Sediment Plumes and Blooms: Using Earth Observations and Modeling to Forecast Post-Fire impacts to Reservoir Water Quality and Quantity

Our primary goal is to improve forecasting and long-term monitoring tools for watershed managers dealing with post-fire hydrological impacts on critical watersheds and reservoirs. Within the continental US, 67 of the 100 largest cities obtain their drinking water solely from surface sources. Thousands of smaller communities with limited budgets also rely on surface water. The majority of these headwater catchments are forested, some are in rangelands and grasslands ecosystems, and are subject to wildfires with droughts and past fire management policies causing an abundance of fuels. When wildfires occur, there is a high likelihood of impaired water quality (excess nitrogen, carbon and phosphorous), high sediment loads, increased stream temperatures, and suspended ash particles that are transported to water intakes and reservoirs. Dramatic increases in post-fire runoff, erosion and sedimentation is well documented. The loss of vegetation and forest litter results in decreased evapotranspiration and surface cover. Post-fire peak flows can be as high as 300 m3 s-1 km-2 resulting in catastrophic floods. Water utilities in watersheds recently impacted by wildfire are spending millions of dollars treating water supplies and dredging post-fire sediments that reduce vital water storage capacity. The cost of replacing the water treatment plant impacted by the Hermits Peak-Calf Canyon Fire in New Mexico is projected to be 145 million dollars. In this step 1 proposal we will leverage MTRI's hydrology, fire, and water sensing expertise to forecast and monitor threats from wildfire to water quality and quantity in the Western US. We propose three primary objectives. (1) We will identify reservoirs and watersheds at potential risk by merging fire detections and burn scars within reservoir watersheds. This analysis will be carried out for historical and current fires. Hydrological modeling of fire effects typically occurs shortly after the fire, however there is a growing interest and need for modeling watershed recovery as well. Field studies have shown the amount of surface cover after a wildfire is a dominant control on post-fire erosion rates under a given climatic regime. We will leverage both process-based models such as the Water Erosion Prediction Project in conjunction with the NASA developed Rapid Response Erosion Database along with empirical curve number models used by Burned Area Emergency Response teams on larger watersheds. Last summer our team collaborated with CALFIRE to create an ESRI toolbox capable of rapidly creating inputs to over a dozen empirical post-fire hydrology models frequently used by Watershed Emergency Response Teams (WERT) teams in California. We are proposing to incorporate these models into an online watershed database along with easy-to-follow instructions for verifying the assumptions and identifying the best models. (2) We will leverage NASA-developed remote sensing tools for mapping sediment plumes in conjunction with monitoring vegetation recovery in order to advance capabilities for monitoring and forecasting hydrological recovery. After a fire the increased influx of sediments, nutrients and metals threaten both water quality and quantity. Low water inflows due to drought, elevated temperatures and increased nutrients elevate risks for Harmful Algal Blooms (HABs). (3) Finally, we will adapt existing algorithms for detecting algal blooms developed for the Great Lakes to these smaller watersheds so that reservoir managers have additional monitoring tools for protecting their communities from sedimentation, water quality issues and HABs.

Enhances the capabilities for existing science instruments for monitoring pre-fire, active-fire, and post-fire situations, reduces the power and mass of these instruments, and enables unprecedented observations in support of wildfire science through distributed observing systems and the information technologies needed for their support.

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