Impacts of Climate Change on Water Resources: Assessment and Modeling—First Edition

1. Introduction

The measurement and assessment of water resources are almost as old as the earliest civilizations: the Egyptians depended on the regular inundation of the Nile for their livelihood, and records of the Nile levels can be traced back to about 3000 to 3500 B.C. In ancient India, rain gauges were in use for agricultural purposes since at least the 4th century BC. In the 1st century AD, the Greek mathematician and engineer Hero of Alexandria proposed a method of measuring spring discharge [1], while the water commissioner Sextus Julius Frontinus left a wonderful description of the aqueducts of the city of Rome [2]. Renaissance genius Leonardo da Vinci pondered the principles of the hydrological cycle and the flow of water in an open channel [3]. With the advent of the Enlightenment, a new era began in the assessment and modeling of water resources. Among a number of pioneers of research in this area, contributions to hydrometry made by Henry De Pitot [4], Daniel Bernoulli [5], Antoine Chézy [6], Reinhard Woltman [7], John Dalton [8], and Henry Darcy [9] can be mentioned.

Despite the passage of centuries and further development of hydrological knowledge, the assessment and modeling of water resources still remain some of the key issues in contemporary studies. A significant impulse for the development of research in this field was undoubtedly given by the changes in the water cycle in connection with human activity and the observed climate change [10,11]. This affects the elements of the water cycle and leads to the intensification of hydrological extreme phenomena, such as floods and droughts, changes in the availability of water for agricultural, industrial, and municipal purposes, adverse impacts on ecosystems, etc., which are predicted to intensify in the future [12].

This Special Issue includes articles focused on a wide range of issues related to the assessment and modeling of water resources and their availability and demand under the conditions of climate change. In our opinion, the intended goal was successfully reached. We received valuable contributions from different corners of the world, including Brunei, China, Greece, India, Iran, Kazakhstan, Poland, and the USA, presenting noticeably diversified analyses in terms of the investigated topics and applied methodologies and based on measurements and mathematical modeling.

We believe that a wide spectrum of readers of Water can enjoy these new findings and learn more about the assessment and modeling of the impacts of climate change on water resources and related topics using the papers published here. They are also encouraged to disseminate and share the presented results among the scientific community, policymakers, and stakeholders.

2. Review of New Advances

Rhymee et al. (contribution 1) studied the effects of climate change on paddy cultivation in Brunei Darussalam, focusing on the Wasan rice scheme located in Brunei Muara district. In order to project irrigation water requirements (IWRs) and crop water requirements (CWRs) or the main and off season using the WEAP-MABIA model, the authors employed historical data analysis over the past 30 years and future projections up to 2100 to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socio-economic pathways (SSPs) (SSP245, SSP370, and SSP585), were utilized to project the IWRs and CWRs. Using downscaled CMIP6 data, three future periods were bias-corrected with the help of quantile delta mapping (QDM) for three periods: 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). It was determined that changes in future temperatures would lead to higher average ETc, resulting in elevated demands in irrigation water during the off season, especially prominent in high-emission scenarios. This study revealed that while the main season experienced a relatively stable or slightly increasing IWR trend, the off season consistently showed a decreasing trend. The off season also benefited from substantial rainfall increases, effectively reducing IWRs despite the rise in both maximum and minimum temperatures.

Jha et al. (contribution 2) explored the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand, taking as an example the state of Bihar in the northeastern part of India. The changes in rice yield, water demand, and crop phenology were estimated with varying CO₂ concentrations and an ensemble of general circulation models (GCMs) using a decision support system for agrotechnology transfer (DSSAT). The measured CO₂ concentration of 400 ppm from the Keeling curve was used as the default CO₂ concentration to estimate yield, water demand, and phenology. The authors compared these outputs obtained with the default concentration with the results from climate change scenarios’ concentrations. In the next step, the outputs corresponding to the ensemble GCMs’ climate data were computed, and the results were compared with the ensemble crop model outputs simulated with each GCM. The yield was concluded to increase with the increase in the CO₂ concentration up to a certain threshold, while water demand and phenology were observed to decrease with the increase in the CO₂ concentration. It was determined that the two approaches of the ensemble technique to obtain final outputs from the DSSAT results did not show significant differences in the predictions.

