Agro-meteorological risks to maize production in Tanzania: sensitivity of an adapted water requirements satisfaction index (WRSI) model to rainfall

The water requirements satisfaction index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tan- zania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R2 = 0.52- 0.61 for maize varieties of 120-160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R2 = 0.46-0.55 and 0.44-0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications

Evaluation of CHIRPS rainfall estimates over Iran

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) dataset, first released in 2014, is a high-resolution blended rainfall product with quasi-global coverage that has not been previously evaluated over Iran. Here, we assess the performance of the CHIRPS rainfall estimates against ground-based rainfall observations across Iran over the time period from 2005 to 2014 inclusive. Results show that CHIRPS’ performance is better over areas and during the months of predominantly convective precipitation with highest correlations in the southern coastal lowlands characterized by heavy rains from convective origin. Correlations are stronger with variables such as altitude, particularly alongside coastal regions in the north and south, where surface water produces more moisture in the atmosphere. Results of pairwise comparison statistics and categorical skill scores reveal the influence of altitude and precipitation amount, while categorical skill metrics vary more with changes in precipitation amount than with latitudinal or longitudinal changes

Exploiting satellite-based rainfall for weather index insurance: the challenges of spatial and temporal aggregation

Lack of access to insurance exacerbates the impact of climate variability on smallholder famers in Africa. Unlike traditional insurance, which compensates proven agricultural losses, weather index insurance (WII) pays out in the event that a weather index is breached. In principle, WII could be provided to farmers throughout Africa. There are two data-related hurdles to this. First, most farmers do not live close enough to a rain gauge with sufficiently long record of observations. Second, mismatches between weather indices and yield may expose farmers to uncompensated losses, and insurers to unfair payouts – a phenomenon known as basis risk. In essence, basis risk results from complexities in the progression from meteorological drought (rainfall deficit) to agricultural drought (low soil moisture). In this study, we use a land-surface model to describe the transition from meteorological to agricultural drought. We demonstrate that spatial and temporal aggregation of rainfall results in a clearer link with soil moisture, and hence a reduction in basis risk. We then use an advanced statistical method to show how optimal aggregation of satellite-based rainfall estimates can reduce basis risk, enabling remotely sensed data to be utilized robustly for WII

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

The utility of impact data in flood forecast verification for anticipatory actions: case studies from Uganda and Kenya

Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data-scarce regions are limited to only a few sparse locations at pre-existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river-gauge observations in Uganda and Kenya. The flood impact data contains semi-quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river-gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data-scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning

The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia

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

Identifying the barriers and opportunities in the provision and use of weather and climate information for flood risk preparedness: the case of Katakwi District, Uganda

The provision of weather and climate information (WCI) can help the most at-risk communities cope and adapt to the impacts of extreme events. While significant progress has been made in ensuring improved availability of WCI, there remain obstacles that hinder the accessibility and use of this information for adaptation planning. Attention has now focused on the ‘usability gap’ to ensure useful and usable WCI is used to inform practice. Less attention has however been directed on barriers to active production and use of WCI. In this study, we combine two frameworks to present a more coordinated institutional response that would be required to ensure a better flow of information from the information providers to the users and vice versa. Focusing on Uganda, a bottom-up approach is used to identify the barriers and opportunities in the production/provision and use of WCI for flood risk preparedness. First, a use-case is developed for Katakwi District where smallholder farming communities have recorded their coping practices and barriers to the use of WCI in practice. Second, online interviews with practitioners from disaster management institutions are used to identify the barriers to the production and provision of WCI to the local farming communities. Findings show that for providers, barriers such as accessibility and completeness of data hinder the production of useful WCI. In situations where useful information is available, technical language used in the format and timeliness in dissemination hinder usability by local farmers. Useful and usable WCI is not used in practice due to the social-economic capacity of the communities e.g., lack of access to improved seeds. Our study highlights the importance of a more coordinated response that would ensure a shift of focus from only the users to a more inclusive approach where even the data and information needs of the providers are considered to improve disaster risk management. While such considerations alone might not be enough to ensure effective use of WCI at the local level, we argue that understanding these data and information gaps across the provider-user landscapes can help in shaping disaster management activities at both the national and local level

A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa

Rainfall information is essential for many applications in developing countries, and yet, continually updated information at fine temporal and spatial scales is lacking. In Africa, rainfall monitoring is particularly important given the close relationship between climate and livelihoods. To address this information gap, this paper describes two versions (v2.0 and v3.0) of the TAMSAT daily rainfall dataset based on high-resolution thermal-infrared observations, available from 1983 to the present. The datasets are based on the disaggregation of 10-day (v2.0) and 5-day (v3.0) total TAMSAT rainfall estimates to a daily time-step using daily cold cloud duration. This approach provides temporally consistent historic and near-real time daily rainfall information for all of Africa. The estimates have been evaluated using ground-based observations from five countries with contrasting rainfall climates (Mozambique, Niger, Nigeria, Uganda, and Zambia) and compared to other satellite-based rainfall estimates. The results indicate that both versions of the TAMSAT daily estimates reliably detects rainy days, but have less skill in capturing rainfall amount—results that are comparable to the other datasets

Dynamic hydrological modeling in drylands with TRMM based rainfall

This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR). Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions