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• Probability density prediction can measure uncertainties of runoff. • B-spline quantile regression is proposed to predict the probability density of runoff. • Different evaluation criteria of probabilistic predictions is adopted. • The new method is used in Shigu station in Jinsha River of China. Exact and dependable runoff forecasting plays a vital role in water resources management and utilization. This paper proposes a B-spline quantile regression probability density prediction method to predict future runoff and quantify the uncertainty of prediction. The method includes three steps. First, the B-spline function is used to perform spline processing on the runoff data. Secondly, the spline-processed training data is input into the quantile regression model to calculate the parameters of the B-spline quantile regression model, and the successfully constructed B-spline quantile regression model is combined with kernel density estimation to construct a probability density prediction method. Finally, the constructed B-spline quantile regression probability density prediction method is used to forecast future runoff flow, and quantitatively analyze the relevant prediction uncertainty. Six evaluation indicators are constructed, among which the root mean square error, the deterministic coefficient, the pass rate are the evaluation metrics of point predictions based on probability mean, median and mode; the prediction interval coverage probability, prediction interval normalized average width and continuous ranked probability score are the evaluation criteria of interval predictions and probabilistic forecasting. The presented method is able to depict the probability density curve of future runoff flow, and obtain more comprehensive information than point forecasting and interval predictions. As a case study, this method is applicable to the Shigu station of the Jinsha River in China. The results show that the results of the proposed method are better than that of existing some state-of-the-art methods. From the perspective of application, the pass rate and the deterministic coefficient of the method have reached the grade A of accuracy. Therefore, the B-spline quantile regression model provides an alternative scheme to runoff prediction. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

• Calculation unit division induced soil spatial aggregation in SWAT model. • Effect of soil spatial aggregation on simulation was separated and quantified.Soil spatial aggregation caused a significant increase in annual runoff and sediment load. • The effect of soil spatial aggregation on sediment load was greater than on runoff. • Optimal sub-watershed division scheme was discussed. Distributed hydrological model divides watersheds into distinct calculation units to simulate hydrological processes. Within the model, different calculation unit division schemes induce input elements, such as soil and land use, to certain degrees of generalization, thus affecting the simulation result. Analyzing the effect of the calculation unit division on the simulation result is an important methodology to reduce uncertainty and improve the simulation accuracy of the model. Previous researchers have focused on the comprehensive effect of underlying surface generalization (such as soil, land use, and terrain generalization) caused by the calculation unit division on the simulation results, while it did not separate the effect of the generalization of the individual input factor on the simulation result. In this paper, the Dongjiang headwater region in southern China was selected as the study area. The single type of land use and slope were input into the Soil and Water Assessment Tool (SWAT) model to fix the effects of land use and terrain caused by calculation unit division on the simulation results. Eleven calculation unit division schemes were used to analyze the impact of soil spatial aggregation on runoff and sediment load simulations. The calculation unit division schemes included 11 sub-watershed division schemes and their hydrological response unit (HRU) thresholds were set at 8 %. The drainage area thresholds of the 11 sub-watershed division schemes were 30 ha, 60 ha, 120 ha, 240 ha, 480 ha, 1000 ha, 1600 ha, 2200 ha, 2800 ha, 3400 ha and 4000 ha. The results demonstrated that the soil spatial distribution showed an obvious aggregation with an increase in the drainage area threshold in the SWAT model. Of these, the Krasnozem soil type which covers the largest area in the watershed, increased significantly with an increase in the drainage area threshold. However, the areas of all the other soil types decreased significantly. The Lioth soil type covers the smallest area in the watershed decreased and even disappeared when the threshold in the SWAT model was greater than 1000 ha. Furthermore, soil spatial aggregation caused a significant increase in the annual runoff and sediment load (P < 0.05), with the sediment load (Re = 29.78 %) increasing at a greater rate compared to runoff (Re = 1.74 %). Increased soil aggregation significantly increased the sediment loads in the maximum 1-day, the continuous maximum 5-day, the continuous maximum 7-day, and the sediment load in flood processes (P < 0.001). Refining the calculation unit division scheme will cause a significant increase in the number of calculation units. Due to the computer processing power limitations, we cannot further refine the calculation unit division scheme. Based on our current operational calculation unit division scheme, we did not identify the optimal division scheme in the study region. These results can provide reference information for future research into hydrological model uncertainty and can improve the SWAT model simulation accuracy. [ABSTRACT FROM AUTHOR]

Accurate and reliable runoff prediction is of great significance to water resources management, disaster monitoring and rational development and utilization of water resources. In this paper, a metaheuristic evolutionary deep learning model based on Temporal Convolutional Network (TCN), Improved Aquila Optimizer (IAO) and Random Forest (RF) is proposed for rainfall-runoff simulation and multi-step runoff prediction. In this study, the influence of various input variables on the prediction accuracy is discussed. First of all, in order to avoid the dimensional disaster problems and reduce the calculation time, RF is used to calculate the correlation between the input variables and the prediction object, and the data with high correlation is selected as the final inputs. Then, the filtered data are sent to the TCN model, and the parameters of the TCN model are optimized using the IAO algorithm, and the final prediction results are obtained. In this study, the rainfall and runoff data of five stations in the middle reaches of Jinsha River, China were selected, and the runoff of Panzhihua station was simulated and predicted by establishing multiple models. By analyzing and comparing the predictive results of several models, it shows that the models and improvements proposed in this study are effective. [ABSTRACT FROM AUTHOR]