Hedden-Nicely and Kaiser (contribution 3) developed the irrigation demand model to assess the impact of how climate change would affect the interrelationship between irrigation demand with decreasing water supply in the western part of the United States. This study focused on 36 agricultural areas exhibiting heterogeneous land ownership (i.e., a mixture of tribal and non-Indian lands) and/or containing both tribal and non-Indian communities. The proposed model was based on the soil–water balance equation, and data were used in a system dynamics model to integrate crop–water requirement estimation techniques with climate change estimates. A Monte Carlo analysis was also applied to assess how irrigation demand could change because of changing temperature, precipitation, incoming radiation, and wind speed caused by climate change. The results indicated that climate change would cause increases in irrigation requirements at most locations. Furthermore, climate change was expected to significantly increase seasonal variability in many locations. The model could provide a useful tool based upon publicly available data that would allow individual water users to make conservation decisions necessary to preserve their water rights in the conditions of climate change.

Zhou et al. (contribution 4) analyzed the impact of climate change on the agricultural and industrial water demands in the Beijing–Tianjin–Hebei (BTH) region of China, coping with an extreme imbalance between the supply and demand of water resources, restricting further socio-economic development in that area. The authors used a statistical downscaling model to generate future climate data under the scenarios RCP2.6, RCP4.5, and RCP8.5, respectively, and then coupled them with agricultural and industrial water demand prediction models to simulate the impact of climate change on the agricultural and industrial water demands in the BTH region. The results showed growing rates of reference crop evapotranspiration (ET₀) under each scenario in the Beijing, Tianjin, and Hebei areas in the forecasted period of 2020–2035. Moreover, it was determined that under each climate scenario, the increase in evapotranspiration in the Hebei area was the largest, followed by that in the Tianjin area, and that in the Beijing area was the smallest. Regarding water consumption per CNY 10,000 of industrial added value during the forecast period, a downward trend was detected in the Beijing area, while in the Tianjin and Hebei areas, upward trends were found under the three different climate scenarios.

Asfari et al. (contribution 5) examined the impact of climate change on meteorological droughts in six metropolises in Iran: Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz. In this study, CMIP6 climate models under varying climate change scenarios (SSPs) were employed to forecast severe meteorological droughts. A wide range of drought indices (SPI, DI, PN, CZI, MCZI, RAI, and ZSI) were also used to assess the drought severity in each analyzed city in the period from 2025 to 2100. Based on the results obtained by the authors, box plots and heat maps were generated to present the distribution of the drought values under different indices and scenarios, and a depiction of the probability of severe drought occurrences until the end of the century for each city was provided. This study was carried out as a response to the growing concerns of rapidly decreasing water levels in Iran’s dams, declines in groundwater aquifers, and the compounding issues of land subsidence and soil erosion due to excessive groundwater withdrawal in the face of severe droughts affecting the Iranian metropolises.

Liu et al. (contribution 6) proposed a deep learning-based method (UNET-GRU) to quantitatively evaluate the effect of artificial precipitation enhancement, which is one of the most important and challenging issues in meteorology. Two cities, Wuhan and Shiyan in China, were selected to represent typical plains and mountainous areas, respectively, and the method was evaluated using 6 min resolution radar weather data from 2017 to 2020. During the experiment, the UNET-GRU algorithm was used, and separate algorithms for comparison against common persistent baselines were developed. The authors proved that the prediction of mean squared error for these three algorithms was significantly lower than that of the baseline data, and the indicators for these algorithms were satisfactory, further demonstrating their efficacy. Furthermore, based on the comparison between the residual results of the estimated 7 h grid rainfall and the actual recorded rainfall, it was concluded that the estimated rainfall was consistent with the recorded precipitation for that year, thus indicating that deep learning methods can be successfully used to evaluate the impact of artificial precipitation. The results obtained demonstrated that the method proposed by the authors improved the accuracy of the effect evaluation and enhanced the generalization ability of the evaluation scheme.

