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The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results. Plain Language Summary: Frequent urban flooding and waterlogging pose significant threats to human life and property. Accurate modeling urban floods is crucial for minimizing damages caused by urban flooding. However, the delayed initiation of urban observations has resulted in prevalent data scarcity in urban areas, leading to difficulties and low accuracy in estimating model parameters for ungauged urban catchments. This study proposes a parameter regionalization framework for an urban hydrological model to improve the accuracy of flood simulations in ungauged urban catchments. This framework involves classifying gauged catchments into multiple groups and identifying the optimal regression‐based methods in each group for parameter regionalization. The flood simulation performance and stability of the parameter regionalization framework is evaluated by comparing it with eight widely‐used regionalization methods in 37 urban catchments located in Shenzhen City, China. Our results show that the classification of gauged catchments can significantly improve the simulation performance of linear regression methods. Additionally, the parameter regionalization framework outperforms the other regionalization methods for both urban flood simulation in the study area and urban flood prediction in the ungauged urban catchments. This study provides a novel and efficient parameterization method for accurate flood prediction in ungauged urban catchments to reduce potential losses. Key Points: TVGM_Urban presents satisfying performance in urban flood modelingThe PRF integrating donor catchment clustering and optimal regression methods in each cluster outperforms single regionalization methodsCurve number, land cover area ratio, and slope are critical factors for runoff generation parameters of TVGM_Urban [ABSTRACT FROM AUTHOR]

Predicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management. Plain Language Summary: The prediction of flood event classes is more effective and informative than that of individual events for obtaining comprehensive dynamic characteristics of flood events for early warning and development of emergency plans. However, this is challenging owing to massive flood events with remarkable spatiotemporal heterogeneity and unclear class membership functions. We propose a class prediction approach for flood events using the class membership functions of flood behavior metrics based on frequency analysis and catchment hydrological modeling. This approach deeply mines the behavior characteristics of historical flood events with a strong mathematical basis and cooperates with hydrological models to predict flood classes. Over thousand unimpacted flood events in 68 headstream catchments across China were selected to validate the robustness of the prediction scheme. Our results show that the proposed approach well predicts the flood class with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Our study has strong implications for scientific flood warning research and flood mitigation management. Key Points: A prediction approach for flood event classes is developed using class membership functions of flood regime metrics with hydrological modelClass membership degree is estimated using the frequency distribution function, which fits well with statistical significanceAverage class hit rates are 68.3% ± 0.4% for all the events and predictions of dumpy flood class are better than those of spike flood classes [ABSTRACT FROM AUTHOR]

The nonstationary impacts of climate change and multi-reservoirs on extreme floods cannot be ignored due to the potential risk to flood control and river security. Nonstationary regional flood frequency analysis (NS-RFFA) provides an effective way to consider these influences. But there is currently no standard framework of NS-RFFA and insufficient consideration of uncertainty in quantile estimates. In the study, we proposed an improved framework of NS-RFFA considering the influence of multi-reservoirs as well as climate change and involving uncertainty estimation. The framework was applied to analyze the frequency of extreme floods in the Huai River Basin (HRB) where multi-reservoirs have been constructed and to reveal the extreme flood variation under the impact of climate change and reservoir group. Results show that precipitation during flood season and reservoir index played a dominant role in the variations of annual maximum streamflow in the HRB. The impact of reservoirs was more significant in the upstream tributaries than in the mainstream. The nonstationary models with location parameters varying with time and/or main influencing factors perform better on fitting extreme streamflow in the HRB compared with stationary and at-site analysis. The quantile estimates and their uncertainty based on NS-RFFA are dynamic, which gives an expression of the changing environment effects on annual maximum streamflow. Thus, our work contributes an improved framework of NS-RFFA considering the influence of climate change and reservoirs, providing more accurate and reliable estimates for river flood risk management. [ABSTRACT FROM AUTHOR]

The water conservation function (WCF), as one of the most critical ecosystem services, has an important impact on the ecological sustainability of a region. Accurately characterizing the spatiotemporal heterogeneity of WCF and further exploring its driving factors are of great significance for river basin management. Here, the WCF of the upper Yangtze River basin (UYRB) from 1991 to 2020 was calculated using the water yield module in the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. Also, we innovatively applied emerging hot spot analysis (EHSA), which could describe the location and pattern of historical changes more accurately, to investigate the spatiotemporal heterogeneity and evolution of WCF. Based on the Geographical Detector Model (GDM), the main driving factors of WCF and their interactions were revealed. The results showed the following: (1) the WCF in the UYRB experienced a temporal increase at a growth rate of 1.48 mm/a, while remarkable differences were observed across the change rates of sub-watersheds. (2) The spatial variation of the WCF showed a gradual increase from northwest to southeast. Interestingly, the Jinshajing River upstream (JSJU) source area with a low WCF showed an increasing trend (with diminishing cold spots). On the contrary, the downstream regions of the JSJU watershed (with intensifying cold spots) underwent a weakening WCF. (3) Among all driving factors, precipitation (q = 0.701) exhibited the most remarkable prominent impact on the spatial heterogeneity of the WCF. Additionally, the interaction of factors exhibited more explanatory power than each factor alone, such as precipitation and saturated soil hydraulic conductivity (q = 0.840). This research study is beneficial to water resource management and provides a theoretical basis for ecological restoration. [ABSTRACT FROM AUTHOR]

Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO2 dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO2 and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm−2year−1a−1, 12.34 gCm−2year−1a−1, 24.91 gCm−2year−1a−1, and 39.62 gCm−2year−1a−1. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered the regional hydrological processes, posing a great challenge to the sustainable development of cities. The TVGM-USWM model, a new urban hydrological model considering the nonlinear rainfall-runoff relationship and the flow routing in an urban drainage system, was developed in this study. We employed this model in the Huangtaiqiao drainage basin of Jinan City, China, and examined the impact of land cover changes due to urbanization on rainfall-runoff processes. Two urbanization scenarios were set up in the TVGM-USWM model during the design rainfall events with different return periods. Results showed that (1) the TVGM-USWM model demonstrated good applicability in the study area, and the RNS values of the flood events are all greater than 0.75 in both calibration and validation periods; (2) the proportion of impervious areas increased from 44.65% in 1990 to 71.00% in 2020, and urbanization played a leading role in the process of land cover change and manifested itself as a circular extensional expansion; and (3) urbanization showed a significant amplifying effect on the design flood processes, particularly for relatively big floods with small frequency, and the impact of urbanization on the time-to-peak of the design flood gradually decreased as the frequency of the design rainfall decreased. The results of this study can provide technical support for flood mitigation and the construction of a sponge city in Jinan City. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) changes are dynamic and have been extensively studied; the change in LULC has become a crucial factor in decision making for planners and conservationists owing to its impact on natural ecosystems. Deriving accurate LULC data and analyzing their changes are important for assessing the energy balance, carbon balance, and hydrological cycle in a region. Therefore, we investigated the best classification method from the four methods and analyzed the change in LULC in the middle Yangtze River basin (MYRB) from 2001 to 2020 using the Google Earth Engine (GEE). The results suggest that (1) GEE platform enables to rapidly acquire and process remote sensing images for deriving LULC, and the random forest (RF) algorithm was able to calculate the highest overall accuracy and kappa coefficient (KC) of 87.7% and 0.84, respectively; (2) forestland occupied the largest area from 2001 to 2020, followed by water bodies and buildings. During the study period, there was a significant change in area occupied by both water bodies (overall increase of 46.2%) and buildings (decrease of 14.3% from 2001 to 2005); and (3) the simulation of LULC in the MYRB area was based on the primary drivers in the area, of which elevation changes had the largest effect on LULC changes. The patch generated land use simulation model (PLUS) was used to produce the simulation, with an overall accuracy and KC of 89.6% and 0.82, respectively. This study not only was useful for understanding the spatial and temporal characteristics of LULC in the MYRB, but also offered the basis for the simulation of ecological quality in this region. [ABSTRACT FROM AUTHOR]

