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Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn , last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities and plays an important role in the hydrological cycle, as it is directly related to the development of agricultural water management. Therefore, it is a requirement to correctly simulate R and have the ability to separate the impacts due to climate change and human activities. In this paper, five single-type simulation models (Back Propagation Neural Network (BP), Non-Autoregressive (NAR), Radial Basis Function (RBF), Support Vector Machine (SVM) and TOPMODEL Hydrological Model (TOPMODEL)) were adopted to simulate the R to analyze the simulating quality by comparing the evaluation indexes like relative error (RE), relative mean squared error (RMSE) and Nash–Sutcliff Efficiency (NSE) with the combined hierarchical structure hydrological (CHSH) simulation model. In traditional studies, only the relative contribution of the impacts of human activities and climate change on R are considered; however, in this study, the relative contribution of each meteorological factor affecting R is included. To quantitatively analyze the impact of human activities and climate change on R, we used a CHSH simulation model to calculate runoff values for the Lancang River of China for a period of nine years (2005–2013). Our objective was to use this type of model to improve both the accuracy and stability of calculated values of R. For example, the RE, RMSE and NSE of simulated monthly R calculated with the CHSH model were 6.41%, 6.67 × 108 m3 and 0.94, respectively. These values substantiate the improved accuracy and stability of calculated values of R obtained with single-type simulation models (the SVM model, for instance, widely used in runoff simulations, and the RE, RMSE and NSE were 14.1%, 12.19 × 108 m3 and 0.87, respectively). The total contribution of human activities and climate change to R, respectively, accounted for 34% and 66% for the nine-year period based on the CHSH model. Furthermore, we adopted a vector autoregressive (VAR) model to analyze the impacts of the meteorological factors on R. The results from this analysis showed that R has a strong fluctuation response to the changes in precipitation (P) and potential water evaporation (Ep). The contribution rates of Ep, P and air temperature (Ta) to R were 15%, 14% and 2%, respectively. Based on the total climate change contribution, the corresponding contribution rates of Ep, Ta and P in the Lancang River of China were 32%, 30% and 5%, respectively. The values of R calculated with the CHSH model are more accurate and stable compared to values obtained with single-type simulation model. Further, they have the advantage of avoiding drawbacks associated when using a single-type simulation model. Moreover, moving away from the traditional method of separating the impact of meteorological factors on R, the vector autoregressive model proposed in this paper can describe the contribution of different meteorological factors on R in more detail and with precision. [ABSTRACT FROM AUTHOR]

The Xin'anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM's effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model's state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins. [ABSTRACT FROM AUTHOR]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

Featured Application: The application provides a new tool for reducing the error in the simulated runoff of a hydro-logical model by integrating time series models with hydrological models. Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher's area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash–Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models. [ABSTRACT FROM AUTHOR]

Many studies quantify the impact of climate change and human activities on runoff changes on an annual scale, but few studies have examined this on multiple time scales. This paper quantifies the contribution of different factors to the variability of Jinsha River runoff at multiple time scales (annual, seasonal and monthly). First, the trend analysis of Jinsha River runoff is carried out, and the Mann–Kendall mutation test was then applied to the runoff data for mutation analysis. According to the mutation year, the research period is divided into the base period and the mutation period. By constructing an ABCD hydrological model simulation and monthly scale Budyko model, the contribution rate of human and climate factors to the multitime-scale runoff of Jinsha River is calculated. The results showed that: (1) The sudden year of change in the Jinsha River runoff is 1978, and the Nash coefficients of the ABCD hydrological model in the base period and sudden change period were 0.85 and 0.86, respectively. (2) Climate factors were the dominant factor affecting annual runoff changes (98.62%), while human factors were the secondary factor affecting annual runoff changes (1.38%). (3) The contribution rates of climate factors in spring, summer, autumn, and winter to runoff were 91.68%, 74.08%, 95.30%, and 96.15%, respectively. The contribution rates of human factors in spring, summer, autumn, and winter to runoff were 8.32%, 25.92%, 4.70%, and 3.85%, respectively. (4) The contribution rates of climate factors to runoff in May, June, and July were 95.14%, 102.15%, and 87.79%, respectively. The contribution rates of human factors to runoff in May, June, and July were 4.86%, −2.15%, and 12.21%, respectively. [ABSTRACT FROM AUTHOR]

