Project Name: Implementation of Drought Early-Warning System over IRAN (DESIR)

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1 Project Name: Implementation of Drought Early-Warning System over IRAN (DESIR) IRIMO's Committee of GFCS, National Climate Center, Mashad November

2 Contents Summary 3 List of abbreviations 5 Introduction 7 Methods 7 Outcomes 9 Governances 10 Activities and timeline 10 2

3 Summary Iran s precipitation is approximately one third of global average and distribution of the monthly rainfall has been changed in recent years. Water scarcity has many environmental and socio-economical impacts over Iran. Unlike to the floods that have limited coverage areas, water scarcity impacts cover vast regions. By increasing global mean temperature, drought and population, water and its consumption has become important. This may even become more significant in those countries where the volume of rainfall is limited. Occurrence of drought is one of the main reasons of the water crisis. Implementation of a drought early warning system is the most important priority for I. R. of Iran Meteorological Organization (IRIMO). In parallel to the objectives of Global Framework of Climate Services (GFCS), the proposed project of National Center for Climatology (Mashad Climate Center) on the prediction of seasonal precipitation and temperature and drought early warning system over Iran has been approved by IRIMO s National Committee of GFCS in November The project Drought Early-warning System over Iran (DESIR) aims to building capacity for improved national climate services, drought risk reduction in agriculture and water resource sectors and produce knowledge-based information for policy makers to develop adaptation and mitigation strategies. The project is the first pilot project of National GFCS Committee. It has hosted by National Center for Climatology (Climate Research Institute) and funded by I. R. of Iran Meteorological Organization (IRIMO), Atmospheric Science and Meteorological Research Center (ASMERC), National Drought Warning and Monitoring Center (NDWMC) and Khorasan Razavi Disaster Management Organization (KHDMO). Main components of the project are: Producing high resolution gridded precipitation data over the country Validation of monthly to seasonal reforecast gridded data output from different global models. Investigation of the statistical relationship between observed gridded precipitation and other related gridded reforecast data such as precipitation, temperature, humidity and other related dynamical output. 3

4 Implementation of monthly historical relationships into model forecast to produce weighted based precipitation and temperature forecast over the region. Issuing drought early warning maps over the country by computing drought standard indices mainly Standard Precipitation Index (SPI). Broadcast the results to stakeholders by holding NCOF, providing newsletter and announcing by media. It is expected that after handover of the project, it can be implemented over the west Asian countries and also other countries located in ECO region under ECO-RCRM program. 4

5 List of abbreviations WMO World Meteorological Organization GFCS Global Framework of Climate Services IRIMO Islamic Republic of Iran Meteorological Organization DESIR Drought Early-warning System over Iran CRI Climate Research Institute (Mashad, Iran) MCC Mashad Climate Center/CRI ECO Economic Cooperation Organization(Tehran, Iran) ASMERC NDWMC KHDMO ECO-RCRM SPI Atmospheric Science and Meteorological Research Center (Tehran, Iran) National Drought Warning and Monitoring Center (Tehran, Tehran) Khorasan-Razavi Disaster Management Organization (Mashad, Iran) ECO Regional Center for Risk Management of natural Disasters (Mashad, Iran) Standard Precipitation Index APHRODITE Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources 5

6 JMA Japan Meteorological Administration MRI-CGCM3 GPV Meteorological Research Institute-Coupled General Circulation Model version 3 Grid Point Value NOAA National Oceanic and Atmospheric Administration/USA CFS v2 Climate Forecast System Version 2 WMO-LCLRF WMO Lead Center For Long Range Forecast (Seoul) KMA Korea Meteorological Organization NIMR National Institute for Meteorological Research/KMA NMHS National Meteorological and Hydrological Services NCOF National Climate Outlook Forum DECOF Drought Early-warning Climate Outlook Forum 6

7 1. Introduction Iran has a variable climate and its mean annual precipitation is 250 mm. The major exceptions is higher mountainous area of Zagros, Alborz and the Caspian coastal plain, where mean precipitation is more than 500 mm. In the western part of the Caspian Sea, rainfall exceeds 1500mm annually and is distributed relatively evenly throughout the year, which is in contrast with some basins in the Central Plateau that receive less than 100mm of precipitation annually. The climate of Iran has experienced gradual warming of approximately 0.5 o C during In recent years, Iran has experienced many extreme weather and climate events such as heavy flooded weather systems and droughts. Frequent occurrences of drought in Iran and its harmful impacts on agriculture and water resources management, significantly increased the need to set up a drought early warning and prediction system. Also, the west and central parts of Iran have experienced sand and dust storms in recent years due to extreme drought conditions in the Middle East. In this regard, GFCS Committee of IRIMO which is located in Mashad Climate Center has approved the project Drought Early-warning System over IRAN (DESIR) aims to issue drought early warning at least three months in advance over Iran. 2. Method There are many researches on precipitation and other meteorological forecast in seasonal scale, but the number of researches involving drought prediction using dynamical model outputs is limited. As drought situation in the future months can help in agriculture water management and water resources over Iran, therefore, most of the researches use the statistical approaches including regression, time series, fuzzy, fuzzy-logic, neural network and genetic algorithm to predict the drought conditions of Iran for 3 months to one year in advance. Two types of data are used in this project. The first is observation data including precipitation of weather stations of Iran in the period of (28 years) and APHRODITE gridded data from JMA. The second type of data is seasonal forecast GPVs. 7