The Approximate Bayesian Computation (ABC) provides a powerful tool for signature-domain calibration of hydrological models where hydrological signatures are incorporated into calibration objectives. However, the efficiency of ABC relies strongly on the use of a vector of sufficient signatures that can fully represent relevant information in raw data. The application of ABC with randomly chosen signatures can result in inaccurate calibration results. To fill this gap, a hybrid time- and signature-domain Bayesian inference framework for calibration of hydrological models is proposed. In this framework, a set of approximately sufficient signatures is pursued through simultaneous consideration of the information redundancy analysis (IRA) and discriminatory power analysis (DPA) procedures. While the IRA deals with the information redundancy inherent in the pool of available signatures, DPA quantifies the discriminatory power of a given signature as the reliability and sharpness of the associated probabilistic predictions generated by ABC. The verified residual error scheme in time-domain inference is approximated as the probabilistic model in the acceptance test of ABC. The proposed framework is then tested on the Xin'anjiang rainfall-runoff model applied to the Ren River basin (RRB) of China. The use of IRA and DPA provides a probabilistic model prediction statistically equivalent to that of classical time-domain inference in terms of the reliability and sharpness. The comparison to signature-domain inference using the complete set of hydrological signatures further demonstrates the importance of IRA and DPA in improving the quality of Bayesian model calibration in the signature domain and reducing the total predictive uncertainty. The framework makes it practically possible to maintain adequate accuracy of model predictions produced by signature-domain inference, improving the efficiency of ABC in solving the model calibration problems and consequently promotes the use of ABC in signature-domain model calibration. [ABSTRACT FROM AUTHOR]

Global warming and associated frequent extreme hydrological events with increasingly severe climate change threaten human life and economic development. Consequently, climate change has become a significant focus of the scientific community. The middle reaches of the Yangtze River Basin (MYRB) contain abundant water resources and is an area with intensive human activities; it is not only the center of social and economic development in China but also the region with the highest frequency of drought and flood disasters. This study analyzed the spatiotemporal characteristics of several climate change indicators, including temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI), in the MYRB during 1961–2017, using principal component analysis (PCA). The main results are as follows: (1) mean temperature, monthly air temperature sum, precipitation sum, maximum daily precipitation, and number of days with precipitation exceeding 30 mm were the primary variables that experienced variations in the MYRB; (2) the average temperature during spring and autumn and precipitation intensity in summer increased significantly in the MYRB, primarily in the east and the north. Increased aridity during spring, autumn, and winter was apparent in the MYRB; and (3) changes in the distribution of temperature and the intensification of drought were the principal variables that changed across the study period in the Hanjiang River Basin, while the dominant variables in Dongting Lake Basin and Yichang to Hukou Basin were precipitation regime and temperature-related indicators. The results of this study can improve our understanding of how climate change affects the MYRB, allowing the development of mitigation measures for meteorological disasters. [ABSTRACT FROM AUTHOR]

Changes in wetland ecosystems have a critical impact on the local ecology and species diversity. Different development scenarios and policies are key factors influencing their changes. Therefore, we studied changes of wetlands in the middle Yangtze River basin (MYRB) in 2001–2020, and a patch-generated land use simulation (PLUS) model and random forest (RF) method were applied to predict and analyze the changes under different scenarios in the MYRB in the future (i.e.2035–2095). The results indicated that: (1) The regions with high wetland proportions were concentrated in the central and eastern MYRB in 2001–2020, with a 1.5% decrease in overall wetland area; (2) The RF could simulate the future ecological quality with training and testing accuracies of 0.98 and 0.92, respectively; (3) Remote Sensing Ecological Index (RSEI) less than 0.5 in the central and eastern regions and 13.3% reduction in the northwest in the SSP245 scenario. In general, the study provides a basis for future regional studies of ecosystem quality and provides data to support wetland conservation and management. [ABSTRACT FROM AUTHOR]

Watersheds are the basic unit of Earth's terrestrial systems and are therefore ideal units for the study of critical zones (CZs). However, grand challenges remain regarding the observation, modeling, and management of CZs and watershed systems. We synthesize the progress and breakthroughs associated with watershed science and CZ research in the Heihe River basin (HRB), a large‐scale endorheic river basin with unique mountain cryosphere‐oasis‐desert landscapes and prominent human‐nature competition for water resources. The HRB observation system consists of mountain cryosphere, agricultural oasis, and natural oasis observatories and is promoted by large‐scale comprehensive experiments to achieve multiscale observations. A watershed system model that couples ecohydrological models with socioeconomic models is developed to investigate the complex interactions among water, ecology, and socioeconomics in the HRB. The model is embedded in a decision support system to bridge science and decision‐making and to better serve river basin sustainability. Significant progress and breakthroughs have been made in CZ and watershed process research (e.g., cryospheric hydrological processes, ecological and hydrological interactions, and surface‐groundwater interactions) in the HRB. Nevertheless, observation and modeling of geochemical and geomorphological processes in the CZ have not been well addressed in integrated watershed studies of the HRB. In the future, new observation technologies, agent‐based models, machine learning, and data assimilation will benefit both watershed science and CZ science and help to address complexity and dynamics in the CZ at the river basin scale. Overall, the HRB has successfully demonstrated how an experimental river basin can link CZ science with watershed science. Key Points: Progress and breakthroughs associated with watershed science and critical zone (CZ) research in the Heihe River basin (HRB) are reviewed and synthesizedThe observation system, model, and decision support system of the HRB contribute to new findings on the watershed and CZ processesLinking critical zone science with watershed science can achieve a better understanding of the Earth system [ABSTRACT FROM AUTHOR]

Rivers play an important role in water supply, irrigation, navigation, and ecological maintenance. Forecasting the river hydrodynamic changes is critical for flood management under climate change and intensified human activities. However, efficient and accurate river modeling is challenging, especially with complex lake boundary conditions and uncontrolled downstream boundary conditions. Here, we proposed a coupled framework by taking the advantages of interpretability of physical hydrodynamic modeling and the adaptability of machine learning. Specifically, we coupled the Gated Recurrent Unit (GRU) with a 1‐D HydroDynamic model (GRU‐HD) and applied it to the middle and lower reaches of the Yangtze River, the longest river in China. We show that the GRU‐HD model could quickly and accurately simulate the water levels, streamflow, and water exchange rates between the Yangtze River and two important lakes (Poyang and Dongting), with most of the Kling‐Gupta efficiency coefficient (KGE $\mathrm{K}\mathrm{G}\mathrm{E}$) above 0.90. Using machine learning‐based predicted water levels, instead of the rating curve approach, as the downstream boundary conditions could improve the accuracy of modeling the downstream water levels of the lake‐connected river system. The GRU‐HD model is dedicated to the synergy of physical modeling and machine learning, providing a powerful avenue for modeling rivers with complex boundary conditions. Key Points: Coupling machine learning into physical hydrodynamics for river modeling with complex boundary conditionsStreamflow and water levels were modeled with Kling‐Gupta efficiency coefficient above 0.90 at most hydrologic stations and 38 times faster than traditional 1‐D/2‐D coupled modelsThe proposed machine learning‐based downstream boundary condition showed better performance than the rating curve method [ABSTRACT FROM AUTHOR]

