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Soil saturation, coupled with intense rainfall can lead to severe flooding and geological disasters, and understanding the spatiotemporal distribution of hydrological elements within a given region is critical for effective disaster mitigation. The CREST distributed hydrological model, incorporating distributed parameters such as soil water capacity and saturated hydraulic conductivity, was used to simulate runoff processes in Beijiang River Basin from 2019 to 2022. The analysis focused on the spatiotemporal distribution characteristics of precipitation, soil moisture, and runoff before and after the once-in-a-century flood that occurred on June 21, 2022, and further explored the relationship among hydrological elements, landslides, and floods. The results show that the Nash-Sutcliffe efficiency coefficients (NSEs) of the model are 0.84 and 0.82, and Pearson correlation coefficients (CCs) are 0.92 and 0.91, respectively, in the periodic and validation periods. The relative deviation is small, indicating that the CREST model has high simulation accuracy in the Beijiang River Basin. The results find that prior to the once-in-a-century flood event on June 21, 2022, the soil moisture levels across the basin were generally high. As the precipitation continued, the soil approached full saturation, and downstream discharge exceeded 16 000 m3/s. After the flood, as precipitation decreased, both soil moisture and runoff levels dropped. A comparison between the simulated average soil moisture for 2022 and historical landslide locations reveals that areas with high kernel density of historical landslides overlap with regions with high soil moisture in the southeastern part of the basin. Comprehensively, the southeastern region, located downstream, faces a higher risk of combined flood and geological hazards during the flood season.

The distributed hydrological model is the development trend of hydrological models. The model of Nanjing Research Institute of Hydrology (NRIHM) is a lumped hydrological model with a flexible architecture. In order to explore the feasibility and adaptability of using a distributed hydrological model based on NRIHM to carry out hydrological simulation in the humid watershed, the Nandawa-Shahe nested watershed in the humid area was studied. Firstly, based on the construction idea of the NRIHM runoff generation model, a one-layer runoff generation model and a two-layer runoff generation model were constructed. The runoff generation simulation verification at the grid point scale was carried out by using the observation results of the natural experimental watershed. The simulation results were compared, and it was found that the two-layer parabola combination method had a better simulation effect. Based on this runoff generation calculation method, a distributed hydrological model with a two-layer soil structure was constructed. The application was carried out in the catchment area above the Shaheji Reservoir in the Chuhe River system of the Yangtze River. The average deterministic coefficients of the calibration period and the verification period were 0.80 and 0.86, respectively. The relative errors of average flood peak and flood volume were also less than 20 %, indicating that the model could better simulate the actual runoff process, which preliminarily proved that the model had good applicability in humid areas.

The complex and varied climatic conditions, short duration and high intensity of rainfall, and complex subsurface properties of semi-humid and semi-arid watersheds pose challenges for sub-daily flood forecasting. Previous studies have revealed that lumped models are insufficient because they do not effectively account for the spatial variability in hydrological processes. Extending the lumped model to a distributed modeling framework is a reliable approach for runoff simulation purposes. However, existing distributed models do not adequately characterize the high spatiotemporal variability in sub-daily hydrological processes. To address the above concerns, a distributed modeling framework was proposed that is extended from a lumped model and accounts for the effects of time-varying rainfall intensity and reservoir regulation on hydrological processes. The results indicated that the proposed distributed model could simulate sub-daily flood events with mean values of the NSE, BIAS, RPE, and PTE evaluation metrics of 0.80, 9.2%, 13.0%, and 1.05, respectively, which are superior to those of the lumped model. Furthermore, to evaluate the difference between the proposed and existing distributed models, the proposed distributed model was compared with the variable infiltration capacity (VIC) model at various time steps. The proposed distributed model could better capture the flooding processes at shorter time steps, especially at 3 h. Therefore, it could be considered a practical tool for sub-daily flood forecasting in semi-humid and semi-arid watersheds. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Study region: The Ganjiang River Basin, China Study focus: Parameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insights: Compared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

This research evaluates natural flood management strategies in a mountain basin, the Olivares-Minitas creek in Manizales, Colombia, to quantify the effectiveness of their application in reducing floods. Hydraulic and hydrological modeling was performed simultaneously in Iber software. Multiple scenarios were proposed for three natural flood management alternatives: (1) making room for the channel, eliminating contractions and structures limitations, (2) changing the vegetation cover of the upper part of the basin, increasing the ground roughness, and (3) reconnect the stream with their floodplains, allowing the stream to dissipate flow and energy. Applying these strategies, the simulations resulted of the decrease the high discharges and the delay in peak times for the hydrographs. In conclusion, from natural solutions like rehabilitating, recovering the water body, and their ecosystem, is possible to manage and reduce the flooding risk, Attaining benefits through long-term reduction of flooding and enhancement of the river and its ecosystem. [ABSTRACT FROM AUTHOR]

While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era. Key Points: A fully differentiable framework that seamlessly integrates physics and deep learning was developed for distributed hydrological modelingThe framework flexibly fuses multi‐source observations and improves the efficiency and accuracy of large‐scale hydrological modelingThe hybrid model for the Amazon Basin exhibits excellent fidelity and physical plausibility and provides insights into the ET process [ABSTRACT FROM AUTHOR]

