Search Results (597)

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

The construction of cascade reservoir systems (CRSs) is increasing globally, providing reliable energy and water resources for human social development, while also having significant impacts on the watershed water environment, particularly in terms of nitrogen and phosphorus distribution in the rivers and lakes of these areas. Watershed management authorities urgently need model tools that can comprehensively analyze the sources of nitrogen and phosphorus in CRSs and the nitrogen and phosphorus cycling in lakes and reservoirs. Therefore, this study establishes a model framework that includes a watershed nutrient load model and a hierarchical reservoir nutrient cycling model, validating and analyzing this framework in the Water Diversion Basin from the Luanhe River to Tianjin (WDBLT) in North China, which yields nitrogen and phosphorus substance flows over different time scales. The conclusions show that banning cage culture and curbing point sources improved reservoir water quality, and the internal TP flux serves as a key environmental indicator. This model framework is scientifically sound, easy to operate, and does not require high data demands, demonstrating high practical value for similar water environmental management in CRS. Full article

Engineered wetland has been used as a Best Management Practice (BMP) to remove pollutants and maintain water quality in watersheds. This study is focused on developing models to analyze the impacts of discharges on the efficiency of wetlands to improve water quality downstream. The watershed hydrological Soil & Water Assessment Tool (SWAT) and wetland (Personal Computer Storm Water Management Model—PCSWMM) models were developed to analyze the efficiency of engineered wetlands to remove the pollutants for different basins under three different climatic conditions (i.e., dry, average and wet year). The SWAT was calibrated and validated to simulate discharge and water quality parameters. The wetland model was developed using inflow hydrographs and concentrations of the water quality parameters biochemical oxygen demand (BOD), total suspended solids (TSSs), total nitrogen (TN) and total phosphorous (TP), simulated from a Soil & Water Assessment Tool (SWAT) model. A PCSWMM (wetland) was developed based on the physical and first order decay process within the wetland system for three basins in Prairie du Pont watershed in Illinois, USA. The results showed that pollutant removal efficiencies decreased from low to high discharges (dry to wet climatic conditions) for all watersheds and pollutants (except for BOD) based on trendline analysis. Nevertheless, the efficiencies were highly variable, specifically during low discharges. Furthermore, the sensitivity of the k-parameter (areal rate constant) was pollutant dependent. Overall, this study is helpful to understand the efficacy of wetlands’ pollutant removal as a function of discharge. The approach can be used in watersheds located in other geographic regions for the preliminary design of engineered wetlands to remove non-point source pollution and treat stormwater runoff. Full article

With the rapid advancement of industrialization and urbanization, the systematic assessment of water environment security in lake-type basins and the identification of key restoration zones have become critical scientific challenges for sustainable watershed management. This study focused on the Dongting Lake Basin, where a comprehensive evaluation system comprising 24 indicators was developed based on the Driving forces–Pressure–State–Impact–Response model. Indicator weights were determined using the entropy method. An obstacle degree model was applied to quantitatively identify the primary factors constraining water environment security. Additionally, spatial autocorrelation analysis was introduced to examine spatial dependency characteristics, enabling the delineation of priority restoration areas. The results demonstrated the following: (1) During 2000–2020, the Dongting Lake Basin exhibited significant spatial heterogeneity, with higher water environment security risks in the southeastern region, while the central-eastern region showed a continuous improvement trend. (2) Quantitative analysis identified the core obstacle factors affecting regional water environment security: wastewater treatment capacity (obstacle degree: 16.87%), ecological water use proportion (12.71%), effective irrigation area ratio (9.29%), environmental protection investment as a percentage of GDP (8.54%), and wastewater treatment rate (7.10%). (3) Based on these key constraints, targeted governance strategies are proposed, including enhancing wastewater treatment capacity, optimizing ecological water allocation, and increasing environmental protection investment. This study established an integrated “diagnosis–restoration–regulation” analytical framework for assessing water environment security and identifying priority restoration zones in lake-type basins, providing both theoretical foundations and practical references for global lake-type basin management. Full article