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05 ° , correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of 0.25 ° or 0.1 ° , correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations. [ABSTRACT FROM AUTHOR]

Water resources systems in arid regions are sensitive to climate change, which critically impacts the water cycle. In this study, we applied historical hydrometeorological data, CMIP5 data, and a large‐scale hydrological model (Community Land Model–Distributed Time‐Variant Gain Model [CLM–DTVGM]) to study the impact of climate change on the hydrological cycle (i.e., runoff, actual evapotranspiration (ETa), and terrestrial water storage [TWS]) under different scenarios at the Aksu River basin (ARB) located in the arid region of northwest China. The primary findings of this study: (a) As the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) reached desirable levels (R2 ≥ 0.583, NSE ≥ 0.371, and root‐mean‐square error ≤ 155.727), the CLM–DTVGM achieved a better simulation of runoff in the ARB; under climate change, the runoff depth in the irrigation area became shallower and followed a decreasing trend, with a minimum depth of 0.5 mm and a significant decreasing trend of −8 mm·a−1. (b) Along with changes in the precipitation and temperature of the baseline period (i.e., 1980–2010), runoff, ETa, and TWS were predicted to change significantly in future scenarios. (c) Climate change significantly impacted the historical runoff from the Shaliguilanke and Xiehela hydrological stations, with correlation coefficients larger than 0.9; however, the runoff from the Alaer hydrological station was not affected. However, runoff, ETa, and TWS in the ARB were very closely correlated with climate change in the future scenarios, with correlation coefficients exceeding 0.9. Related research in the future would be important for guiding the sustainable development of the ARB. [ABSTRACT FROM AUTHOR]

In a changing environment, changes in terrestrial water storage (TWS) in basins have a significant impact on potential floods and affect flood risk assessment. Therefore, we aimed to study the impact of TWS on potential floods. In this study, we reconstructed the TWS based on precipitation and temperature, evaluated the reconstructed TWS data based on Gravity Recovery and Climate Experiment (GRACE)-TWS data, and analyzed and calculated the flood potential index (FPI) in the Yangtze River Basin (YRB). The related influencing factors were analyzed based on the Global Land Data Assimilation System (GLDAS) data and Granger's causality test. The main conclusions are as follows: (1) although the GRACE-TWS anomaly (GRACE-TWSA) in the YRB showed an increasing trend for the averaged TWSA over all grids in the whole basin (i.e., 0.31 cm/a, p < 0.05), the variable infiltration capacity-soil moisture anomalies (VIC-SMA) showed a decreasing trend (i.e., −0.048 cm/a, p > 0.05) during April 2002–December 2019; (2) a larger relative contribution of detrended precipitation to FPI was found in the Jialingjiang River Basin (JRB), Wujiang River Basin (WRB), Dongting Lake Rivers Basin (DLRB), YinBin-Yichang reaches (YB-YC), and Yichang-Hukou reaches (YC-HK), while the contribution of detrended TWS to FPI in the Poyang Lake Rivers Basin (PLRB) was larger than that in other basins; and (3) the original and detrended soil moisture (SM) and TWS in the YRB showed a significant positive correlation (p < 0.05), while the significant effect of SM on TWS caused a change in FPI in the YRB and its sub-basins. This study is of great significance for the correct understanding of the FPI and the accurate assessment of flood risk. [ABSTRACT FROM AUTHOR]

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales. [ABSTRACT FROM AUTHOR]

Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of "source-sink" landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by "source" landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) "source" landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution. [ABSTRACT FROM AUTHOR]

This study conducts in the Bahe River Basin, an agricultural basin in Northwest China. We use the Soil and Water Assessment Tool (SWAT) model to identify the spatial distribution characteristics of non-point source (NPS) pollution and determine the critical source areas (CSA). Then the relationship between landscape pattern and NPS pollution is analyzed by spearman correlation analysis and redundancy analysis (RDA). On this basis, we set up eight landscape management practices in the CSA and evaluate their reduction effects on NPS pollution loads. The results show that the spatial distribution of nitrogen and phosphorus loss intensity has a certain correlation with rainfall and runoff, and the correlation between phosphorus loss intensity and sediment loss intensity is more significant. The NPS pollution load is closely related to the landscape pattern of the river basin, and is affected by the fragmentation, aggregation and complexity of the landscape. Farmland, forest land, and grassland are the main landscape components of the river basin. Farmland is the main source of NPS pollution, whereas forest land and grassland can effectively inhibit the output of NPS pollution, and the reduction effect of forest land is significantly better than that of grassland. The largest patch index (LPI), landscape shape index (LSI), patch density (PD) are the main landscape factors that affect the output of NPS pollution load. Among all the scenarios, the reduction effect of returning farmland to forest land in slopes above 15° is the best, and the reduction rates of total nitrogen (TN) and total phosphorus (TP) loads have reached about 25%. This study provides some reference for the management of NPS pollution in the Bahe River Basin and other similar basins. [ABSTRACT FROM AUTHOR]

The Yangtze River, the largest river in China, has been facing major challenges in massive flooding and eco-environmental health over the past decades. Sustainable socioeconomic development in the Yangtze River Basin depends on water and ecosystem security. This overview addresses eco-water security under the changing environment of the Yangtze River Basin. Looking forward to a healthy Yangtze River in the future, there are still uncertainties regarding how to assess and wisely manage the Yangtze River through a systematic, integrated approach applied to multiple dimensions, water, biodiversity, ecological services, and resilience, for the sustainable development of ecosystems and human beings. The Yangtze Simulator, an integrated river basin model powered by artificial intelligence and interdisciplinary science, is introduced and discussed, and it will serve as a robust tool for good governance of the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Key Points: Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data [ABSTRACT FROM AUTHOR]

• An urban hydrological model considering complicated overland flow processes is proposed. • The TVGM-USWM model demonstrated good applicability in the piedmont city. • Disconnected impervious area is an important factor in urban flood simulations. Urbanization has great potential to adversely affect the local hydrological cycle, as well as result in severe ecological and environmental problems. Piedmont cities have a special topography and complicated overland flow processes, thus creating challenges for the simulation of urban flooding/waterlogging processes. The TVGM-USWM model, a new distributed urban hydrological model that considers the flow routing in urban drainage systems and complicated overland flow processes, was developed in this study. We applied this model to the Huangtaiqiao drainage basin of Jinan City, China and set up two simulation scenarios. The spatial heterogeneity of the proportion of directly connected impervious area (DCIA) and disconnected impervious area (DIA) in sub-catchments was considered in scenario B, but not in scenario A. We conducted sensitivity analysis on model parameters by using the Sobol method. Results showed that: (1) model parameters have a certain degree of uncertainty, and the parameter characterizing the proportion of the DCIA and DIA showed a relatively high sensitivity under rainstorms of different levels and different accuracy assessment standards; (2) The simulation of scenario A was poor (with a mean Nash coefficient of 0.59), which ignored the different flow paths followed by runoff from DCIA and DIA; and (3) Considering the spatial differences in the overland flow process, the prediction accuracy of scenario B significantly increased (with a mean Nash coefficient of 0.82). This indicated the importance of the disconnected impervious area and the lateral water exchange across the impervious-pervious interface on the flood simulation in a piedmont city. Compared to most urban hydrological models, the TVGM-USWM model can represent complex rainfall-runoff mechanisms in urban areas with varied terrain. The findings of this study can provide scientific decision-making for the planning and construction of a sponge city in Jinan City. [ABSTRACT FROM AUTHOR]

