Variability Of Water Resources Research Articles

Studying the impact of climate change on spatiotemporal variability of blue and green water resources

ABSTRACT Nowadays, the focal point of water resources planning and management in the river basin scale, especially in the arid/semi-arid regions, is aimed at enhancing the efficient utilization of water resources. Therefore, knowledge on the spatiotemporal variations of water resources concerning blue water (BW) and green water (GW) indicators under climate change scenarios is inevitable. The present research was conducted to study the interaction of climate variability on the BW and GW components in the Seymareh River Basin (SRB), Iran, with the aid of the Soil and Water Assessment Tool (SWAT). Future climate scenarios for the period of 2020–2040 were generated based on the LARS-WG 6.0 model. According to the results, all climate change scenarios indicate an increase in precipitation in the range of 29–33%. Furthermore, the average monthly surface runoff was projected to increase approximately by 75, 88, and 98% under RCP 4.5, 6.0, and 8.5 scenarios, respectively. The results revealed that the SRB witnessed a significant increase in BW due to climate changes. Meanwhile, the magnitude of the GW indicator was different within the SRB, with minor changes. Identifying areas with high blue/green water potentials is effective in planning for rain-fed or irrigated agriculture in the SRB.

Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics

Abstract. Advances in satellite Earth observation have opened up new opportunities for global monitoring of soil moisture (SM) at fine to medium resolution, but satellite remote sensing can only measure the near-surface soil moisture (SSM). As such, it is critically important to examine the potential of satellite SSM measurements to derive the water resource variations in deeper subsurface. This study compares the SSM variability captured by the Soil Moisture Active and Passive (SMAP) satellite and the Soil Water Index (SWI) derived from SMAP SSM with subsurface SM and groundwater (GW) dynamics simulated by a high-resolution fully integrated surface water–groundwater model over an agriculturally dominated watershed in eastern Canada across two spatial scales, namely SMAP product grid (9 km) and watershed (∼4000 km2). SMAP measurements compare well with the hydrologic simulations in terms of SSM variability at both scales. Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of ∼1 d for the 25–50 cm SM, ∼6 d for the 50–100 cm SM, and ∼11 d for the GW storage for both scales. Modeled subsurface SM dynamics agree well with the SWI derived from SMAP SSM using the classic characteristic time lengths (15 d for the 0–25 cm layer and 20 d for the 0–100 cm layer). The simulated GW storage showed a slightly delayed variation relative to the derived SWI. The quantified optimal characteristic time length Topt for SWI estimation (by matching the variations in SMAP-derived SWI and modeled root zone SM) is comparable to Topt obtained in other agricultural regions around the world. This work demonstrates SMAP SM measurements as a potentially useful aid when predicting root zone SM and GW dynamics and validating fully integrated hydrologic models across different spatial scales. This study also provides insights into the dynamics of near-surface–subsurface water interaction and the capabilities and approaches of satellite-based SM monitoring and high-resolution fully integrated hydrologic modeling.

Exploring Spatio-Temporal Variations in Water and Land Resources and Their Driving Mechanism Based on the Coupling Coordination Model: A Case Study in Western Jilin Province, China

Water and land resources (WLR) are the most important basic resources for social and economic development. The effective alignment of WLR is crucial for maximizing resource utilization and promoting sustainable regional development. This study focuses on Western Jilin Province (WJP), China, employing the degree of coupling coordination model, spatial autocorrelation, and the center of gravity transfer model to assess and characterize the spatio-temporal differentiation patterns of water and land resource matching from 2006 to 2020. Five indicators—annual average temperature (AAT), urbanization rate (UR), population density (PD), reclamation rate (RR), and water resource utilization rate (WRUR)—were selected as influencing factors. A Tobit model was constructed to elucidate the driving mechanisms behind the evolution of the WLR coupling coordination degree (CCD) in WJP. The results indicate the following: (1) From a temporal perspective, the coupling coordination degree of WLR in WJP has shown a year-on-year increase from 2006 to 2020, transitioning from a moderate imbalance to intermediate coordination, reflecting a trend of continuous improvement. (2) Regarding spatial distribution, the overall center of gravity of water and land resource coupling coordination remained relatively stable between 2006 and 2020; however, the direction of distribution gradually shifted from the northeast to the southwest and then from the northwest to the southeast. (3) The AAT, PD, and RR from 2006 to 2020 were all statistically significant at p < 0.01. Notably, the RR positively influences the CCD of WLR, whereas the AAT and PD exert a negative impact. In contrast, the UR and WRUR do not significantly affect the CCD of WLR.

