OUTLAST - Development of an operational, multi-sectoral global drought hazard forecasting system

Abstract

<p>Climate change and drought have imposed tremendous pressure on water resources in most parts of the world. Therefore, addressing drought impacts on water resources and socioeconomic conditions has gained attention to develop mitigation and adaptation strategies. Many innovative and cutting-edge modeling systems set up by academic institutions are exclusively available to academics and are not accessible or used by practitioners or policymakers. These constraints are in particular relevant in data poor regions where decision making and drought management are hampered by a lack of drought information. Therefore, within OUTLAST project (<strong>D</strong>evelopment of an <strong>O</strong>perational, m<strong>U</strong>l<strong>T</strong>i-sectoral globa<strong>L</strong> drought h<strong>A</strong>zard forecasting Sys<strong>T</strong>em), we aim to develop and implement the first worldwide, multi-sectoral, and operational drought forecasting system for quantifying drought hazard in water supply, riverine and non-agricultural land ecosystems, rainfed and irrigated agriculture. The forecasts provided by OUTLAST can also support better drought management and therefore contribute to the achievement of several Sustainable Development Goals (SDGs) of the United Nations, particularly SDG1(no poverty), SDG2 (zero hunger), SDG6 (clean water and sanitation), SDG13 (climate action) and SDG15 (life on land). In cooperation with pilot users in the project regions of Lake Victoria (Burundi, Kenya, Rwanda, Tanzania, and Uganda) and West and Central Asia (e.g. Afghanistan, Armenia, Azerbaijan, Iran, Iraq, Lebanon, Oman, Syria, Tajikistan, Turkey, Uzbekistan), the value of global-scale forecasts released for the subsequent six months for data-poor and transboundary basins will be tested. Through co-design, relevant drought indicators will be defined and the web portal and pilot applications of these global forecasts for drought management and water governance will be developed. We also systematically investigate the predictive ability of global-scale drought forecasts based on bias-corrected seasonal ensemble weather forecasts, as well as the factors influencing the predictive skill in terms of (i) the type of drought (soil moisture and hydrological droughts) and the affected sector, (ii) the length of the forecast period, (iii) seasonal and regional differences in predictive quality. Two global-scale models (WaterGAP and GCWM) will be further developed to provide operational and monthly drought forecasts globally at 0.5-degree spatial resolution and for a six-month forecast period. The applicability of the data and models for drought forecasting will be rigorously examined for different locations, sectors, and periods using historical reanalysis data and historical ensemble forecasts. This will entail using historical meteorological "forecasts" to generate drought forecasts for historical drought events reported for these regions and validating them regionally. Finally, an automated operational modeling system will be created to download and process the necessary meteorological input data, calculate the drought indicators, visualize them appropriately, and transfer them to the Global Hydrological Status and Outlook System (HydroSOS) at World Meteorological Organization (WMO). Through a flexible implementation (cloud-based), we will enable a potential transfer of the forecast system to different users. The use of the HydroSOS-portal of WMO to visualize project results and as an outlet of the drought forecast products will make optimal use of synergies and ensure high visibility and impact of the research performed in OUTLAST.</p>