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Locations of the Hotan River Basin, Hotan (HT) and Pishan (PS) climate stations, as well as the Tongguziluoke (TGZLK) discharge gauging station; inset maps show the locations of Xinjiang Autonomous Region within China and the Hotan River Basin within Xinjiang.
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The effects of global climate change threaten the availability of water resources worldwide and modify their tempo-spatial pattern. Properly quantifying the possible effects of climate change on water resources under different hydrological models is a great challenge in ungauged alpine regions. By using remote sensing data to support established mo...
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Study region
India
Study focus
India boasts the largest irrigated agricultural system in the world relying on groundwater. To address the strong linkages between the natural groundwater and the anthropogenic irrigated system requires innovative hydrological modeling geared at informing national policies on groundwater management and future develop...
Citations
... Unfortunately, hydrological modeling is relatively difficult in HCMA regions of China where only sparse hydrometeorological data are available [20]. The spatial heterogeneity of the basin and the variable hydrological characteristics and morphology of the HCMA result in runoff time series that are nonlinear and nonstationary, making it difficult to accurately predict runoff and capture the characteristics of runoff changes [21,22]. The above methods require parameterization with sufficient data to achieve the best prediction and simulation results, and are not robust and cumbersome, making it difficult to provide scientifically accurate advice [23,24]. ...
Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.
... This finding validated the effectiveness of the TVGSSREM-3D model for assessing microscopic influences at a small regional point scale. Due to the climatic characteristics of the arid zone, soil relative humidity plays a crucial role in the development and growth of desert vegetation (Luo et al., 2017). Specifically, lower soil relative humidity during soil moisture variations indicates low soil water content. ...
Soil salinization and water deficits are considered the primary factors limiting economic development and environmental improvement in arid areas. However, there remains limited knowledge of the adaptability of typical shrubs to salinization of desert areas in arid zones. This study was conducted in a desert oasis transition zone (Tarim River, China), aiming to investigate: i) the spatial-temporal changes in soil salinity; ii) the interactions between the pedoenvironment vs typical shrub (Calligonum mongolicum). The van Genuchten soil salinity retention ensemble model (TVGSSREM-3D) was developed to simulate variations in soil water-salt transport in the desert-oasis zone and to accurately explain the main factors influencing Calligonum mongolicum desert-oases transition areas. The results showed that monthly average salinity ranged from 2.0 to 8.0 g kg-1, with a peak in August (9.17 g kg-1). The presence of human activities (Salt Drainage Canal) and the distribution of Calligonum mongolicum resulted in a clear spatial salinity zonation. Moreover, analysis of environmental indicators using the TVGSSREM-3D model revealed strong correlations between the distribution of salinity in Calligonum mongolicum desert-oases transition areas and groundwater depth (GD), minimum relative humidity (MRH), and water vapor pressure (WVP). These findings provide a scientific basis for stabilizing, restoring, and reconstructing the ecosystem of the oasis-desert transition zone.
... Several prior studies have assessed the influence of climate change on streamflow in different watersheds with various climatic types around the world for current and future conditions; however, the majority of these studies did not include future land use changes in their studies (Zhang et al. 2007;de Oliveira et al. 2017;Dlamini et al. 2017;Luo et al. 2017;Su et al. 2017;Neves et al. 2020;Ndhlovu & Woyessa 2021;Quansah et al. 2021). Although few studies have attempted to incorporate climate change and LULC on streamflow prediction (Tarigan & Faqih 2019;Farinosi et al. 2019;Sinha et al. 2020;Raihan et al. 2021), investigating the relative impacts of climate change and LULC on regional hydrological processes under various scenarios is critical for establishing effective adaptation measures and assisting decision makers in achieving watershed stability (Haleem et al. 2022). ...
... The Coupled Model Intercomparison Project-5 (CMIP5) multi-model mean outperforms the CMIP3 multi-model mean for climate change impact studies and is more skillful at representing precipitation patterns (Annamalai et al. 2007;Sperber et al. 2013;Farinosi et al. 2019). Many research studies have proved the necessity for region specific selection of climate models (Raju & Kumar 2015;Ruan et al. 2018;Yang et al. 2020) and bias correction of RCMs (Fang et al. 2015;Luo et al. 2017;Mudbhatkal & Mahesha 2018) for reliable simulation of hydrologic processes. ...