• Parameter uncertainty varies over time scales due to land use change (LUC). • The calibrated daily runoff model is deficient for the extreme flow changes. • Model structure impacts the extreme runoff direction change for moderate LUC. Hydrological modeling has provided key insights into the mechanisms of land-use change impacts on runoff. However, the uncertainty of this impact is poorly understood. This paper examines the uncertainty originated from hydrological models' parameters and structure in simulating hydrological responses to land use change on daily, monthly and annual time scales. Two hydrological models, SWAT and HSPF, were applied to simulate the runoff response in the Xitiaoxi basin of eastern China. The effects of three land use scenarios (in 1985, 2002 and 2014) were analyzed with respect to the expansion of urban areas and reduction of cropland and forests. The changes in streamflow with multi-timescales between two models were compared at the whole basin and the sub-basin scales. The parameter uncertainty was estimated based on a sequential uncertainty analysis method (SUFI-2). The results indicate that the two models (SWAT and HSPF) could reproduce the observed daily and seasonal flows well, but they cannot accurately reconstruct the extreme flows (such as annual 7-day minimum discharge, R_7dMIN). The effect of parameter uncertainty on streamflow varies over time scales. The simulated annual and monthly runoff change values show large discrepancies but with same trends. The simulated changes for annual maximum discharge (R_1dMAX) and R_7dMIN often show different signs. The average width of the predictive interval in the relative change of different flow characteristics exceeded 100% of the average relative changes for both models. HSPF tends to present larger relative changes for the annual and monthly runoffs compared to SWAT. Moreover, the model choice could affect the direction change in R_1dMAX and R_7dMIN. However, when land use change was significant to a certain degree, both models simulated increased R_1dMAX and decreased R_7dMIN, but with different significance levels. The study suggests that the model structure represents an additional uncertainty, which should be accounted for in the land-use change impact assessment. [ABSTRACT FROM AUTHOR]

Abstract The need to estimate event soil loss better than the USLE and the RUSLE has led to the development of WEPP and the USLE-M, a modification of the USLE that estimates event soil loss for bare fallow runoff and soil loss plots in the USLE database better than the USLE/RUSLE when runoff is known or predicted well. Although the erosivity indices in the USLE-M and the USLE/RUSLE focus on raindrop driven erosion, the calibration of the erodibility index on plots where rilling occurs enables the USLE-M and the USLE/RUSLE to be applied on areas where sheet and rill erosion occur. This paper presents results from the application of both WEPP and the USLE-M to modelling event soil loss from 6 runoff and soil plots at the Ansai Research Station of Soil and Water Conservation, Chinese Academy of Sciences (36°56′N, 109°16′E) produced from steep slopes (8.7%–53.2%) where rills frequently developed under natural rainfall during 1985–1992. WEPP was calibrated to minimize the mean squared error (MSE) between measured runoff and predicted runoff for the events where WEPP predicted runoff and soil loss to occur. The number of events where WEPP predicted runoff and soil loss to occur varied from 25% to 41% of events that actually produced soil loss. In the comparison with WEPP, the USLE-M erodibility factor was calibrated by dividing the total observed soil loss where WEPP predicted runoff and soil loss to occur by the total of the USLE-M erosivity factor for the same set of events. Consequently, the total amount of soil lost estimated for these events by both models were the same. This enabled the ability of the models to predict event soil loses to be examined using the Nash-Sutcliffe model efficiency index. In every case, the efficiency index values for the USLE-M were higher than for WEPP irrespective of whether the values of USLE-M erosivity index were determined using observed or WEPP predicted runoff. As a model of rainfall erosion, focusing on a process based approach has not resulted in WEPP performing as well as or better than the USLE-M on the steep bare fallow plots of loessal soil at the Ansai Research Station in China. Highlights • WEPP and USLE-M models were applied to slopes up to 53%. • WEPP predicted runoff on <45% of events where runoff occurred. • USLE-M was consistently more effective in estimating event soil loss than WEPP. [ABSTRACT FROM AUTHOR]