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

In the past decade, more than 300 people have died per year on average due to mountain torrents in China. Mountain torrents mostly occur in ungauged small and medium-sized catchments, so it is difficult to maintain high accuracy of flood prediction. In order to solve the problem of the low accuracy of flood simulation in the ungauged areas, this paper studies the influence of different methods on the parameter regionalization of distributed hydrological model parameters in hilly areas of Hunan Province. According to the terrain, landform, soil and land use characteristics of each catchment, we use Shortest Distance, Attribute Similarity, Support Vector Regression, Generative Adversarial Networks, Classification and Regression Tree and Random Forest methods to create parameter regionalization schemes. In total, 426 floods of 25 catchments are selected to calibrate the model parameters, and 136 floods of 8 catchments are used for verification. The results showed that the average values of the Nash–Sutcliffe coefficients of each scheme were 0.58, 0.64, 0.60, 0.66, 0.61 and 0.68, and the worst values were 0.27, 0.31, 0.25, 0.43, 0.35 and 0.59. The random forest model is the most stable solution and significantly outperforms other methods. Using the random forest model to regionalize parameters can improve the accuracy of flood simulation in ungauged areas, which is of great significance for flash flood forecasting and early warning. [ABSTRACT FROM AUTHOR]

Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [ABSTRACT FROM AUTHOR]

Precipitation has been recognized as the most critical meteorological parameter in hydrological studies. Recent developments in space technology provide cost-effective alternative ground-based observations to simulate the hydrological process. Here, this paper aims to evaluate the performance of satellite-based datasets in the hydrological modeling of a sensitive area in terms of water quality and safety watershed. Three precipitation products, i.e., rain gauge observations (RO), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), and Tropical Rainfall Measuring Mission Multi-satellite (TRMM) products, were used to develop the Soil and Water Assessment Tool (SWAT) model to simulate the streamflow in the Danjiang River Basin (DRB). The results show that: (1) these three precipitation products have a similar performance with regard to monthly time scale compared with the daily scale; (2) CMADS and TRMM performed better than RO in the runoff simulations. CMADS is a more accurate dataset when combined with satellite-based and ground-based data; (3) the results indicate that the CMADS dataset provides reliable results on both monthly and daily scales, and CMADS is a possible alternative climate product for developing a SWAT model for the DRB. This study is expected to serve as a reference for choosing the precipitation products for watersheds similar to DRB where the rain gauge data are limited. [ABSTRACT FROM AUTHOR]

Error correction is an important postprocess technique in hydrological modeling. The paper aims to investigate the effectiveness and suitability of a recently proposed error correction method, the regularized dynamic system response curve (RDSRC) method in the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. In the HBV model, the effective precipitation can reflect the error of model inputs so it is used for runoff error correction. Three commonly used statistics [Nash-Sutcliffe efficiency (NSE) coefficient, root-mean-squared error (RMSE), and percent bias (PBIAS)] were used to evaluate the model performance. In this study, the RDSRC method was tested by a synthetic case and a real case. Results of the synthetic case showed that the NSE, RMSE, and PBIAS of runoff simulation before correction were 0.943, 182.73 m3/s , and −0.626% , respectively, while those statistics after correction by the RDSRC method were 1,0.61 m3/s , and 0.002%, respectively. In the real case, the HBV model was used for daily runoff modeling in the Shiquan catchment in China; meanwhile, the RDSRC method and the AR(2) model were compared for runoff error correction. The corrected daily runoff by the RDSRC method had much better accuracy than the second-order autoregressive [AR(2)] model and the HBV model. After correction of the RDSRC method, the NSE of the HBV model increased from 0.83 to 0.946, whereas the RMSE of the HBV model decreased by 43.6%, from 246.37 to 138.96 m3/s. This case study provides two main conclusions: (1) the RDSRC method had better performance in runoff error correction than the AR(2) model, and (2) the RDSRC method is an effective alternative for daily runoff error correction when used for the HBV model. [ABSTRACT FROM AUTHOR]

Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility functions of raw residuals. Additionally, the AR(1) model and location-mixture Gaussian distribution are used to estimate the temporal correlation structure and innovation distribution. Through two case studies in South-East China, this research demonstrates the superiority of the NRE model over the benchmark Box-Cox transformation approach in terms of prediction reliability, precision, and bias correction capabilities. Simulation experiments further reveal that the NRE model can effectively fit the residual regression function, conditional volatility function, and innovation distribution under varying scenarios. The proposed residual error model is anticipated to promote the adoption of probabilistic predictions in hydrological modeling applications. • Residual Error Handling: This paper tackles complex residual errors in hydrological modeling. • Accuracy Improvement: The NRE model boosts accuracy with a local linear estimator. • Predictive Superiority: The NRE model excels in predicting reliability, precision, and bias correction. • Data-Driven Flexibility: The NRE model reduces reliance on assumptions for hydrological modelers. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]

