Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

Remote Sensing

Carolien Toté⁶  Domingos Patricio⁴  Hendrik Boogaard⁵  Raymond van der Wijngaart⁵  Elena Tarnavsky²  Chris Funk¹  George P. Petropoulos³ 

[1] United States Geological Survey/Earth Resources Observation and Science (EROS) Center and the Climate Hazard Group, Geography Department, University of California Santa Barbara, Santa Barbara, CA 93106, USA; E-Mail:;Department of Meteorology, University of Reading, Earley Gate, PO Box 243, Reading RG6 6BB, UK; E-Mail:;id="af1-remotesensing-07-01758">1Flemish Institute for Technological Research (VITO), Remote Sensing Unit, Boeretang 200, 2400 Mol, Belgi;Instituto Nacional de Meteorologia (INAM), Rua de Mukumbura 164, C.P. 256, Maputo, Mozambique; E-Mail:;Alterra, Wageningen University, PO Box 47, 3708PB Wageningen, The Netherlands; E-Mails:;Flemish Institute for Technological Research (VITO), Remote Sensing Unit, Boeretang 200, 2400 Mol, Belgium

关键词: Mozambique;    rainfall;    satellite;    rain gauge;    pairwise comparison;    categorical validation;    drought;    flood;   

DOI  :  10.3390/rs70201758

来源: mdpi

【 摘 要 】

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.