The power generation plan formulated based on the forecasted runoff is an important basis for the operation of hydropower station. However, due to the unavoidable forecast error, the ideal practical operation process usually does not match with the plan. In view of this, after a detailed analysis on the current processes of making and implementing power generation plan, this paper constructed an early warning mechanism according to different operation states of cascade hydropower stations, and considering the uncertainty of runoff forecast and the security constraints of power grid, a new load adjustment model with characteristics of real-time tracking, early warning and timely adjusting was proposed, and three basic principles of load adjustment were put forward in this model. Taking the cascade hydropower stations in Yalong River of China as an instance, two typical scenarios that threaten the operation safety of hydropower stations are simulated and analyzed. Results show that the proposed model can be used to scientifically adjust the output of hydropower stations through the proposed adjustment principles and strategies, and ensure the safe and stable operation of power grid, and avoid the output shortage and water abandonment of hydropower stations caused by the uncertainty of runoff forecast. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool (SWAT) model is diffusely applied to areas with uniform precipitation distribution and gentle slopes. However, the SWAT has apparent defects in the process of ecohydrological simulation in mid-high latitudes and arid regions. This model can merely be used in agricultural catchments with gentle slopes and uniform rainfall, and the simulation is not suitable for high-altitude alpine catchment. To this point, this paper makes appropriate improvements to the runoff module, and specifically corrects the key sensitive parameters. The global sensitivity and performance assessment of the model's parameters were carried out before and after the improvement using the monthly data of runoff and evaporation from 1990 to 2018 in the Kaidu River basin in northwestern China. It is indicated that the Modified SWAT simulates the performance of monthly runoff and evapotranspiration better than the original SWAT, which overestimates the runoff volume throughout hydrological changes in different periods. Compared with the monthly water production under different land use/cover conditions, the highest water production was found in forest and grassland. By modifying the SWAT algorithm, the structural non-determinacy of the Modified SWAT is significantly reduced, and the adaptability of the model is improved. Thus, the Modified SWAT shows a better simulation performance in arid areas with considerable terrain variation and scarce rainfall data. [Display omitted] [ABSTRACT FROM AUTHOR]

The importance of glaciers for providing runoff is reflected in the extensive glacier measurement record for China. In this paper, we summarized the mass-balance and front-variation data of glaciers in China that have long-term records. Mass balance has been measured for 22 glaciers; length (or front-position) records exist for 96 glaciers. We found that the mean rate of glacial mass balance decreased by −0.015 m w.e. (water equivalent) per year during the period 1959–2015, which was lower than that for glaciers globally (−0.013 m w.e. per year). It was indicated that the rate of glacial melting (mass loss) increased in China, which was higher than that in glaciers worldwide. However, glacial frontal positions varied among mountains in time and space. Glaciers in the Karakoram Mountains mean advanced 379 m during the 1968–2000 period, while glaciers in the Pamir Mountains remained relatively stable from the 1960s through the 2000s, i.e., retreated 28 m. Glaciers in Hengduan Mountains retreated farthest (1250 m maximum) from 1930 to 2005. [ABSTRACT FROM AUTHOR]

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition–prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD–RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD–RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention. [ABSTRACT FROM AUTHOR]

The water resources in the arid region of northwestern China, which are impacted by climate change, tend to be more unstable, and the environment and ecosystems will suffer from severe water shortage. In this paper, potential future climate trends were predicted based on CMIP5 simulations in this region. The water availability and agricultural water demand under future climate change scenarios were estimated. Impacted by increases in temperature, the irrigation water demand will increase by 4.27–6.15 billion m 3 in this region over the next 60 years, compared to the demand of 32.75 billion m 3 during 1971–2000. However, the annual runoff will only increase by 4.8–8.5 billion m 3 , which is equivalent to or even less than the increased irrigation water demand. In fact, the increased demand for industrial, domestic and ecological water were not considered here. Thus, the water supply/demand contradiction will result in more severe water shortages in the future. According to a comparison with simulated irrigation water demand under three adaptation strategy scenarios, we should take effective measures such as improving the efficiency of irrigation water utilization, reducing crop planting areas and adjusting crop planting structures to alleviate the impacts of future climate changes and human activities on the water supply and water use in this region. [ABSTRACT FROM AUTHOR]

Summary Land cover and rainfall regime are two important factors that affect soil erosion. In this paper, three land cover types – grass cover, litter cover and bare land – were employed to analyze surface runoff, subsurface flow and sediment loss processes in relation to the rainfall regimes in the red soil region of China. Five rainfall regimes were classified according to 393 rainfall events via a k-means clustering method based on the rainfall depth, duration and maximum 30-min intensity. The highest surface runoff coefficient and erosion amount were found on bare land in all five rainfall regimes, and the lowest were found on grass cover. The litter cover generated the highest subsurface flow rate, followed by the grass cover; the lowest was on bare land. For grass cover and litter cover plots, rainfall events of rainfall regime IV which had the longest duration, greatest depth and lowest intensity had the highest surface runoff coefficient, soil erosion amount and subsurface flow rate. For bare land, storm rainfall events of rainfall regime V had the highest intensity, lowest depth and duration, had the highest surface runoff coefficient and soil erosion amount, but the lowest subsurface flow rate. The highest subsurface flow rate of bare land happened in rainfall regime IV. Surface cover was urgently needed to reduce soil erosion. When the lands under dense surface cover, more attention should be paid to rainfall events that of long duration, high depth but low in intensity which commonly occurred in spring. The interactions of surface–subsurface flow and its effects on soil erosion and nutrient loss were worth considering in the red soil region. [ABSTRACT FROM AUTHOR]

