Abstract
This study investigated the accuracy and drought monitoring application of two newly-released long-term satellite precipitation products (i.e., the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 CHIRPS) and the latest reanalysis precipitation product (i.e., the Global Precipitation Climatology Centre full data monthly version 2018, GPCC 8.0). Satellite- and reanalysis-based precipitation sequences and standardized precipitation indices (SPIs) were compared comprehensively with background estimates of the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset at spatial and multiple temporal scales over the Yellow River Basin (YRB) in China during 1983–2016. Results indicated the PERSIANN-CDR, CHIRPS and GPCC 8.0 precipitation products generally had good consistency with CGDPA (correlation coefficient, CC > 0.78). At spatial, monthly and seasonal scales, the consistency between GPCC 8.0 and CGDPA precipitation was found to be better than that of the two satellite products. Due to their good performance at the spatiotemporal scale, the satellite with long-time record and GPCC 8.0 products were evaluated and compared with CGDPA to derive SPI-1 (1-month SPI), SPI-3 (3-month SPI), and SPI-12 (12-month SPI) for drought monitoring in the YRB. The results showed that they had good application in monitoring droughts (CC > 0.65 at spatial scale, CC > 0.84 at temporal scale). The historical drought years (i.e., 1997, 1999, and 2006) and the spatial distribution of drought area in August 1997 were captured successfully, but the performance of GPCC 8.0 was found to be the best. Overall, GPCC 8.0 is considered best suited to complement precipitation datasets for long-term hydrometeorological research in the YRB.
Highlights
Drought can be the most frequent, longest duration, widespread, and serious natural disasters in the world [1]
The standardized precipitation indices (SPIs) has the advantage of simple calculation and applicability on multiple timescales, and it can be applied to comparative analysis of droughts and floods in different areas [11,12,13]
Long-term continuous historical records include the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) [23] and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 (CHIRPS) [24], produced in accordance with the recommendations of the World Meteorological Organization (WMO) [25]. These satellite precipitation products can be used for precipitation evaluation and drought monitoring
Summary
Drought can be the most frequent, longest duration, widespread, and serious natural disasters in the world [1]. Long-term continuous historical records (over 30 years) include the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) [23] and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 (CHIRPS) [24], produced in accordance with the recommendations of the WMO [25] These satellite precipitation products can be used for precipitation evaluation and drought monitoring. Meteorological station data have been used to produce surface reanalysis precipitation products such as the Global Precipitation Climatology Centre (GPCC) full data [26] Such reanalysis precipitation estimates provide basic data for studying spatiotemporal variations of precipitation patterns and drought monitoring. Both satellite-derived precipitation products and reanalysis precipitation estimates have their own limitations. In order to match the spatial resolution of satellite and reanalysis products, the CGDPA with 0.5◦ spatial resolution from 1983 to 2009 was resampled to obtain the CGDPA dataset with 0.25◦ spatial resolution through cubic function of ArcGIS software
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