integrating TN and TP as core water-quality variables, the method is tailored to agriculture-dominated catchments where nutrient pressures are more critical than organic loads traditionally represented by BOD. The dual-scenario assessment (RCP4.5 and RCP8.5) further extends the approach, allowing decision-makers to gauge sustainability trajectories under both moderate and high-emission futures. Beyond methodological refinement, the findings have practical implications: the results highlight the need for adaptive governance, targeted nutrient management, and basin-scale planning tools that remain robust across various climate pathways. Rather than claiming causality, the analysis illustrates correlations between climatic stressors, hydrological responses, and socio-economic pressures, offering policy actors a transparent basis for prioritizing interventions under uncertainty. Unlike earlier WSI applications, this study does not simply adapt existing indices; instead, it introduces a methodological advance by embedding IPCC AR6 discontinuities, expanding scenario analysis, and systematically testing weighting and uncertainty.

Several limitations should be acknowledged. The analysis is based on a single basin context and therefore may not capture dynamics in other hydrological or socio-economic settings. Although both RCP4.5 and RCP8.5 were included, a wider set of scenarios could further enhance robustness. Finally, some climate sub-indicators remain semi-quantitative, which may understate their influence relative to fully quantitative metrics. These caveats suggest directions for future refinement while not diminishing the utility of the framework presented here.

6. Conclusions

Integrated watershed management that increases adaptive capacity to climate change impacts requires a sustainable management approach [80]. This study aims to raise awareness on climate change by trying to emphasise the importance of participation and cooperation in achieving the Sustainable Development Goals. The main contribution of this study is to enrich the WSI framework by linking it to the key climate change impacts highlighted in the IPCC 6th Assessment Report and to provide a methodological example through a case study. The impacts of climate change on water resources, the environment, and social and political areas are assessed through an innovative approach using WSI for different periods.

The proposed WSI approach was applied to two periods (baseline: 2016–2020 and future: 2021–2030) to assess the sustainable management of the Central North Aegean Basin, where the negative impacts of climate change are visible and agricultural activities are highly intensive. The 2021–2030 period has been assessed under the RCP4.5 and RCP8.5 scenarios. Subsequently, baseline WSI and future WSI results have been compared to assess the potential impacts of climate change on watershed sustainability. WSI has been calculated as 0.66 for the baseline period. Under the RCP4.5 and RCP8.5 scenarios, it has decreased slightly to 0.65 and 0.64, respectively. The amount of decrease in future WSI values under climate scenarios appears low when compared to the baseline period. However, considering that the periods are two short periods consisting of consecutive five-year and ten-year periods, it should be emphasised that −2.4% and −2.8% represent a significant trend. The results indicate that the problems arising in the basin due to climate change could lead to a decline in the watershed’s sustainability. This situation emphasizes that merely controlling the existing pressures would not be sufficient to improve the status of the watershed and ensure its sustainability; instead, it highlights the necessity of developing an integrated water management approach. Furthermore, the results suggest that strong policy and managerial responses could play a decisive role in the future.

The study’s findings reveal the strengths and weaknesses of the hydrological, environmental, social, and political characteristics of the basin. Furthermore, the results indicate that to strengthen the identified weaknesses of the basin, cooperation and coordination between decision-makers and different stakeholders need to be increased, and governance linkages need to be strengthened. As effective governance requires interactive and comprehensive communication between non-governmental organizations, institutions involved in basin management, and citizens, conflicts of opinion and governance failures can be avoided [81]. The study provides an innovative framework for addressing climate change. Assessing the potential impacts of climate change on hydrology, environment, life, and policy will contribute to developing appropriate solutions to the risks posed by the climate crisis.