Abstract: Land use change has significant impacts on hydrologic processes at the watershed level. This paper combines an empirical land use change model and an event-scale, rainfall-runoff model to quantify the impacts of potential land use change on the storm-runoff generation in the Xitiaoxi basin upstream of Taihu Lake watershed. The HEC–HMS rainfall-runoff model is calibrated and validated for 7 storm events in the study area, and the results show good consistency between the simulated and measured hydrographs at the outlet of the basin with Nash–Sutcliff efficiency ranging from 75% to 95%. Then two future land use scenarios for the year 2050 are forecasted using the CLUE-s model based on the land use of 2002: (1) scenario A with an increase in urban area from 9.2% to 17% of the entire watershed and (2) scenario B with an increase from 9.2% to 14%. Finally, the calibrated HEC–HMS model is applied for these future land use scenarios under designed storms with different recurrence intervals to assess the potential land use impacts on the storm-runoff generation. The results indicate that the future land use scenarios are projected to increase the total runoff as well as the peak discharge, and that the magnitude of increment relates to the expansion rate of built-up area. In the case of rainstorms with recurrence intervals of 10, 50 and 100 years, the simulated peak discharges increase by 3.8%, 2.6% and 2.3% under scenario A, and by 2.8%, 1.9% and 1.7% under scenario B, respectively; while the total runoff is projected to increase by 4.7%, 3.4% and 3.0% under scenario A, and by 3.3%, 2.5% and 2.1% under scenario B. It can be concluded that the sensitivity of hydrologic response to land use change tends to increase as the recurrence interval of rainfall events decreases. Additionally, remarkable spatial variations of the land use changes and its impacts can be observed at the subbasin level, and the most sensitive subbasin is closeby the city. The results of this paper can provide useful support for land use planning and management, and the methods applied here have proved a useful tool for land use impact studies. [Copyright &y& Elsevier]

Runoff has been in decline in most river basins in China during the last 50 years. The Weihe River, the largest tributary of the Yellow River, has experienced runoff declines as large as 35% in the last century. Little is known regarding the relative contributions of climate and human impact to the observed hydrological trends in the Weihe River Basin. In the present paper, data from seven hydrological stations and 21 meteorological stations are used to analyze the long-term trends of precipitation, temperature, and streamflow. Using the daily climatic data, a Variable Infiltration capacity (VIC) hydrological model is calibrated and verified to a baseline period from 1956 to 1970. Subsequently, natural runoff for the following years (1971–2006) is reconstructed using the VIC model without considering local human impacts. On the basis of observed meteorological data and runoff and the reconstructed runoff data from 1971 to 2006 in the Weihe River Basin, we quantified long-term trends and decadal and annual variations. The results showed that precipitation and runoff have decreased since the baseline decade. We further estimated the relative contributions of human activity and climate change to the hydrological response of the Weihe River Basin and determined that human activity (such as large-scale soil conservation practices and large irrigation areas) has a greater impact on basin runoff than do climate change factors. The percentages in change of runoff due to climate change ( PC ) are 36%, 28%, 53% and 10% in the 1970s, 1980s 1990s and 2000s, respectively. The percentages in change of runoff caused by human activity ( PH ) are 64%, 72%, 47%, and 90%, respectively. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

• A deep convolutional generative adversarial network (DCGAN) combined with Wasserstein generative adversarial network (WGAN) called DC-WGAN is proposed as a runoff generation model. • A runoff generation method based on Copula function is developed. • The applicability of DC-WGAN in the field of runoff stochastic generation for a reservoir system is verified. • The spatial–temporal correlation of runoff samples of DC-WGAN compared with the Copula function is analyzed. The implicit stochastic optimization (ISO) model is a methodology that has been extensively employed in the mid-long term optimal operation of a reservoir system. The generation of runoff series is the key technology to make up for the poor representativeness and reliability of historical runoff samples and is the premise to guarantee the accuracy of the ISO model. However, the traditional methods are not suitable for the stochastic generation of runoff series, among reservoirs with spatial–temporal correlation. In this work, the generative adversarial network called DC-WGAN is developed as a runoff generation model. DC-WGAN is based on a data-driven model and can capture the correlation of runoff series in time and space dimensions simultaneously. The proposed method is applied to the downstream Jinsha river cascade and Three Gorges cascade reservoirs in China to generate ten-day runoff series and is compared with the stochastic generation method based on Copula function. The focus of this paper is the model's performance in replicating the spatial–temporal correlation of ten-day runoff series. Results are as follows. (1) DC-WGAN has strong learning and generalization ability, which can learn the correct distribution of original runoff samples and generate runoff series similar to that from the validation set. (2) The runoff series among reservoirs with spatial–temporal correlation can be generated through DC-WGAN without assuming the probability distribution of samples in advance. (3) DC-WGAN is better than Copula function in capturing the spatial–temporal correlation, especially the moderate and weak correlation. The proposed methodology has a high practical value and application prospect for the stochastic generation of runoff series with spatial–temporal correlation. [ABSTRACT FROM AUTHOR]