This study evaluated the individual as well as integrated impacts of climate and land use change on streamflow over Meenachil River Basin, Kerala, India. The hydrological model SWAT was used to simulate future streamflow simulations under representative concentration pathway (RCP) 4.5 and 8.5 scenarios for the medium term (2025–2055) and long term (2056–2086). The land use land cover change was projected using land change modeler (LCM) of TerrSet software for the future period. Climate model simulations were taken to predict future streamflow at the regional scale, and an attempt was made to reduce the uncertainty associated with future predictions. According to the findings, streamflow was influenced by climate change (68.07%) and land use change (31.92%), with climate change having a higher contribution rate. The findings suggest that the combined impact of climate and land use change would increase streamflow in the future. The annual average streamflow is expected to decline (5.9%) in the medium term (2025–2055) under RCP 4.5 scenario and rise by 5.3% under RCP 8.5 scenario with reference to the observed streamflow for the period 1987–2017. However, in the long term, it is expected to rise by 10.56% under RCP 4.5 and 22.61% under RCP 8.5.
HIGHLIGHTS
Climate models were employed to simulate precipitation, maximum temperature and minimum temperature for the future period.;
Precipitation bias correction methods were assessed for their hydrological impact in SWAT.;
The SWAT model was used to separate the influence of climate and land-use change on streamflow.;
Future land use for the years 2030 and 2060 was predicted using TerrSet software.;
Streamflow projections were evaluated for changes in the climate and land use.;
... Runoff from ice meltwater is mainly concentrated in reservoirs and other water projects. However, the surface areas of these water bodies have been declining due to a gradual reduction in the area of glaciers (Bai et al., 2021a;Luo et al., 2017). ...
Hydraulic Engineering Infrastructure Projects (HEIPs) typically show profound effects on hydrological systems and ecosystems. However, data restrictions have limited the exploration of the influences of compound HEIPs on ecosystems to a few studies. This study proposes a watershed-wide ecosystem assessment framework to investigate the impact of HEIPs in the Tarim River Headwaters-Hotan River Basin on the ecosystem of the arid zone. The framework includes a deep learning-meta cellular automata algorithm (DLMCAA) based on the spatiotemporal characteristics of HEIPs and hydro-meteorological and human activities. Moreover, the spatiotemporal relationships between compound HEIPs and ecosystem variances were quantified. The framework including DLMCAA showed a good performance in simulating landcover in 2020, with a Kappa coefficient of 0.89. Therefore, the DLMCAA could be used to simulate and predict ecosystem changes under the HEIPs, which suggested that the framework is effective and practical. An analysis of the spatiotemporal distribution of each ecosystem from 1980 to 2020 showed that the low shrub ecosystems changed most significantly (26.38 %) between 1980 and 2020. Also, the use of spatially driven hydrological project data from different ABC scenarios showed that ecosystems driven by HEIPs were more stable compared to those without HEIPs under future climate change. In particular, the DLMCAA indicated that compound HEIPs had a more positive impact on ecosystem oases in arid lands compared with that of single HEIPs. The results of this study can serve as a scientific reference for assessing the impact of HEIPs, as well as for understanding ecosystem changes and facilitating sustainable water resource management in the arid regions.
... Our results partly agree with the conclusion of previous studies, for instance, Chen et al. (2014Chen et al. ( , 2015, which suggest that precipitation and temperature are the main factors regulating the runoff variations in the Kaidu River Basin. Unlike other alpine river basins (e.g., the Kunmalik and Hotan River Basins), the major influential factor of the runoff variation is the temperature (Shen et al., 2018;Luo et al.,2017). Chen et al. (2017) has highlighted the importance of including glacier melt in the hydrological modeling of the glacierized catchments, which can be a critical perspective that should be accounted for in the study of hydrological responses to future climate change. ...
Study region
The Kaidu River Basin (KRB) is located on the central southern slope of the Tianshan Mountain in Northwest China.
Study focus
This work aimed to assess changes and main drivers of snowmelt-driven runoff in KRB associated with three future climate scenarios. Six versions of the "Cemaneige" snowmelt module embedded in the hydrological model "GR4J" were calibrated and evaluated. The bias-corrected climate datasets from CMIP5 Models were used to drive the optimal snowmelt-hydrological model for runoff prediction. The factors that lead to runoff variations were also assessed.
