Performance Assessment of Spatial Interpolation of Precipitation for Hydrological Process Simulation in the Three Gorges Basin

Abstract

Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash-Sutcliffe Coefficient (NSE), the coefficient of determination (R 2 ), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

“…In the hydrological simulation, the default spatial resolution of XRAIN data (250 m) was used as the input, and we used radar rainfall at different spatial resolutions without changing the optimized parameters for each event in the model to allow us to compare the results when using different resolutions. A number of interpolation techniques for reproducing the spatial continuity of rainfall fields have been described in the literature [6,18,[50][51][52][53][54][55]. These techniques are normally divided into two approaches: deterministic and geostatistical.…”

“…These techniques are normally divided into two approaches: deterministic and geostatistical. The most frequently used deterministic methods are nearest neighbor, inverse distance weighting, Thiessen polygon, and Kriging, which are fairly straightforward interpolation techniques that provide reliable results [18,[52][53][54]. Some studies also apply a geostatistical approach to interpolate the spatial and temporal resolution of the rainfall data [7,25].…”

“…Some studies also apply a geostatistical approach to interpolate the spatial and temporal resolution of the rainfall data [7,25]. However, each method shows some biases when compared to real datasets [50,[52][53][54][55]. Spatial interpolation techniques basically obtain fine resolution data from the lowest amount of available data [52][53][54][55], and they differ in their assumptions or perspectives (local vs. global).…”

Spatial Variation Pattern Analysis of Hydrologic Processes and Water Quality in Three Gorges Reservoir Area

“…There are 22 meteorological stations and 113 rain gauges located (see Figure 1) in or close to the TGRA, which were acquired from the National Meteorological Information Center and Hydrologic Statistical Yearbooks. Since the rain gauges only have rainfall data, other meteorological factors, including solar radiation, air temperature (maximum and minimum), relative humidity, and wind speed were interpolated from 22 meteorological stations to the 113 rain gauges by the Inverse Distance Weighted method introduced in our previous work [30]. The meteorological data were collected from 2009 to 2013.…”

“…Our previous research indicated that localized database and rational parameterizations of specific basins are required to improve the application of SWAT [2], and the finer the subdivision scheme and hydrologic response unit (HRU) definition, the better the model performance [29]. Obtaining more detailed precipitation data, based on the precipitation interpolation method, will improve the hydrological simulation performance of SWAT [30]. However, adapting SWAT in a large-scale region faced a trade-off involving the time used for the simulation and the description accuracy of natural characteristics [31][32][33].…”