In this paper, we present the Daily based Morgan–Morgan–Finney model. The main processes in this model are based on the Morgan–Morgan–Finney soil erosion model, and it is suitable for estimating surface runoff and sediment redistribution patterns in seasonal climate regions with complex surface configurations. We achieved temporal flexibility by utilizing daily time steps, which is suitable for...

Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the ...

In the last decades RS and GIS technology have been increasingly used for hydrological applications. Hydrological parameters estimation is strongly related to land cover composition. This study examines the impact of different approaches to estimate land cover distribution on the prediction and the spatial pattern of surface runoff. Land cover fractions are derived at sub-pixel scale from CHRIS...

The Australian water balance model (AWBM) is a catchment water balance model that calculates runoff from rainfall at daily or hourly time increments. The daily version iss used for water yield and water management studies; the hourly version is used for design flood estimation. This paper describes the origin and development of the AWBM beginning with elementary modelling components of saturati...

The prediction uncertainty of a hydrologic model is closely related to model formulation and the uncertainties in model parameters and inputs. Currently, the foremost challenges concern not only whether hydrologic model outputs match observations, but also whether or not model predictions are meaningful and useful in the contexts of land use and climate change. The latter is difficult to determ...

A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One-hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omissio...

[1] The optimal parameters and uncertainty estimation of land surface models require that appropriate length of forcing and calibration data be selected for computing error functions. Most of the previous studies used less than two years of data to optimize land surface models. In this study, 18-year hydrometeorological data at Valdai, Russia, were used to run the Chameleon Surface Model (CHASM...