Oum Er Rbia, Morocco
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IMPROVED DROUGHT EARLY WARNING AND FORECASTING TO STRENGTHEN PREPAREDNESS AND ADAPTATION TO DROUGHTS IN AFRICA DEWFORA A 7th Framework Programme Collaborative Research Project Implementation of improved methodologies in comparative case studies Final report for the Oum er-Rbia case study WP6-D6.2 July 2013 Coordinator: Deltares, The Netherlands Project website: www.dewfora.net FP7 Call ENV-2010-1.3.3.1 Contract no. 265454 DEWFORA Project Report WP6-D6.2 Page intentionally left blank DEWFORA Project Report WP6-D6.2 DOCUMENT INFORMATION Title Final report for the Oum er-Rbia case study Lead Author IAV Contributors JRC, CSIR, ECMWF CO: Confidential, only for members of the consortium (including the Commission Distribution Services) Reference WP6-D6.2 DOCUMENT HISTORY Date Revision Prepared by Organisation Approved by Notes Yasmina 17/06/2013 Imani; Ouiam IAV Lahlou 18/06/2013 Ana Iglesias UPM Francesca Di 19/06/2013 ECMWF Giuseppe Paulo Barbosa; 10/07/2013 JRC Gustavo Naumann Yasmina 15/07/2013 Imani; Ouiam IAV Lahlou Paulo Barbosa; 16/07/2013 JRC Gustavo Naumann ACKNOWLEDGEMENT The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement N°265454 i DEWFORA Project Report <WP6-D6.2> Page intentionally left blank ii DEWFORA Project Report <WP6-D6.2> SUMMARY The objective of the Oum er-Rbia case study is to test different approaches that can improve drought mitigation and preparedness strategies in the region. To reach this goal, three different analyses were performed: The implementation and skill assessment of medium range to seasonal forecasts, a test on statistical drought agricultural forecasts, and a drought vulnerability assessment at the rural commune level. The first objective (section 2) was to assess the skill of medium range weather forecasts. The analysis focused on the growing season in rainfed areas planted with cereal crops. Those regions were selected given their predominance and socio-economic importance around the basin and throughout Morocco. Both, daily and medium-range forecasts appears not to be able to predict rainfall amount, but seemed to show better skills in forecasting dry periods. Indeed, some significant potential exist both for early warning and mitigation operations; this is particularly true for herders which cannot afford major food purchases to save their cattle, but also for crop imports, subsidies, and some agricultural practices. An efficient drought early warning system would allow farmers to evaluate more accurately their production options and insurance companies anticipate payments to farmers. In section 3, a statistical-dynamical approach to estimate durum wheat yield over the Oum er-Rbia basin was outlined. Different low level circulation fields were used as predictors using a principal component regression to test the predictability of seasonal crop yields in the region. Both deterministic and stochastic approaches hold a good potential for yield predictions over the mountains and coastal areas. Particularly, with a two months lead-time, high and low yield are well discriminated for both areas. On the other hand a very low predictability was identified over the basin plains. The drought vulnerability study in section 4 aimed to assess and map agricultural drought vulnerability at the rural commune level in the Oum er-Rbia basin in order to assist and guide drought management authorities in the development of efficient mitigation actions, tailored to the needs and specificities of each rural community. This has been achieved through the implementation of the improved methodology developed in DEWFORA Work Package 3. It consists in computing a Drought Vulnerability Index (DVI) that integrates several variables into four dimensions: Renewable Natural Capital; Economic capacity; Human and Civic Resources; Infrastructure and Technology. The drought vulnerability map that was derived from the computation of the DVI shows that except for the provinces of Azilal, Khenifra, Settat and El Kelaa, most of the other parts of the Oum er Rbia basin are highly vulnerable to drought. The provinces of Azilal and Khenifra are mountainous areas that present the most favorable annual rainfall. That contributes to explain their low DVI. Although being more arid than coastal areas, the inner plains of Settat and El kelaa have lower DVI’s. This should be considered carefully and should be object of further analysis. The analysis of the four dimensions (Renewable Capital, Economic Capacity, Human and Civic Resources, Infrastructure and Technology) of the drought vulnerability index showed iii DEWFORA Project Report <WP6-D6.2> that at the river basin level, the following sub-indicators represent the major drivers of vulnerability that may therefore be targeted in prioritizing mitigation and adaptation actions: • The mean annual rainfall and the percentage of irrigated lands as well as the belonging to a big irrigated perimeter, • The Cereal / Fruit trees and market crops ratio, the land status and the farms size, • The adult literacy rate and the access to improved drinking water. iv DEWFORA Project Report <WP6-D6.2> Page intentionally left blank v DEWFORA Project Report <WP6-D6.2> TABLE OF CONTENTS 1. INTRODUCTION: OUM ER-RBIA BASIN CASE STUDY .................................................... 