Charalampopoulos et al. (contribution 7) pointed to bioclimate alteration possessing a current, but also a potential future threat to natural and agricultural ecosystems and their services. On this basis, the present and future bioclimatic footprint of five Central European countries, Austria, Switzerland, the Czech Republic, Hungary, and Slovakia, was studied. The analysis was performed by taking the years 1981–2010 as the reference period for three time series, namely 2011–2040, 2041–2070, and 2071–2100, respectively, under two emissions scenarios (SSP370 and SSP585) to determine the potential short-term and distant future bioclimatic change trends. The results revealed higher xerothermic trends over the lowland agricultural areas of Central Europe mostly in 2071–2100 and under the extreme SSP585, with the classes’ spatial distributions going from 0.0% to 2.3% for the semi-dry class and from 0.0% to 30.1% for the presiding Mediterranean class. More than half of the territory’s agricultural area was foreseen to be dependent on supplementary irrigation by 2100. More intense dry thermal conditions were expected to impact the agricultural areas of the Czech Republic, Slovakia, and Hungary, with the latter emerging as particularly vulnerable.

Charalampopoulos et al. (contribution 8) carried out research on the present and projected bioclimate evolution over Greece’s phytogeographical regions. The assessments were performed over the reference period (1970–2000) and two future time frames, 2021–2040 and 2041–2060, under the RCP4.5 and RCP8.5 emission scenarios. The authors, for the first time, analyzed and illustrated ultra-high-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterization. The application of this index provided an in-depth view of the Greek area’s bioclimatic regime and the potential alterations due to climate change per phytogeographical region. By 2060 and under the extreme RCP8.5, intense xerothermic trends were demonstrated owing to the resulting significant spatial evolution mainly of the Arid–Hot, Semi-Arid–Very Hot, Semi-Arid–Hot, and Semi-Arid–Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%), and North Central (up to 31%) in Greece. The RCP8.5 long-term period exhibited the strongest impacts over approximately the right half of the Greek territory, with the bioclimate appearing more dry–thermal in the future.

Abdrakhimov et al. (contribution 9) analyzed the impacts of climate change on the water regime in the Ile River basin in Kazakhstan. The authors investigated the trends in selected meteorological and hydrological variables, as temperature and precipitation for elevations above and below 1500 m a.s.l. during cold and warm periods, and runoff within the basin, emphasizing the importance of incorporating these intra-annual variations when planning water management strategies and hydraulic structures. It was found that rising average annual air temperatures were leading to a larger area experiencing snowmelt and a longer warm period within the runoff formation zone, which had direct impacts on the water balance of the Ile River basin. This research demonstrated that changes in temperature regime and precipitation patterns in the Ile River basin resulted in observable alterations to the water regimes of the Ile River and its tributary rivers located in various basin locations and elevations. An increase in total annual precipitation, particularly during the cold season within the runoff formation zone, was also determined, suggesting a potential growth of water resources in the future. The researchers pointed to the importance of their findings in adjusting the existing plans of water management and developing new strategies in the analyzed area.

Marsz et al. (contribution 10) investigated the relationships between changes in the annual surface temperature of the North Atlantic (SST) and the number of days per year experiencing low flows in the Warta River catchment (WRC) in Poland, Central Europe, in 1951–2020. In order to describe the conditions of hydrological drought in the analyzed area, the number of days with low flows (T_LF) was applied in this study. This study revealed moderately strong, but statistically highly significant, relationships between T_LF and the SST in the subtropical and subpolar North Atlantic. On this basis, it was concluded that with the increase in the annual SST in these parts of the North Atlantic, the number of days in a year with low flows in the study area also increased. Moreover, the authors determined that besides synchronous relationships between T_LF and SST, asynchronous relations also occurred: the SST changes were one year ahead of the T_LF changes. With the increase in the SST in the subtropical and subpolar North Atlantic, the sunshine duration and air temperature in the WRC increased, while the relative humidity decreased. This indicated that the impact of SST changes on T_LF in the WRC was mainly caused by shaping the amount of surface evaporation, which strongly increased in the periods of high SST, and the climatic water balance became negative, resulting in an increase in extremely low flows in the study area. These findings shed a new light on the role of teleconnections in shaping the temporal variability of hydrological droughts in Central Europe.

