Impact of Precipitation Pre-Processing Methods on Hydrological Model Performance using High-Resolution Gridded Dataset

Abstract

Effective representation of precipitation inputs is one of the essential components in hydrological model structures, especially when gauge measurements for the modelled catchment are sparse. Assessment of the impact of precipitation pre-processing is often nontrivial as precipitation data are very limited in the first place. In this paper, we demonstrate a study using a semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT) to examine the impact of different precipitation pre-processing methods on model calibration and the overall model performance with regards to the operational use. A river catchment in the UK is modelled to test against the three pre-processing methods: the Centroid Point Estimation Method (CPEM), the Grid Area Method (GAM) and the Grid Point Method (GPM). Cross-calibration and validation are then carried out by using the high-resolution Centre for Ecology & Hydrology–Gridded Estimate Areal Rainfall (CEH-GEAR) dataset. The results show that the proposed methods GAM and GPM can improve the model calibration significantly against the one calibrated with the existing CPEM method used by the model; the performance differences in the validation among the calibrated models, however, remain small and become irrelevant. The findings indicate that it is preferable to always make use of high-quality rainfall data, when available, with a better pre-processing method, even with models that are previously calibrated with low-quality rainfall inputs. It is also shown that such improvements are affected by the size of catchment and become less significant for smaller catchments.

Assessing the National Water Model’s Streamflow Estimates Using a Multi-Decade Retrospective Dataset across the Contiguous United States

“…Due to its nonlinearity, even small variations in input can lead to substantial changes in output. Previous studies have demonstrated how alterations in precipitation can have significant impacts on streamflow estimation [18][19][20][21]. Moreover, the hydrological modeling process uncertainty raises concerns from the simplified or incorrect representation of the hydrological system.…”

Inverse Trend in Runoff in the Source Regions of the Yangtze and Yellow Rivers under Changing Environments

“…Climate change has had significant effect on the water cycle in the basin through factors such as precipitation, temperature, sunshine, wind speed, etc. [5,6], with precipitation being the crucial driving factor [7]. In addition, with rapid economic development, human activities are altering the ecological environment of a basin in the form of greenhouse gas emissions, land-use changes, water conservancy construction, and human water consumption, resulting in an increasingly significant effect on river runoff [8,9].…”

Impacts of Spatial Interpolation Methods on Daily Streamflow Predictions with SWAT

“…Precipitation inputs are the main driving force of hydrological models [1,2]. Soil & Water Assessment Tool (SWAT) is a widely recognized hydrological model utilized to simulate long-term hydrologic outputs such as streamflow [1][2][3][4][5][6][7][8][9], and water quality [1,4] in gauged and ungauged catchments [10]. Alongside precipitation, SWAT requires five more weather inputs: minimum and maximum temperatures, humidity, solar radiation, and wind speed [10,11].…”