The Loess Plateau region is seriously affected by soil erosion, water scarcity, and fragile ecosystems, which severely hinder social and economic development in the region. There is currently a lack of quantitative analysis and research on how human activities will affect the changes in water and sediment on the Loess Plateau and how to allocate the areas for reforestation and grassland restoration. This study focuses on the Beiluo River Basin in the Loess Plateau, analyzing the spatiotemporal variations of precipitation, runoff, water resources, sediment yield and normalized difference vegetation index (NDVI) over the past 20 years. The study establishes a distributed hydrological model for the basin, quantitatively evaluates the impact of returning farmland to forests and grassland on water resources and sediment transport, and explores the optimal land retirement plan under different decision-making conditions. The results show that the total water resources, runoff, groundwater resources and sediment yield in the basin are decreasing, and the annual reductions are 7×108 m3, 1×108 m3, 1.2×108 m3, and 1.6×104 tons respectively. While NDVI and precipitation is increasing, and the annual increases are 0.0064 and 65×108 m3 respectively. The continuous increase in NDVI has resulted in a reduction in runoff volume and sediment transport. Returning farmland to forests and grassland or increasing vegetation density can reduce the amount of runoff and sediment transport, with sediment transport being more sensitive. Considering both the impact on runoff and sediment transport, based on multi-objective optimization, an optimal allocation proposal for the area of returning farmland to forest and grass is proposed. The optimal land retirement area is 28.1%. This study provides decision-making support for ecological environment construction, soil and water conservation, and rational utilization of water and sediment resources in the Loess Plateau region.

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields. [ABSTRACT FROM AUTHOR]

Against the backdrop of global climate warming, the issue of flash flood disasters in small watersheds triggered by heavy rainfall is gradually becoming more prominent. Selecting an appropriate hydrological model is crucial for flash flood disaster defense. This article focuses on the Daxi Water Basin in Lianping County, Guangdong Province, as the research area. Firstly, organize the data and subject it to standardization processing. Subsequently, establish the topological relationships within the basin, construct a hydrological model for simulating flood processes in Chinese mountainous regions, and obtain a set of model parameters applicable to the specific basin. The results indicated that: ① the relative errors of flood runoff depth were all less than 7%, with an average of 4.5%; ② the relative errors of peak flow for all events were less than 6%, with an average of 4.2%; ③ peak time errors were all within ±2 h, either earlier or later than the actual peak by 1 h; ④ the Nash-Sutcliffe efficiency coefficient for floods were all greater than 0.8, with an average of 0.86. The research results above will serve as a reference and guidance for flood defense management in the Daxi Water Basin. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a case study of riverside villages in Hubei Province. Fully taking into account previous rainfall and assuming different rainfall conditions, this work developed a dynamic early warning threshold chart by determining critical rainfall thresholds at different soil moisture levels (dry, normal, wet, and saturated) through pilot calculations, to support a quick query of the critical rainfall at any soil moisture level. The research results show that of the 74 counties and districts in Hubei Province, more than 50% witnessed higher mean critical rainfall than empirical thresholds when the soil was saturated, and about 90% did so when the soil was dry. In 881 towns, a total of 456 early warnings were generated based on dynamic thresholds from 2020 to 2022, 15.2% more than those based on empirical thresholds. From the perspective of total rainfall, dynamic early warnings were generated more frequently in wet years, while empirical early warnings were more frequent in dry years, and the frequency of two warnings were roughly the same in normal years. There were more early warnings based on empirical thresholds in May each year, but more based on dynamic thresholds in June and July, and early warnings generated based on the two methods were almost equal in August and September. Spatially, after dynamic early warning thresholds were adopted, Shiyan and Xiangyang, both northwestern cities in Hubei Province, witnessed significant increases in early warnings. In terms of the early warning mechanism, dynamic early warning took into account the impact of soil moisture and analyzed the flood discharge capacity of river channels according to the flood stage of the riverside villages. On this basis, the rainfall early warning thresholds under different conditions were determined. This is a refined early warning method that could improve the accuracy of flash flood warnings in Hubei Province. [ABSTRACT FROM AUTHOR]

Mediterranean temporary ponds are a priority habitat according to the Natura 2000 network of the European Union, and complete inventories of these ecosystems are therefore needed. Their small size, short hydroperiod, or severe disturbance make these ponds undetectable by most remote sensing systems. Here we show, for the first time, that the distributed hydrologic model IBER+ detects ephemeral and even extinct wetlands by fully exploiting the available digital elevation model and resolving many microtopographic features at drainage basin scales of about 1000 km2. This paper aims to implement a methodology for siting flood-prone areas that can potentially host a temporary wetland, validating the results with historical orthophotos and existing wetlands inventories. Our model succeeds in dryland endorheic catchments of the Upper Guadalquivir Basin: it has detected 89% of the previously catalogued wetlands and found four new unknown wetlands. In addition, we have found that 24% of the detected wetlands have disappeared because of global change. Subsequently, environmental managers could use the proposed methodology to locate wetlands quickly and cheaply. Finding wetlands would help monitor their conservation and restore them if needed. [ABSTRACT FROM AUTHOR]

The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds. [ABSTRACT FROM AUTHOR]