Perz et al. (contribution 11) assessed relationships between rainfall and river flood risk in the upper Nysa Kłodzka River catchment and Kłodzko town located on the Nysa Kłodzka River in southwestern Poland, which are two of the most flood-prone areas in the Odra River basin. In this study, the well-established methods of potential flood losses (PFLs) estimation and the copula-based model, allowing an assessment of connections between rainfall and flood losses in a probabilistic way, were applied. Seventeen significant summer (rainfall-driven) flood waves recorded in the multi-annual period 1971–2021 were selected, for which PFLs were estimated. With regard to different periods of rainfall aggregation, the authors calculated cumulative rainfall for five temporal variants, 24, 48, 72, 96, and 120 h, preceding the flood peak. The results were presented using the ‘synchronicity’ measure. The estimated PFLs for the flood events ranged from EUR 2.61 million to EUR 77.89 million. It was found that the synchronicity of PFLs and the 24 h rainfall was the lowest among the analyzed variants, while for the 48 to 120 h rainfall, the highest synchronicity was identified at precipitation gauge Podzamek located east of Kłodzko. The findings of this research may be useful in preparing and adjusting warning systems or planning protection measures and infrastructure in areas exposed to flood hazards.

3. Conclusions

The articles published in this Special Issue reflect a wide range of topics and methodological approaches of contemporary research related to water resources’ assessment and modeling. While the majority of them (six in total) addressed the predicted effects of climate change on crop production and irrigation and industrial and municipal water demand in selected parts of the world, different methodologies in these studies were applied, including those proposed by Rhymee et al.’s (contribution 1) WEAP-MABIA model utilizing a dual crop coefficient approach to evaluate crop evapotranspiration, and an ensemble of four GCM models under the fifth phase of the Coupled Model Intercomparison Project introduced by Jha et al. (contribution 2). In turn, Hedden-Nicely and Kaiser (contribution 3) developed a model based on soil–water balance to predicting the irrigation demand in relation to different water law systems, and Zhou et al. (contribution 4) used the statistical downscaling model (SDSM) to estimate the impacts of climate change on agricultural and industrial water demands under different future emission scenarios. Then, the CMIP6 Multi-Model Analysis was analyzed by Asfari et al. (contribution 5) to assess the impact of climate change on the availability of water resources for municipal needs in the context of increasingly severe droughts. Closely related to the aforementioned set of papers is the article of Liu et al. (contribution 6), who in response to growing concerns about the availability of water resources developed a deep learning-based algorithm for rainfall estimation, allowing for the quantitative assessment of the effects of artificially increased precipitation.

A separate group of articles includes two papers by Charalampopoulos et al. (contributions 7 and 8) who addressed current and projected changes in the bioclimate, which, due to climate change, may pose a threat to natural and agricultural ecosystems and their services. In their studies, the authors utilized for the first time the ultra-high-resolution computation results on the spatial distribution of the Emberger index’s Q2 classes of bioclimatic characterization and the de Martonne Aridity Index (IDM) to project the future climatic conditions in selected countries of Europe. In another paper, published in this Special Issue, Abdrakhimov et al. (contribution 9) employed cumulative integral curves and linear trend coefficients to analyze selected hydro-meteorological variables and identify and quantify their trends. The application of these statistical tools allowed for emphasizing the role of climate change impacts on the water regime in relation to water management planning and hydraulic structures. Marsz et al. (contribution 10) described the low-flow periods with the use of a few parameters and adopted principal component analysis to determine teleconnections between the surface water temperature of the North Atlantic Ocean and the occurrence of droughts in a Central European river catchment. Finally, Perz et al. (contribution 11) assessed the synchronous occurrence of rainfall and flood risk with the help of the copula method and the “synchronicity” measure.

In our view, the articles published in this Special Issue are valuable in several respects. Firstly, they show how far the methodology of these studies has come over the past few millennia and centuries: from the earliest measurements of water levels on the Nile, through the observations of the ancient Greeks and Romans, the Renaissance concepts of the hydrological cycle, and the experimental investigations initiated in the 17th century. Secondly, they prove a considerable diversity in contemporary hydrological studies in the field of water resources’ assessment and modeling. Thirdly, these papers indicate directions for further research in the field of the assessment and modeling of water resources at the local, regional, and global scales. Last but not least, the findings presented in these contributions, apart from purely scientific significance, may be of interest to practitioners dealing with the effects of alterations in the hydrological cycle under the conditions of a changing climate.