A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Abstract

Abstract:Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25˝daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

“…The peak values were positive and the valley values were slightly negative, except that the valley value reached −0.12 in August, when monthly precipitation reached its peak of 195.22 mm in the year. The reasons for such the complicated periodicity might be the combined effect of the following facts: (1) Hengduan Mountains is controlled by southwest Asian monsoon, the southeast Asian monsoon, the winter monsoon, and local circulation of Tibetan Plateau in turn and/or simultaneously; (2) there are time lags between vegetation growth withering and precipitation increase/decrease; (3) vegetation is restricted by both water and temperature during dry season, but much less restricted by water than temperature during flood season. Standardized slopes of DEM were negative and smaller than −0.2 all the year except for February, May, and June.…”

“…As a fundamental component in material and energy circulation, precipitation is of great significance for hydrological, meteorological, and ecological studies (e.g., [1][2][3]). Traditionally, spatial distribution of precipitation is obtained through rain gauge data interpolation [4].…”

Error Analysis and Evaluation of the Latest GSMap and IMERG Precipitation Products over Eastern China

“…Nevertheless, previous studies have found that inherent biases and errors of satellite precipitation products might notably influence their applications in hydrological modeling and extreme weather impact assessment [14]. To facilitate the applications of these products at regional and global scales, it is important to quantitatively validate them and identify the error characteristics before being used in applied settings.…”

Scale effects of topographic ruggedness on precipitation over Qinghai‐Tibet Plateau

“…Due to the physical connection between precipitation and topography in mountainous areas, local altitude, TR and related meteorological factors (e.g., wind and humidity) are commonly used to estimate the spatial variability of precipitation through statistical downscaling (Fang et al , ; Masson and Frei, ; Long et al , ; Qi et al , ) and numerical model simulation (Giorgi et al , ; Gao et al , ; Shi et al , ; Ogwang et al , ; Dong et al , ; Pan et al , ). It is noted that the use of topographical information compensates for the interpolation method to improve the estimation of the extreme rainfall (Prudhomme, ) and enhances the activities of mesoscale disturbances in the model simulations (Shi et al , ).…”