The determination of critical management areas for nitrogen (N) and phosphorus (P) losses in large-scale basins is critical to reduce costs and improve efficiency. In this study, the spatial and temporal characteristics of the N and P losses in the Jialing River from 2000 to 2019 were calculated based on the Soil and Water Assessment Tool (SWAT) model. The trends were analyzed using the Theil-Sen median analysis and Mann–Kendall test. The Getis-Ord Gi* was used to determine significant coldspot and hotspot regions to identify critical regions and priorities for regional management. The ranges of the annual average unit load losses for N and P in the Jialing River were 1.21–54.53 kg ha−1 and 0.05–1.35 kg ha−1, respectively. The interannual variations in both N and P losses showed decreasing trends, with change rates of 0.327 and 0.003 kg ha−1·a−1 and change magnitudes of 50.96% and 41.05%, respectively. N and P losses were highest in the summer and lowest in the winter. The coldspot regions for N loss were clustered northwest of the upstream Jialing River and north of Fujiang River. The coldspot regions for P loss were clustered in the central, western, and northern areas of the upstream Jialing River. The above regions were found to be not critical for management. The hotspot regions for N loss were clustered in the south of the upstream Jialing River, the central-western and southern areas of the Fujiang River, and the central area of the Qujiang River. The hotspot regions for P loss were clustered in the south-central area of the upstream Jialing River, the southern and northern areas of the middle and downstream Jialing River, the western and southern areas of the Fujiang River, and the southern area of the Qujiang River. The above regions were found to be critical for management. There was a significant difference between the high load area for N and the hotspot regions, while the high load region for P was consistent with the hotspot regions. The coldspot and hotspot regions for N would change locally in spring and winter, and the coldspot and hotspot regions for P would change locally in summer and winter, respectively. Therefore, managers should make specific adjustments in critical regions for different pollutants according to seasonal characteristics when developing management programs. • Quantify the trends in nitrogen and phosphorus losses over long series of grids. • Identify the critical management regions for nitrogen and phosphorus losses. • Compare the critical management regions for nitrogen and phosphorus losses between annual and different seasons. [ABSTRACT FROM AUTHOR]

The modeling of changes in surface water and groundwater in the areas of inter-basin water diversion projects is quite difficult because surface water and groundwater models are run separately most of the time and the lack of sufficient data limits the application of complex surface-water/groundwater coupling models based on physical laws, especially for developing countries. In this study, a distributed surface-water and groundwater coupling model, named the distributed time variant gain model-groundwater model (DTVGM-GWM), was used to assess the influence of climate change and inter-basin water diversion on a watershed hydrological cycle. The DTVGM-GWM model can reflect the interaction processes of surface water and groundwater at basin scale. The model was applied to the Haihe River Basin (HRB) in eastern China. The possible influences of climate change and the South-to-North Water Diversion Project (SNWDP) on surface water and groundwater in the HRB were analyzed under various scenarios. The results showed that the newly constructed model DTVGM-GWM can reasonably simulate the surface and river runoff, and describe the spatiotemporal distribution characteristics of groundwater level, groundwater storage and phreatic recharge. The prediction results under different scenarios showed a decline in annual groundwater exploitation and also runoff in the HRB, while an increase of groundwater storage and groundwater level after the SNWDP’s operation. Additionally, as the project also addresses future scenarios, a slight increase is predicted in the actual evapotranspiration, soil water content and phreatic recharge. This study provides valuable insights for developing sustainable groundwater management options for the HRB. [ABSTRACT FROM AUTHOR]

Urbanization-induced landuse changes alter runoff regimes in complex ways. In this study, a detailed investigation of the urbanization impacts on runoff regimes is provided by using multiple runoff metrics and with consideration of landuse dynamics. A catchment hydrological model is modified by coupling a simplified flow routing module of the urban drainage system and landuse dynamics to improve long-term urban runoff simulations. Moreover, multivariate statistical approach is adopted to mine the spatial variations of runoff metrics so as to further identify critical impact factors of landuse changes. The Qing River catchment as a peri-urban catchment in the Beijing metropolitan area is selected as our study region. Results show that: (1) the dryland agriculture is decreased from 13.9% to 1.5% of the total catchment area in the years 2000–2015, while the percentages of impervious surface, forest and grass are increased from 63.5% to 72.4%, 13.5% to 16.6% and 5.1% to 6.5%, respectively. The most dramatic landuse changes occur in the middle and downstream regions; (2) The combined landuse changes do not alter the average flow metrics obviously at the catchment outlet, but slightly increase the high flow metrics, particularly the extreme high flows; (3) The impacts on runoff metrics in the sub-catchments are more obvious than those at the catchment outlet. For the average flow metrics, the most impacted metric is the runoff depth in the dry season (October ∼ May) with a relative change from −10.9% to 11.6%, and the critical impact factors are the impervious surface and grass. For the high flow metrics, the extreme high flow depth is increased most significantly with a relative change from −0.6% to 10.5%, and the critical impact factors are the impervious surface and dryland agriculture; (4) The runoff depth metrics in the sub-catchments are increased because of the landuse changes from dryland agriculture to impervious surface, but are decreased because of the landuse changes from dryland agriculture or impervious surface to grass or forest. The results of this study provide useful information for urban planning such as Sponge City design. [ABSTRACT FROM AUTHOR]

Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRIvar) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRIvar is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration ( ET 0 ) is important for agricultural, environmental and other studies, and understanding the attribution of its change is helpful to provide information for irrigation scheduling and water resources management. The present study investigates the attribution of the change of ET 0 at 49 meteorological stations in the middle reaches of Yellow River basin (MRYRB) of China from 1960 to 2012. Results show that annual ET 0 increases from the northwest to the southeast of MRYRB in space. We find that annual ET 0 clearly presents a zigzag change pattern rather than a monotonically change during the whole period. The detected three breakpoints at 1972, 1988 and 1997 divide the whole period into four subperiods. The sensitivity analysis indicates that the ET 0 is the most sensitive to surface solar radiation ( Rs ), followed by relative humidity ( RH ) and mean air temperature ( T ), and the least sensitive to wind speed ( u ) in our study area. Furthermore, we find that ET 0 is becoming less sensitive to RH and more sensitive to T during 1960–2012. The attributions of the change in ET 0 vary largely at different regions and subperiods. The declined wind speed is the dominant factor, followed by Rs to the ET 0 reduction during 1960–2012. Further analysis shows that Rs and u are the two major contributing factors that control the change of ET 0 at most stations and during most subperiods. Our study confirms that the change of ET 0 is influenced by the complex interactions of climatic factors, and the dominant factor to the change of ET 0 is different in various regions and time periods. The results presented here can provide a reference for agricultural production and water resources management in MRYRB as well as other semi-arid and semi-humid regions. [ABSTRACT FROM AUTHOR]