Abstract: Vegetation recolonization has often been used to control roadside slope erosion, and in this paper, four restoration models – Natural Restoration, Grass, Grass & Shrub, Sodded Strip – were chosen to recolonize the plants on a newly built unpaved roadside slope in the Three Gorges Reservoir Area. After eight months growth, eight rainfall simulations (intensity of 90mmh−1 for 60min) and in-situ soil shear strength test were then carried out to identify the impacts of vegetation on roadside slope erosion and soil shear strength. The erosion on cutslopes was higher than that on fillslopes. The runoff coefficient and soil detachment rate were significantly lower on the Grass & Shrub model (4.3% and 1.99gm−2 min−1, respectively) compared with the other three, which had the highest surface cover (91.4%), aboveground biomass (1.44kgm−2) and root weight density (3.94kgm−3). The runoff coefficient and soil detachment rate on roadside slopes showed a logarithmic decrease with the root weight density, root length density and aboveground biomass. The soil shear strength measured before and after the rainfall was higher on Grass & Shrub (59.29 and 53.73kPa) and decreased on Grass (46.93 and 40.48kPa), Sodded Strip (31.20 and 18.87kPa) and Natural Restoration (25.31 and 9.36kPa). Negative linear correlations were found between the soil shear strength reduction and aboveground biomass, root weight density and root length density. The variation of soil shear strength reduction was closely related to the roadside slope erosion, a positive linear correlation was found between runoff coefficient and soil shear strength reduction, and a power function was shown between soil detachment rate and soil shear strength reduction. This study demonstrated that Grass and Grass & Shrub were more suitable and highly cost-effective in controlling initial period erosion of newly built low-volume unpaved road. [Copyright &y& Elsevier]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

Summary: The hydrological cycle has been substantially influenced by climate change and human activities. It is therefore of utmost importance to analyze the impact of climate change on hydrology, particularly on a regional scale, in order to understand potential future changes of water resources and water-related disaster, and provide support for regional water management. However, during the evaluation of climate change impact on hydrology or water resources, large uncertainty exists. In this paper, the Soil Water Assessment Tool (SWAT) model is used to investigate the potential impact of climate change on hydrology of the upper reaches of Qiantang River Basin, East China, for the future period 2011–2100. The uncertainty is considered by employing upgraded reliability ensemble averaged GCM climate projections under three emission scenarios A1B, A2 and B1 for three different stages of the future period. These projections are downscaled and used in the hydrological model. Impact of climate change on precipitation, potential evapotranspiraton and river runoff is then investigated. The model calibration and validation outcomes show reasonable performance of the SWAT model. The final results suggest that annual river runoff will likely decrease almost under all emission scenarios and time stages of the future period. Particularly, at Jinhua Station, substantial decrease of annual river runoff can be noticed, indicating less water resource possibly available for the region in future. Simulated monthly patterns show that the largest decrease will likely occur in winter while increases will occur in summer, implying possible more water-related disasters in this region. However, it is also noticed that the change signs/amount could be different under different emission scenarios and time stages, indicating large uncertainty involved in the impact analysis. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to provide an enhanced understanding of the parameter sensitivities of the Soil and Water Assessment Tool (SWAT) using a variance-based global sensitivity analysis, i.e., Sobol′’s method. The Yichun River Basin, China, is used as a case study, and the sensitivity of the SWAT parameters is analyzed under typical dry, normal and wet years, respectively. To reduce the number of model parameters, some spatial model parameters are grouped in terms of data availability and multipliers are then applied to parameter groups, reflecting spatial variation in the distributed SWAT model. The SWAT model performance is represented using two statistical metrics – Root Mean Square Error (RMSE) and Nash–Sutcliffe Efficiency (NSE) and two hydrological metrics – RunOff Coefficient Error (ROCE) and Slope of the Flow Duration Curve Error (SFDCE). The analysis reveals the individual effects of each parameter and its interactions with other parameters. Parameter interactions contribute to a significant portion of the variation in all metrics considered under moderate and wet years. In particular, the variation in the two hydrological metrics is dominated by the interactions, illustrating the necessity of choosing a global sensitivity analysis method that is able to consider interactions in the SWAT model identification process. In the dry year, however, the individual effects control the variation in the other three metrics except SFDCE. Further, the two statistical metrics fail to identify the SWAT parameters that control the flashiness (i.e., variability of mid-flows) and overall water balance. Overall, the results obtained from the global sensitivity analysis provide an in-depth understanding of the underlying hydrological processes under different metrics and climatic conditions in the case study catchment. [ABSTRACT FROM AUTHOR]

Abstract: This paper analyzes runoff and soil loss in relation to the rainfall regimes and land use changes in a small mountainous watershed in the Three Gorges Area (TGA) of China. Based on 10years of rainfall measurements and K-means clustering, 152 rainfall events were classified into three rainfall regimes. The mean statistical features of different rainfall regimes display a marked difference. Rainfall Regime I is events of medium amounts (31.8mm) and medium duration (1371min). Rainfall Regime II is events with high amounts (54.0mm), long duration (2548min), and an infrequent occurrence. Rainfall Regime III is events of low amount (22.2mm), short duration (494min) and high frequency. Each rainfall regime results in differing levels of runoff and erosion and Rainfall Regime I causes the greatest proportion of accumulated discharge (368.7mm) and soil loss (4283t). In the different rainfall regimes, the values of the mean runoff coefficient and the mean sediment load were ordered as follows: Rainfall Regime II>Rainfall Regime I>Rainfall Regime III. These results suggest that greater attention should be paid to Rainfall Regimes I and II because they had the most erosive effect. In the Wangjiaqiao watershed, the changes in land use primarily affected the paddy fields, where the cropland decreased significantly and the forest and orchards increased by 9.9% and 7.7%, respectively, during 1995–2004. The ANOVA shows land use changes caused significant decreasing trends in the runoff coefficients (P<0.01) and sediment loads (P<0.01). In order, the most sensitive response of runoff and erosion to land use was Rainfall Regime II>Rainfall Regime>Rainfall Regime III. Rainfall characteristics are decisive for the relative importance of different storm runoff generation mechanisms. The land use changes in the study watershed have considerably decreased runoff and soil loss. [Copyright &y& Elsevier]

