The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

Maidment, Ross; Grimes, D. I. F.; Allan, Richard P.; Tarnavsky, Elena; Stringer, Marc; Hewison, Tim J.; Roebeling, Rob; Black, Emily

doi:10.1002/2014jd021927

Cited by 226 publications

(195 citation statements)

References 106 publications

(185 reference statements)

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“…There are a number of African rainfall datasets available at high resolution and for a sufficient time period for use in WII [4,34]. This study uses one such dataset, TARCAT, which is the historical product, based on the TAMSAT method.…”

Section: Tamsat and Tamsat Rainfall Ensemblesmentioning

confidence: 99%

The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

et al. 2016

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Remotely sensed rainfall is increasingly being used to manage climate-related risk in gauge sparse regions. Applications based on such data must make maximal use of the skill of the methodology in order to avoid doing harm by providing misleading information. This is especially challenging in regions, such as Africa, which lack gauge data for validation. In this study, we show how calibrated ensembles of equally likely rainfall can be used to infer uncertainty in remotely sensed rainfall estimates, and subsequently in assessment of drought. We illustrate the methodology through a case study of weather index insurance (WII) in Zambia. Unlike traditional insurance, which compensates proven agricultural losses, WII pays out in the event that a weather index is breached. As remotely sensed rainfall is used to extend WII schemes to large numbers of farmers, it is crucial to ensure that the indices being insured are skillful representations of local environmental conditions. In our study we drive a land surface model with rainfall ensembles, in order to demonstrate how aggregation of rainfall estimates in space and time results in a clearer link with soil moisture, and hence a truer representation of agricultural drought. Although our study focuses on agricultural insurance, the methodological principles for application design are widely applicable in Africa and elsewhere.

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Section: Tamsat and Tamsat Rainfall Ensemblesmentioning

confidence: 99%

The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

et al. 2016

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“…The TAMSAT group provides a 30-year rainfall climatology (from 1983 to 2012) covering Africa with a spatial resolution of 0.0375° [31]. Ten-daily and monthly rainfall estimates, along with the corresponding anomalies, are derived from archived Meteosat thermal infrared imagery, calibrated against rain gauge records collected from numerous African agencies [32].…”

Section: Tamsatmentioning

confidence: 99%

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

et al. 2015

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Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. Finally, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results OPEN ACCESSRemote Sens. 2015, 7 6455 also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.

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“…Mean annual precipitation sums (Fig. 1) mainly follow a north-south gradient with mean annual precipitation <100 mm in the north and 600-700 mm in the outermost south (based on ten-daily cumulative rainfall estimates from TAMSAT data from the TARCAT v2.0 dataset for the period 1983-2012; Maidment et al, 2014;Tarnavsky et al, 2014). Most of the precipitation occurs during the rainy season (Jun-Sep) and is related to the West African monsoon (Nicholson, 2013).…”

Section: Study Areamentioning

confidence: 99%

Biomass estimation to support pasture management in Niger

Schucknecht

Meroni

Francois

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998-2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R 2 = 0.55, R 2 cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.

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The 30 year TAMSAT African Rainfall Climatology And Time series (TARCAT) data set

Cited by 226 publications

References 106 publications

The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia

High-Resolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model

Biomass estimation to support pasture management in Niger

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