Hydrological simulation in areas with complex topography and pronounced climatic heterogeneity has a certain degree of uncertainty, and how precipitation data from multiple types of sources influence hydrological simulation remains unclear. Performances of precipitation datasets based on multiple interpolation methods (i.e., inverse distance weighted [IDW], cokriging, and ANUSPLIN) and satellite remote sensing datasets (i.e., tropical rainfall measuring mission multi‐satellite precipitation analysis 3B42 version7 [TRMM 3B42V7], precipitation estimation from remote sensing information using artificial neural networks–climate data record [PERSIANN‐CDR], climate predication centre morphing technique, and sian precipitation–highly resolved observational data integration towards evaluation of water resources [APHRODITE]) were obtained for comparison. Hydrological responses from these precipitation datasets were evaluated through distributed hydrological model variable infiltration capacity. The Yalong river basin (YRB), located in the Hengduan Mountains region and characterized by the significant heterogeneity of precipitation in China, was focused. Four sub‐regions (i.e., sub‐region I to IV) were divided. The results indicated that: (a) TRMM 3B42V7 and APHRODITE data were superior in detection capability from daily to annual measurement than other satellite precipitation data sets generally; (b) under the scale of gauges, TRMM 3B42V7, APHRODITE, and cokriging performed well in sub‐regions I, II, and IV, respectively. In sub‐region III, performance of the interpolation methods was better than that of TRMM 3B42V7; (c) the interpolation datasets exhibited a poor capability to simulate peak runoff. The combination dataset COM could improve the overall runoff simulation in flood periods; and (d) the interpolation method has the problem of excessive smoothness. Whatever the interpolation dataset is, hydrological simulation tends to result in smaller runoff, which was related to that the measurements from meteorological stations in mountainous valleys are not necessarily representative of the entire region. In this paper, the applicability of multi‐source precipitation data in the hydrological simulation process of YRB was comprehensively analysed, which could provide effective references for improving the accuracy of precipitation data and the applicability of hydrological simulation. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface-subsurface hydrological model that employs a finite volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma Watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://shuddata.com), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

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In this paper, a distributed catchment-hydrology model and a physically based lake hydrodynamic model were used to simulate the large-scale and highly dynamic lake catchment system of Poyang Lake, in the middle reach of the Yangtze River basin, China. The simulation of the hydrodynamics of the lake is a significant extension to previous efforts to simulate Poyang Lake's considerable variability in lake extent and flow rates. Further, the combination of the distributed catchment-hydrology model and the lake-hydrodynamic model, applied to a highly dynamic and large-scale system, is a rare attempt to develop a physically based management model of this complexity and scale. Model calibration and validation were undertaken to evaluate the model's performance and to enhance its effectiveness in simulating catchment discharges, lake water levels, lake water surface areas, and lake flow patterns. The results showed a satisfactory agreement with field observations, with Nash-Sutcliffe efficiencies of 0.71-0.84 for catchment discharges, 0.88-0.98 for lake water levels, and 0.80 for lake outflows during the calibration period. The Nash-Sutcliffe efficiency values for the validation period, ranging from 0.62 to 0.97, were largely consistent with the calibration values. Further investigation of the results showed that the modeling approach simulated adequately the lake hydrodynamics in terms of the flow fields within the lake and the seasonal changes in the lake water surface area. The outcomes of this paper will benefit future modeling efforts by providing a tool for predicting the hydrology of Poyang Lake and its catchment under climate variability and land-use changes. [ABSTRACT FROM AUTHOR]

This paper investigates the probability of integrating the Xinanjiang model, the most widely used watershed hydrological model in China, with the fully distributed groundwater model MODFLOW for processing a coupled flow simulation. Based on the flow simulation of the Xinanjiang and the SWAT model in the case study area, the correlation between the groundwater recharge and the total flow runoff is analyzed, which is then applied to compute the groundwater recharge in subbasins in the Xinanjiang model. By applying a loosely coupled modeling approach, the spatially distributed groundwater recharge is introduced into MODFLOW with the RCH package via the interface of the subbasins in the Xinanjiang model and the cells in MODFLOW; thus the integration of the two models is successfully realized. The application of the integrated model simulation in Zhangji, a groundwater-dominated area in the Huai River basin, China, demonstrates that the coupled Xinanjiang-MODFLOW simulation produces reasonable groundwater levels comparing to the observations. However the study also shows the need for further research in regarding to the improvement on Xinanjiang model in order to enable its direct integration with MODFLOW. Furthermore, the approach presented in this paper is limited to a unidirectional coupling of Xinanjiang and MODFLOW, which only accounts for groundwater level simulations, while the possible feedbacks from the surface water is neglected. More efforts are required to further facilitate the applicability and capability of the models integration. [ABSTRACT FROM AUTHOR]

Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

(Objective) The China meteorological assimilation driving datasets is a database built up over the past decades specifically for application to the SWAT model (CMADS) in data-poor regions. This paper studies its application in upper reach of the Kaidu river in Northwestern China. (Method) We assessed two aspects. The first one is to assess the performance of the CMADS dataset in terms of estimating precipitation, maximum and minimum temperatures. The second is to compare the simulated results using the CMADS dataset and ground-true meteorological data.(Result) 1 The CMADS dataset is accurate for estimating temperature with its correlation with ground-true data being more than 0.9, but is less accurate for estimating daily precipitation, with its correlation with ground-truth data being 0.59. However, the correlation between calculated and measured monthly precipitations was high, reaching 0.84. 2 The simulated runoff using SWAT combined with CMADS dataset was in general more accurate than the results simulated using SWAT combined with the ground-truth data, indicating that, for regional hydrological modelling, CMADS dataset is more accurate than point-measured data. 3 Using the CMADS overestimated the baseflow and the lowest flow and underestimate the peak flow in model calibration stage, while underestimated the peak flow and overestimated baseflow slightly in model validation stage. In general, the runoff calculated using the CMADS was acceptable, except in a few extreme cases.(Conclusion) Testing the CMADS dataset against the upper reach of Kaidu river basin shows it is accurate and reliable. It can be used to simulate hydrological processes in regions short of measured meteorological data. [ABSTRACT FROM AUTHOR]