Abstract: Characterizing the landscape changes and hydrologic alterations associated with dam construction is very important for watershed management. This paper presents a case study of the Lancang River in Yunnan Province following dam construction. The landscape patterns and dynamics indicate the fragmentation, shape, and diversity of the river in 1980, 1990, and 2000. The Range of Variability Approach (RVA) is used to evaluate the degree of hydrologic alteration (DHA) using 44 years (1957–2000) of hydrologic data. The results indicate that the midstream and downstream landscapes were affected by dam construction, becoming more complex and fragmented during the 1980–2000 period; the upstream area was not influenced by dam construction and the reservoir impoundment exhibited less change. The variability in maximum runoff occurrence in the post-dam period was less than that in the pre-dam period. The integrated DHAs of the Jiuzhou (upstream), Gajiu (midstream), and Yunjinghong (downstream) stations were relatively low, reaching 26.28%, 33.40%, and 37.14%, respectively. However, the alteration became obvious in the midstream area, and the situation worsened when the river was simultaneously influenced by dam construction and other human activities (downstream). The results of the regression analysis show strong relationships of landscape metric changes with DHA, and the forestland and water areas with DHA. The DHA increased along with the aggravation of landscape fragmentation, the complexity of the landscape shape, and the diversification of the landscape. [Copyright &y& Elsevier]

Abstract: The Xitiaoxi catchment is one of the most important catchments in the Taihu Basin in southeastern China. It contributes a significant amount of surface runoff and nutrient to Taihu Lake. Understanding the nutrient cycling and identification of critical non-point source pollution in this catchment are therefore of primary importance. In this paper, the Xinanjiang-Nitrogen (XAJ-N) model, a conceptual model of nutrient mobilization and transport is developed by integration of the Xinanjiang rainfall–runoff model, the Integrated Nitrogen CAtchment (INCA) model and the Modified Universal Soil Loss Equation (MUSLE). It is implemented with the environmental modelling language PCRaster and estimates the water fluxes and nutrient loadings on a cell-by-cell basis in daily time steps. The model includes the nitrogen cycling processes of mineralization, leaching, fixation, volatilization, nitrification, denitrification and plant uptake. Nitrogen is assumed to be mobilized by surface runoff and groundwater. The model performance was verified by comparing simulated and measured daily discharge and nutrient loadings. The results showed a fairly good relationship between predicted and observed values. Due to the scarce observed data, the simulation results were also validated using an internal mass balance method and values from the literature. It showed that the modelling approach can be used as a tool to estimate the export of nutrient with a daily resolution at a catchment scale. [ABSTRACT FROM AUTHOR]

Summary: The commonly used irrigation system in the irrigation districts (with a combined irrigation area of 3.334×106 ha) along the lower Yellow River of China is canal network. It delivers water from the Yellow River to the fields, collects surface runoff and drainage from cropland, and stores both of them for subsequent irrigation uses. This paper developed a new combined irrigation, drainage, and storage (CIDS) module for the SWAT2000 model, simulated the multiple roles of the CIDS canal system, and estimated its performance in improving water reuse in the irrigation districts under different irrigation and water diversion scenarios. The simulation results show that the annual evapotranspiration (ET) of the double-cropping winter wheat and summer maize was the highest under the full irrigation scenario (automatic irrigation), and the lowest under the no irrigation scenario. It varied between these two values when different irrigation schedules were adopted. Precipitation could only meet the water requirement of the double-cropping system by 62–96% on an annual basis; that of the winter wheat by 32–36%, summer maize by 92–123%, and cotton by 87–98% on a seasonal basis. Hence, effective irrigation management for winter wheat is critical to ensure high wheat yield in the study area. Runoff generation was closely related to precipitation and influenced by irrigation. The highest and lowest annual runoff accounted for 19% and 11% of the annual precipitation under the full irrigation and no irrigation scenarios, respectively. Nearly 70% of the annual runoff occurred during months of July and August due to the concentrated precipitation in these 2months. The CIDS canals play an important role in delivering the diversion water from the Yellow River, intercepting the surface runoff and drainage from cropland (inflow of the CIDS canal) and recharging the shallow aquifer for later use. Roughly 14–26% of the simulated total flow in the CIDS canal system recharged shallow aquifer through canal seepage. The water flowing out of the canal system accounted for approximately 32% of the water in the CIDS canals. The storage capacity of the CIDS canals is negatively correlated to the precipitation. In years with abundant precipitation, the volume of the surface runoff and drainage from the cropland may surpass the storage capacities of the CIDS canals, while in years with less precipitation, partial storage capacity of the CIDS canal may be occupied by the diversion water from the Yellow River. Proper maintenance of the storage capacity of the CIDS has the potential in improving the efficiency of reusing the surface runoff and field drainage for irrigation practices to mitigate the increasing water shortage along the lower Yellow River. [Copyright &y& Elsevier]