New hydrological insights
The significant declining trends of runoff were only predicted in the RCP4.5 and RCP8.5 scenarios. The declining trends of runoff were found in all the seasons. For the annual and summer runoff, compared to the historical period, both the RCP2.6 and RCP 4.5 periods showed a decline in the mid-century and a rise in the end-century; however, RCP8.5 showed a continuous decline during this period. Precipitation and evapotranspiration were ranked as the two most important factors regulating future runoff variations in all RCPs. In contrast, snowmelt timing is the second factor in the historical period, and its importance decreases in the warmer RCP scenarios. These results highlighted that the importance of snowmelt and snowmelt timing to the future runoff depends on the runoff responses to the trajectory of future changes in temperature and precipitation.
... It is not known if the same results could be generated in our study area with a different hydrological model. For instance, in the Hotan River Basin in southwestern Xinjiang in Central Asia, from 2004 to 2008, Luo et al. [31] assessed the performance of the SWAT and MIKE SHE hydrological models. The results showed that the SWAT model performs better (NSE = 0.77) than the MIKE SHE model (NSE = 0.66) for the same climate input. ...
In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.
... When target and input variables have opposite associations at different time scales, like in Simulation-based models considering dynamical processes have also been used to understand runoff variability in this region-with similar conclusions. For instance, Luo et al. (2017) employed two hydrological models, the fully distributed MIKE SHE model and the semi-distributed SWAT model, to detect the effects of climate change on runoff in this region. They found that runoff was much more sensitive to temperature variation than precipitation using both models. ...
Understanding the contributions of potential drivers on runoff is essential for the sustainable management of water resources; however, the impacts of climate variability and human activities on runoff at inter-annual and inter-decadal scales have rarely been assessed quantitatively. To achieve this goal, this study develops a nonlinear hybrid model, which integrates extreme-point symmetric mode decomposition (ESMD), back propagation artificial neural networks (BPANN) and weights connection method. ESMD allows to separate the times series of drivers and runoff into different time scales. BPANN is then used to simulate the relation between the drivers and runoff at each time scale separately. Weights connection method is employed to quantify the impacts of climate variability and human activities on runoff. The performance of this proposed model is compared with multiple linear regression (MLR). The mountainous area of the Hotan River Basin is selected as case study area. Results reveal that runoff exhibits significant fluctuations at inter-annual (2 and 9 years) and inter-decadal (14 years) scales. Climate variables are responsible for 81% of the runoff variations, while human activities account for 8%. The nonlinear hybrid model substantially outperforms MLR in all performance measures. We attribute this improvement to the ability of the proposed model to represent nonlinear relations and to simulate the association between drivers and runoff at different time scales. For instance, water vapor affects runoff positively at the inter-annual time scale but negatively at the inter-decadal time scale. Such opposing relations cannot be represented by MLR or many other, more traditional methods.
... Later, it needs efficient calibration or optimization approaches to approximate the phenomena (Abbaspour et al. 2015). Different approaches are used to enhance the hydrological and climate models separately (Abbaspour et al. 2015;Luo et al. 2017Luo et al. , 2019Osei et al. 2018), but hybridizing both the components with the help of a single framework is not addressed. The hybrid models can afford the characteristics of two or more systems and provide a detailed analysis of the structure (Zhang et al. 2012). ...