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation. [ABSTRACT FROM AUTHOR]

• Hillslope erosion dynamic model integrating vegetation effect was established. • Unified baseline parameters were obtained across different land uses. • Effect of vegetation on soil detachment and sediment transport was revealed. • Contribution rates of vegetation to sediment reduction along slopes were quantified. Developing a process-based soil erosion model that comprehensively considers the effects of vegetation is complex but crucial for evaluating sediment reduction during vegetation restoration. Current understanding of the effect of vegetation on sediment reduction along hillslopes across different land uses is limited. In this paper, we developed a dynamic model of hillslope erosion that integrates the effects of vegetation on soil detachability and hydrodynamics (VED) based on the feedback mechanism between soil detachment and sediment transport. The VED was calibrated and validated with runoff plot data for woodland, grassland, and farmland in the Wuding, Yan, Jing and Wei Rivers of the Loess Plateau in China. Unified baseline parameters and decay coefficients of vegetation were obtained. The model validation results indicated that the coefficient of determination, Nash–Sutcliffe simulation efficiency and relative error ranged from 0.59 to 0.99, 0.56 to 0.90, and −45.99 % to 46.36 %, respectively. The decay coefficients for soil detachment capacity (Φ), sediment transport capacity (T c), and the sediment reduction effect of vegetation exhibited the following order: woodland > grassland > farmland. Compared with VED, existing process-based soil erosion models failed to effectively characterize the influence of vegetation on Φ and T c on the Loess Plateau, with differences of several orders of magnitude. The individual contributions of runoff, soil detachability, and hydrodynamics with vegetation impact on sediment yield were quantified by VED. The contribution rates of vegetation to sediment reduction decreased gradually with increasing slope length because the soil detachment capacity of bare slopes exceeded that of vegetated slopes, resulting in a faster increase in sediment yield. The contribution rates of vegetation to sediment reduction for woodland, grassland, and farmland with 20 %–60 % vegetation covers were 66.75 %–99.95 %, 57.43 %–99.63 %, and 27.91 %–88.63 % in the boundary conditions of this study, respectively. The results reveal the potential for integrating VED into other distributed watershed hydrological and sediment models to assess the effects of vegetation restoration on sediment reduction at a watershed scale. [ABSTRACT FROM AUTHOR]

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas. [ABSTRACT FROM AUTHOR]

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases. [ABSTRACT FROM AUTHOR]

• A spatial random forest-based downscaling and merging method (SRF-DM) is proposed. • SRF-DM performs better than the original satellite-based precipitation products. • SRF-DM is more accurate than the classical machine learning-based methods. • Spatial autocorrelation is the most important variable. • SRF-DM has a good performance even when the gauge density is low. Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography. [ABSTRACT FROM AUTHOR]

Coal mining is one of the core industries that contribute to the economic development of a country but deteriorate the environment. Being the primary source of energy, coal has become essential to meet the energy demand of a country. It is excavated by both opencast and underground mining methods and affects the environment, especially hydrological cycle, by discharging huge amounts of mine water. Natural hydrological processes have been well known to be vulnerable to human activities, especially large scale mining activities, which inevitably generate surface cracks and subsidence. It is therefore valuable to assess the impact of mining on river runoff for the sustainable development of regional economy. In this paper, the impact of coal mining on river runoff is assessed in one of the national key coal mining sites, Gujiao mining area, Shanxi Province, China. The characteristics of water cycle are described, the similarities and differences of runoff formation are analyzed in both coal mining and pre-mining periods. The integrated distributed hydrological model named MIKE SHE is employed to simulate and evaluate the influence of coal mining on river runoff. The study shows that mining one ton of raw coal leads to the reduction of river runoff by 2.87 m3 between 1981 and 2008, of which the surface runoff decreases by 0.24 m3 and the baseflow by 2.63 m3. The reduction degree of river runoff for mining one ton of raw coal shows an increasing trend over years. The current study also reveals that large scale coal mining initiates the formation of surface cracks and subsidence, which intercepts overland flow and enhances precipitation infiltration. Together with mine drainage, the natural hydrological processes and the stream flows have been altered and the river run off has been greatly reduced. [ABSTRACT FROM AUTHOR]

High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at 10.6084/m9.figshare.21901074 (Hou and Li, 2023). [ABSTRACT FROM AUTHOR]

