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Distributed rainfall-runoff model simulations are often used to evaluate the impact of changes on the generation of runoff. These models have the advantage of reflecting the effects of land use on spatially distributed model parameters. The article deals with changes in forest associations as a result of global climate changes. In this article the WetSpa model was used for estimating the impact of forest changes on the runoff regime in the Hron and Topla river basins, with an emphasis on the parameterization of the land cover properties in the runoff simulations. The parameters of the model were estimated using climate data and three digital map layers: a land-use map, soil map and digital elevation model. This work contains two land use change scenarios of forest associations and also two scenarios of global climate change. Both types of scenarios of changes were prepared, and the runoff under the new conditions was simulated.

Different approaches for flow-direction determination have been continuously proposed to perform a more realistic simulation of surface runoff, yet the diversity of the existing methods causes significant differences in the extractions of geomorphic parameters as well as the results of rainfall-runoff simulations. In this study, the three most widely used flow-direction methods were separately applied in hydrological models to thoroughly investigate their effects on the simulation output. The results show that the drainage area is a significant factor that affects not only the flow-collecting ability but also the time to peak discharge; however, the definition and calculation of the drainage area become different when the consideration of multiple flow directions is involved in a terrain analysis. This study adopted two area indexes, flow concentration area and flow dispersion area, to understand their consequences in the aspects of the flow volume of simulated hydrograph and the delay of time to peak discharge. The multiple-flow-direction methods show the later time to flow peak and less amount of outflow volume in comparison with the single flow-direction method. By merely extracting two area indexes, a transformation approach is suggested to predict hydrograph shape and to quantify the extent of hydrograph deviations induced by using different flow-direction methods.

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol’ method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results.

Hourly flood flow estimation for gauged and ungauged catchments is a prerequisite for planning and water management. Various methods have been applied in a multitude of studies to calculate the peak flow for ungauged catchments. However, it is not simple for engineers to use the existing methods in practical applications. An easier method is suggested for this purpose in this study. The authors estimated the relationship between the runoff coefficient, intensity of rainfall, and curve number, and then utilized the relationship to calculated the peak flow using the rational method for ungauged catchments. Rainfall and flood time series for ungauged study catchments were generated by a simple data generation method and a distributed rainfall⁻runoff model. Results showed that the runoff coefficients simulated using the estimated relationship reasonably agree with the runoff coefficients in the studied ungauged catchments. In addition, the peak flow simulated using the rational method and the relationship highly agree with the peak flow in the ungauged catchments. Therefore, the peak flow in ungauged catchments can be easily calculated by this method, which is more pragmatic for engineers.

The hydrological model assessment and development (hydromad) modeling package is an R-based package that can be applied to simulate hydrological models and optimize parameters. As the hydromad package is incompatible with hydrological models outside the package, the parameters of such models cannot be directly optimized. Hence, we proposed a method of optimizing the hydrological-model parameters by combining the executable (EXE) file of the hydrological model with the shuffled complex evolution (SCE) algorithm provided by the hydromad package. A physically based, spatially distributed, grid-based rainfall–runoff model (GRM) was employed. By calibrating the parameters of the GRM, the performance of the model was found to be reasonable. Thus, the hydromad can be used to optimize the hydrological-model parameters outside the package if the EXE file of the hydrological model is available. The time required to optimize the parameters depends on the type of event, even for the same catchment area.

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

Radar systems have been widely employed to measure precipitation and predict flood risks. However, radar as a rainfall measuring device and the produced rainfall estimate contain uncertainties and errors resulting from sources such as mis-calibration, beam blockage, anomalous propagation, and ground clutter. Previously, these radar errors have been individually studied. However, in practical applications, separating and estimating these errors are not possible. In the current study, to analyze the effects of radar rainfall errors, especially for their effect on the peak discharge, through a synthetic runoff simulation, a spatial error model based on univariate Gaussian random numbers was employed. Furthermore, a Monte Carlo simulation, one of the most widely used techniques for intensive simulation toward obtaining practical results, was performed. The results indicated that the variability of the peak discharge increases as the assumed true rainfall increases. In addition, the higher standard deviation of the tested radar rainfall error leads to a higher peak discharge bias. To investigate the cause of this bias, an additional simulation was performed. This simulation revealed that the regression line for the peak discharge corresponding to rainfall amount increases quadratically. The results show that the higher bias is a result of the higher deviation of peak discharges in the cells, with a greater than mean rainfall, even with the same number of cells for lower and higher rainfall amounts. [ABSTRACT FROM AUTHOR]

The article presents a study on the comparison between Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA), and Micro-Genetic Algorithm (μGA). The study uses the parameter calibration of a grid-based distributed rainfall-runoff model (GBDM) and a database for model calibration and verification. The study shows close calibration results among SCE, SGA, and μGA and no superiority within performance measures such as errors of time to peak and total runoff volume.