This paper presents the Green Watershed Index (GWI) methodology, focusing on the 17 sustainability indicators selected in the Razey watershed, NW Iran. Field surveys and data collection have provided the possibility of field inspection and measurement of the present condition of the watershed and the indicators taken. Based on the degree of compliance with the required process, each indicator was scored from 0 to 10 and classified into three categories: unsustainable, semi-sustainable, and sustainable. Using the Entropy method to assign weight to each indicator and formulating a proportional mathematical relationship, the GWI score for each sub-watershed was derived. Spatial changes regarding the selected indicators and, consequently, the GWI were detected in the study area. Development of water infrastructure, particularly in the upstream sub-watersheds, plays a great role in increasing the GWI score. The highest weight is related to environmental productivity (0.26), and the five indicators of water footprint, knowledge management and information quality system, landscape attractiveness, waste recycling, and corruption control have approximately zero weight due to their monotonous spatial distribution throughout sub-watersheds. Only sub-watershed R1 has the highest score (5.13), indicating a semi-sustainable condition. The rest of the sub-watersheds have unsustainable conditions (score below 5). Concerning the GWI, the watershed is facing a critical situation, necessitating the implementation of management and conservation strategies that align with the sustainability level of each sub-watershed. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

The U.S. Environmental Protection Agency (EPA) developed the concept of Water Quality Volume (WQv) as a Best Management Practice (BMP) to treat the first 25.4 mm of rainfall in urban areas, aiming to capture approximately 90% of annual runoff. However, applying this urban-based standard—designed for areas with over 50% imperviousness—to rural regions with higher infiltration and pervious surfaces may result in overestimated facility capacities. In Korea, a uniform WQv criterion of 5 mm is applied nationwide, regardless of land use or hydrological conditions. This study examines the suitability of this 5 mm standard in rural catchments using the Hydrological Simulation Program–Fortran (HSPF). Eight sub-watersheds in the target area were simulated under varying cumulative runoff depths (1–10 mm) to assess pollutant loads and runoff characteristics. First-flush effects were most evident below 5 mm, with variation depending on land cover. Nature-based treatment systems for constructed wetlands were modeled for each sub-watershed, and their effectiveness was evaluated using Flow Duration Curves (FDCs) and Load Duration Curves (LDCs). The findings suggest that the uniform 5 mm WQv criterion may result in overdesign in rural watersheds and highlight the need for region-specific standards that consider local land-use and hydrological variability. Full article

The Angat Reservoir serves as a major water source for Metro Manila, providing most of the region’s domestic, agricultural, and hydropower needs. However, its dependence on rainfall makes it sensitive to climate variability and future climate change. This study assesses potential long-term impacts of climate change on water availability in the Angat watershed using the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS). Historical rainfall data from 1994 to 2023 and projections under both RCP4.5 (moderate emissions) and RCP8.5 (high emissions) scenarios were analyzed to simulate future hydrologic responses. Results indicate projected reductions in wet-season rainfall and corresponding outflows, with declines of up to 18% under the high-emission scenario. Increased variability during dry-season flows suggests heightened risks of water scarcity. While these projections highlight possible changes in the watershed’s hydrologic regime, the study acknowledges limitations, including assumptions in rainfall downscaling and the absence of direct streamflow observations for model calibration. Overall, the findings underscore the need for further investigation and planning to manage potential climate-related impacts on water resources in Metro Manila. Full article

