Spatiotemporal Evaluation of Satellite-Based Precipitation Products in the Colorado River Basin

Abstract

Abstract Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in the Upper Colorado River basin (UCRB) for the period 2000–20. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3–4 mm day−1 differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November–April) than in the dry season (May–October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50%–60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features.