Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present

Tarnavsky, Elena; Grimes, D. I. F.; Maidment, Ross; Black, Emily; Allan, Richard P.; Stringer, Marc; Chadwick, Robin; Francois, Kayitakire

doi:10.1175/jamc-d-14-0016.1

Cited by 214 publications

(156 citation statements)

References 46 publications

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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%

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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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Section: Study Areamentioning

confidence: 99%

“…Figure 1. Biomass sample sites and mean annual precipitation 1983-2012 calculated from ten-daily TAMSAT data Tarnavsky et al, 2014) In this study we focus on the pastoral zone (approximate outline in Fig. 2 provided by the MoL), which is roughly represented by the livelihoods zone 3 "transhumant and nomad pastoralism" (FEWS NET, 2011) (Fig.…”

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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“…In many parts of the world the gauge station network is sparse, and hence monitoring cannot rely on ground-based measurements. An alternative is to use remotely sensed data, such as satellite-based rainfall estimates (i.e., rainfall that is estimated based on calibrated satellite imagery-for example, [3][4][5]). Although they both provide estimates of rainfall, satellite and ground-based methods are fundamentally different.…”

Section: Introductionmentioning

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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“…We replaced the precipitation in the WFDEI data with Tropical Applications of Meteorology using SATellite data and groundbased observations (TAMSAT) rainfall monitoring products (Maidment et al, 2014;Tarnavsky et al, 2014). TAMSAT produces 20 daily rainfall estimates over Africa at a 4 km resolution with data ranging back to 1983.…”

Section: Tamsat Rainfall Observationsmentioning

confidence: 99%

Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

Pinnington

Quaife

Black

2017

Preprint

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Abstract. Early warning of agricultural drought can enable decision makers to act to improve food security. Land-surface models are useful tools to inform such monitoring systems, but model errors are problematic. We show that satellite-derived estimates of shallow soil moisture can be used to calibrate a land-surface model at the regional scale in Ghana, using data assimilation techniques. The modified calibration significantly improves model estimation of soil moisture. Specifically, we find a 44% reduction in root-mean-squared error for a 5-year hindcast after assimilating a single year of soil moisture ob-5 servations to update model parameters. The use of an improved remotely-sensed rainfall dataset contributes to 10% of this reduction in error. Improved rainfall data has the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying down. The significant reduction in root-mean-squared error we find after assimilating a single year of observations bodes well for the production of improved soil moisture forecasts over sub-Saharan Africa where subsistence farming remains prevalent.

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Extension of the TAMSAT Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present

Cited by 214 publications

References 46 publications

Biomass estimation to support pasture management in Niger

Biomass estimation to support pasture management in Niger

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

Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model

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