Conflicts between humans and land use in the process of using water and conflicts between humans and water resources in the process of using land have led to an imbalance between natural ecosystems and socio-economic systems. It is difficult to understand the impact of the processes of water production and consumption on land patches and their ecological effects. A grid-type, basin-distributed hydrological model was established in this study, which was based on land-use units and coupled with groundwater modules to simulate the water production and consumption processes in different units. By combining land use and net primary productivity, the runoff coefficient and the water use efficiency (NPP/ET) of different land units were used as indicators to characterize the interaction between water and land resources. The results showed that the average runoff coefficients of cultivated land, forest land and grassland were 0.7, 0.5 and 0.9, respectively. Moreover, the average runoff coefficients of hills, plains and basins were 0.7, 0.7 and 0.8, respectively. The NPP produced by the average unit, evapotranspiration, in cultivated land, forest land and grassland was 7 (gC/(m2•a))/mm, 0.7 (gC/(m2•a))/mm and 0.2 (gC/(m2•a))/mm, respectively. These results provide quantitative scientific and technological support in favor of the comprehensive ecological management of river basins. [ABSTRACT FROM AUTHOR]

The data assimilation of remotely sensed soil moisture observations provides a feasible path of improving river flow simulation. In this work, we studied the performance of the error subspace transform Kalman filter (ESTKF) assimilation algorithm on the assimilation of remotely sensed soil moisture from SMAP, including the improvement of soil moisture and river flow in the hydrological model. Additionally, we discussed the advantages and added value of ESTKF compared to the ensemble Kalman filter (EnKF) in a hydrological model. To achieve this objective, we solved the spatial resolution gap between the remotely sensed soil moisture and the simulated soil moisture of the hydrological model. The remotely sensed soil moisture from SMAP was assimilated into the first layer soil moisture in the distributed hydrological model. The spatial resolution of the hydrological model was 600 m, while the spatial resolution of the SMAP remotely sensed soil moisture was 9 km. There is a considerable gap between the two spatial resolutions. By employing observation operators and observation localization based on geolocation, the distributed hydrological model assimilated multiple remotely sensed soil moisture values for each grid, thereby ensuring the consistent updates of soil moisture in the model. The results show the following: (1) In terms of improving soil moisture, we found that both ESTKF and EnKF were effective, and the ubRMSE of ESTKF was lower than that of EnKF. (2) ESTKF improved most cases where open-loop high river flow simulations were too low, but EnKF did not improve this situation. (3) In ESTKF, the relative error of flood volume was reduced on average to 2.52%, but the relative error of flood peak did not improve. The results provide evidence of the value of ESTKF in the hydrological model. [ABSTRACT FROM AUTHOR]

The frequency and magnitude of flash floods in the olive orchards of southern Spain have increased because of climate change and unsustainable olive-growing techniques. Affected surfaces occupy > 85 % of the rural regions of the Upper Guadalquivir Basin. Dangerous geomorphic processes record the increase of runoff, soil loss and streamflow through time. We report on ripple/dune growth over a plane bed on overland flows, deep incision of ephemeral gullies in olive groves and rock-bed erosion in streams, showing an extraordinary sediment transport capacity of sub-daily pluvial floods. We develop a novel method to design optimal solutions for natural flood management and erosion risk mitigation. We adopt physical-based equations and build a whole-system model that accurately reproduces the named processes. The approach yields the optimal targeted locations of nature-based solutions (NbSs) for active flow-control by choosing the physical-model parameters that minimise the peak discharge and the erosion-prone area, maximising the soil infiltration capacity. The sub-metric spatial resolution used to resolve microtopographic features of terrains/NbS yields a computational mesh with millions of cells, requiring a Graphics Processing Unit (GPU) to run massive numerical simulations. Our study could contribute to developing principles and standards for agricultural-management initiatives using NbSs in Mediterranean olive and vineyard orchards. [ABSTRACT FROM AUTHOR]

• The structure of Liuxihe (LXH) model was improved to develop an improved Liuxihe (ILXH) model. • The runoff generation and confluence and parameter calibration algorithms were optimized. • The flood simulation performance was greatly improved after model improvement. • This ILXH model can provide important theoretical guidance for extreme flood disaster mitigation. Recently, the high incidence of extreme rainfall and flood events worldwide has severely harmed regional economies and societies. Therefore, flood simulations and forecasts, which can provide key technical support for regional flood control and disaster reduction, are urgently needed. The Liuxihe model, as a fully distributed, physically based hydrological model, was improved in this study to simulate extreme rainfall flood events in the Beijiang River Basin. This basin is a famous rainstorm centre in Guangxi Province, China. In this work, the Liuxihe model is improved in two aspects: first, its structure and runoff generation and confluence algorithm are improved, and second, the parameter calibration method is optimised. These two adjustments improve the flood simulation performance of the model and reduce the uncertainty of the simulation results. The results revealed that the flood simulated by the improved Liuxihe model was strongly consistent with the measured values, and the index values of the Nash coefficient, correlation coefficient, process relative error, and flood peak flow error performed very well in the scheme evaluation; in particular, the mean process relative error, flood peak error, and peak time difference decreased by 62%, 63%, and 80%, respectively, after model improvement. The error indicators of the simulation were within the allowable error range from the Standard for Hydrological Information and Hydrological Forecasting (GB/T-22482–2008), which meets the accuracy requirements of flood forecasting in the local hydrological department and can be used as a practical operational plan for flood forecasting. These satisfactory flood simulation results showed that the model and algorithm were improved; thus, the improved Liuxihe model can provide important theoretical guidance for regional flood forecasting and flood disaster mitigation. [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]

