In the Western United States (US), the current mountain pine beetle (MPB; Dendroctonus ponderosae) epidemic has affected more than five million hectares since its start in 1996, including headwater catchments that supply water to much of the Western US. There is widespread concern that the hydrologic consequences of the extensive pine tree die-off will impact water supply across the Western US. While forest disturbance studies have shown that streamflow increases in response to tree harvest, the actual effect of bark beetle infestations on water supply remains widely debated. The current study evaluates watershed-level response following bark beetle outbreak for 33 watersheds in seven western states. Streamflow records were investigated to assess whether the timing and amount of stream discharge during bark beetle outbreak and early recovery periods were significantly different to preoutbreak conditions. Results show no significant modification in peak flows or average daily streamflow following bark beetle infestation, and that climate variability may be a stronger driver of streamflow patterns and snowmelt timing than chronic forest disturbance.
This study develops a new, highly efficient method to produce accurate, high‐resolution surface water maps. The “active‐passive surface water classification” method leverages cloud‐based computing resources and machine learning techniques to merge Sentinel 1 synthetic aperture radar and Landsat observations and generate monthly 10‐m‐resolution water body maps. The skill of the active‐passive surface water classification method is demonstrated by mapping surface water change over the Awash River basin in Ethiopia during the 2015 East African regional drought and 2016 localized flood events. Errors of omission (water incorrectly classified as nonwater) and commission (nonwater incorrectly classified as water) in the case study area are 7.16% and 1.91%, respectively. The case study demonstrates the method's ability to generate accurate, high‐resolution water body maps depicting surface water dynamics in data‐sparse regions. The developed technique will facilitate better monitoring and understanding of the impact of environmental change and climate extremes on global freshwater ecosystems.
Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (>5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.
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