Publications

This study proposes a method to detect global irrigated areas by combining satellite and reanalysis datasets. The proposed method assumes that irrigation is an unmodeled land surface process, while satellite observations can effectively detect irrigation signals in near real-time. The study uses three irrigation-dependent variables, soil moisture (SM), land surface temperature (LST), and surface albedo (AL), to derive the spatial extents of irrigation by calculating the difference between the remotely sensed and reanalysis datasets. The proposed irrigation map is compared to commonly used global irrigation maps, and the study finds that combining the individual detection maps shows reasonable agreement with the reference irrigated maps, overlapping with approximately 70% of the irrigated areas. The study suggests that the proposed method, alone or in combination with existing irrigation maps, can benefit studies regarding water and energy balance closure in near-real time for large-scale land surface models, minimizing uncertainties in model parameterization. Overall, the study highlights the importance of understanding the reliable extent and distribution of global irrigated areas, given the significant role of irrigation in meeting the world's food demand and modifying water and energy cycles.

This study explores the amount of precipitation stored in the topsoil layer (0-10 cm) across different vegetation and aridity indices on a global scale. The study uses data from four satellites and two reanalysis data sets to investigate spatial trends of stored precipitation. The study finds that drier and less vegetated soil retains more precipitation in the top layer of the soil, while wet and forested areas have a lower retention rate due to large runoff fluxes and plants intercepting water. Specifically, the topsoil retains 37% ± 11% of precipitated water three days after a rainfall event where the aridity index was greater than 5, while wet and forested areas retain 21% ± 2%. The study also conducts a sensitivity analysis of different sampling frequency values using modeled data sets to calculate the stored precipitation fraction metric. Overall, the study highlights the importance of understanding the spatial trends of stored precipitation in the topsoil layer for better land-atmosphere interactions.

Accurate climate forecasting affects our daily lives. Large-scale farmers depend on weather forecasts to decide when to plant their crops. Bad timing can impact the whole years' harvest and thus the farmers' livelihoods. Even more importantly, people who live in floodplains and hurricane zones trust their lives to accurate weather forecasts. For these reasons and more, hydrologists need up-to-date knowledge of Earth's climate systems. And one of the most important sources of data may surprise you. The amount of moisture in just the first 8 mm of topsoil affects all of Earth's climate systems. Currently, National Aeronautics and Space Administration keeps track of soil moisture levels with a satellite called Soil Moisture Active Passive. However, it only provides soil moisture data every 2–3 days. We believe that we can do better, and we believe that we can do it with preexisting satellite systems. In 2017, National Aeronautics and Space Administration (NASA) launched eight microsatellites, called Cyclone Global Navigation Satellite System (CyGNSS), to predict cyclone paths. We have found that while the CyGNSS satellites are predicting cyclone paths, they can simultaneously measure changes in soil moisture around 5 times per day. Augmenting the Soil Moisture Active Passive data with CyGNSS would give us detailed prediction of weather changes in near-real time, protecting livelihoods and lives.

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