This paper presents an energy model for determining the overland flow velocity in order to improve the low accuracy problem in flow concentration simulation. It furnishes a novel idea for studying flow concentration in ungauged basins. The model can be widely applied in analysis of spatial velocity field, extraction of instantaneous geomorphic unit hydrograph and development of distributed hydrological model. A distributed flood-forecasting model is constructed for Lianyuan Basin in Hunan Province of China. In the proposed method, gravitational potential energy is transformed into kinetic energy via an analysis of energy distribution of water particles in the basin. Based on the kinetic energy equation, the overland flow velocity simulating the geomorphic unit hydrograph is computed. Rainfall-runoff simulation is then performed by integrating with runoff yield and concentration model. Results indicate that the model based on energy conversion leads to more accurate results. The model has the following advantages: firstly, the spatial distribution of the velocity field is appropriate; secondly, the model has only one parameter, which is easily determined; and finally, flow velocity results can be used for the computation of river network flow concentration. HIGHLIGHTS Propose a geomorphic unit hydrograph based on the principle of energy conversion.; Develop a distributed flood-forecasting model using the proposed geomorphic unit hydrograph.; The process of parameter determination of the developed model is very simple.; The velocity distribution obtained from the analysis is reasonable.; The developed hydrological model has a significant improvement in flood forecasting accuracy.;

• Coupling conceptual models and deep learning models for rainfall-runoff simulation. • Fusion over-infiltration and full-storage runoff mode to improve conceptual model. • Integrate the advantages of LSTM and Transformer to enhance deep learning. • Using deep learning for error correction of conceptual models can improve accuracy. • This study provides a direction for conceptual rainfall-runoff model improvements. Due to the complex runoff and concentration situation, flood forecasting for small and medium-sized catchments is very difficult. To improve the accuracy of flood forecasting, this study constructs a distributed model for flood forecasting based on the Xainanjiang (XAJ) model and the North China (NC) model respectively, and takes the deep learning model including LSTM and transformer to compare. Taking the Podi Basin and Shibazi Basin as study cases. LSTM, Transformer, and LSTPencoder models were used to correct the error of distributed models, and the differential evolution (DE) algorithm was used to optimize the model parameters. Taking the observed rainfall and distributed model results as input, the residuals of the simulation flow are fitted to improve the accuracy of the distributed model. The research results show that the NC model performs better than the XAJ, LSTM, and transformer models. Compared with the XAJ model, the average Nash-Sutcliffe coefficient of the NC model for the two catchments increased by 25.7 % and 5.87 % respectively. The performance of the NC+LSTPencoder model is better than other models. Compared with the NC model, the average Nash-Sutcliffe coefficient of the NC+LSTPencoder model for two catchments increased by 89.7 % and 1.12 % respectively, and the Root Mean Square Errors (RMSE) of the two catchments reduced by 79.1 % and 63.4 % respectively. The model proposed in this paper has strong correction ability, which has important significance for improving the accuracy of flood forecasts. [ABSTRACT FROM AUTHOR]

Cold season air warming was more rapid than warm season air warming on the Qinghai‐Tibetan Plateau (QTP). However, the effect of this asymmetrical seasonal air warming on permafrost hydrological changes has not been fully understood. This study applied a distributed cryospheric hydrological model to evaluate the effects of different seasonal air warming on the changes in frozen soil and hydrological processes in a typical catchment, the source region of the Lancang River on the eastern QTP. The results show that the area of permafrost reduced by 14.0%. The maximum frozen depth of seasonally frozen ground (MFDSFG) decreased at 5.0 cm decade−1, and the active layer thickness (ALT) of permafrost increased by 3.3 cm decade−1. Controlled experiments illustrate that cold season air warming dominated the reduction in MFDSFG which caused the liquid soil moisture increase in seasonally frozen ground, and warm season air warming primarily determined the increase in ALT which enhanced the liquid soil moisture in permafrost. Cold season air warming had a greater effect on runoff than warm season air warming because it dominated the permafrost degradation into seasonally frozen ground. In the region where permafrost degraded into seasonally frozen ground, both the cold and warm season air warming contributed to the soil liquid water increase, and the cold season warming had a greater effect due to its more important role in thermal degradation of permafrost. The findings of this study reveal different complex impacts of cold and warm season air warming on permafrost hydrological changes on the QTP. Plain Language Summary: The more rapid air warming in cold season than that in warm season on the Qinghai‐Tibetan Plateau (QTP) has been reported. However, the influences of this asymmetrical season warming on permafrost hydrological changes have been rarely studied. We used a hydrological model to distinguish the impacts of cold and warm season air warming on the changes in permafrost hydrology in a typical catchment on the QTP. The different influences of cold and warm season air warming on permafrost, runoff, and liquid soil moisture were investigated. The results suggest that cold season air warming had a greater effect on runoff than the warm season air warming due to its major role in permafrost degradation into seasonally frozen ground (SFG). Cold season air warming reduced the maximum frozen depths, leading to the liquid soil moisture increase in SFG. Warm season air warming thickened the active layer thickness which increased the liquid soil moisture in permafrost. In the region where permafrost degraded into SFG, cold season air warming played a more significant role on soil liquid water content increase. Key Points: Different effects of cold and warm season air warming on the permafrost hydrological processes were investigatedThe cold season air warming had a greater impact on runoff than the warm season air warming due to its major role in permafrost degradationIn permafrost degradation zone, cold season air warming had a greater effect on liquid soil water increase than warm season air warming [ABSTRACT FROM AUTHOR]

