Performance Evaluation of Three Satellites-Based Precipitation Data Sets Over Iran
- PDF / 2,819,146 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 76 Downloads / 175 Views
RESEARCH ARTICLE
Performance Evaluation of Three Satellites-Based Precipitation Data Sets Over Iran Morteza Miri1 • Reyhaneh Masoudi2 • Tayeb Raziei3 Received: 22 April 2019 / Accepted: 20 September 2019 Ó Indian Society of Remote Sensing 2019
Abstract The present study aims to evaluate the performance of daily and monthly precipitation data relative to GPM-IMERG, TRMM_3B42 and PERSIANN satellite-based precipitation estimations against historical data for the period 2014–2017 as observed at 70 synoptic stations distributed over Iran. The coefficient of determination (R-squared), root mean square error and the Nash–Sutcliffe model efficiency coefficient were used to evaluate the performance of the used data sets against observed precipitation records at the considered stations. The statistics showed that the considered data sets are generally less successful in estimating daily precipitation at nationwide as the estimation errors were found high at almost all the studied stations. The errors of daily precipitation estimation of GPM-IMERG, TRMM_3B42 and PERSIANN-CDR data sets showed that although there is a considerable similarity between the estimated precipitation by the three data sets, especially between the TRMM_3B42 and GPM-IMERG, the accuracy of GPM-IMERG daily precipitation over Iran is higher than that of TRMM_3B42 and PERSIANN-CDR. The highest R2 value for GPM-IMERG, TRMM_3B42 and PERSIANN-CDR remotely sensed daily precipitation is equal to 0.6, 0.46, and 0.37, respectively. Similarly, on the monthly time scale, the GPM-IMERG, with an average R2 value of 0.83 over the country, performs better than the other two data sets. The TRMM_3B43 with mean nationwide R2 = 0.80 also showed comparative performance with GPMIMERG, but the PERSIANN-CDR data set with an average R2 value of 0.4 over the stations is not as accurate as the GPMIMERG and TRMM_3B43. Keywords Remote sensing data GPM-IMERG TRMM_3B43 PERSIANN-CDR Performance statistics Precipitation
Introduction Precipitation is one of the main inputs in hydrological systems, and therefore, knowledge on areal precipitation characteristics plays an important role in understanding the cycle and management of water resources (Su et al. 2008; Qi et al. 2016). The amount, intensity and time distribution & Morteza Miri [email protected] 1
Department of Geography, University of Tehran, Tehran, Iran
2
Department of Reclamation of Arid and Mountainous Zones Regions, Faculty of Natural Resources, University of Tehran, Tehran, Iran
3
Soil Conservation and Watershed Management Research Institute, Tehran, Iran
of rainfall can be obtained from various sources of data such as meteorological stations, weather RADAR, LiDar and meteorological satellites. Rainfall is typically measured by rain gauges and weather RADARs, which are the most reliable sources of rainfall data for a given location (Miri et al. 2016; Gairola et al. 2015). However, the satellite’s measurements are affected by systematic errors such as losses due to wetting, evaporation and aerodynamic effects
Data Loading...
