Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.
Abstract. Lakes can be effective indicators of climate change, and this is especially so for the lakes over the Qinghai–Tibet Plateau (QTP), the highest plateau in the world, which undergoes little direct human influence. The QTP has warmed at twice the mean global rate, and the lakes there respond rapidly to climate and cryosphere changes. The QTP has ∼ 1200 lakes larger than 1 km2 with a total area of ∼ 46 000 km2, accounting for approximately half the number and area of lakes in China. The lakes over the QTP have been selected as an essential example for global lakes or water body studies. However, concerning lake data over the QTP are limited to the Landsat era and/or available at sparse intervals. Here, we extend the record to provide comprehensive lake evolution data sets covering the past 100 years (from 1920 to 2020). Lake mapping in 1920 was derived from an early map of the Republic of China and in 1960 from the topographic map of China. The densest lake inventories produced so far between 1970 and 2020 (covering all lakes larger than 1 km2 in 14 epochs) are mapped from Landsat MSS, TM, ETM+, and OLI images. The lake evolution shows remarkable transitions between four phases: significant shrinkage in 1920–1995, rapid linear increase in 1995–2010, relative stability in 2010–2015, and further increase in 2015–2020. The spatial pattern indicates that the majority of lakes shrank in 1920–1995 and expanded in 1995–2020, with a dominant enlargement for central-north lakes in contrast to contraction for southern lakes in 1976–2020. The time series of precipitation between 1920 and 2017 indirectly supports the evolution trends of lakes identified in this study. The lake data set is freely available at https://doi.org/10.5281/zenodo.4678104 (Zhang et al., 2021a).
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