Cloud Computing-Based Delivery of Drought Information at Multiple Scales

Cloud Computing-Based Delivery of Drought Information at Multiple Scales Cloud Computing-Based Delivery of Drought Information at Multiple Scales Satellite remote sensing products can form the backbone for water resources decision-making, in particular for applications at the water-energy-food-climate nexus. Unfortunately, the large abundance, variety and complexity of remote sensing products make these relatively inaccessible to water managers with limited technical capabilities. For example, inferences can be made on drought and water stress in agricultural areas from the analysis of remotely-sensed land surface temperature (LST) and normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) at high resolutions in space and time. However, conducting this analysis requires certain expertise that is typically unavailable to irrigation district managers in charge of water delivery decisions. An approach to disseminate water resources products through an advanced delivery system would: (1) provide a platform for integrating data from ground and remotely-sensed platforms, and (2) yield analytical products that can help make better decisions in water resources applications. The proposed project aims at producing a prototype of a water resources application in the Planetary Skin Institute (PSI) decision support platform for drought prediction in support of food security and efficiency. PSI is a not-for-profit organization dedicated to harness the power of information technology to help decision-makers manage scarce resources more effectively in a changing world. While the system will have global capabilities, the prototype is focused on water stressed regions of Latin America (LATAM). These arid and semiarid areas presents many water resources challenges, including a large susceptibility to drought that impacts crop production and export. We will collect, process and analyze MODIS imagery for LST and NDVI to produce a water stress index (WSI) using the Triangle Method over two specific study sites in LATAM during the period 2000-2010. The sites are northeast Brazil and northwest Mexico where large agricultural sectors are possible due to irrigation infrastructure projects. Dynamic maps of water stress over the entire regions will be made available through a geospatial platform under a disturbance layer category at 1 km, 16-day composite resolutions. Change detection analysis will be applied to the water stress fields to derive areas with large negative changes in WSI. These will be overlaid on agricultural region maps to detect the presence of stress for different crop types throughout the country. In this way, specific events can be mapped and additional details provided to the user and production communities, including time series of specific drought events, periods of intense water stress and recovery from drought. We will partner with local scientists and producers to receive input and demonstrate the drought monitoring platform. This project builds upon the water resources expertise at Arizona State University (ASU) and the technological innovations at PSI. Our interests are: (ii) to integrate agricultural water stress and drought monitoring in the framework of the PSI platforms, (ii) provide a country-wide drought detection product for decision makers in Latin American countries, specifically Mexico and Brazil in this prototype, and (iii) conduct more detailed assessments in specific agricultural areas where water stress disturbances are identified over 2000-2010. To do so, MODIS products will be integrated into geospatial platforms that facilitate interpretation and provide actionable knowledge. This will allow us to add drought indicators into a robust, information delivery system that can be disseminated to decision makers. In this way, we will demonstrate the capabilities of the new PSI platform for water resources applications and lay the groundwork for the expansion of the capabilities and the global deployment, including the integration of soil moisture estimates from current and forthcoming missions.