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• 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]

• Climate change, hydrological and hydraulic model are combined to analyze runoffs. • Four Global Climate Model: BCC, BNU, CanESM and CSIRO are used. • The future design runoffs and water levels increase in Qu River. • Disaster management and infrastructure construction should be paid more attention to. The impact of climate change on regional hydrological extreme events has been a research topic that has received much attention in recent years. This paper aims to provide a general overview of changes on future runoffs and water levels in the Qu River Basin, upper reaches of Qiantang River, East China by combining future climate scenarios, hydrological model and 1D hydraulic model. The LARS-WG (Long Ashton Research Station Weather Generator) statistical downscaling method is used to downscale the GCM (Global Climate Model) outputs (including BCC, BNU, CanESM and CSIRO under RCP4.5 and RCP8.5 scenarios) and generate 50 years of synthetic precipitation and maximum and minimum temperatures to drive the GR4J hydrological model and the 1D hydraulic model for the baseline period 1971–2000 and the future period 2021–2050. Finally, the POT (Peaks over Threshold) method is applied to analyze the change of extreme events in the study area. The results show that design runoffs and water levels all indicate an increasing trend in the future period for Changshangang River and Jiangshangang River at most cases, especially for small return periods, and for Qu River the increase becomes larger, which suggests that the risk of flooding will probably become greater and appropriate adaptation measures need to be taken. [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]

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]

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]

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]

• 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]