Land use and management practice inputs to the Soil and Water Assessment Tool (SWAT) are critical for evaluating the impact of land use change and best management practices on soil erosion and water quality in watersheds. We developed an algorithm in this study to maximize the usage of land use and management records during the setup of SWAT for a small experimental watershed in New Brunswick, Canada. In the algorithm, hydrologic response units (HRUs) were delineated based on field boundaries and associated with long-term field records. The SWAT model was further calibrated and validated with respect to water flow and sediment and nutrient (nitrate and soluble phosphorus) loadings at the watershed outlet. As a comparison, a baseline version of SWAT was also set up using the conventional way of HRU delineation with limited information on land use and management practices. These two versions of SWAT were compared with respect to input and output resolution and prediction accuracy of monthly and annual water flow and sediment and nutrient loadings. Results show that the SWAT set up with the new method had much higher accuracies in generating annual areas of crops, fertilizer application, tillage operation, flow diversion terraces (FDT), and grassed waterways in the watershed. Compared with the SWAT set up with the conventional method, the SWAT set up with the new method improved the accuracy of predicting monthly sediment loading due to a better representation of FDT in the watershed, and it also successfully estimated the spatial impact of FDT on soil erosion across the watershed. However, there was no definite increase in simulation accuracy in monthly water flow and nutrient loadings with high spatial and temporal management inputs, though monthly nutrient loading simulations were sensitive to management configuration. The annual examination also showed comparable simulation accuracy on water flow and nutrient loadings between the two models. These results indicate that SWAT, although set up with limited land use and management information, is able to provide comparable simulations of water quantity and quality at the watershed outlet, as long as the estimated land use and management practice data can reasonably represent the average land use and management condition of the watershed over the target simulation period. [ABSTRACT FROM AUTHOR]

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events. [ABSTRACT FROM AUTHOR]

Study region: Hekou karst basin, southwestern China. Study focus: The duality and complexity of hydrological behavior of karst systems pose challenges in precipitation-streamflow modelling, reliable hydrological models with high temporal resolution are warranted for predicting floods in humid flood-prone karst-dominated basins. This study proposes a grid-based distributed karst Xin’anjiang hydrological model (DK-XAJ), the model structure of each grid was developed using the conceptual Xin’anjiang model. Water exchange among each grid and the spatial heterogeneities of land use types and the karstification in each grid were considered. Information on two new linear reservoirs was added to represent the rapid-conduit and slow-matrix underground flow to improve the karst dual-porosities, the range of the underground runoff partitioning parameter was estimated from the observed flood recessions. New hydrological insights: The DK-XAJ model demonstrated good successive hourly streamflow modelling performance with mean values of 0.84, 0.83, 0.28, and 0.88 for NSE, KGE, RRE, and R2, and results during validation of one nested interior grid without recalibration was also good. The results indicate that the DK-XAJ model can help obtain detailed results for successive streamflow processes, the peaks of floods under different magnitudes and the marked increase and recession of the flash floods can be accurately reproduced and depicted. The DK-XAJ model can therefore be considered a new tool for the prediction of sub-daily precipitation-streamflow and flood events in karst-dominated basins.

Flood in small and medium-sized catchments can be characterized by the short concentration time, high flow velocity, fierce attack, and violent stage change. Mountain torrents also occur frequently in these small and medium-sized catchments. But, it is very difficult to carry out flood forecasting and early warning, due mainly to those located in the ungauged areas. Therefore, it is a high demand to improve the forecast accuracy in the operational hydrology, especially in the areas without enough measured data. In this study, an energy model was proposed to estimate the velocity of overland flow for the high accuracy in the simulation of flow concentration. Firstly, the spatial distribution of the energy was determined for the water particles in the basin. The gravitational potential energy was gradually transformed into kinetic energy using the iterative computation from the upstream to the downstream, according to the flow direction. Secondly, the spatial energy field was constructed considering the energy loss, and then the overland flow velocity was estimated to generate the spatial velocity field. Thirdly, the concentration time was calculated to count the number of grids, when the water particles on each grid reached the outlet of the watershed. Finally, the geomorphic unit hydrograph was generated to determine the relationship between the catchment area and concentration time. The study area was set as the Zhuxipo basin in Yiyang City, Hunan Province, China, located at the source of Yixi, a tributary of the Zishui River. The Zhuxipo basin was divided into 57 sub watersheds using Digital Elevation Model (DEM) data with a resolution of 30 m×30 m. A distributed model was then constructed to simulate 36 floods in the study area from 1984 to 2020. A Xinanjiang model, geomorphic unit hydrograph model, and Muskingum Routing were used to calculate the runoff generation, overland flow concentration, and river network flow concentration, respectively. The geomorphic unit hydrograph was also extracted by the Energy Conversion Method (EC-GUH) and Slope Rain Intensity Method (SR-GUH). At the same time, the EC-GUH and SR-GUH were also used to compute the overland flow concentration for the evaluation. The average flow velocity of the hydrological section was then calculated to estimate the range of energy residual coefficient (the only parameter of EC-GUH), according to the total water volume and kinetic energy of 36 floods. The results show that the EC-GUH method performed better than the SR-GUH method, where the proportion of floods with a peak time error no more than 1 h increased from 30.5% to 83.3%, the number of floods with Nash-Sutcliffe efficiency coefficient no less than 0.9 increased from 9 to 17, and the average Nash-Sutcliffe efficiency coefficient increased from 0.82 to 0.89, indicating a significantly improved simulation accuracy. It was estimated that the range of energy residual coefficient was [0.008, 0.014] under the flow velocity. In this case, the proportion of flood simulation with the Nash-Sutcliffe efficiency coefficient not less than 0.9 was 44%-50%, which was close to the calibration. It infers that the parameter can be estimated indirectly using the average velocity of the outlet section. Consequently, the concentration model presented a clear physical meaning, whose parameters were determined by the calibration or measurement for the cross and vertical section of the channel. As such, the obtained velocity can be used to simulate the flow concentration of overland and river networks. The finding can also provide a reliable idea for the concentration evaluation in the ungauged basins. [ABSTRACT FROM AUTHOR]