Spreading the Desalination Technology as the Appropriate Option for the Drinking Water in Vietnam

Water scarcity is the one of the global risks. In order to compensate the sudden decrease in the water supply due to the increase in water demand and sudden variations of water resources due to the climate change, the alternative water resource could be required. Desalination technology is the water production method which can produce the fresh water from the seawater, the unlimited water resource on earth. In the Mekong delta area, Vietnam, the feasible water resources are the rainwater, groundwater, and Mekong river water. Because the climate in the Mekong delta area, Vietnam has the distinct rainy and dry seasons, the seawater intrusion has been occurred during the dry season due to the decreased Mekong river water by the Dam construction in the upper estuary and the increased seawater level. Therefore, the Mekong river water and groundwater have been affected by the seawater intrusion and become salty which are not proper for the agricultural and drinking water purposes. In order to provide the sustainable water in the rural areas in the Mekong delta area, Vietnam, the KIST and VKIST have been collaborated and suggested several the solar-power desalination systems. In this study, the collaborations and applications of the reverse osmosis plants were summarized.

Assessment of climate change impact on meteorological variables of Indravati River Basin using SDSM and CMIP6 models.

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (Tmax, Tmin, and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of Tmax, Tmin, and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts.

A framework for evaluating the combined effects of water transfer and storage strategies on water stress alleviation

Water transfer and storage are two effective anthropogenic management strategies to alleviate the contradictions between water supply and demand. However, the trade-off and synergistic impacts of management strategies in alleviating water stress remain unclear at the national level. Therefore, this study proposes a framework that integrates the fraction of runoff being withdrawn (scarcity coefficient) and the variation of runoff weighted by reservoir (variability coefficient) to evaluate the multifaceted impacts of management strategies on water stress mitigation. The proposed framework evaluates the changes in both the water supply–demand balance and the historical variability of runoff by considering physical water transfers, virtual water flows, and reservoir operations. This study applied the framework to evaluate the spatiotemporal patterns of water stress and used principal component analysis to estimate the relative contributions of management strategies across ten first-order basins in China for the period of 2014–2018. Results show that water-resource scarcity coefficient varied between −37.15% and 13.28% at the basin scale (the national average varied −5%) and water-resource variability coefficient varied between −100% and −19.26% at the basin scale (the national average varied −61.11%). Management strategies, incorporating water transfer and storage strategies, shifted the distribution patterns of national water stress. The attribution analysis revealed that reservoir storage capacity was the largest contributor to the first principal component representing infrastructure element, whereas the second component representing economic element was affected by the net virtual water inflows. Overall, through exploring the outcomes of combined effects among management strategies, this proposed framework provides a comprehensive perspective for investigating how human activity alleviates regional water stress.

Monitoring Harmful Algal Blooms and Water Quality Using Sentinel-3 OLCI Satellite Imagery with Machine Learning

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms.

