Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Linking seasonal forecasts into risk - view to enhance food security contingency planning

RiskView is a tool developed by the World Food Programme (WFP) to translate weather data (real-time and historical) and other spatial information (e.g., crops, drought risk, population, etc.) into food security needs and response costs. It serves as a swift way of estimating costs in advance of food insecurity outlooks for financial planning, and for facilitating better resource allocations to disasters before on-the-ground needs assessments are produced. One of the cores of RiskView is the calculation of Water Requirement Satisfaction Index (WRSI) to estimate drought risks at the aggregate scale. WRSI is used to determine the risk of a region being food insecure in a coming season. To accomplish this, RiskView applies a forward- looking approach in which observed rainfall from the current season is used to calculate WRSI up to the current date, then samples rainfall from historical years to calculate WRSI for the remaining part of the cropping season. This forward-looking approach allows RiskView to include estimates of uncertainty in the calculations of WRSI from historical information. However, this capability could potentially be enhanced by using seasonal climate forecasts to weight historical years instead of assigning them equal weights. For this reason, WFP partnered with IRI to develop weights to be applied to WRSI values calculated for administrative regions in Africa. These would weight projections of end-of-season WRSI values based on the IRI Net Assessment seasonal precipitation forecasts. In RiskView, for a particular agricultural or pastoral season and administrative region, WFP calculates projected end-of-season WRSI values based upon historical RFE precipitation estimates from previous seasons (beginning with 1996) to reflect potential uncertainty in the projected WRSI estimate. The IRI weights based upon the latest IRI Net Assessment seasonal precipitation forecast would be applied to the historical end-of-season WRSI values from previous seasons to provide a forecast-based shift in the PDF to adjust the projected end-of-season WRSI values for the current agricultural or pastoral season of interest. This report outlines the procedure used to develop these weights and how it can be incorporated within Africa RiskView and its outputs

Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study

Drought is of global concern for society but it originates as a local problem. It has a significant impact on water quantity and quality and influences food, water, and energy security. The consequences of drought vary in space and time, from the local scale (e.g. county level) to regional scale (e.g. state or country level) to global scale. Within the regional scale, there are multiple socio-economic impacts (i.e., agriculture, drinking water supply, and stream health) occurring individually or in combination at local scales, either in clusters or scattered. Even though the application of aggregated drought information at the regional level has been useful in drought management, the latter can be further improved by evaluating the structure and evolution of a drought at the local scale. This study addresses a local-scale agricultural drought anatomy in Story County in Iowa, USA. This complex problem was evaluated using assimilated AMSR-E soil moisture and MODIS-LAI data into a crop model to generate surface and sub-surface drought indices to explore the anatomy of an agricultural drought. Quantification of moisture supply in the root zone remains a gray area in research community, this challenge can be partly overcome by incorporating assimilation of soil moisture and leaf area index into crop modeling framework for agricultural drought quantification, as it performs better in simulating crop yield. It was noted that the persistence of subsurface droughts is in general higher than surface droughts, which can potentially improve forecast accuracy. It was found that both surface and subsurface droughts have an impact on crop yields, albeit with different magnitudes, however, the total water available in the soil profile seemed to have a greater impact on the yield. Further, agricultural drought should not be treated equal for all crops, and it should be calculated based on the root zone depth rather than a fixed soil layer depth. We envisaged that the results of this study will enhance our understanding of agricultural droughts in different parts of the world