The calibration processes of current distributed hydrological model has been a critical issue in data-scared or ungauged regions. In this study, we setup a hydrological model, in which, the variable saturated zone concept originated from the real-time interactive basin simulator is applied for runoff generation and the grid water droplet method for flow concentration. We also proposed a method for parameter estimation based on the characteristics of underlying surface. Based on field infiltration experiments and parameter sensitivity analysis, the quantitative statistical relationships were built between two sensitive parameters (surface saturated hydraulic conductivity K0z, coefficient of attenuation of saturation hydraulic conductivity with depth f) and the topographic parameters and soil types. The overland confluence parameters were determined by field overland flow observation experiments. The proposed parameter estimation method was verified in selected basins. Our results showed that: ① The proposed method for K0z estimation contributes to a better modeling performance for flood simulation in Jiangwan experimental watershed, the average Nash- Sutcliffe efficiency coefficient increased from 0. 82 to 0. 86, and the average absolute values of peak and flood volume errors decreased by 2. 2% and 0. 95%, respectively, but the average absolute value of the peak present time error increased by 4% (still controlled within 2 h). ② Using the measured flood data of 14 basins such as Jiangwan to calibrate the parameter f, we established the quantitative relationship between the calibrated parameter f and the soil type data of different depths was built in 14 basins including Jiangwan, and further tested in other six basins. The parameter f estimated by the soil type data of different depths was very close to that determined by traditional model calibration processes, the average absolute relative error is 2. 8%, the average Nash-Sutcliffe efficiency coefficient of the flood simulation is 0. 83, and the average absolute values of flood peak error and flood volume error were 10. 07% and 6. 86%, respectively, and the average absolute peak present time error was 2. 61 h. Our results indicated that the proposed methods for determining sensitive runoff generation parameters are applicable in data-sparse areas and could provide a better or comparable parameter estimation and flood simulation than that determine by field measurements, model calibration, remote sensing data estimation, and other methods. [ABSTRACT FROM AUTHOR]

The currently used hydrological forecast system in China is mainly focused on flood, and the flood forecasting frameworks are typically based on point discharge measurements and predictions at discrete locations, hence they can't provide spatio-temporal information of various hydrological elements, such as surface runoff, soil moisture, ground water table, and flood inundation extents over large scales and at high spatial resolutions. The use of distributed hydrological model has recently appeared to be the most suitable option to bridge this gap. An open source GIS-based distributed hydrological forecast system was established recently, and the watershed delineation and hydrological modelling were integrated together seamlessly. The time and human consuming work of processing the spatial data in building distributed hydrological model could be reduced significantly, and the spatial distribution of hydrological information could be quickly simulated and predicted using this system. The system was applied successfully to forecast the flood caused by super strong typhoon “Mangkhut” which attacked the south China in 2018.

Large-scale hydrological models are important tools for simulating the hydrological effect of climate change. As an indispensable part of the application of distributed hydrological models, large-scale flow routing methods can simulate not only the discharge at the outlet but also the temporal and spatial distribution of flow. The aggregated network-response function (NRF), as a scale-independent routing method, has been tested in many basins and demonstrated to have good runoff simulation performance. However, it had a poor performance and produced an unreasonable travel time when it was applied to certain basins due to a lack of consideration of the influence of the underlying surface. In this study, we improve the NRF routing method by combining it with a velocity function using a new routing parameter b to reflect the wave velocity's sensitivity to slope. The proposed method was tested in 15 catchments at the Yangtze River basin. The results show that it can provide better daily runoff simulation performance than the original routing model and the calibrated travel times in all catchments are more reasonable. Therefore, our proposed routing method is effective and has great potential to be applied to other basins. HIGHLIGHTS This study coupled the aggregated network-response function (NRF) flow routing method with a velocity function.; The improved NRF method reflects the sensitive wave velocity to slope and gets better runoff simulation performance.; The calibrated travel time is closer to benchmark value after improvement.; The improved NRF method adapts to basins of various underlying surfaces.; The improved NRF method gets more reasonable wave velocity after improvement.;

Study region: The Qinghai Lake Basin, Qinghai-Tibetan Plateau. The Qinghai Lake is the largest inland saltwater lake in China. Study focus: Significant increase in runoff into the Qinghai Lake has been reported; however, the relationship between frozen soil changes and runoff remains poorly understood. This study investigated the temporal and spatial variations in frozen soil and associate effects on streamflow and soil moisture in the study region by a distributed eco-hydrological model. New hydrological insights: The results illustrate that the coverage of permafrost decreased by about 13% from 1971 to 2015, and permafrost degradation mainly occurred in the elevation interval of 3600–4200 m. The maximum frozen depth averaged in the seasonally frozen ground significantly decreased by 0.06 m/10a, while the active layer thickness averaged in the permafrost enhanced by 0.02 m/10a. Permafrost degradation caused enhanced soil liquid water storage and an increase in freezing season runoff. The increase in runoff in the thawing season was dominated by changes in precipitation. The results suggest that frozen soil degradation altered the seasonal flow regime, leading to lags in the monthly runoff peak, and it increased the base flow and reduced the thawing season runoff. This offset of the competing impacts of frozen soil changes in different seasons led to a negative effect on annual runoff. This study provides new understandings of cryospheric hydrological responses to climate change.