Future freshwater security relies on hydroclimatic (HC) shifts and regimes for sustainable development. The approximation of the HC system faces major uncertainties and complexities due to the incorporation of heavy datasets, characteristics, and constraints. The proposed study focused on the parallel computing of emulator modeling-based spatial optimization to enhance the HC systems with the perspective of future freshwater security in the Upper Chattahoochee River basin (UCR). Here, the framework compiles both physical and machine learning concepts with adaptive technology for the replication of real-world scenarios. Besides, it contains 2Emulator Model Fitting, Spatial Optimization, Parallel Computing, and Initial and Adaptive sampling to upgrade model efficiency, while UCR has inadequate groundwater and the assessment of freshwater security in UCR is more necessary for varying future climatic conditions. The results displayed that the proposed spatial optimization algorithm proved to be an effective and efficient approach in the approximation of HC models. The assessment of water security in UCR was showed in terms of scarcity and vulnerability indicators for median and low-level conditions, respectively. Moreover, this study provides the potential framework for the enhancement of physical model predictions with the incorporation of hybrid concepts for problem-solving technology which can provide significant information on HC issues. HIGHLIGHTS
A comprehensive framework for the integration of the physical and machine learning concepts to enhance the hydroclimatic system.;
Adaptive emulator-based spatial optimization was introduced to control expensive simulations.;
Parallel computing was incorporated in the framework to restrict the spatial variability in large-scale watersheds.;
Assessed the future freshwater security based on the Blue/Green Water dynamics.;
... Water security in arid regions is extremely sensitive to climate change and increasing human activities (Timpane et al., 2017;Waseem et al., 2020;Zhong et al., 2020). It is closely tied to knowledge of the existing available water resources and the spatial-temporal variations of water demand from different water users (Masafu et al., 2016;Deng and Chen, 2017;Luo et al., 2017). The Amu Darya River (ADR) is vital to life in four central Asian countries because it is the major source of water in these regions and provides water resources for domestic, agriculture, power generation, and industrial purposes (Jiang et al., 2014;Omurakunova et al., 2020). ...
High water consumption and inefficient irrigation management in the agriculture sector of the middle and lower reaches of the Amu Darya River Basin (ADRB) have significantly influenced the gradual shrinking of the Aral Sea and its ecosystem. In this study, we investigated the crop water consumption in the growing seasons and the irrigation water requirement for different crop types in the lower ADRB during 2004–2017. We applied the FAO Penman-Monteith method to estimate reference evapotranspiration (ET0) based on daily climatic data collected from four meteorological stations. Crop evapotranspiration (ETc) of specific crop types was calculated by the crop coefficient. Then, we analyzed the net irrigation requirement (NIR) based on the effective precipitation with crop water requirements. The results indicated that the lowest monthly ET0 values in the lower ADRB were found in December (18.2 mm) and January (16.0 mm), and the highest monthly ET0 values were found in June and July, with similar values of 211.6 mm. The annual ETc reached to 887.2, 1002.1, and 492.0 mm for cotton, rice, and wheat, respectively. The average regional NIR ranged from 514.9 to 715.0 mm in the 10 Irrigation System Management Organizations (UISs) in the study area, while the total required irrigation volume for the whole region ranged from 4.2×109 to 11.6×109 m3 during 2004–2017. The percentages of NIR in SIW (surface irrigation water) ranged from 46.4% to 65.2% during the study period, with the exceptions of the drought years of 2008 and 2011, in which there was a significantly less runoff in the Amu Darya River. This study provides an overview for local water authorities to achieve optimal regional water allocation in the study area.
... The global climate change influence on Xinjiang has been obvious science the 1980s, where the temperature tread has been rising [45], while the trend of temperature was increased especially from the 21st Century in Hotan Oasis [46]. Therefore, the predictions of the evapotranspiration were very likely to happen, while some studies used the GCMs climate model data in Hotan Oasis [47]. ...
Studying the pattern of agricultural water demand under climate change has great significance for the regional water resources management, especially in arid areas. In this study, the future pattern of the irrigation demand in Hotan Oasis in Xinjiang Uygur Autonomous Region in Northwest China, including Hotan City, Hotan County, Moyu County and Luopu County, was assessed based on the general circulation models (GCMs) and the Surface Energy Balance System model (SEBS). Six different scenarios were used based on the GCMs of BCC_CSM1.1, HadGEM2-ES and MIROC-ESM-CHEM under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5. The results showed that the method integrating the GCMs and SEBS to predict the spatial pattern was useful. The irrigation demand of Hotan Oasis will increase in 2021–2040. The annual irrigation demand of Hotan City is higher, with 923.2 and 936.2 mm/a in 2021–2030 and 2031–2040, respectively. The other three regions (Hotan County, Moyu County and Luopu County) are lower in the six scenarios. The annual irrigation demand showed a spatial pattern of high in the middle, low in the northwest and southeast under the six scenarios in 2021–2040. The study can provide useful suggestions on the water resources allocation in different regions to protect water resources security in arid areas.