13 1.1 DESCRIPTION OF THE CASE STUDY ................................................................. 13 1.2 OBJECTIVES OF THE CASE STUDY ................................................................... 15 2. MEDIUM RANGE FORECASTS OF PRECIPITATION ........................................................ 17 2.1 FOLLOWED APPROACH ...................................................................................... 17 2.2 DATA COLLECTION .............................................................................................. 17 2.2.1 Daily rainfall (observed and forecasted) ................................................................. 17 2.2.2 Seven days accumulated precipitation data (observed and forecasted) ................ 18 2.2.3 Forecasts: ECMWF data: ....................................................................................... 18 2.2.4 Observed data ........................................................................................................ 19 2.2.5 Information on irrigation scheduling and farmers behaviour during the mid-February to mid-April 2012 period. .................................................................................. 19 2.3 METHODS AND TOOLS ........................................................................................ 19 2.3.1 Comparison of daily observed and forecasted rainfall............................................ 19 2.3.2 Comparison of the 7 days accumulated observed and forecasted rainfall ............. 19 2.4 RESULTS AND OUTCOMES ................................................................................. 20 2.4.1 Medium range forecasts of daily rainfall ................................................................. 20 2.4.2 Medium range forecasts of 7 days accumulated rainfall ........................................ 24 2.5 ASSESSMENT OF THE POTENTIAL USE OF MEDIUM RANGE FORECASTS OF PRECIPITATION FOR DROUGHT MITIGATION ........................................ 26 2.6 MEDIUM-RANGE FORECAST AND EARLY WARNING....................................... 30 2.6.1 Seed supply ............................................................................................................ 30 2.6.2 Measures for livestock ............................................................................................ 30 2.6.3 Measures for water supply ...................................................................................... 31 2.6.4 Drought insurance................................................................................................... 31 2.7 MEDIUM-RANGE FORECAST AND SUPPLEMENTARY IRRIGATION ............... 32 2.8 MEDIUM-RANGE FORECAST AND OTHER CROPS TECHNIQUES .................. 34 2.9 CONCLUSION ........................................................................................................ 34 3. SEASONAL FORECASTS FOR CEREAL YIELDS PREDICTION ...................................... 36 4. DROUGHT VULNERABILITY MAPPING AT THE RURAL COMMUNE LEVEL ................. 38 4.1 FOLLOWED APPROACH ...................................................................................... 38 vi DEWFORA Project Report <WP6-D6.2> 4.1.1 Evaluating vulnerability ........................................................................................... 38 4.1.2 Drought Vulnerability Index (DVI) ........................................................................... 39 4.2 SELECTION AND RELEVANCE OF INDICATORS ............................................... 39 4.2.1 Indicators of Renewable Natural Capital ................................................................ 40 4.2.2 Indicators of Economic capacity ............................................................................. 41 4.2.3 Indicators of human and civic resources ................................................................ 43 4.2.4 Indicators of infrastructure and technology ............................................................. 44 4.3 DATA COLLECTION .............................................................................................. 44 4.4 DROUGHT VULNERABILITY INDICATORS ......................................................... 47 4.4.1 Missing