Abstract
During the last decade, satellite-based precipitation products have been believed to be a potential source for forcing inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively evaluates several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, at daily and monthly time-scales over Austria. We show that all the examined products are able to reproduce the spatial precipitation distribution over the country. MSWEP, followed by IMERG-V05B and -V06A, show the strongest agreement with in situ observations and perform better than other products with respect to spatial patterns and statistical metrics. Both IMERG and ERA5 products seem to have systematic precipitation overestimation at the monthly time-scale. IMERG-V06A performs slightly better than IMERG-V05B. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, the ERA5 and MSWEP indicate robust average POD and FAR values with respect to light/moderate precipitation (10 mm > P ≥ 0.1 mm) with 0.94 and 0.11, respectively. Such robustness of MSWEP may be rooted in applying the daily rain gauges in calibration processes. Moreover, although all products accurately map the spatial precipitation distribution they still have difficulty capturing the effects of topography on precipitation. The overall performance of the precipitation products was lower in the peripheries of the study area where most stations are situated in the mountainous area and was higher over the low altitude regions. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06S and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain.
Highlights
Droughts and floods are water-related natural phenomena which have large negative impacts on society and activities related to agriculture, and local economies
The results indicated IMERG-V05-RT, ERA5 the, and SM2RAIN are unreliable at detecting precipitation at heavy precipitation category
To elucidate the strengths and weaknesses of recently released gridded precipitation datasets, we conducted a comprehensive evaluation of the performance of IMERG-FR-V05B, -V06A, IMERG-V05B-RT, ERA5, SM2RAIN-ASCAT, and Multi-Source Weighted-Ensemble Precipitation (MSWEP)-V2.2 at daily and monthly time-scales for Austria using a dense network of gauges (882 stations) as a reference
Summary
Droughts and floods are water-related natural phenomena which have large negative impacts on society and activities related to agriculture, and local economies. Drought is one of the most important natural disasters, since it affects wide areas for long time (months to years) and, has a serious impact on regional or countries economic performance, etc. Large-scale extreme events (i.e., droughts) have been observed in many places around the world leading to high negative impacts on economic, ecological resources, food shortages, etc. Gridded daily surface precipitation data are important for many water-related applications, such as drought and flood monitoring systems. The information derived from SPEs provides tremendous potential for identification, monitoring, and assessment of droughts, flood, etc., especially for regions with sparse rain gauges or limited radar coverage [4]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.