• The drought events in Central Asia retrieved by ASWI are consistent with the records of actual situation. • The drought centroid of Central Asia hardly occurs in the southeast. • Central Asia has experienced drought in the past two decades, followed by gradual wetting. • The water vapor flux, precipitation, and temperature have obvious influence on the occurrence of drought in Central Asia. Under warm climates, the more complex and malleable relationship between water and heat and the changes in the water components at the land–atmosphere interface pose greater challenges to understanding hydro-meteorological processes. However, the emergence of the advanced microwave scanning radiometer (AMSR) has facilitated the precise monitoring of these changes. Therefore, this study investigated and analyzed the spatiotemporal transformation characteristics of drought–wetness and its driving mechanisms in Central Asia from July 2002 to December 2018 based on the AMSR surface wetness index (ASWI) coupled with multiple water components. Three major conclusions were drawn from this investigation. First, the spatiotemporal differences in multiple water components in Central Asia caused substantial spatiotemporal differences in the extent of changes in drought–wetness. For example, arid areas were mainly concentrated in the lower reaches of the Amu Darya and Syr Darya river basins, where the dominant drought periods were 2007 and 2018. Second, the centroids of the largest drought patches were mainly located in the northeast, northwest, and southwest regions of Central Asia. Central Asia experienced a continuous drought from 2002 to 2005, and then a gradual wetting from 2008 to 2017. Third, the drought–wetness variation can be attributed to the variation of water vapor flux in the typical barometric layer as well as the variation of precipitation and temperature. The ASWI may play an essential role in drought monitoring and tracking prediction, particularly for drought risk mitigation and aversion. [ABSTRACT FROM AUTHOR]

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran's index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin. [ABSTRACT FROM AUTHOR]

This study investigates the changes of rainfall patterns along with the underlying reasons in the Nansi Lake Basin (NLB), China during 1960-2009. Results show that the annual rainfall increases from the northwest to the southeast of the NLB. From the temporal variation perspective, annual rainfall decreases slightly in the majority of stations. Furthermore, in spite of no pronounced trends are detected in all stations, the annual rainfall series fluctuate intensely, and present step changes around the year of 1974 and 2002. This change pattern of rainfall is verified by the approximately wet-dry-wet phase pattern, which is exhibited in the standardized departures of annual rainfall series, during the three sub-periods divided by the pre-obtained two change years. In particular, the parametric t test demonstrate that the step change in 2002 is significant. The variations in the rainy season (RS, June-September) rainfall contributed mostly to the changes in the annual rainfall, and a high similarity of change patterns between the RS and annual rainfall is also observed. The long term mean RS and annual rainfall decreases largely from the sub-period of 1960-1974 to 1974-2002, and increased largely from the sub-period of 1974-2002 to 2002-2009 in the NLB. Besides, various elements, such as the summer East Asian summer monsoon and summer Pacific decadal oscillation, may together lead to the step changes in summer rainfall over our study area. [ABSTRACT FROM AUTHOR]

The hydrological variations of Ganjiang River Basin have significant influence on the ecological environment of Poyang Lake, which is one of the largest freshwater lake and wetland in the world. This study analyzed the spatial and temporal characteristics of rainfall across Ganjiang River Basin. The analyses include annual total rainfall amount (ATRA), annual total rainy day (ATRD) and annual mean daily rainfall intensity (AMDRI). To detect changes in the hydrological trends, data from 19 rainfall stations from 1953 to 2013 were used in the analyses. First, quality control and homogeneity detection was carried out to examine the annual rainfall series. Second, the spatial correlation analysis was used to identify the spatial relationship among the measurements from different stations. Finally, the statistics of coefficient of variation (CV) and average were used to analyze the interannual variation trend. The modified Mann-Kendall (MMK) trend test method was used to detect the temporal characteristics of rainfall. The results include: (1) Some outliers were detected and corrected. (2) The correlation of ATRA and ATRD series of all the stations had lower spatial variability while the AMDRI series of all the stations had higher spatial variability. On the other hand, the ATRA, ATRD and AMDRI series of single stations show similar spatial variability and the influencing radius of the rainfall events was less than 50 km. (3) the rainfall at the northern and southern parts of the basin had smaller CV. Further, by applying the MMK test method to the ATRA, ATRD, and AMDRI series, the ATRA series has an increasing trend which is opposite to that of the ATRD series. The increasing trend in the ATRA series also led to the increasing trend in the AMDRI series. These stations are mainly in central and northern parts of the basin which are more likely to experience droughts and floods. Thus, for the central and northern parts of the basin, they should receive particular attention on the prevention and treatment of droughts and floods. [ABSTRACT FROM AUTHOR]

Climate variability modulates spatio-temporal variability of dry spells (DSs) and wet spells (WSs) within a river basin and will affect water resources management practices leading to various impacts on the socio-economic development in river basins. In this study, we evaluated spatio-temporal variability of DS and WS in Huai River basin (HRB), China, by developing copula-based severity-duration-frequency (SDF) curves. The result shows that the upper reach and the southern part of middle reach of HRB are prone to both DS and WS; however, the duration and severity of WS are comparatively higher in comparison to DS. It was observed that DS is more frequent in spring and summer, whereas WS in summer and autumn. The choice of copula plays an important role in deriving the SDF curves, and an inappropriately chosen copula function may result in a large bias of SDF estimation. The arch12 copula was found to be the best choice in the majority of stations for deriving the SDF curves. The constructed SDF curves primarily shows two major patterns for DS and WS, i.e. concave down pattern and convex up pattern. The frequency of extreme DS decreases from 1960s to 1990s, and increases after 2000s, while the frequency of extreme WS increases from 1970s to 1990s and then decreases from 1990s to 2000s. The results in this study can provide useful information for designing conservation structures and to develop water allocation strategies at different temporal scales. [ABSTRACT FROM AUTHOR]

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To manage water resources effectively, a multiscale assessment of the vulnerability of water resources on the basis of political boundaries and watersheds is necessary. This study addressed issues on the vulnerability of water resources and provided a multiscale comparison of spatial heterogeneity under a climate change background. Using improved quantitative evaluation methods of vulnerability, the Theil index and the Shannon-Weaver index, we evaluated the vulnerability of water resources and its spatial heterogeneity in the Haihe River Basin in four scales, namely, second-class water resource regions (Class II WRRs), third-class water resource regions (Class III WRRs), Province-Class II WRRs, and Province-Class III WRRs. Results show that vulnerability enhances from the north to south in the different scales, and shows obvious spatial heterogeneity instead of moving toward convergence in multiscale assessment results. Among the Class II WRRs, the Tuhai-Majia River is the most vulnerable area, and the vulnerability of the Luanhe River is lower than that of the north of the Haihe River Basin, which in turn is lower than that of the south of the Haihe River Basin. In the scales of Class III WRRs and Province-Class III WRRs, the vulnerability shows obvious spatial heterogeneity and diversity measured by the Theil index and the Shannon-Weaver index. Multiscale vulnerability assessment results based on political boundaries and the watersheds of the Haihe River Basin innovatively provided in this paper are important and useful to characterize the real spatial pattern of the vulnerability of water resources and improve water resource management. [ABSTRACT FROM AUTHOR]

