Abstract
Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.
Highlights
Precipitation, as an important hydrometeorological variable, plays a foremost role in the energy and water cycle, and in weather, climatology, hydrology, ecosystems, and even the Earth system [1,2]
The 30-year mean daily, monthly, and annual Pall of OBS and PERSIANN-CDR were calculated by averaging over the Huai River Basin (HRB) (Table 3), i.e., 2.21 vs. 2.45 mm/day, 67.24 vs. 74.66 mm/month, and 806.92 vs. 895.90 mm/year
Comparison between OBS and PERSIANN-CDR (Figure 3(a1) vs. Figure 3(a2), Figure 3(b1) vs. Figure 3(b2), and Figure 3(c1) vs. Figure 3(c2)) suggests that PERSIANN-CDR can well capture the spatial distributions of climatological Pall on daily, monthly, and annual scales, with a spatial R larger than 0.94
Summary
Precipitation, as an important hydrometeorological variable, plays a foremost role in the energy and water cycle, and in weather, climatology, hydrology, ecosystems, and even the Earth system [1,2]. It is noted that directly gauging precipitation is available in specific places, with some records dating back to several thousand years [21], but most areas have no ground sites as a result of inaccessibility and higher costs for installations and maintenance This limits the representativeness of gauge precipitation, because precipitating weather systems generally have high spatial and temporal variability [2,7,22,23,24,25,26]. Because of the backwardness of radar technology in some countries, radar blockage, due to topography, and the nearly negligible radar measurements overseas, satellite-based precipitation estimates have become the most attractive and viable approach to fulfill various requirements of academic studies and practical applications [19,27,28,29]
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