Assessment of forest impact on river runoff plays an important role in the rational use of water resources and the choice of optimal land use management. A top priority task of this research is to find out whether the forest increases or decreases the annual river runoff and how significant these changes can be. The influence of forest on river runoff generation is a result of complicated interactions of hydrological, soil and biological processes and, consequently, for reliable estimation of changes of the annual water yield caused by forests, it is necessary to carry out experimental and theoretical studies of the entire hydrological cycle of forest watersheds. Lack of data of systematic observations and experimental studies significantly limited the possibilities of desired research and led to highly variable, often contradictory, qualitative and quantitative estimations of forest impact. It is obvious that these estimations may depend on physiographic conditions and research methods. However, according to experimental studies carried out in most countries, deforestation results in an increase of annual water yield, but in most published Russian studies deforestation decreases annual water yield of large rivers. In addition to geographical differences of the studied watersheds, these findings may be due to methodological errors in investigation or public beliefs. In this paper, we consider the peculiarities of the hydrological cycle of forested watersheds, compare methods and results of experimental investigations of the forest impact on annual water yield carried out in different countries discuss the causes of differences in estimation of the forest effect on annual runoff. We also discuss possibilities of applying mathematical modelling for improving the reliability of assessments of the impact of forest on annual runoff. [ABSTRACT FROM AUTHOR]

The frequency and magnitude of flash floods in the olive orchards of southern Spain have increased because of climate change and unsustainable olive-growing techniques. Affected surfaces occupy >85% of the rural regions of the Upper Guadalquivir Basin. Dangerous geomorphic processes record the increase of runoff, soil loss and streamflow through time. We report on ripple/dune growth over a plane bed on overland flows, deep incision of ephemeral gullies in olive groves and rock-bed erosion in streams, showing an extraordinary sediment transport capacity of sub-daily pluvial floods. We develop a novel method to design optimal solutions for natural flood management and erosion risk mitigation. We adopt physical-based equations and build a whole-system model that accurately reproduces the named processes. The approach yields the optimal targeted locations of nature-based solutions (NbSs) for active flow-control by choosing the physical-model parameters that minimise the peak discharge and the erosion-prone area, maximising the soil infiltration capacity. The sub-metric spatial resolution used to resolve microtopographic features of terrains/NbS yields a computational mesh with millions of cells, requiring a Graphics Processing Unit (GPU) to run massive numerical simulations. Our study could contribute to developing principles and standards for agricultural-management initiatives using NbSs in Mediterranean olive and vineyard orchards.

Regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman-Monteith-Leuning (PML) equation into the Distributed Time-Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration process. We calibrated six key model parameters grid-by-grid across China using a multivariable calibration strategy, which incorporates spatiotemporal runoff and evapotranspiration (ET) datasets (0.25°, monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters, but performed better in humid than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at national scale, though the improvement is not significant pertaining to watershed streamflow validation due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters by using watershed properties in ungauged regions. Keywords: Regionalization; Gradient Boosting Machine; Distributed hydrological model; Soil; Terrain; [ABSTRACT FROM AUTHOR]

Precipitation phase (e.g., rainfall and snowfall) and snow (e.g., snowpack and snowmelt runoff) in high-mountain regions may largely affect runoff generation, which is critical to water supply, hydropower generation, agricultural irrigation, and ecosystems downstream. Accurately modeling precipitation phase and snow is therefore fundamental to developing a better understanding of hydrological processes for high-mountain regions and their lower reaches. The Lancang River (LR, or the Upper Mekong River) in China, among the most important transboundary rivers originating from the Tibetan Plateau, features active dam construction and complex water resources allocation of various stakeholders in Southeast Asian countries under climate change. This study aims to improve precipitation phase and snow modeling for the LR basin with a hydrological model and multisource remotely sensed data. Results show that joint use of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product with high spatial resolution (1 km×1 km) and an air temperature product can more precisely distinguish precipitation phase than air and wet-bulb temperature products in the LR basin. Snowfall and snowmelt were found to be controlled primarily by rainfall and snowfall temperature thresholds in snow modeling. The rainfall and snowfall temperature thresholds derived from the hydrological model through calibration with remotely sensed snowpack at basin scales were considerably lower than those derived from in situ observations. Rainfall and snowfall temperature thresholds derived from in situ observations could lead to the overestimation of snowmelt runoff due mostly to the lack of representation of point-based measurements at basin scales. This study serves as a basis for better modeling and predicting snow for the LR basin and potentially other similar basins globally. [ABSTRACT FROM AUTHOR]