The basin water cycle affected by human activities presents the duality of natural and human characteristics. The effect of human activities to hydrology is becoming point of focus in the world with the development of society. As a physically based distributed parameter model, soil and water assessment tool is deficient when applied to the area with more human activities. In areas of increased activities, monthly changes in irrigation and consumptive water use has been observed over the years. In the past, this module was applied to a system with very little human influence. In this study we revised the theory and made appropriate improvement on irrigation module and consumptive water use module in order to apply it to the water system in Tianjin City, China, where water cycle is strongly affected by the human activities. The watershed model has been calibrated and validated using measured data available for the main river. By comparing the simulation results between initial model and improved model, the Nash-Sutcliffe efficiency is adjusted from 0.62 to 0.89 and correlation coefficient is improved from 0.79 to 0.91. The resulting validated model improves the simulation precision, especially amends the peak value of runoff, and adequately describes the impact of consumptive water use and irrigation on the natural water cycle. The model developed in this paper can be used to study water resource and water environment management in area of high human activities. [ABSTRACT FROM AUTHOR]

This paper presents an application of the TOPKAPI model, a physically based, fully distributed hydrologic model, to flood simulation at three unique and flood prone sites in China. The TOPKAPI model was applied with a grid size of 1 km×1 km and a time step varying from 1 to 24 h based on the basin conditions and data availability. Model calibration was chiefly based upon moderate variations of parameter values from the ones estimated on physical grounds. The three case studies demonstrated that the model performed well for flood simulation. The TOPKAPI model represents a comprehensive process-based model, which offers a wide range of capability for flood forecasting application, and has potential in prediction of ungauged basins. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and machine learning (ML) are widely used in hydrological modelling, which plays a critical role in improving the accuracy of hydrological predictions. However, the trade-off between model performance and computational cost has always been a challenge for hydrologists when selecting a suitable model, particularly for probabilistic post-processing with large ensemble members. This study aims to systematically compare the quantile regression forest (QRF) model and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) model as hydrological probabilistic post-processors. Specifically, we evaluate their ability in dealing with biased streamflow simulations driven by three satellite precipitation products across 522 nested sub-basins of the Yalong River basin in China. Model performance is comprehensively assessed using a series of scoring metrics from both probabilistic and deterministic perspectives. Our results show that the QRF model and the CMAL-LSTM model are comparable in terms of probabilistic prediction, and their performances are closely related to the flow accumulation area (FAA) of the sub-basin. The QRF model outperforms the CMAL-LSTM model in most sub-basins with smaller FAA, while the CMAL-LSTM model has an undebatable advantage in sub-basins with FAA larger than 60 000 km 2 in the Yalong River basin. In terms of deterministic predictions, the CMAL-LSTM model is preferred, especially when the raw streamflow is poorly simulated and used as input. However, setting aside the differences in model performance, the QRF model with 100-member quantiles demonstrates a noteworthy advantage by exhibiting a 50 % reduction in computation time compared to the CMAL-LSTM model with the same ensemble members in all experiments. As a result, this study provides insights into model selection in hydrological post-processing and the trade-offs between model performance and computational efficiency. The findings highlight the importance of considering the specific application scenario, such as the catchment size and the required accuracy level, when selecting a suitable model for hydrological post-processing. [ABSTRACT FROM AUTHOR]

Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]

This paper applied a HIMS (hydroinformatic modeling system) model to simulate streamflow in the Luanhe River Basin. This model was compared with SIMHYD and XAJ models for eight sub-basins of the Luanhe River. The results showed HIMS model performed better than SIMHYD and XAJ models, in these areas. We then investigated the streamflow response to climate changes in the different sub-basins. Twenty hypothetical climate change scenarios (perturbed temperatures and precipitation) were used to test the sensitivity of HIMS model simulated annual and mean monthly streamflow. Our results demonstrated that: (i) the annual streamflow was positively related to precipitation, and there was a negative relationship between streamflow and temperature for all the eight sub-basins; (ii) in all sub-basins, the relationship of annual streamflow change to precipitation change was highly non-linear, but the relationship of annual streamflow change with temperature change was approximately linear; (iii) the annual streamflow response to precipitation change was more sensitive when increasing than decreasing; (iv) the annual streamflow response to climate change was more sensitive in the Xingzhouhe River sub-basin, followed by the Wuliehe River sub-basin, and the Sahe River sub-basin was least sensitive; (iv) there were few differences in inner-streamflow response to climate change in the Laoniuhe, Yimatuhe, and Yixunhe Rivers. But for other rivers, when the temperature changed, larger streamflow differences happened in winter and summer; when the precipitation decreased or was unchanged, the larger differences happened in winter months, and when the precipitation increased, larger differences happened in winter and summer. [ABSTRACT FROM AUTHOR]

