10 research outputs found
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Recent observed and simulated changes in precipitation over Africa
Multiple observational data sets and atmosphere-only simulations from the Coupled Model Intercomparison Project Phase 5 are analyzed to characterize recent rainfall variability and trends over Africa focusing on 1983–2010. Data sets exhibiting spurious variability, linked in part to a reduction in rain gauge density, were identified. The remaining observations display coherent increases in annual Sahel rainfall (29 to 43 mm yr−1 per decade), decreases in March–May East African rainfall (−14 to −65 mm yr−1 per decade), and increases in annual Southern Africa rainfall (32 to 41 mm yr−1 per decade). However, Central Africa annual rainfall trends vary in sign (−10 to +39 mm yr−1 per decade). For Southern Africa, observed and sea surface temperature (SST)-forced model simulated rainfall variability are significantly correlated (r~0.5) and linked to SST patterns associated with recent strengthening of the Pacific Walker circulation
Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa
Deciding when to plant is critical for smallholders in Africa. If they plant too early, farmers risk seedling death if the rains are not established; if they plant too late, there will not be enough rain to sustain the crop through critical development periods. In this study, we present a new decision support tool (DST) that accounts for the trade-off in the risks of early and late planting through advisories based on both short- and long-range forecasts of crop water availability. Unlike most existing operational systems, which are based solely on rainfall, the DST presented here uses ensemble forecasts of soil moisture to estimate the optimal planting date at a local scale. Evaluations using >30,000 observations of planting date and yield in Kenya, Rwanda, Uganda, Zambia and Malawi demonstrate that that planting at the optimal time would increase yield by 7–10% overall, and up to 20% for late planting farmers. The DST has been piloted by One Acre Fund for the 2019–2020, 2020–2021, and 2021–2022 seasons and there is strong demand for the service to be extended further. We conclude from the evaluations and pilots that the planting date DST has the potential to strengthen farmer decision making and hence their resilience to climate variability and change
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A new drought model for disaster risk management in the Punjab, Sindh and Baluchistan provinces of Pakistan
Drought poses a continual threat to both lives and livelihoods in the Global South. Although the impact on food security from drought could be reduced through early release of funds, the humanitarian sector typically reacts to crises
rather than anticipates them. A significant challenge lies in devising a drought monitoring and forecasting system that can function across environmentally and economically diverse regions. This is particularly evident in Pakistan, which encompasses environments ranging from fertile riverbeds to arid deserts. This paper details the development, implementation, and operation of an anticipatory drought Disaster Risk Financing (DRF) programme for the provinces of Punjab, Sindh, and Baluchistan in Pakistan. Key to the DRF development are a new yield model for the primary crop in the target season (winter wheat), and a novel forecasting system for four seasonal drought indicators - namely winter
wheat yield, precipitation, normalised difference vegetation index (NDVI) and vegetation health index (VHI). Formal evaluations demonstrate that the forecasts are skillful up to 2 months in advance of the end of the season – enabling anticipatory release of funds. The work presented here is applicable beyond Pakistan. Indeed, the model and the methodologies are sufficiently broad and adaptable to be utilised in arid and semi-arid regions across the Global South
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The 30-year TAMSAT African rainfall climatology and time-series (TARCAT) dataset
African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time
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Application of TAMSAT-ALERT soil moisture forecasts for planting date decision support in Africa
Deciding when to plant is critical for smallholders in Africa. If they plant too early, farmers risk seedling death if the rains are not established; if they plant too late, there will not be enough rain to sustain the crop through critical development periods. In this study, we present a new decision support tool (DST) that accounts for the trade-off in the risks of early and late planting through advisories based on both short- and long-range forecasts of crop water availability. Unlike most existing operational systems, which are based solely on rainfall, the DST presented here uses ensemble forecasts of soil moisture to estimate the optimal planting date at a local scale. Evaluations using >30,000 observations of planting date and yield in Kenya, Rwanda, Uganda, Zambia and Malawi demonstrate that that planting at the optimal time would increase yield by 7-10% overall, and up to 20% for late planting farmers. The DST has been piloted by One Acre Fund for the 2019-2020, 2020-2021 and 2021-2022 seasons and there is strong demand for the service to be extended further. We conclude from the evaluations and pilots that the planting date DST has the potential to strengthen farmer decision making and hence their resilience to climate variability and change