Research on watershed ecological protection and restoration within the framework of territorial spatial planning serves as a critical approach to ensuring national ecological security and plays a vital role in enhancing ecosystem stability. In recent years, scholarly interest in this topic has grown significantly. However, development trends and optimization strategies remain unclear, especially regarding comparative insights between Chinese and English research articles within the territorial spatial planning paradigm. A comprehensive review is therefore needed to bridge this gap. This study utilizes bibliometric analysis with CiteSpace, based on publications from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, to visualize and compare Chinese and English research articles on watershed ecological protection and restoration. By combining quantitative and qualitative approaches, this study identified research hotspots and trajectories and provided directions for future research. The main findings are as follows: (1) A quantitative analysis indicates that the number of publications has increased significantly since 1998, with growing interdisciplinary and cross-sector collaboration. (2) The qualitative analysis reveals three fundamental theoretical principles: holistic management, multi-scale interactions, and dynamic coordination. (3) The Chinese Academy of Sciences led in research output, while other institutions showed wider geographic coverage, stronger collaboration networks, and a decentralized, multi-core structure. (4) Keyword clustering highlights three major themes: evaluation methodologies for ecological protection and restoration, spatiotemporal evolution and driving mechanisms, and integrated governance system development. (5) Within the territorial spatial planning paradigm, future researchers should employ big data analytics and monitoring technologies to better diagnose and address ecological challenges. Full article

To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 ${\mathrm{m}}^3/\mathrm{s}$ (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems. Full article

Sediment fingerprinting was utilized to identify potential hotspots of soil erosion and sediment transport pathways in the Nile Nyabarongo Upper Catchment (NNYU) in Rwanda, where rivers and reservoirs are suffering from alarmingly high levels of sedimentation. Sediment fingerprinting is a practical approach used to identify erosional hotspots and sediment transport processes in highly mountainous regions undergoing swift land use transformation. This technique involves a statistical comparison of the elemental composition of suspended sediments in river water with the elemental composition of soils belonging to different geological formations present in the catchment, thereby determining the sources of the suspended sediment. Suspended sediments were sampled five times over dry and wet seasons in all major headwater tributaries, as well as the main river channel, and compared with soils from respective delineated watersheds. Elemental composition was obtained using laser ablation inductively coupled plasma mass spectrometry, and elements were chosen that could reliably distinguish between the various geological types. The final results indicate different levels of sediment contribution from different geological types. A three-level intervention priority system was devised, with Level 1 indicating the areas with the most serious erosion. Potential sources were located on an administrative map, with the highest likely erosion over the study period (Level 1) occurring in Kabuga cell in the Mwogo sub-catchment, Nganzo and Nyamirama cells in the Nyagako sub-catchment and Kanyana cell in the NNYU downstream sub-catchment. This map enables the pinpointing of site visits in an extensive and rugged terrain to verify the areas and causes of erosion and the pathways of sediment transport. Sediment concentrations (mg L⁻¹) were the highest in the Secoko and Satinsyi tributaries. The composition of suspended sediment was seen to be temporally and spatially dynamic at each sampling point, suggesting the need for an adequate number of sampling locations to identify erosion hotspots in a large mountainous watershed. Apart from prioritizing rehabilitation locations, the detailed understanding of critical zone soil–land cover–climate processes is an important input for developing region-specific watershed management and policy guidelines. Full article

Urbanization and climate change have disrupted natural water circulation by increasing impervious surfaces and altering rainfall patterns, leading to reduced groundwater infiltration, deteriorating water quality, and heightened flood risks. This study investigates the application of Low Impact Development (LID) and flood control facilities as structural measures to address these challenges in the upper watershed of the Fucha River in Bogotá, Colombia. The methodology involved analyzing watershed characteristics, defining circulation problems, setting hydrological targets, selecting facility types and locations, evaluating performance, and conducting an economic analysis. To manage the target rainfall of $26.5$$\mathrm{m}$$\mathrm{m}$ under normal conditions, LID facilities such as vegetated swales, rain gardens, infiltration channels, and porous pavements were applied, managing approximately 2362 ${\mathrm{m}}^3$ of runoff. For flood control, five detention tanks were proposed, resulting in a 31.8% reduction in peak flow and a 7.3% decrease in total runoff volume. The flooded area downstream was reduced by $46.8$$\mathrm{h}\mathrm{a}$, and the benefit–cost ratio was calculated at 1.02. These findings confirm that strategic application of LID and detention facilities can contribute to effective urban water cycle management and disaster risk reduction. While the current disaster management approach in Bogotá primarily focuses on post-event response, this study highlights the necessity of transitioning toward proactive disaster preparedness. In particular, the introduction and expansion of flood forecasting and warning systems are recommended as non-structural measures, especially in urban areas with complex infrastructure and climate-sensitive hydrology. Full article