Study region: Upper region of the Greater Chao Phraya River (GCPR) basin in Thailand. Study focus: The upper GCPR basin is highly regulated by multipurpose reservoirs, which obviously have altered the natural streamflow. Understanding quantitative effects of such alteration is crucial for effective water resource management. Therefore, this study aims to assess how reservoir operation affects the water balance, daily flow regime and extreme flows in this basin. For this purpose, we reconstructed streamflow in the naturalized (no reservoir) and baseline operation scenarios using the (∼1 km resolution) distributed model. To overcome data scarcity, we ran the model with global data and parameterization. A target storage-and-release-based reservoir operation module was applied in the baseline operation scenario. The model results were analyzed in comparison to observations in a wet year, a dry year, and the period 1989–2014. New hydrological insights for the region: The reservoir operation resulted in more evaporation. It inverted the natural flow seasonality and smoothed the daily flow regime with decreasing high flows, increasing mean flows and low flows, greater baseflow contribution, and lower flashiness. It prevented or mitigated many historical extreme flow incidents. The annual flood peaks and minimum flows were markedly mitigated in terms of both magnitudes and frequencies, but their timing became more variable and difficult to predict. Altogether, the results highlighted the importance of effective decision making for real-time operation, which remain challenging in practice.

A distributed hydrological model was applied to a 69 km2 experimental catchment, Eddleston Water, Scotland, UK. The impact on model outputs of applying progressively simpler representations of spatial variability in land use and superficial geology was assessed. Alternative representations of the spatial distribution of superficial geology and land use produced differences in model outputs. These differences were generally small with the exception of the maximum absolute error (Emax). Inter-model differences were most sensitive to the largest precipitation events. Although variations in superficial geology dominated over those for land use, exceptions were seen in two sub-catchments. These were connected with particularly large variations in land use and/or the small spatial extent of superficial geology. Lower resolution spatial data produced superior model performance in the majority of sub-catchments. This has implications for modelling other catchments, especially in situations where the high-resolution data employed herein are not available. [ABSTRACT FROM AUTHOR]

In order to study the runoff changes in the arid oasis irrigation area,this paper constructs a distributed hydrological model of Yanqi Basin based on SWAT model with the Chinese meteorological assimilation driving datasets (CMADS) from 1955 to 2017,traditional hydrometeorological station data,land use data,soil data,digital elevation data,analyzes the sensitivity of parameters by SUFI-2 (Sequential Uncertainty Fitting) algorithm,calibrates several important parameters influencing runoff simulation results through SWAT-CUP (SWAT Calibration Uncertainty Procedures) software,and verifies the model by the measured runoff from 1991 to 2017.The results show that:the simulated runoff of Yanqi Basin in Xinjiang for a long time scales coincides with the measured runoff.During the calibration period and verification period,the Nash coefficient (NES) is above 0.82,the Pearson correlation coefficient (R2) is 0.86 and 0.90 respectively,and the relative error (Re) is less than 10.Therefore,SWAT model is well applicable to the runoff simulation of Yanqi Basin.Based on this,runoff changes from 2016 to 2100 in Yanqi Basin under the scenarios of RCP4.5 and RCP8.5 are simulated.It can be found that precipitation changes in Kaidu River basin have a significant impact on runoff in Yanqi Basin,that is,when precipitation increases by 20% and temperature increases by 2°C,the runoff increases by 26.53% compared with the current year.

Study region: The Atibaia and Jacaré-Guaçu watersheds from the Tietê river basin, São Paulo, Brazil. Study focus: This study aims to compare estimates of flow, sediment yield and nutrients loads obtained from two distinct models with different structures and degree of complexity. The Generalized Watershed Loading Function (GWLF) and the Soil Water Assessment Tool (SWAT). We are particularly interested in understanding under which conditions the use of each model is to be recommended, namely when does the addition effort required to run the SWAT model leads to effective better results. As SWAT’s calibration procedure is cumbersome, the advantage of using a more detailed and distributed model fails to materialize when detailed data are not available or when monthly estimates are enough. GWLF model provides useful results with a reduced data gathering and calibration effort. New hydrological insights of the region: The joint calibration of both models to two watersheds offered a robust set of parameter values for prevalent conditions of Tiête river basin given the existing data set, although not all modelled variables are reproduced accurately. The performance of both models is adequate when estimating streamflow at a monthly time, but decreases when estimating daily flow, sediment yield, and nutrients loads. The poor monitoring of sediments and nutrients concentration hinders the ability to fully calibrate the model’s water quality component.

The heavy rain event of July 2018 and Typhoon Hagibis in October 2019 caused severe flash flood disasters in numerous parts of western and eastern Japan. Flash floods need to be predicted over a wide range with long forecasting lead time for effective evacuation. The predictability of flash floods caused by the two extreme events is investigated by using a high-resolution (~ 150 m) nationwide distributed rainfall-runoff model forced by ensemble precipitation forecasts with 39 h lead time. Results of the deterministic simulation at nowcasting mode with radar and gauge composite rainfall could reasonably simulate the storm runoff hydrographs at many dam reservoirs over western Japan for the case of heavy rainfall in 2018 (F18) with the default parameter setting. For the case of Typhoon Hagibis in 2019 (T19), a similar performance was obtained by incorporating unsaturated flow effect in the model applied to Kanto Region. The performance of the ensemble forecast was evaluated based on the bias ratios and the relative operating characteristic curves, which suggested the higher predictability in peak runoff for T19. For the F18, the uncertainty arises due to the difficulty in accurately forecasting the storm positions by the frontal zone; as a result, the actual distribution of the peak runoff could not be well forecasted. Overall, this study showed that the predictability of flash floods was different between the two extreme events. The ensemble spreads contain quantitative information of predictive uncertainty, which can be utilized for the decision making of emergency responses against flash floods. [ABSTRACT FROM AUTHOR]