Prediction of Surface Water Resources in Danjiangkou Basin Based on ESP and Distributed Hydrological Model

<p> The prediction of surface water resources in the Danjiangkou Basin is of great significance for the design of the water transfer plans for the South-to-North Water Diversion Project. However, it is difficult to obtain high-precision simulations for mid- and long-term hydrological forecasting. Based on the thought of extended streamflow prediction (ESP) and distributed hydrological models, this paper proposed a set of forecasting systems for predicting the annual surface water resources in the Danjiangkou Basin. Firstly,  the Wetspa model  was established to forecast the inflow of Danjiangkou reservoir. The Nash efficiency coefficients of the monthly average runoff during the calibration period (2006-2012) and verification period (2013-2016) were 0.97 and 0.95, respectively. Secondly, it was assumed that the rainfall of 2017 could be predicted by the rainfall forecasting model, then the rainfall process was obtained based on the ESP and the runoff process of the basin outlet was calculated through the Wetspa model. Finally, the predicted surface water resources of the Danjiangkou Basin in 2017 was 45.448 billion m<sup>3</sup>, and the actual surface water resources is 40.395 billion m<sup>3</sup>, with a relative error of 12.51%. The results showed that the prediction of surface water resources in Danjiangkou Basin based on ESP and distributed hydrological model could provide a certain reference for the design of water transfer plans of the Danjiangkou Reservoir.</p><p><strong>Key words: </strong>Water resources prediction; ESP; Wetspa model; Nash coefficient</p>

Application of Xin'anjiang Model and Wetspa Model in the Inflow Forecasting of Shiquan Reservoir

<p>In order to improve the accuracy of the inflow forecasting of Shiquan Reservoir in the Han River Basin, this paper compared the application effects of Xin'anjing model and Wetspa model. The study collected the rainfall and runoff data from 2009 to 2015, as well as the DEM, land use and soil data with 1000´1000m grid size. The model calibration and verification periods were from 2009 to 2012 and from 2013 to 2015, respectively. In addition to using the runoff depth and the determination coefficient to evaluate the accuracy of the two models, the flow relative error CR1, model confidence coefficient CR2, Nash-Sutcliffe efficiency CR3, logarithmic version of Nash-Sutcliffe efficiency CR4 for low flow, improved Nash-Sutcliffe efficiency CR5 for high flow were adopted to analyze the simulation results of the two models. The results showed that the simulation results of the Wetspa model could be used as a supplement to the simulation results of the Xin'anjiang model, providing high-precision flood forecasting results for the scheduling decisions of Shiquan Reservoir in terms of time and space.</p>

Estimation of the Effect of Changes in Forest Associations on Runoff Processes in Basins: Case Study in the Hron and Topľa River Basins

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.

Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

Spatial sensitivity analysis of snow cover data in a distributed rainfall–runoff model

As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.