Evaluation and analysis of spatio-temporal variation of water resources carrying capacity and restraining factor: a case study in Anhui Province, China

Evaluation and analysis of spatio-temporal variation of water resources carrying capacity and restraining factor: a case study in Anhui Province, China

Research on provincial water resources carrying capacity and coordinated development in China based on combined weighting TOPSIS model

With the continuous development of the economy and society, along with the sustained population growth, the issue of water resources carrying capacity in China has attracted increasing attention. This paper constructs a model for evaluating the provincial water resources carrying capacity in China from four dimensions: water, economy, society, and ecology. Utilizing this model, we analyze the spatiotemporal variations in water resources carrying capacity among 31 provinces in China from 2005 to 2021. Additionally, we delve into the coupling coordination and influencing factors of water resources carrying capacity. The study reveals an overall increasing trend in China’s water resources carrying capacity index, with the ecological indicator exhibiting the most significant growth while the water resources sub-indicator lags behind. There are notable regional differences, with higher water resources carrying capacity observed in the eastern coastal areas and relatively lower capacity in the western regions. The ecological criterion becomes a core factor constraining water resources carrying capacity from 2005 to 2015, gradually giving way to the prominence of the social criterion since 2015. The coordination degree is relatively higher in the eastern regions, more scattered in the western regions, and relatively stable in the central regions. Based on the research findings, a series of recommendations are proposed, including strengthening environmental protection policies, optimizing water resources management mechanisms, improving water use efficiency, and promoting economic structural diversification. These suggestions aim to facilitate the sustainable development of water resources carrying capacity in China.

Exploring the Impact of Climate Change on Water Resources for Vegetation Covers in Extremadura (Spain)

Mediterranean areas will likely undergo climate shifts in the near future that modify the water resources for vegetation. However, in some regions of southwestern Spain, such as Extremadura, the impact of different future scenarios on the water resources for vegetation has not been studied extensively. This study focused on the quantification and spatial distribution of water resources for vegetation covers in Extremadura and analyzed the impact of future climate change scenarios on those water resources. For this, five downscaled global climate models from Coupled Model Intercomparison Project phase 6 (CMIP6) were used in four future periods (from 2021 to 2100) following two Shared Socioeconomic Pathways (SSP-2.45 and SSP-5.85). These projections were compared with a historical baseline period (1970–2000) to obtain the variation of water resources. The results showed decreases in the water resources for all the scenarios and periods analyzed compared to those observed in the historical baseline period. The smallest decreases were noted over 2041–2060 for SSP2-4.5, with almost 74% of the region decreasing between 15 and 18% (with an average of 16.4%). The greatest decreases were over 2081–2100 for SSP5-8.5, in which 90% of the region displayed water resource declines of greater than 50%. In this last situation, the three more widespread vegetation covers (agrosilvopastoral systems of dehesas, grasslands, and crops) underwent similar declines of around 55% of their water resources (from ≈203 to ≈93 mm), while the fourth widely spread vegetation cover, forests, declined by 49% (from ≈261 to ≈133 mm). If any of these future projections occur, the decline in water resources could modify the forest composition and structure of these water-dependent ecosystems, compromising their maintenance and ecological, cultural, and economic functions.

Assessing future intra-basin water availability in madagascar: Accounting for climate change, population growth, and land use change

The Major River Basins in Madagascar (MRBM) play a crucial role in providing water to the Malagasy population as well as the ecosystem. Little is known about the impact of climate change on these basins, and it is not clear what factors have the most significant impact on them. There are two central objectives of this study: 1. To assess the future potential water available for daily life and agriculture use across the MRBM. 2. To compare the projected change within the MRBM with the historical trends analysis and identify the water-stressed basins. In this paper, a new method for assessing the future available Intra-basin water resources combined with the impacts of climate change, land use, and population is proposed. Three imbalance indicators are introduced to quantify the spatial availability (indicator N°1), distribution (indicator N°2), and variability (indicator N°3) of the Potential Water Resources (PWR) available and have been applied to the MRBM. Under the SSP2–4.5 scenario, results showed a decreasing trend of the PWR in most of the basins by 2050 with a rise in evapotranspiration and a decline in precipitation. The increasing trend and uneven distribution of the population and agricultural land upstream/downstream are found to cause the reduction of the PWR available per capita (by 37 %) and agriculture area (by 69 %) across the MRBM. This study predicts water scarcity for most of the basins by 2050, especially in the Mangoro and Onilahy Basins. Upstream populations are expected to grow in Mahajamba, Mahavavy, Betsiboka, Manambolo, Tsiribihina, Mangoro, Onilahy, Mananara, and Mandrare basins, along with an expansion of the downstream agricultural land in Sofia, Betsiboka, Manambolo, Mangoky, and Mandrare basins. These findings enhance the cause-effect relationship between climate change, land use change, population growth, and water scarcity in the MRBM. Urgent action is therefore needed for an efficient and sustainable management of these water-stressed basins.