One of the potential impacts of global warming is likely to be experienced through changes in flood frequency and magnitude, which poses a potential threat to the downstream reservoir flood control system. In this paper, the downscaling results of the multimodel dataset from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) were coupled with the Variable Infiltration Capacity (VIC) model to evaluate the impact of climate change on the Feilaixia reservoir flood control in the Beijiang River basin for the first time. Four emissions scenarios [A1B and representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5, and RCP8.5] were chosen. Results indicate that annual distribution and interannual variability of temperature and precipitation are well simulated by the downscaling results of the CMIP3 and CMIP5 multimodel dataset. The VIC model, which performs reasonably well in simulating runoff processes with high model efficiency and low relative error, is suitable for the study area. Overall, annual maximum 1-day precipitation in 2020-50 would increase under all the scenarios (relative to the baseline period 1970-2000). However, the spatial distribution patterns of changes in projected extreme precipitation are uneven under different scenarios. Extreme precipitation is most closely associated with extreme floods in the study area. There is a gradual increase in extreme floods in 2020-50 under any of the different emission scenarios. The increases in 500-yr return period daily discharge of the Feilaixia reservoir have been found to be from 4.35% to 9.18% in 2020-50. The reservoir would be likely to undergo more flooding in 2020-50. [ABSTRACT FROM AUTHOR]

China has begun a rapid move towards Sponge City implementation, which will include widespread installation of various Low Impact Development (LID) stormwater infrastructure. Ambitious goals have been set for all Sponge Cities, including a target capture rate of 80–85% of annual precipitation in Beijing. However, there has so far been limited investigation into whether these goals are obtainable at the catchment scale given the physical restrictions of the existing urban landscape. This paper demonstrates the use of long-term continuous hydrologic modelling to evaluate the potential of meeting rainfall capture goals over a long time period for a case study area in Beijing. Detailed land cover classification derived from satellite imagery aided in the parameterization of a SWMM (Stormwater Management Model) model representing a 133 km2 urban study area. The calibrated and validated model demonstrated satisfactory agreement between computed and observed runoff for a variety of precipitation events ranging from 10 to 207 mm. A synthetic high resolution rainfall time series was generated from historic daily observations then used as input to run the model continuously for a 35 year period. Feasible LID model scenarios based on varying degrees of implementation of rain gardens, permeable pavements and green roofs were compared in terms of their potential volume capture of annual rainfall. Modelling results demonstrated that the conversion of 30% of roof area to green roof, 10% of green areas to rain gardens and 35% of paved areas to permeable pavement could increase the baseline volume capture ratio from 59.9% to 82.2%, indicating that Sponge City goals can be met within the case study area with realistic levels of LID implementation. This study provides a decision-making basis for future planning in the study area. • Satellite imagery based hydrologic model shows good agreement with observed flows. • Continuous modelling approach suggested for evaluation of Volume Capture Ratio. • >80% of annual rainfall captured with moderate catchment wide implementation of LID. [ABSTRACT FROM AUTHOR]

Drought simulation and prediction are of great significance to drought early warning. However, it is difficult to predict hydrological drought in data-scarce areas. To address this problem, Tarim River Basin was selected as a typical representative of the data-scarce inland river basin in China, we constructed a hybrid model by combining the complete ensemble empirical mode decomposition with adaptive noise and the long short-term memory method to predict hydrological drought from 2022 to 2100 based on CMIP6. The results show that meteorological drought has quasi-3-month, quasi-5-month, quasi-7-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-17-year and quasi-54-year cycles. Hydrological drought has quasi-3-month, quasi-5-month, quasi-6-month, quasi-1-year, quasi-2-year, quasi-4-year, quasi-9-year, quasi-29-year and quasi-32-year cycles. The components of meteorological drought and hydrological drought have significant correlations on monthly, interannual, and interdecadal scales, with correlation coefficients of 0.282, 0.573, and 0.340, respectively, and p values of 0.000. The hybrid model had a better prediction accuracy (R2 = 0.951, MAE = 0.131, NSE = 0.951, d index = 0.987) than previous studies. The trend of the hydrological drought index in the sustainable development model (SSP1-2.6) shows a trend of increasing severity with a rate of − 0.004/10 years from 2022 to 2100. And from the sustainable development model (SSP1-2.6) to the unbalanced development model (SSP5-8.5), the hydrological drought gradually becomes more serious. This study provides a new mechanism for predicting hydrological drought in data-scarce areas and is of great significance for the early warning of hydrological drought in this area. [ABSTRACT FROM AUTHOR]