Abstract: Severe pollution in Lake Taihu in the lower reach of the Yangtze River has led to the need to evaluate transport pathways and the overall water balance of the catchment. This paper describes the development of a grid-based spatially distributed hydrological model for integrated simulations of surface runoff and groundwater flow at catchment scales. Surface water flow is modeled based on rainfall-runoff transformation and stream flow routing, while the saturated groundwater is modeled by MODFLOW-2005. Surface water flow is coupled to the groundwater flow regime through the soil layer. Relatively simple equations are used to describe the soil storage and soil water percolation. These equations are computationally inexpensive and overcome some of the limitations in existing models for coupled simulation of surface and subsurface domains, such as either multiple soil layer data are needed in the vertical direction or numerical difficulties are encountered in dealing with the high non-linearity of the equations representing the variably-saturated subsurface. The model was verified against the V-catchment benchmark problem and was compared to existing models to demonstrate its accuracy and capability. The model was applied to Xitiaoxi catchment of Lake Taihu watershed. The accuracy of the model was satisfactory with daily and monthly efficiencies of 0.80 and 0.92, respectively for stream discharge comparison, and an absolute error of 0.144m for groundwater level comparison. The model indicated that for Xitiaoxi catchment, a percentage of 37.4% of rainfall transferred to surface overland flow, and a percentage of 16% infiltrated as groundwater recharge, of which 81% returns to rivers as base flow and the rest is lost to the system via evapotranspiration. The model can be used as an alternative to more complex methods to assess impacts of land use or/and climate changes on water resources availability, and the interactions between surface and subsurface flow domains. [Copyright &y& Elsevier]

Summary: Debris flows (especially those of high-density or high viscosity) usually appear in the form of surge sequence. Each debris flow event consists of tens or hundreds of surges. A debris flow is thus equal to a surge series which is expected to feature the intrinsic properties. Statistics of observation data indicates that different events have the similar distributions in parameters such as velocity, flow depth, discharge, runoff, and time interval, which can be considered as the signs of an underlying dynamics. But there are currently difficulties in extracting the dynamics from the surge series. This paper is concerned with the temporal variations of surge series based on observation data in the last forty years in Jiangjia Gully (JJG). The temporal process of a surge series is characterized by the accumulative curve of the interval time. A surge series is found to be dominated by the peak discharge in that the peak discharge determines the exponent of the distribution. Moreover, the average discharge evolves with surge progress and finally decays in a power-law form. It follows that a surge series behaves as a whole which requires a unified dynamical framework to encompass all the appearances. [Copyright &y& Elsevier]

Applying the wavelet theory and methods to investigate the hydrologic processes such as precipitation and runoff is a hot field. However, several aspects in research are usually ignored: the effect of admissible condition of wavelet functions and the disturbance of noises for the detection of periods, the effect of the length of a hydrologic time-series on the final result, and the choice between the anomaly and the original time series for wavelet analysis. In this paper, these issues are fully discussed. Precipitation data from Lanzhou Precipitation Station are taken for case study. The result indicates that in the wavelet analysis of hydrologic series, denoise methods should be used to eliminate the influence of noises. The MexHat wavelet function satisfies the admissible condition, which ensures that the periodic properties of hydrologic processes can be well represented by using the MexHat wavelet for decomposition. The affected range of hydrologic series which should be discarded before analysis is given. It is also suggested that the anomaly series should be used to highlight the actual undulation of the hydrologic series. [ABSTRACT FROM AUTHOR]

Summary: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]

Storm Water Management Model (SWMM), a hydrodynamic rainfall-runoff and urban drainage simulation model, is widely applied in planning, analysis, and design. It is worth mentioning that the hydrological and hydrodynamic simulation functions of SWMM can also provide decision support for real-time urban stormwater management. However, it remains challenging to directly apply traditional SWMM to real-time urban stormwater management based on web technology. Here we designed and implemented a web service framework based on SWMM (WEB-SWMM), which can provide real-time computing services for urban water management. To test the functionality, efficiency, and stability of the WEB-SWMM, WEB-SWMM was applied to an urban area in China. Test results show that WEB-SWMM could provide real-time computing services stably, quickly, and accurately. In general, the implementation of WEB-SWMM enables traditional SWMM to be quickly and efficiently applied in real-time urban stormwater management. What is more, the web-based hydrological model framework proposed in this paper also applicable to most existing hydrological models. • The real-time simulation of SWMM is implemented. • WEB-SWMM can provide real-time computing services for urban stormwater management. • WEB-SWMM can provide web services and exchange data with other applications. • The core four tasks and four modules guarantee the regular running of WEB-SWMM. • The framework can be installed and deployed across platforms. [ABSTRACT FROM AUTHOR]