Analyzing river disruption factors and ecological flow in China's Liu River Basin amid environmental changes.

Water resources variability and availability in a basin affect river flows and sustain river ecosystems. Climate change and human activities disrupt runoff sequences, causing water environmental issues like river channel interruptions. Therefore, determining ecological flow in changing environments is challenging in hydrological research. Based on an analysis of long-term changes in hydrological and meteorological variables and interruption conditions in the semi-arid Liu River Basin (LRB), this study summarizes the controlling factors of river interruption at different temporal and spatial scales and proposes a framework to determine ecological flow under changing environments. Hydrological model and the monthly optimal probability distribution were used to determine the optimal ecological runoff of LRB. The results showed that from 1956 to 2017, precipitation and potential evapotranspiration in the basin showed no significant decreasing trend, but the streamflow significantly decreased, and the downstream interruption worsened, with an average annual interruption duration of 194days at Xinmin Station from 1988 to 2017. The controlling factors of river interruption are as follows: (1) soil and water conservation measures in the upstream significantly reduce the runoff capacity; (2) the operation mode of the controlling reservoir in the middle reaches changes from "all-year discharge" to "winter storage and spring release" to "combined storage and supply," severing the hydraulic connection between upstream and downstream; and (3) siltation in the downstream river channel coupled with over-extraction of groundwater increases the seepage capacity of the river. The monthly ecological flow of Naodehai Reservoir was determined by considering the monthly seepage losses after reconstructing the natural runoff using the SWAT model and determining the optimal probability distribution function for monthly runoff. The findings are important for downstream LRB ecological restoration and for determining the ecological flow of other river basins in changing environments.

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

Contrasting Changes of Debris-Free Glacier and Debris-Covered Glacier in Southeastern Tibetan Plateau

Debris-free and debris-covered glaciers are both extensively present in the southeastern Tibetan Plateau. High-precision and rigorous comparative observational studies on different types of glaciers help us to accurately understand the overall state of water resource variability and the underlying mechanisms. In this study, we used multi-temporal simultaneous UAV surveys to systematically explore the surface elevation change, surface velocity, and surface mass balance of two representative glaciers. Our findings indicate that the thinning rate in the debris-free Parlung No. 4 glacier UAV survey area was consistently higher than that in the debris-covered 24K glacier in 2020–2021 (−1.16 ± 0.03 cm/d vs. −0.36 ± 0.02 cm/d) and 2021–2022 (−0.69 ± 0.03 cm/d vs. −0.26 ± 0.03 cm/d). Moreover, the surface velocity of the Parlung No. 4 glacier was also consistently higher than that of the 24K glacier across the survey period, suggesting a more dynamic glacial state. The surface mass balance of the Parlung No. 4 glacier (2020–2021: −1.82 ± 0.09 cm/d; 2021–2022: −1.30 ± 0.09 cm/d) likewise outpaced that of the 24K glacier (2020–2021: −0.81 ± 0.07 cm/d; 2021–2022: −0.70 ± 0.07 cm/d) throughout the observation period, which indicates that the debris cover slowed the glacier’s melting. Additionally, we extracted the melt contribution of the ice cliff area in the 24K glacier and found that the melt ratio of this ‘hotspot’ area ranged from 10.4% to 11.6% from 2020 to 2022. This comparative analysis of two representative glaciers provides evidence to support the critical role of debris cover in controlling surface elevation changes, glacier dynamics, and surface mass balance.

Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine

In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of − 0.65 and − 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

Analysis of Meteorological Element Variation Characteristics in the Heilongjiang (Amur) River Basin

Located in the Heilongjiang (Amur) River in north-east Asia, spanning four countries, plays a crucial role as an international border river, and its meteorological changes significantly impact the variation in water resources in the basin. This study utilizes daily average temperature and precipitation data from 282 meteorological stations in the Heilongjiang (Amur) River Basin and its surrounding areas for the period 1980–2022. The analysis employs spatial interpolation, change point testing, and model construction prediction methods. The results indicate a significant increasing trend in both overall temperature and precipitation changes within the Heilongjiang (Amur) River Basin. At the spatial scale, the annual warming rate increases gradually from the southeastern coastal region to the northwestern plateau region, while the rate of precipitation increase decreases from the southern area towards its surroundings. Temporally, the warming amplitude during the growing season decreases gradually from east to west, and the trend in precipitation changes during the growing season aligns with the overall annual precipitation trend. During the non-growing season, the warming trend shows a decrease in the plains and an increase in the plateau, while precipitation increase concentrates in the central and southern plains, and precipitation decrease predominantly occurs in the northwestern plateau region. Temperature and precipitation change points occurred in the years 2001 and 2012, respectively. In precipitation prediction, the Long Short-Term Memory (LSTM) model exhibits higher accuracy, with R (Pearson correlation coefficient) and NSE (Nash-Sutcliffe efficiency coefficient) values approaching 1 and lower NRSME values. This study provides a research foundation for the rational development and utilization of water resources in the Heilongjiang (Amur) River Basin and offers valuable insights for research on climate change characteristics in large transboundary river systems.

Impact of water resources variation on winter–spring rice yield in the upper Vietnamese Mekong Delta: A case study of An Giang Province

AbstractNatural and socio‐economic development under climate change has affected the distribution of surface water resources (SWRs) in many deltas worldwide. The dynamics in the SWRs has negatively impacted agricultural production, which is one of the growing issues in the Vietnamese Mekong Delta (VMD) as the national rice bowl. Our study aims to assess SWR dynamics related to its effects on rice production in the upper VMD. We applied Mann–Kendall, linear regression and Pettit tests to statistically determine trends of SWRs in terms of water level, total water volume and rainfall in 1996–2020. The findings show that high water level trends decrease significantly after 2011, and this reduction has been due to climate variability and dam development upstream of the Mekong River. Moreover, rice yield data analysis from 1996 to 2020 indicated a decreasing trend in the yield of the winter–spring rice crop in the An Phu district of An Giang Province after 2011. Our interviews with 33 water experts and 90 local farmers revealed 80% agreement that the decline in water resources has affected rice yield. This study provides empirical evidence and a platform for knowledge sharing and learning across different deltas and initiatives worldwide.

Long‐Term Variability and Trends in Snow Depth and Cover Days Throughout Iranian Mountain Ranges

AbstractIn Iran, the mountain snow cover generally feeds major rivers and thereby largely provides water resources required for improving human lives and protecting nature. Hence, understanding historical variability and trends in mountainous snowpack water resources in Iran in response to global warming and climate change can play a critical role in the sustainable development of this country. Accordingly, this study investigated long‐term (1982–2018) snowpack climatology at 13 hydrometeorological measurement stations scattered throughout the Iranian mountain ranges, with a focus on Elburz, Azerbaijan, Zagros, and Khorasan mountainous regions. The non‐parametric Mann‐Kendall test was used to detect statistically significant (p < 0.05) trends, the Pettitt test to identify possible abrupt shift years, the Pearson's correlation coefficient to measure relationships among different time series, and the partial correlation to determine the most important climate factor influencing snowpack dynamics The annual snow depth (maximum snow depth) significantly declined throughout Iranian mountain ranges during 1982–2018, with an average rate of 1.0 (3.4) cm decade−1. The annual snow cover days (SCDs) also showed significant decreasing trends, ranging from 3 to 15 days decade−1 during 1982–2018, in 69% of the stations studied. Such considerable reductions in snow depth and cover days were mainly related to the compound effects of substantial increases in temperature, sunshine, and wind speed as well as decreases in precipitation and cloudiness during the SCDs across the Iranian mountain ranges. However, precipitation was the most influential climate factor controlling snow resources throughout both the Elburz and Zagros mountains in Iran.