The Jinsha River basin (JRB), known as China's largest hydropower base, has been facing a surge in hydrological drought occurrences in the past several years. This study used the drought index model and soil and water assessment tool (SWAT) hydrological model to uncover the linkages between meteorological and hydrological drought using long-term datasets in the JRB. The results revealed that: (1) Over the past six decades, the JRB has experienced recurrent meteorological droughts, with the upper reaches being the most affected, accounting for a frequency of 17.5%. However, the frequency of drought in the middle and lower reaches has shown a marked increase in the last 15 years. (2) The frequency of hydrological drought in the JRB has been on the rise over the past six decades, with a particularly notable increase observed in the last two decades. Furthermore, a noticeable upward trend has been observed in the duration of these hydrological droughts. (3) The propagation durations from meteorological drought to hydrological drought exhibited noticeable seasonal differences in the JRB. The transmission duration during the flood season was shorter, whereas in the dry season, it was more protracted. Additionally, the connection between meteorological drought and hydrological drought demonstrates a weakening trend. The findings of this study hold significant implications for crafting an efficient reservoir dispatching strategy to safeguard the water security of the JRB. [ABSTRACT FROM AUTHOR]

Agricultural Best Management Practices (BMPs) are effective ways to reduce agricultural nonpoint source pollution from their source area to receiving water bodies. Characterization of BMPs in a watershed model is a critical prerequisite for evaluating their impacts on water quantity and water quality in a complex system. However, limited research has reported about the representation of BMPs in fully distributed models. This paper presents a stepwise procedure for representation of several BMPs and assessment of their hydrologic impacts with a fully distributed model, SEIM (Spatially Explicit Integrated Modeling). A case study is conducted in the 73 km2 Luoyugou watershed located in the Loess Plateau of China, where rainstorm erosion accounts for more than 60% of annual sediment load in average. Three BMPs are selected in this study including (i) conversion from farmland to forest, (ii) terrace, and (iii) no-till farming. These management practices are represented in the model through the alteration of model parameters characterizing their physical processes in the field. The results of scenario assessment for a historical storm event showed that the maximum sediment reduction after terrace is about 97.3%, the average sediment reduction after no-till farming is about 9.5%, and the average sediment reduction after conversion from farmland to forest is 75.6%. [ABSTRACT FROM AUTHOR]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]

Karst watershed refers to the total range of surface and underground recharge areas of rivers (including subterranean rivers and surface rivers) in karst areas. Karst water resources, as the primary source of domestic water supply in southwest China, are vital for the social and economic development of these regions. It is greatly significant to establish a high-precision hydrological model of karst watershed for guiding water resources management in karst areas. Choosing the Daotian river basin in the Wumeng Mountains of Southwest China as the study area, this paper proposed a method for simplifying karst subterranean rivers into surface rivers by modifying the digital elevation model (DEM) based on a field survey and tracer test. This method aims to solve the inconsistency between the topographical drainage divides and actual catchment boundaries in karst areas. The Soil and Water Assessment Tool (SWAT) model was modified by replacing the single-reservoir model in the groundwater module with a three-reservoir model to depict the constraints of multiple media on groundwater discharge in the karst system. The results show that the catchment areas beyond topographic watershed were effectively identified after simplifying subterranean rivers to surface rivers based on the modified DEM data, which ensured the accuracy of the basic model. For the calibration and two validation periods, the Nash–Sutcliffe efficiencies (NSE) of the modified SWAT model were 0.87, 0.83, and 0.85, respectively, and R2 were 0.88, 0.84, and 0.86, respectively. The NSE of the modified SWAT model was 0.09 higher than that of the original SWAT model in simulating baseflow, which effectively improved the simulation accuracy of daily runoff. In addition, the modified SWAT model had a lower uncertainty within the same parameter ranges than the original one. Therefore, the modified SWAT model is more applicable to karst watersheds. [ABSTRACT FROM AUTHOR]

This paper develops an operation-based numerical scheme for simulating storage in and outflow from a multiyear and multipurpose reservoir at a daily time step in order to enhance the simulation capacity of macroscale land surface hydrologic models. In the new scheme, besides the purpose of flood control, three other operational purposes-hydropower generation, downstream water supply, and water impoundment-are considered, and accordingly three related decision-based parameters are introduced. The new scheme is then integrated into the Soil and Water Assessment Tool (SWAT), which is a macroscale hydrologic model. The observed water storage and outflow from a multiyear and multipurpose reservoir, the Xinfengjiang Reservoir in southern China, are used to examine the new scheme. Compared with two other reservoir operation schemes-namely, a modified existing reservoir operation scheme in SWAT (i.e., the target release scheme) and a multilinear regression scheme-the new scheme can give a consistently better simulation of the reservoir storage and outflow. Furthermore, through a sensitivity analysis, this study shows that the three decision-based parameters can represent the significance of each operational purpose in different periods and the new scheme can advance the flexibility and capability of the simulation of the reservoir storage and outflow. [ABSTRACT FROM AUTHOR]

