Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation.

Hydrologic extremes often involve a complex interplay of several processes. For example, flood events can have a cascade of impacts, such as saturated soils and suppressed vegetation growth. Accurate representation of such interconnected processes while accounting for associated triggering factors and subsequent impacts of flood events is difficult to achieve with conceptual hydrological models alone. In this study, we use the 2019 flood in the Northern Mississippi and Missouri Basins, which caused a series of hydrologic disturbances, as an example of such a flood event. This event began with above-average precipitation combined with anomalously high snowmelt in spring 2019. This series of anomalies resulted in above normal soil moisture that prevented crops from being planted over much of the corn belt region. In the present study, we demonstrate that incorporating remote sensing information within a hydrologic modeling system adds substantial value in representing the processes that lead to the 2019 flood event and the resulting agricultural disturbances. This remote sensing data infusion improves the accuracy of soil moisture and snowmelt estimates by up to 16% and 24%, respectively, and it also improves the representation of vegetation anomalies relative to the reference crop fraction anomalies.

Realizing ecosystem-safe hydropower from dams.

For clean hydropower generation while sustaining ecosystems, minimizing harmful impacts and balancing multiple water needs is an integral component. One particularly harmful effect not managed explicitly by hydropower operations is thermal destabilization of downstream waters. To demonstrate that the thermal destabilization by hydropower dams can be managed while maximizing energy production, we modelled thermal change in downstream waters as a function of decision variables for hydropower operation (reservoir level, powered/spillway release, storage), forecast reservoir inflow and air temperature for a dam site with in situ thermal measurements. For data-limited regions, remote sensing-based temperature estimation algorithm was established using thermal infrared band of Landsat ETM+ over multiple dams. The model for water temperature change was used to impose additional constraints of tolerable downstream cooling or warming (1-6 °C of change) on multi-objective optimization to maximize hydropower. A reservoir release policy adaptive to thermally optimum levels for aquatic species was derived. The novel concept was implemented for Detroit dam in Oregon (USA). Resulting benefits to hydropower generation strongly correlated with allowable flexibility in temperature constraints. Wet years were able to satisfy stringent temperature constraints and produce substantial hydropower benefits, while dry years, in contrast, were challenging to adhere to the upstream thermal regime.