Summary Understanding the effects of climate change on runoff is important for the sustainable management of water resources. However, the mechanism of such effects in the Asian monsoon region remains unclear. This study revisits Fu’s two-parameter climate elasticity index and enhances it by using the Gardner function to strengthen the former’s prediction reliability when the future climate condition is beyond the historical range. Then the improved method was applied to study the elasticity change with temperature and precipitation in the eastern monsoon basins of China, whereas to explore the mechanism of climate change on runoff. Furthermore, the runoff change and the elasticity of the study area from 2020 to 2050 under representative concentration pathways (RCPs) were predicted. Results show that the trend of elasticity change assumes a centrosymmetric picture with the symmetric point (0, 0). Different catchments respond differently to the same climate change scenario: the sensitivity of the Haihe Basin is the highest; those of Yellow, Huaihe, Liaohe, Songhua, Pearl, Yangtze, and Southeast Rivers are lower, in descending order. The changing mode of precipitation and temperature differs greatly to keep the runoff unchanged. For semi-humid regions in which the mean annual temperature ranges from 0.71 °C to 9.0 °C, such as the basins of Songhua, Liaohe, Haihe, and Yellow, a 1 °C increase in temperature requires a corresponding 3.2–4.0% increase in precipitation to keep the runoff unchanged. However, in wet regions, such as the basins of Yangtze, Southeast Rivers, and Pearl, the same change in temperature requires a less than 2.8% increase in precipitation to keep the runoff unchanged. In the future, the runoff in most basins may decrease in different degrees. The decreasing velocity of the runoff is the fastest in the RCP8.5 scenario and the decreasing trend of the runoff slows down under the RCP4.5 and RCP2.6 scenarios. The proposed method can be applied to other basins to assess potential climate change effects on annual runoff. The results of the basins studies can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

High regulation of dams or sluices disturbs flow regimes and pollutant transformation process significantly in most basins over the world. The water quality and quantity simulation in highly regulated river basins is always a very complicated task. The Huai River Basin is a typical area in China with the highest density of water projects and serious pollution problems simultaneously. In this paper, a procedure based on Soil and Water Assessment Tool (SWAT) was proposed to carry out the water quantity and quality simulation of Huai River Basin by incorporating the operation rules of dams or sluices into the reservoir regulation module. The water quality module in SWAT is extended to suit the actual situation of river basin in China. The results show that: for runoff simulation, 28 of all the 38 stations (73.7%) have the acceptable performance, with the average correlation coefficient and efficiency coefficient of 0.83 and 0.66, respectively in the calibration period. In the validation period, 17 of all the 34 stations (50%) have the acceptable performance, with the average correlation coefficient and efficiency coefficient of 0.77 and 0.54, respectively. The model performs the worst for reservoirs, little better for sluices and the best for unregulated stations. Comparing to the low flow and high flow simulation of original SWAT model, the low flow simulation of reservoirs and the high flow simulation of sluices are much better because of their different main purposes. For water quality simulation, the standard reaching rates of NH-N and COD are 55.9% (19/34) and 67.6% (23/34) respectively for all the stations, and the average relation coefficients are 0.46 and 0.48 respectively. Comparing with the results of original SWAT model, the improved model better reproduces the long-term water quantity and quality processes in the Huai River Basin of China. This study provides a new approach and reference to understand the variation of water quantity and quality in highly regulated river basin, and is expected as technical support for the environment restoration and integrated management in the basins, especially in China. [ABSTRACT FROM AUTHOR]

In this research, drought in Yellow River basin has been studied by using dry spells. Three indices, including the maximum length (MxDS), mean length (MDS) and number of dry spells (NDS), and five periods (annual, winter, spring, summer and autumn) are considered. The results show that a south to north gradient for mean MxDS and MDS has been dominantly found in all periods except summer, in which a southwest and southeast to north gradient exists. Mean NDS shows an opposite distribution to that of mean MxDS and MDS. It is surely that the northern part of Yellow River basin, with a higher MxDS and MDS and lower NDS, is much drier than southern part in a regional scale. According to temporal analysis by using the Mann-Kendall trend method, MxDS of most stations show negative but insignificant trends during annual and winter, while the majority of stations show positive trends during spring, summer and autumn. Trends of MDS and NDS dominantly depict positive and negative for most periods, respectively. By comparing the frequency of dry spells during the ENSO events, it can be found that the frequency of intermediate and long dry spells is almost tantamount during the occurrence periods of El Niño and La Niña. [ABSTRACT FROM AUTHOR]

Research on the impact of sluice regulation on water quantity and quality (WQQ) processes in regulated rivers is a fundamental requirement in basin management at present but remains a research puzzle at the basin scale because of insufficient monitoring data, irregular order of sluice or dam regulation, and numerous influencing factors in real-world river basins. This paper presents a laboratory experiment in a single reach as an artificial case study and develops a dynamic numerical model. Through analysis of the observed data and modeling, the river WQQ processes under sluice regulation are investigated and the interaction of WQQ and its regulation capacity downstream of a sluice is explored. Furthermore, a sensitivity analysis of model parameters is carried out and the model validity is tested on a prototype (real-world) scale. Results show that the WQQ variation downstream of a sluice has complicated nonlinear relationships with upstream flow, pollutant load into a river, and sluice regulation. The impact of sluice regulation on water quantity and thus, water quality, is inversely proportional to the relative opening of sluice and upstream flow, respectively. The WQQ results simulated by the numerical model in a real-world-scale system are reasonable, and the patterns are similar to experimental observation. Thus, the experiment and the numerical model will provide a potential approach to mimicking real reaches in a river system with dense sluices where the reaches between adjacent sluices are short and the change in sluice regulation is a dominant factor, such as in the Huai River basin of China. This study provides the foundation and reference for exploration of the mechanism of WQQ changes in regulated river systems and the technical support for the joint WQQ regulation of dams and sluices in complicated real river basins. [ABSTRACT FROM AUTHOR]

Global losses caused by floods are gradually increasing under the influence of climate change and human activities. The Yangtze River (YR) economic belt has continued to experience frequent flood disasters over the years; therefore, clarifying the significant flood risk factors is highly relevant for the development of the region. In this study, we investigated the flood risk factors in the Yangtze River Basin (YRB) during 2002–2018 based on meteorological data, reconstructed terrestrial water storage (TWS) data from Gravity Recovery Climate Experiment (GRACE) (GRACE-TWS), and Landsat data. The major conclusions were as follows: (1) the principal components (i.e., the first, third, and fourth principal components (PC1, PC3, and PC4)) of Standardized Precipitation Evapotranspiration Index (SPEI) at a 12-month scale (SPEI12) were more significantly (P < 0.05) correlated with teleconnection indices (i.e., Atlantic Multi-decadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), Southern Oscillation Indices (SOI), and Multivariate El Niño-Southern Oscillation (ENSO) Index (MEI)), than the SPEI at 6-month scale (SPEI6) in the YRB during 2002–2018; (2) the changes in water bodies reflected by Landsat data (i.e., Landsat 5, Landsat 7, and Landsat 8 OLI) for the YRB confirmed the wet periods (i.e., 2002–2005, 2010, 2012–2013, and 2015–2018) monitored by SPEI (i.e., SPEI6 and SPEI12); and (3) while the Pearson correlation coefficients indicated a significant linear relationship between the major hydrological factors (e.g., precipitation, runoff, GRACE-TWS, flood potential index (FPI), and soil moisture (SM)) in the YRB, the precipitation detected by the GRACE-TWS and SM with one-month lag phase, had the maximum correlation coefficients of 0.83 and 0.85, respectively. Significant relationships (r2 = 0.48 and r2 = 0.92, P < 0.05) were found between variable infiltration capacity (VIC)-runoff and predicted runoffs based on these factors (i.e., precipitation, GRACE-TWS, flood potential index (FPI), and SM) and the random forest regression. This study provides significant hydrological information and contributes to approaches aimed at supporting climate resilience and investments in the YRB. • The correlation between SPEI12 and teleconnection index is more significant than SPEI6. • The changes in wet-dry in the YRB was consistent with the changes in waterbodies from remote sensing. • The wetness duration and intensity are concentrated in the middle and lower reaches of in the YRB. • Random forest can predict the runoff changes in the YRB well based on the major hydrological factors. [ABSTRACT FROM AUTHOR]

