Abstract
Satellites are capable of observing precipitation over large areas and are particularly suitable for estimating precipitation in high mountains and poorly gauged regions. However, the coarse resolution and relatively low accuracy of satellites limit their applications. In this study, a downscaling scheme was developed to obtain precipitation estimates with high resolution and high accuracy in the Heihe watershed. Shannon’s entropy, together with a semi-variogram, was applied to establish the optimal precipitation station network. A combination of the random forest (RF) method and the residual correction approach with the established rain gauge network was applied to downscale monthly precipitation products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results indicated that the RF model showed little improvement in the accuracy of IMERG-based precipitation downscaling. Including residual modification could improve the results of the RF model. The mean absolute error (MAE) and root mean square error (RMSE) values decreased by 19% and 21%, respectively, after residual corrections were added to the RF approach. Moreover, we found that enough rain gauge records are necessary for and remain an important component of tuning model performance. The application of more rain gauges improves the performance of the combined RF and residual modification methods, with the MAE and RMSE values reduced by 8% and 9%, respectively. Residual correction, together with enough precipitation stations, can effectively enhance the quality of the precipitation patterns and magnitudes obtained in the RF downscaling process. The proposed downscaling scheme is an effective tool for increasing the accuracy and spatial resolution of precipitation fields in the Heihe watershed.
Highlights
Precipitation is a primary variable associated with earth science, hydrology, climatology, and agriculture [1,2,3,4,5]
By combining Shannon’s entropy with the semi-variogram, the optimal virtual rain gauge network was established (Figure 3), and the monthly precipitation at each gauge was obtained by using Weather Research and Forecasting Model (WRF)
The monthly precipitation amounts at the candidate rain gauges were obtained by using WRF with some suitable localized parameters
Summary
Precipitation is a primary variable associated with earth science, hydrology, climatology, and agriculture [1,2,3,4,5]. The spatial distribution of precipitation is a necessary input for hydrological and ecological models. Various approaches have been proposed for estimating distributions of precipitation over the last few decades. These methods are based on mathematical and physical principles determined by using rain gauges, weather radar, satellite sensors, and numerical models. The interpolation method is a common approach used to generate spatial precipitation fields. The accuracy of interpolation-based precipitation estimates is significantly dependent on the station network and the degree of the spatial heterogeneity of precipitation [9,10]. It is difficult to gain accurate spatial information on precipitation based on data from stations with sparse coverage and in areas where the spatial heterogeneity of precipitation is high, especially mountainous areas [11].
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