• A new framework is developed for detailed flash flood risk mapping in ungauged basin. • Performance of the developed model is assessed with −0.004 m of BIAS for water level. • Flood inundation parameters and socioeconomic features are considered in the model. Flash floods are among the most devastating natural hazards but the analyses of flash floods are hindered by the lack of hydro-meteorological data. In particular, data issues are critical in ungauged basins. This paper presents an investigation of flash flood modeling using coupled hydrological and hydrodynamic models. Specifically, the TOPography-based hydrological MODEL (TOPMODEL) is employed to obtain the flood hydrograph and the coupled 1D-2D hydrodynamic model MIKEFLOOD is used to simulate floodplain inundation. The methods are tested in the Pajiang River, a mountainous basin in South China. The results show that the TOPMODEL effectively simulates the flood hydrograph and the Nash–Sutcliffe coefficients of the flow rate are larger than 0.80 for both calibration and validation. Also, MIKEFLOOD effectively simulates the flood inundation extent and flooding depth and the BIAS for simulated water level is −0.004 m. Further, using socioeconomic and historical flood disaster datasets, areas with high flash flood risk are identified. A flash flood risk assessment shows a clear trend of change in the flood hazard and the spatial heterogeneity of the flood vulnerability is significant. A sensitivity analysis of the flood vulnerability to the socioeconomic indicators indicates that cultivated land is the most sensitive indicators, and the population and the construction areas have similar influences. Overall, the coupled hydrological and hydrodynamic models prove effective and can be used in practical applications for flash flood risk mapping. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces the development of a dynamic parallel algorithm for conducting hydrological model simulations. This new algorithm consists of a river network decomposition method and an enhanced master–slave paradigm. The decomposition method is used to divide a basin river network into a large number of subbasins, and the enhanced master–slave paradigm is adopted to realize the function of this new dynamic basin decomposition method through using the Message-Passing Interface (MPI) and C++ language. This new algorithm aims to balance computation load and then to achieve a higher speedup and efficiency of parallel computing in hydrological simulation for the river basins which are delineated by high-resolution drainage networks. This paper uses a modified binary-tree codification method developed by to code drainage networks, and the basin width function to estimate the possible maximum parallel speedup and the associated efficiency. As a case study, with a hydrological model, the Digital Yellow River Model, this new dynamic parallel algorithm is applied to the Chabagou basin in northern China. The application results reveal that the new algorithm is efficient in the dynamic dispatching of simulation tasks to computing processes, and that the parallel speedup and efficiency are comparable with the estimations made by using the basin width function. [Copyright &y& Elsevier]

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An economic valuation of flood reductions arising from land use changes induced by the Conversion of Cropland to Forest and Grassland Program (CCFGP) in China is detailed in this paper. A distributed hydrologic model is used to analyze and predict changes in the probability of flood occurrences in the Yellow River Basin (YRB) due to revegetation activities under the CCFGP. The expected value of flood reductions due to the avoided flood damage potential is then estimated. Losses brought about by each type of flood in the YRB over time and the probability change in flood occurrences are used in the calculation. Results show that the economic benefits from flood reductions will total China Yuan 362 million during 2010–2020. The inclusion of this economic benefit into a benefit-cost analysis of the CCFGP provides a more comprehensive assessment of the program. Implications for prioritizing management options to mitigate flood disasters in the YRB are drawn. [ABSTRACT FROM AUTHOR]

This paper targets efforts to integrate rough set theory and the fuzzy inference technique into the multi-element medium and long-term hydrological forecast. Rough set theory is used to predigest the data and deal with the redundant inconsistent initial information table. Accordingly, the factors are reduced with the attribute significance concept. The minimal solution which is as fuzzy inference forecast pattern rule set in the model is achieved according to the principle of maximal attribute significance and combination significance as well as rules frequency. The model is applied to forecast annual runoff of Dahuofang Reservoir in China. The results indicate that the forecast precision is improved with rough set and the model can effectively reflect the non-linear relations between the runoff and factors and provide an effective and adaptable method to solve forecast problems related to complex factors selection and minimal inference rule set generation. [ABSTRACT FROM AUTHOR]

Hydrological variable frequency analysis is a fundamental task for water resource management and water conservancy project design. Given the deficiencies of higher distribution features for the upper tail section of hydrological variable frequency curves and the corresponding safer resulting design of water conservancy projects utilizing the empirical frequency formula and Pearson type III function-based curve fitting method, the normal cloud transform algorithm-based model for hydrological variable frequency analysis was proposed through estimation of the sample empirical frequency by the normal cloud transform algorithm, and determining the cumulative probability distribution curve by overlapping calculation of multiple conceptual cloud distribution patterns, which is also the primary innovation of the paper. Its application result in northern Anhui province, China indicated that the varying trend of the cumulative probability distribution curve of annual precipitation derived from the proposed approach was basically consistent with the result obtained through the traditional empirical frequency formula. Furthermore, the upper tail section of the annual precipitation frequency curve derived from the cloud transform algorithm varied below the calculation result utilizing the traditional empirical frequency formula, which indicated that the annual precipitation frequency calculation result utilizing the cloud transform algorithm was more optimal compared to the results obtained by the traditional empirical frequency formula. Therefore, the proposed cloud transform algorithm-based model was reliable and effective for hydrological variable frequency analysis, which can be further applied in the related research field of hydrological process analysis. [ABSTRACT FROM AUTHOR]