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

Abstract

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)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(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 OPEN ACCESSRemote Sens. 2015, 7 1759 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.

Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

“…TRMM data, in situ measurements and other atmospheric and climatology models were assimilated in [66] to create an ensemble precipitation product Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS, University of California Santa Barbara -Climate Hazards Group, Santa Barbara, California, U.S.) with a superior resolution at 0.05° × 0.05°. Precipitation from CHIRPS performed well statistically for flood and drought monitoring, particularly for meteorological complex regions [67]. The current study combines TRMM7.0 and CHIRPS2.0 rainfall products.…”

Comparison and validation of eight satellite rainfall products over the rugged topography of Tekeze-Atbara Basin at different spatial and temporal scales

“…Nevertheless, the performance varies among the rainfall products because of different data sources and retrieving algorithms (Derin & Yilmaz, 2014;Toté et al, 2015). In addition, the 4 performance also varies for the same data type across different regions and seasons (Gebremichael et al, 2014;Hu et al, 2014).…”

“…Variations of topography can significantly compromise the interpolation of observed rainfall (Thiemig et al, 2012). The rainfall stations are also sparsely and unevenly 14 distributed over the basin, which can be a source of systematic errors when interpolating aerial rainfall (Dembélé & Zwart, 2016;Toté et al, 2015).…”

Spatio-temporal vegetation dynamics and relationship with climate over East Africa

“…The CCD-derived estimates are then merged with World Meteorological Organization's Global Telecommunication System (GTS) gauged data using a 'smart interpolation' approach . Comparisons with other satellite precipitation estimates and observed rainguage data have shown that this data set provides useful long-term precipitation estimates for Africa (Ceccherini et al, 2015;Dembélé and Zwart, 2016;Toté et al, 2015). In addition, this dataset has been used in the East Africa region 15 previously to provide high resolution and combined gauge-satellite precipitation estimates (Ayana et al, 2016;Pricope et al, 2013).…”