Abstract
Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.
Highlights
Precipitation is one of the most important water balance components of the global water cycle, and has great variability across different spatial and temporal scales [1,2]
The Red River Basin drains an area of 156,451 km2, of which 50.3% is in Vietnam, 48.8% is in China, and 0.9% is in Laos [52].The upper catchment of the Red River Basin (URRB) refers to the catchment north of the China–Vietnam border (Figure 1)
The Red River Basin drains an area of 156,451 km2, of which 50.3% is in Vietnam, 48.8% is in China, and 0.9% is in Laos [52].The upper catchment of the Red River Basin (URRB) refers to the catchment north of the China– RVemieottne Saemns.b2o01r8d, e1r0,(1F8i8g1ure 1)
Summary
Precipitation is one of the most important water balance components of the global water cycle, and has great variability across different spatial and temporal scales [1,2]. The accurate observation or estimation of precipitation has important theoretical and practical significance for flood warnings, drought monitoring, and water resource management [3,4]. Gauge observations provide relatively accurate point-based measurements of precipitation [5]; owing to significant precipitation heterogeneity across a variety of spatiotemporal scales, rain gauge observations only represent local conditions, and can result in potential errors when interpolated to larger scales, especially in mountainous areas with complex terrain [6]. With wide coverage and high spatial–temporal resolution (mostly finer than 0.25◦ × 0.25◦at spatial and three-hour temporal scales), SPPs have been extensively applied in many fields, including hydrological simulation [16,17,18], extreme event analysis [19,20,21], and water resource management [22,23]
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