Summary: The aim of this paper is to examine the benefits of updating soil moisture of a distributed rainfall runoff model in forecasting large floods. The updating method uses Ensemble Kalman Filter concepts and involves an iterative similarity approach that avoids calculation of the Jacobian that relates the states and the observations. The soil moisture is updated based on observed runoff in a real-time mode, and is then used as an initial condition for the flood forecasts. The case study is set in the 622km2 Kamp catchment, Austria. The results indicate that the updating procedure indeed improves the forecasts substantially. The mean absolute normalised error of the peak flows of six large floods decreases from 25% to 12% (3h lead time), and from 25% to 19% (48h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3h lead time), and from 0.79 to 0.88 (48h lead time). The flood forecasting system has been in operational use since early 2006. [Copyright &y& Elsevier]

Real time updating of rainfall-runoff (RR) models is traditionally performed by state-space formulation in the context of flood forecasting systems. In this paper, however, we examine applicability of generalized likelihood uncertainty estimation (GLUE) approach in real time modification of forecasts. Real time updating and parameter uncertainty analysis was conducted for Abmark catchment, a part of the great Karkheh basin in south west of Iran. A conceptual-distributed RR model, namely ModClark, was used for basin simulation, such that the basin’s hydrograph was determined by the superposition of runoff generated by individual cells in a raster-based discretization. In real time updating of RR model by GLUE method, prior and posterior likelihoods were computed using forecast errors that were obtained from the results of behavioral models and real time recorded discharges. Then, prior and posterior likelihoods were applied to modify forecast confidence limits in each time step. Calibration of parameters was performed using historical data while distribution of parameters was modified in real time based on new data records. Two scenarios of rainfall forecast including prefect-rainfall-forecast and no-rainfall-forecast were assumed in absence of a robust rainfall forecast model in the study catchment. The results demonstrated that GLUE application could offer an acceptable lead time for peak discharge forecast at the expense of high computational demand. [ABSTRACT FROM AUTHOR]

In Taiwan, the continuously increasing levels of rice imports are likely to result in surplus paddy fields. Hence, the surplus paddy fields may be developed into wetlands to increase ground water recharge, provide appropriate environments for wildlife, and most importantly, store flood water. This study developed a hydrological model incorporating the distributed rainfall-runoff model based on the kinematics wave approach and the distributed tank model for simulation, respectively, in mountainous and flat areas. The hydrological model was found to simulate the rainfall-runoff behavior well in the study area. Furthermore, a decision method based on the genetic algorithm concepts was proposed to give policy makers the optimal location and area size of paddy fields to construct wetlands for flood mitigation. [ABSTRACT FROM AUTHOR]

• Remote-sensed soil moisture information is assimilated into a distributed model. • The influence of updating scheme on assimilation results is considered. • The assimilation improves simulated profile soil moisture and streamflow. Profile soil moisture (PSM), which represents soil moisture content over the whole soil layer depth, is a key variable to control plant growth, biological interactions, and streamflow generation, and its information plays a crucial role in hydrological analysis and agricultural water management. Recent studies have assimilated multi-source satellite PSM information into hydrological modelling to more accurately estimate real PSM. However, the PSM estimated from these studies are normally at coarse spatial resolution (i.e., larger than 25 × 25 km2). In this study, the high-resolution (1 × 1 km2) PSM are generated by assimilating multiple remote-sensed PSM data of coarse resolution into the Digital Elevation Model (DEM) based distributed rainfall-runoff model (DDRM) in three catchments (two humid catchments and one semiarid catchment) in China with the grid scale of 1 × 1 km2. The remote-sensed PSM data are pre-processed from two remote-sensed surface soil moisture datasets, i.e. the multi-satellite-retrieved soil moisture dataset released by the Europe Space Agency Climate Change Initiative (ESA CCI), and the soil moisture product from the Soil Moisture Active Passive (SMAP) satellite. The influence of remote-sensed datasets selection schemes (i.e. only ESA CCI, only SMAP, and ESA CCI and SMAP combined) on assimilation results are investigated. In the assimilation process, two updating schemes are considered, one is to only update the DDRM's state variable, i.e. PSM, and the other updating both the parameters and PSM variable of the DDRM. Thus, six assimilation scenarios are set in the study, whose performances are compared with the DDRM without assimilation for different time periods, including the whole period, the dormant period and the growing period. Results indicate that in any periods, for any of remote-sensed datasets used for assimilation, either the state update or the parameter-state update can improve the accuracy of high-resolution (1 × 1 km2) PSM simulations by the DDRM. Besides, assimilating the SMAP PSM dataset into the DDRM has the potential to improve streamflow simulations for the three catchments. This study has shown that, by assimilating multi-source remote-sensed PSM into a high spatial resolution distributed hydrological model, i.e. DDRM, estimation of PSM can be improved over both the original remote-sensed PSM and the DDRM-simulated PSM. [ABSTRACT FROM AUTHOR]