Ecological environment quality is a long-term continuous concept that is affected by various environmental factors. Its assessment has important implications for implementing the planning and protection of dynamic regional ecosystems. Therefore, this study attempted to obtain these indicators (green, dry, wet, heat) through the Google Earth Engine (GEE) platform, and then coupled the ecological environment quality index in the middle reaches of the Yangtze River Basin (MYRB) between 2000 and 2019, based on the remote sensing ecological index (RSEI). The major results show that: (1) changes in the four indicators in summer were more obvious than those in winter, and the changes were concentrated in the central and northern regions of the MYRB; (2) both the modified normalized difference water index (MNDWI) and normalized differential build-up and bare soil index (NDBI) in summer and winter have higher weighting ratios, implying that water body changes and human activities had a greater impact on the ecological environment; and (3) ecological environment quality in the MYRB between 2000 and 2019 was relatively flat. The ecological conditions began to deteriorate in 2008, and substantial ecological degradation was noted in some areas between 2008 and 2019 (18.7% in the central region, 16.0% in the eastern region). The MYRB has an important position in the Yangtze River economic belt and is an important part of the Yangtze River protection. This research could provide a theoretical basis and decision support for the development and protection of the Yangtze River Basin (YRB) green economy. [ABSTRACT FROM AUTHOR]

The spatiotemporal characteristics of river water quality are the key indicators for ecosystem health evaluation in basins. Land use patterns, as one of the main driving forces of water quality change, affect stream water quality differently with the variations in the spatiotemporal scales. Thus, quantitative analysis of the relationship between different land cover types and river water quality contributes to a better understanding of the effects of land cover on water quality, the landscape planning of water quality protection, and integrated water resources management. Based on water quality data of 2006–2018 at 18 typical water quality stations in the Yangtze River basin, this study analyzed the spatial and temporal variation characteristics of water quality by using the single-factor water quality identification index through statistical analysis. Furthermore, the Spearman correlation analysis method was adopted to quantify the spatial-scale and temporal-scale effects of various land uses, including agricultural land (AL), forest land (FL), grassland (GL), water area (WA), and construction land (CL), on the stream water quality of dissolved oxygen (DO), chemical oxygen demand (CODMn), and ammonia (NH3-N). The results showed that (1) in terms of temporal variation, the water quality of the river has improved significantly and the tributaries have improved more than the main rivers; (2) in the spatial variation respect, the water quality pollutants in the tributaries are significantly higher than those in the main stream, and the concentration of pollutants increases with the decrease of the distance from the estuary; and (3) the correlation between DO and land use is low, while that between NH3-N, CODMn, and land use is high. CL and AL have a negative effect on water quality, while FL and GL have a purifying effect on water quality. In particular, AL and CL have a significant positive correlation with pollutants in water. Compared with NH3-N, CODMn has a higher correlation with land use at a larger scale. The results highlight the spatial scale and seasonal dependence of land use on water quality, which can provide a scientific basis for land management and seasonal pollution control. [ABSTRACT FROM AUTHOR]

• The abrupt change of precipitation and runoff in the upper reaches of Aksu River Basin is not synchronous. • CLM-DTVGM can simulate the hydrological process of rivers in arid area of Northwest China well. • The longer the SRI time scale is, the more obvious their drought characteristics are. • Human activities have aggravated the hydrological drought in the arid area of Northwest China. Under climate change, drought events have a great impact on different regions of the world. Quantitative evaluation of regional drought characteristics is of great significance for regional drought risk aversion. This study simulates the changes in the historical and future hydrological runoff based on historical observation data, the Community Land Model-Distributed Time Variant Gain Model (CLM-DTVGM), and CMIP5 data. Subsequently, it reveals the historical and future hydrological drought characteristics in the Aksu River Basin under a changing environment. The conclusions are as follows: (1) although both the asynchronous mutated precipitation and runoff in the upper reaches of the Aksu River Basin increased, with a maximum increase of 3.3 mm and 1.2 m3/s, the synchronous mutation of precipitation and runoff in the downstream indicated that human activities only affected the runoff in Alaer station; (2) CLM-DTGVM could be well applied in historical and future runoff simulation of the Aksu River basin with complex transformation of different land use under the changing environment; and (3) the seasons of drought development, maximum intensity, and drought termination mainly occurred in winter, followed by spring, summer, and autumn, while human activities had an obvious impact on the drought characteristics in the Aksu River Basin. These findings are of great significance for improving water-saving awareness and warning against drought risk. [ABSTRACT FROM AUTHOR]

The Hanjiang River Basin (HJRB) is an important source area for drinking water in Hubei Province, China, and the vegetation coverage there is important to the ecological system. Due to the spatial heterogeneity and synergistic effect of various factors, it is very difficult to identify the main factors affecting vegetation growth in the HJRB. With the normalized difference vegetation index (NDVI) data from 2001 to 2018 in the HJRB, the spatiotemporal patterns of NDVI and the influences of natural factors and human activities on NDVI were investigated and quantified based on the Mann-Kendall (M-K) test, partial correlation analysis, and Geographical Detector. The individual factors and their interactions and the range/type of factor attributes suitable for vegetation growth were also examined. NDVI in the HJRB increased from 2001 to 2018, and the variation rate was 0.0046 year−1. NDVI was increasing in 81.17% of the area (p < 0.05). Elevation and slope can effectively explain the vegetation distribution. The interactions of factors on NDVI were significant, and the interactions of the elevation and precipitation can maximize the impact among all factors. The range of available landforms is thought to be highly conducive to vegetation growth. The rates of the annual precipitation and annual mean temperature changed from 2001 to 2018, which were 3.665 mm/year and 0.017 °C/year, and the regions where NDVI positively correlated with them were over 85%. Contrary to the general trend, NDVI has obviously decreased in urban areas since 2010. The quantitative findings of this study can help us better understand the effects of various factors on vegetation growth and provide appropriate suggestions for vegetation protection and restoration in the HJRB. [ABSTRACT FROM AUTHOR]