Impact of modified SWAT plant growth module on modeling green and blue water resources in subtropics

The dynamics of water availability within a region can be quantitatively analyzed by partitioning the water into blue and green water resources. It is widely recognized that vegetation is one of the key factors that affect the assessment and modeling of blue and green water in hydrological models. However, SWAT-EPIC has limitations in simulating vegetation growth cycles in subtropics because it was originally designed for temperate regions and naturally based on temperature. To perform a correct and realistic assessment of changing vegetation impacts on modeling blue and water resources in the SWAT model, an approach was proposed in this study to modify the SWAT plant growth module with the remotely sensed leaf area index (LAI) to finally solve problems in simulating subtropical vegetation growth, such as controlling factors and dormancy. Comparisons between the original and modified model were performed on the model outputs to summarize the spatiotemporal changes in hydrological processes (including rainfall, runoff, evapotranspiration and soil water content) under six different plant types in a representative subtropical watershed of the Meichuan Basin, Jiangxi Province. Meanwhile, detailed analysis was conducted to discuss the effectiveness of the modified SWAT model and the impacts of vegetation changes on blue and green water modeling. The results showed that (1) the modified SWAT produced more reasonable seasonal curves of plants than the original model. E<sub>NS</sub> (Nash-Sutcliffe efficiency) and R<sup>2</sup> increased by 0.02 during the calibration period and accounted for an increase of 0.09 and 0.03, respectively, during the validation period. (2) The comparison of model outputs between the original and modified SWAT suggested that evapotranspiration was more sensitive to vegetation changes than other components of green water. In addition, vegetation presented conservation capability in the blue water. (3) The variation in blue and green water resources with different plant types after modifying the SWAT model showed that seasonal changes in vegetation led to a significant difference between forest and non-forest areas.

Spatio-temporal variability and trend of blue-green water resources in the Kaidu River Basin, an arid region of China

Study regionKaidu River Basin, an arid region of China Study focusWater resources are scarce and unstable. Adverse changes in blue-green water resources can disrupt water balance and availability in arid regions. Therefore, it is crucial to effectively assess and predict future blue-green water resources in arid areas. This study utilizes the Soil and Water Assessment Tool (SWAT), Patch-generating Land Use Simulation (PLUS), and Global Circulation Models (GCMs) to analyze the interactive impacts of climate and land use/cover changes (LUCC) on blue-green water in arid basin from 1990 to 2050. New hydrological insights for the regionThis work identified key factors influencing blue-green water changes, including forests, grasslands, and snowmelt. In the historical scenarios, the Kaidu River basin exhibited significant interannual variations in blue water and precipitation. In the future scenarios, blue water increases (+3.6%), and green water decreases (−1.8%) under SSP245 (medium emissions). Under SSP585 (high emissions), blue water decreases (−2.4%), and green water increases (+1.8%). Under SSP126 (low emissions), both blue water (+8.5%) and green water (+0.73%) show an increasing trend. The change in snowmelt is the main reason for the first peak of blue water in April May. Under historical and future snowmelt conditions, blue water shows a significant upward or downward trend with the amount of snowmelt. The findings provided practical hints for the enhancement of the management and allocation of precious water resources.