Green roofs (GRs) are emerging as effective tools for mitigating urban runoff, particularly in cities facing challenges related to increased impervious surfaces and flooding risks. This study evaluates the potential hydrological performance of GR retrofitting in São José dos Campos, Brazil, based on municipal legislation, focusing on the effects of reducing the Effective Impervious Area (EIA) in urban watersheds. Using a range of projected EIA reduction scenarios (Mandatory, Incentivized, and Ideal), this study compares key hydrological indicators such as peak flow attenuation, runoff volume reduction, and hydrograph delay during rainfall events with different return periods. The results show that retrofitting with GRs significantly attenuates peak flows and delays runoff, with the ‘Ideal’ scenario (EIA = 16%) achieving peak flow reductions of up to 41% and runoff volume reductions of 35%. However, the effectiveness of GRs diminishes for high-intensity rainfall events, suggesting that GRs are most effective for frequent, low-intensity storms. These findings demonstrate the potential of GRs in reducing flooding risks in urban environments, highlighting the importance of integrating GRs into broader sustainable drainage systems. This study further emphasizes that while financial support is crucial for promoting GR adoption, it alone is not sufficient. Policies should be complemented by educational efforts and urban regulatory measures to ensure widespread adoption and long-term impact. This research provides urban planners and stakeholders with evidence to enhance urban resilience, sustainability, and effective flood risk management. Full article

Rapidly decarbonizing the electricity grid is crucial for achieving net-zero greenhouse gas (GHG) emissions by mid-century and mitigating climate change impacts. Hydropower facilities can directly support grid decarbonization; however, like all energy systems, they emit GHGs throughout their lifecycle, with reservoirs being an important source. Further research is urgently needed to improve the accounting and mitigation of hydropower reservoir GHG emissions to ensure that this technology is accurately considered in decarbonization policies and to allow project owners and energy buyers to make credible emission claims regarding this energy source. To this end, this paper reviews over seven dozen studies and emerging research to synthesize the current state of the science on reservoir GHG emission pathways as well as advancements in emission measurement tools to identify areas where further research is needed. This paper presents a research roadmap for government agencies, research institutions, and funding organizations covering four action areas: understanding and reducing uncertainties in reservoir GHG estimation and associated publicly accessible estimation tools; reducing the technical and economic barriers for reservoir managers to use GHG estimation tools; setting common standards to enable consistent monitoring, allocation, and reporting of reservoir GHG emissions; and supporting work on reservoir GHG emission mitigation strategies, including watershed-scale strategies. Progress in these areas will enable robust accounting of hydropower reservoir GHG emissions and targeted mitigation efforts to advance grid decarbonization. Full article

Anthropogenic pollution of watersheds significantly threatens aquatic ecosystems, biodiversity, and human health. The present review examines the primary sources of contamination in river catchments, including industrial effluents, agricultural runoff, and urban wastewater discharge. The presence of pollutants degrades water quality, disrupting aquatic habitats and leading to adverse outcomes, including biodiversity loss, eutrophication, and declining fish populations. It also focuses on strategic mitigation approaches, including implementing stricter waste management regulations, adopting sustainable agricultural practices, improving wastewater treatment infrastructure, and public education initiatives. The article summarizes several biotechnological techniques developed to decrease the impact of farming activities on water quality. It also emphasises directions that could be followed concerning specific water chemical indicators, such as the residual quantity of heavy metals. Emphasis is placed on the need for integrated policy frameworks and cross-sector collaboration to safeguard freshwater systems and ensure long-term environmental sustainability. Full article