Different approaches for flow-direction determination have been continuously proposed to perform a more realistic simulation of surface runoff, yet the diversity of the existing methods causes significant differences in the extractions of geomorphic parameters as well as the results of rainfall-runoff simulations. In this study, the three most widely used flow-direction methods were separately applied in hydrological models to thoroughly investigate their effects on the simulation output. The results show that the drainage area is a significant factor that affects not only the flow-collecting ability but also the time to peak discharge; however, the definition and calculation of the drainage area become different when the consideration of multiple flow directions is involved in a terrain analysis. This study adopted two area indexes, flow concentration area and flow dispersion area, to understand their consequences in the aspects of the flow volume of simulated hydrograph and the delay of time to peak discharge. The multiple-flow-direction methods show the later time to flow peak and less amount of outflow volume in comparison with the single flow-direction method. By merely extracting two area indexes, a transformation approach is suggested to predict hydrograph shape and to quantify the extent of hydrograph deviations induced by using different flow-direction methods. [ABSTRACT FROM AUTHOR]

This study aimed to assess the suitability of the parameters of a physically based, distributed, grid-based rainfall-runoff model. We analyzed parameter sensitivity with a dataset of eight rainfall events that occurred in two catchments of South Korea, using the Sobol' method. Parameters identified as sensitive responded adequately to the scale of the rainfall events and the objective functions employed. Parameter sensitivity varied depending on rainfall scale, even in the same catchment. Interestingly, for a rainfall event causing considerable runoff, parameters related to initial soil saturation and soil water movement played a significant role in low flow calculation and high flow calculation, respectively. The larger and steeper catchment exhibited a greater difference in parameter sensitivity between rainfall events. Finally, we found that setting an incorrect parameter range that is physically impossible can have a large impact on runoff simulation, leading to substantial uncertainty in the simulation results. The proposed analysis method and the results from our study can help researchers using a distributed rainfall-runoff model produce more reliable analysis results. [ABSTRACT FROM AUTHOR]

Hourly flood flow estimation for gauged and ungauged catchments is a prerequisite for planning and water management. Various methods have been applied in a multitude of studies to calculate the peak flow for ungauged catchments. However, it is not simple for engineers to use the existing methods in practical applications. An easier method is suggested for this purpose in this study. The authors estimated the relationship between the runoff coefficient, intensity of rainfall, and curve number, and then utilized the relationship to calculated the peak flow using the rational method for ungauged catchments. Rainfall and flood time series for ungauged study catchments were generated by a simple data generation method and a distributed rainfall–runoff model. Results showed that the runoff coefficients simulated using the estimated relationship reasonably agree with the runoff coefficients in the studied ungauged catchments. In addition, the peak flow simulated using the rational method and the relationship highly agree with the peak flow in the ungauged catchments. Therefore, the peak flow in ungauged catchments can be easily calculated by this method, which is more pragmatic for engineers. [ABSTRACT FROM AUTHOR]

The hydrological model assessment and development (hydromad) modeling package is an R-based package that can be applied to simulate hydrological models and optimize parameters. As the hydromad package is incompatible with hydrological models outside the package, the parameters of such models cannot be directly optimized. Hence, we proposed a method of optimizing the hydrological-model parameters by combining the executable (EXE) file of the hydrological model with the shuffled complex evolution (SCE) algorithm provided by the hydromad package. A physically based, spatially distributed, grid-based rainfall–runoff model (GRM) was employed. By calibrating the parameters of the GRM, the performance of the model was found to be reasonable. Thus, the hydromad can be used to optimize the hydrological-model parameters outside the package if the EXE file of the hydrological model is available. The time required to optimize the parameters depends on the type of event, even for the same catchment area. [ABSTRACT FROM AUTHOR]

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results. [ABSTRACT FROM AUTHOR]