The lack of meteorological observation data limits the hydro-climatic analysis and modeling, especially for the ungauged or data-limited regions, while satellite and reanalysis products can provide potential data sources in these regions. In this study, three daily products, including two satellite products (Tropic Rainfall Measuring Mission Multi-Satellite Precipitation Analysis, TMPA 3B42 and 3B42RT) and one reanalysis product (China Meteorological Assimilation Driving Datasets for the SWAT Model, CMADS), were used to assess the capacity of hydro-climatic simulation based on the statistical method and hydrological model in Ganjiang River Basin (GRB), a humid basin of southern China. CAMDS, TMPA 3B42 and 3B42RT precipitation were evaluated against ground-based observation based on multiple statistical metrics at different temporal scales. The similar evaluation was carried out for CMADS temperature. Then, eight scenarios were constructed into calibrating the Soil and Water Assessment Tool (SWAT) model and simulating streamflow, to assess their capacity in hydrological simulation. The results showed that CMADS data performed better in precipitation estimation than TMPA 3B42 and 3B42RT at daily and monthly scales, while worse at the annual scale. In addition, CMADS can capture the spatial distribution of precipitation well. Moreover, the CMADS daily temperature data agreed well with observations at meteorological stations. For hydrological simulations, streamflow simulation results driven by eight input scenarios obtained acceptable performance according to model evaluation criteria. Compared with the simulation results, the models driven by ground-based observation precipitation obtained the most accurate streamflow simulation results, followed by CMADS, TMPA 3B42 and 3B42RT precipitation. Besides, CMADS temperature can capture the spatial distribution characteristics well and improve the streamflow simulations. This study provides valuable insights for hydro-climatic application of satellite and reanalysis meteorological products in the ungauged or data-limited regions. [ABSTRACT FROM AUTHOR]

• A Nonstationary Standardized Precipitation Index is developed. • Nonlinear dependence performed better than linear dependence in the nonstationary model. • The proposed NSPI shows a more robust performance in drought assessment. The widely used probability based drought indices for drought characterization usually underlie the assumption of the considered hydrometeorological variables to be stationary, which is largely challenged under the current global change. Incorporating the parameters of probability distribution with covariates, such as time or climate indices, has become a common way to develop drought index considering nonstationarity. However most previous drought studies emphasized more on the linear relationship between distribution parameters and related covariates, which may neglect the inherent nonlinear relationship and lost useful information for drought assessment. Our study aims to develop a Nonstationary Standardized Precipitation Index (NSPI) with distribution parameters nonlinearly varying with potential influencing climate indices. The middle and lower reaches of the Yangtze River Basin (MLRYRB) of China are selected as the case study area to examine the performance of the NSPI with the performance of the traditional standardized precipitation index (SPI). Results indicate that the NSPI (with climate indices as covariates) is more robust than both the NSPI (with time as the covariate) and the traditional SPI. Furthermore, the nonlinear dependence of distribution parameters on climate indices considered in the nonstationary model can be more suitable for drought assessment than the linear dependence of distribution parameters. Besides, there exists a higher frequency of extreme drought by the NSPI than the SPI in recent decades in most regions of the MLRYRB. Although the NSPI is a little computationally expensive with more required inputs of climate variables, it can depict the influence of changing environment on the drought occurrence, thus can be a feasible alternative for drought assessment considering nonstationarity in view of future change and provide valuable support for further studies. [ABSTRACT FROM AUTHOR]

Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis. [ABSTRACT FROM AUTHOR]

Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments. Key Points: Direct calibration of hydrological models solely against remotely sensed ET (RS‐ET) data is used to predict runoff in ungauged regionsThe RS‐ET runoff‐free calibration approach is tested on 222 catchments across AustraliaThe RS‐ET runoff‐free calibration can estimate mean annual runoff and monthly runoff series in wet catchments reasonably well [ABSTRACT FROM AUTHOR]

The regional frequency analysis (RFA) is a widely used method in analyzing the changes of extreme precipitation (EP). The uncertainties in the identification of homogeneous subregions and the selection of optimal regional frequency distributions can largely influence the results of the RFA. In this study, the fuzzy c-means method combined with the extended Xie-Benn index (FCXB) is used to help determine the optimal division of subregions. In addition, we introduce a new comprehensive index (CI) to overcome the shortcomings of present measures and reduce the uncertainty in regional frequency distribution selection. The changes of EP at 93 meteorological stations in the Songliao River Basin (SRB) during 1960 to 2016 is analyzed. The main results show that: (1) FCXB can effectively identify the optimum number of homogeneous subregions automatically, and the corresponding subregion division is proven to be reasonable and reliable; (2) compared with the single goodness-of-fit measure, the developed CI can reduce the uncertainties in distribution selection and determine the optimal regional distribution in a reliable way; (3) the estimated EP under different return periods both decrease from the south to the north of the SRB, which indicates the risk of high-intensity EP events in the southern SRB is relatively higher. These findings can provide technical support for local policymakers to formulate effective measures to lessen the damages of the EP on ecosystem and society. • FCXB can give a reliable division of homogeneous sub-region. • The CI can select the optimal regional distributions in a reliable way. • The risk of high-intensity EP events in southern SRB is higher than northern SRB. [ABSTRACT FROM AUTHOR]

The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three series of pairwise rainfall and river flow were extracted to capture their dependence. • Pairs of rainfall and river flow were proportionally sampled from their bivariate joint distributions. • Expected annual damage was employed to characterize compound flood risk. • The relationship between flood damage and rainfall and river flow is positively stepped. • Future compound flood risk is expected to increase in most of the Yangtze River Basin. Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 ∼ 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % ∼ 66.13 % and 1.72 % ∼ 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % ∼ 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R 2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % ∼ 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % ∼ 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction. [ABSTRACT FROM AUTHOR]

[Display omitted] • Coupling PT-JPL into DTVGM improved the hydrological simulation accuracy in UHRB. • Except for Pr, which had a high positive correlation with BW, other climatic factors correlated relatively weakly with BW and GW. • Vegetation showed a significant greening trend in UHRB, facilitating the transformation of BW to GW. • Vegetation greening will alter the original correlation between climatic factors and hydrological processes. The characteristics of water fluctuations within the basin have remained ambiguous in recent years due to climate and vegetation changes, posing a challenge to the planning and utilization of regional water resources. This study coupled the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, which can consider vegetation dynamics information and evapotranspiration physical mechanisms, into the Distributed Time Variant Gain Model (DTVGM) to improve the hydrological simulation accuracy under changing environments. Based on different simulation scenarios of the coupled model, the effects of climate and vegetation changes on hydrological processes were fully investigated using blue and green water (BW and GW) as assessment metrics. The upper Han River Basin (UHRB), as water source for the Central Line Project of China's South-North Water Diversion (CSNWD), was selected as a typical basin for the study, results indicated that: (1) The DTVGM coupled with PT-JPL could better reflect the spatiotemporal patterns of major hydrological processes (runoff, evapotranspiration, and soil moisture) in UHRB from 2001 to 2019; (2) The BW in UHRB exhibited decreasing trend from 2001 to 2019, while the GW on the contrary. Surface runoff (Rs), vegetation transpiration (Et), and soil moisture (W) contributed most in the components of BW and GW; (3) Vegetation change dominated by greening in UHRB, which facilitated the transformation of BW to GW in the basin, and it increased the proportion of GW mainly by promoting Et. BW was mainly influenced by precipitation, but vegetation change altered the original correlation between climatic factors and hydrological processes. This study can support the management of water resources in the basin under changing environments. [ABSTRACT FROM AUTHOR]