8 Table 1. Name and definition of the MRI-CGCM3 GPVs Two seasonal forecast GPVs dataset from MRI-CGCM3 of JMA and CFS v2 of NOAA are used in the first phase of the project. Table 1 shows the name and description of GPVs dataset of MRI-CGCM model. For improving the accuracy of seasonal precipitation forecasts and drought early warnings, the output of seasonal forecast models has been used to station scale post processing of the model precipitation over synoptic weather stations of Iran. The outputs of MRI-CGCM3 are available to registered National Meteorological and Hydrological Services (NMHSs) on the website of the Tokyo Climate Center of the Japan Meteorological Agency (JMA/TCC). Model variables and indices which are used in the postprocessing are WIO rain, Z2030, Z5060, WIO SST, T850, T2m, SST, NINOWEST 8

9 SST, WNP RAIN, NINO3 SST, Z3040, H500, MSLP, SAMOI RAIN, MC RAIN, DL RAIN, THMD, THTR and total precipitation (Table 1). In this project monthly statistical relationship between GPVs of seasonal forecast models with observed precipitation is calculated using 70% of reforecast data. Then the above mentioned relationships (calibrated relationship) are used to forecast seasonal precipitation for 30% remaining reforecast data. The skill of multivariate post-processing is evaluated using Mean Square Skill Score, Mean Bias Error, relative error and categorical skill score over the training and evaluation periods. Post-processed precipitation outputs are entered to the SPI software to calculate SPI drought index over weather stations of Iran. Post processed precipitation and SPI drought indices are compared with observed precipitations and drought indices for evaluation of the capability of statistical post processing method used on the model outputs. 3. Outcomes According to the goals of the GFCS, development of a method for drought early warning system is of highly important for agriculture and water resource management. Post processing of the coarse gridded output of seasonal forecast model has a substantial need to improve the skill of forecast in local scale. This method can be improved by utilizing GPVs from different dynamical seasonal forecast models. Some outcomes of the project are as below: Strengthen capacity of IRIMO s experts on using and interpretation of seasonal forecast outputs. Investigate the skill of different seasonal forecast models. Expand climate services to less developed provinces to prevent drought losses and damages. Improve awareness of climate risks in food security and disaster risk reduction sectors. Drought risk reduction using improved seasonal forecast and drought early warning over the country. Submit seasonal drought outlook to policy makers to make decision in the field of agriculture and water resources. 9

10 Improve understanding of the effectiveness of the GFCS in climate risk management and adaptation. Improve the awareness of stakeholders through holding National Drought Early-warning Climate Outlook Forum (DECOF). Skill of DESIR in modeling drought outlook using SPI index by post processed precipitation dataset will be evaluated. 4. Governance The project is governed by GFCS Committee of IRIMO and hosted by Climate Research Institute (Mashad Climate Center). The project is also supported by IRIMO, ASMERC, NDWMC and KHDMO. 5. Activities and timelines Activities of the projects consist of 7 steps. Activities and timeline of the project are shown in the table: Item Activity Time line 1 Review activities, projects and any other research on drought early warning 2 Providing gridded observed precipitation over Iran using available reanalysis dataset 3 Providing monthly relationship between model outputs and observed gridded precipitation 4 Validation of the monthly precipitation relationship over each grid point 5 Validation of the predicted monthly drought index (SPI) over weather stations 6 Implementation of multiple weighting system on precipitation and SPI prediction 7 Establishment of the National Drought Early-warning Climate Outlook Forum(DECOF) January, 2014 April, 2014 August, 2014 October, 2014 December, 2014 February, 2015 March, 2015 We would like to extend our sincere thanks to JMA/Tokyo Climate Center and National Institute for Meteorological Research (NIMR/KMA) for their close 10

11 cooperation and providing seasonal forecast GPVs. The project aims to building capacity for improved national climate services, drought risk reduction in agriculture and water resource sectors and produce knowledge-based information for policy makers to develop adaptation and mitigation strategies. 11

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