• Contour tillage reduced runoff and sediment yield compared to traditional tillage. • Topography and management practices affected the effectiveness of contour tillage. • Climate affected the capacity of contour tillage in reducing runoff. • Soil properties affected the benefits of contour tillage in reducing sediment yield. Contour tillage is a widely practiced conservation tillage in China. The beneficial effects of contour tillage in controlling water erosion have been recognized, further researches are needed to quantify the effectiveness of contour tillage in reducing water erosion and identify the influencing factors in China. We conducted a nationwide meta-analysis based on 229 runoff and 290 sediment paired observations from 47 published papers from national and international literatures. The results showed that compared to traditional tillage, the benefits of contour tillage in China with respect to runoff and sediment reduction were 35.86% and 49.02%, respectively. Sediment yield reduction by contour tillage was greater under simulated rainfall than under natural rainfall. Runoff reduction by contour tillage decreased with the increasing mean annual precipitation and temperature, while sediment yield reduction was not affected by climate factors. Contour tillage in loamy soils and soils with organic carbon content > 1% showed the greatest benefits in reducing sediment yield (64.26% and 52.52%, respectively). The greatest runoff and sediment yield reduction (61.04% and 67.24%, respectively) occurred at slope length > 20 m. The greatest runoff reduction by contour tillage was found for tillage duration > 10 years (44.08%), while the greatest sediment yield reduction occurred at tillage duration ≤ 5 years (52.89%). The effectiveness of contour tillage under cereals was better for runoff and sediment yield reduction compared to legumes and rotation as consequence of the high biomass quantity and lower mineralization rate of cereal straw. Our results conclude that contour tillage is an effective measure for controlling water erosion, and its benefits in reducing runoff and sediment yield are different according to the climate, soil properties, topography and management practices. [ABSTRACT FROM AUTHOR]

• The rainfall-runoff relationship has changed under climate change and human activities in the TRH region. • The impacts climate indicators of rainfall-runoff ratio in the sub-basins of TRH region were different. • The contribution rates of human activity in the YRB were a little higher than that in the YARB. Identifying the impact of climate change and human activities on rainfall-runoff relationship in the Three-River Headwaters (TRH) region is essential for water source conservation, ecological security, and biodiversity maintenance of China and East Asia. In this paper, the runoff-rainfall ratio was used as the index of rainfall-runoff relationship. Different methods were employed to identify the impact indicators and quantitative assessment of rainfall-runoff relationship from 1957 to 2016. The results are the following: (1) the rainfall-runoff relationship has changed in the source regions of Yangtze River basin (YARB) and Yellow River (YRB), and runoff-rainfall ratio has increased by 2.9% and decreased by 3.2%, respectively; (2) the main climate indicators of runoff-rainfall ratio were rainfall, potential evaporation and sunshine hours in the YARB, and rainfall, potential evaporation and temperature in the YRB; (3) the contribution rates of human activity in the YRB were a little higher than that in the YARB, and the contribution rates of climate change and human activity were 90.9% and 9.1% in the YARB and 42.9% and 57.1% in the YRB. The results can provide invaluable information for managing water resources and taking ecological protection measures. [ABSTRACT FROM AUTHOR]

• A new framework based on "feature decomposition-learning reconstruction" was developed for daily runoff forecast. • IPSO algorithm was used to optimize the parameters of DBN model. • PACF was applied to determine the most effective input variables. • VMD-DBN-IPSO model outperforms other comparison models, and it shows consistency in peak flow forecast. Accurate and reliable short-term runoff prediction is of great significance to the management of water resources optimization and reservoir flood operation. In order to improve the accuracy of short-term runoff forecasting, a hybrid model-based "feature decomposition-learning reconstruction" named VMD-DBN-IPSO was proposed. In this paper, variational mode decomposition (VMD) is first used to decompose the original daily runoff series into a set of sub-sequence for improving the frequency resolution. Partial autocorrelation function (PACF) is then applied to determine the input variables of each sub-sequence. The improved particle swarm optimization (IPSO) algorithm is combined with the deep belief network (DBN) model to predict each sub-sequences and finally reconstruct the ensemble forecasting result. Three quantitative evaluation indicators, mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSE), were used to evaluate and compare the established models using the historical daily runoff data (1/1/1988-31/12/2017) at Yangxian and Ankang hydrological station in the Han River Basin of China. Meanwhile, a comparative analysis of the performance of VMD-DBN-IPSO model under different forecast periods (1-, 3-, 5- and 7-day lead time) was performed. In addition, the prediction ability of peak runoff of the VMD-DBN-IPSO model is further verified by analyzing the 10 peak flows during the testing data-series. The results indicate that the VMD-DBN-IPSO model can always achieve the best performance in the training and testing stage, and has good stability and representativeness, the NSE coefficient remains above 0.8, and the prediction error of peak flow is within 20%. It is a preferred data-driven tool for forecasting daily runoff. [ABSTRACT FROM AUTHOR]