Advances in estimating actual evapotranspiration (ETa) with remote sensing (RS) have contributed to improving hydrological, agricultural, and climatological studies. In this study, we evaluated the applicability of Vegetation-Index (VI) -based ETa (ET-VI) for mapping and monitoring drought in arid agricultural systems in a region where a lack of ground data hampers ETa work. To map ETa (2000–2019), ET-VIs were translated and localized using Landsat-derived 3- and 2-band Enhanced Vegetation Indices (EVI and EVI2) over croplands in the Zayandehrud River Basin (ZRB) in Iran. Since EVI and EVI2 were optimized for the MODerate Imaging Spectroradiometer (MODIS), using these VIs with Landsat sensors required a cross-sensor transformation to allow for their use in the ET-VI algorithm. The before- and after- impact of applying these empirical translation methods on the ETa estimations was examined. We also compared the effect of cropping patterns’ interannual change on the annual ETa rate using the maximum Normalized Difference Vegetation Index (NDVI) time series. The performance of the different ET-VIs products was then evaluated. Our results show that ETa estimates agreed well with each other and are all suitable to monitor ETa in the ZRB. Compared to ETc values, ETa estimations from MODIS-based continuity corrected Landsat-EVI (EVI2) (EVIMccL and EVI2MccL) performed slightly better across croplands than those of Landsat-EVI (EVI2) without transformation. The analysis of harvested areas and ET-VIs anomalies revealed a decline in the extent of cultivated areas and a loss of corresponding water resources downstream. The findings show the importance of continuity correction across sensors when using empirical algorithms designed and optimized for specific sensors. Our comprehensive ETa estimation of agricultural water use at 30 m spatial resolution provides an inexpensive monitoring tool for cropping areas and their water consumption.

<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>

<p>Droughts are a significant threat to the agricultural sector in general, and rainfed farming in particular. Therefore, effective and timely responses to manage droughts and their impacts are required so that farming systems can limit the negative effects of droughts on food production. We developed a crop drought index (CDI) by integrating drought hazard and exposure and applied this index at the global scale to evaluate the influence of drought on the exposed rainfed areas for different crops. In an attempt to develop an operational, multisectoral global drought hazard forecasting system, we computed and analyzed CDI for historical periods. We further used bias-corrected seasonal climate forecasts to project the drought development in a 7-month period. The CDI was calculated by using the Global Crop Water Model (GCWM) at a global extent (5 arc-minute resolution) from 1980 to 2020. We compared the drought conditions in specific years to the CDI in the 30-year reference period 1986 to 2015. The CDI was computed for 25 specific crops or crop groups based on the relative deviation of the ratio between actual evapotranspiration (ETa) and potential evapotranspiration (ETp) in a specific year from the long-term mean ratio of ETa/ETp during the crop growing season. To test the skill of the seasonal drought forecasts, CDI computed with bias-corrected ensemble forecasts was compared to simulations with standard ERA5-reanalysis data for the year 2018 when severe drought conditions were observed across Europe and other regions. The skill of the CDI to detect drought impacts was tested for historical years by comparing the time series of the harvested area weighted CDI to detrended yield anomalies for crops and countries with predominantly rainfed production. The results of the comparison with historical yield anomalies showed that the CDI is a good indicator for negative yield anomalies, in particular in regions known to be affected regularly by droughts. The model simulations employing the bias-corrected ensemble forecasts reproduced well the reference drought condition in the year 2018 in countries such as Argentina, Australia, Italy, and Spain but showed little skill to reproduce the severe drought in Western Europe. Data availability constraints also had an impact on the accuracy of historical reconstructions and forecasts. For instance, the hazard and exposure analysis rely on static input data for crop shares and crop calendars, which can impact the results (i.e., as cropping patterns are dynamic and often can change over time). The findings suggest that bias-corrected seasonal ensemble forecasts have a significant potential to enhance seasonal drought forecasts, although the skill of the forecasts varies considerably for specific regions. Further research is needed to analyze this potential across different periods and geographies systematically to increase forecasting system efficiency and minimize processing time before this system can be run operationally. In our study, we hence want to demonstrate the current status of the CDI-based forecasting system and discuss the potential, limitations, and uncertainties of such CDI forecasts for agricultural applications.</p> <p> </p>

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