Research Projects
Landslide susceptibility hindcast and contributions from different hydrological processes
During January 2023, a parade of atmospheric rivers brought record-breaking rain and snowfall to California, causing widespread flooding and over 700 landslides. Flooding and landslide events were triggered by a mixture of hydro-geomorphic processes including infiltration-excess runoff in previously burned terrains, saturation-excess runoff, and snowmelt events.
To enhance our capability to forecast these types of hydro-geomorphic hazards at regional scales, here we employ the National Oceanic and Atmospheric Administration’s National Water Model, WRF-Hydro, to resolve relevant hydrological processes and hindcast California’s susceptibility to flooding and landslide hazards during the January 2023 event. We validate WRF-Hydro baseline simulations using both streamflow and in-situ soil moisture observations. At the 50 U.S. Geological Survey streamflow sites we produce a mean Kling-Gupta Efficiency (KGE) of 0.61 while the mean KGE at the 60 soil moisture sites is 0.59, indicating good model performance.
A cluster analysis of the WRF-Hydro modeled hydrologic conditions revealed mixed physical processes underlying California’s ~700 landslides in Jan 2023.
Cluster 1 is associated with intense precipitation and infiltration excess water.
Cluster 2 is associated with elevated peak discharge by wildfire burn scars.
Cluster 3 is associated with moderate rainfall on already saturated soil.
Cluster 4 is associated with soil ice freeze-thaw cycles and snowmelting.
In summary, in addition to heavy rainfall, processes like rain on snow, rain on wildfire burn scars, and rain on saturated soil have contributed to excessive water which burdens the hillslopes.
We calculated excess water by subtracting evapotranspiration, drainage from soil bottom, and changes in storage from precipitation.
We found a good agreement between excess water and landslide occurrence. For the strongest AR event during Jan 2023, 98% of the 358 reported landslides are located in areas with excess water.
This work is still in preparation. There was a talk on this study during 2023 AGU. Please check the presentation tab on top for details about the talk.
Wildfire burn scar hydrology
In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the ability to accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited.
In this project, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments.
Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peak discharge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located in the simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchments with anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flow observations.
We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-based tool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weather forecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazard forecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.
Read the Natural Hazards and Earth System Sciences paper HERE
Remote sensing of postfire rdNDVI
In our Natural Hazards and Earth System Science publication, we divide WRF-Hydro’s simulated peak overland flow and streamflow discharge by the corresponding catchment area to indicate debris-flow susceptibility. Our results show that WRF-Hydro simulated debris-flow susceptibility estimates agree well with the normalized difference vegetation index (rdNVDI) calculated from Sentinel-2 pre- and post-event satellite imagery, i.e., catchments with high simulated susceptibilities correspond well with remotely sensed vegetation loss – likely due to the occurrence of postfire floods, debris floods. or debris flows.
In a hindcast context, our modeling framework can provide guidance on post-event field observations; In a predictive context, WRF-Hydro can be coupled with weather forecasts to predict such hazards and assist in issuing early warnings.
There was a Geological Society of America (GSA) talk on this study in session T100: Advancements in the Science and Management of Wildfire Impacts on the Critical Zone in Oct 2023.
Improving model simulation of soil moisture
In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations after updating the model’s soil parameters according to POLARIS.
Compared to four well-established soil moisture datasets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations.
More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially-distributed hydrological models, can substantially improve a model’s performance, helping reduce calibration efforts and computational costs.
Read the Water Resources Research paper HERE.
A cluster analysis of atmospheric river tracks over North Atlantic
Using reanalysis and high-resolution ensemble simulations, we characterize cold season (December−March) North Atlantic (NA) atmospheric river (AR) tracks by grouping them into four distinct clusters; then for each cluster, we link the year-to-year variations in track count to large-scale climate variability and examine the climatological effects of the cluster on extreme precipitation and winds.
The four clusters share similar prevailing AR track orientation, but differ in AR genesis locations and dominate over different regions. Cluster 1, with the longest average track of the four clusters, originates near the U.S. East Coast during La Niña and positive North Atlantic Oscillation (NAO) years and produces extreme precipitation and winds primarily over the eastern coast of North America. Cluster 2, which is weak in intensity and short-lived, forms north of 30°N of the open ocean during positive NAO years and contributes to more than 25% of the precipitation and wind extremes along the coasts of Northwestern Europe. Cluster 3, with the strongest intensity and longest duration among the four clusters, is generated surrounding the Gulf of Mexico during El Niño and negative NAO years and produces respectively more than 50% and 40% of the extreme precipitation and wind events over the eastern U.S. Cluster 4, the smallest and weakest among the four clusters, is favored under negative NAO conditions and generates roughly 25% of the extreme precipitation and winds along the coast of the Iberian Peninsula.
Read the Climate Dynamics paper HERE.
Atmospheric river variability using ensemble climate simulations
The sea surface temperature (SST)-forced and internal variability in cold-season (December–March) atmospheric river (AR) occurrence frequency during 1951–2010 over the North Atlantic (NA) basin are examined using a 30-member ensemble of high-resolution atmospheric simulations. The first leading mode of the forced variability features a north–south wobbling pattern modulated by an out-of-phase combination of El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Co-existing El Niño and negative NAO act to shift ARs equatorward, whereas concurrent La Niña and positive NAO tend to displace ARs poleward. The second leading mode is characterized by a meridional concentration and dispersion of AR occurrence at a basin scale and can be linked to the Scandinavian pattern and the SST difference between the central and easternmost tropical Pacific. The third leading mode is dominated by an oscillation of AR occurrence north and south of 40°N in the eastern NA basin, and modulated by an in-phase combination of ENSO and the NAO. Its time series exhibits a significant upward trend, which can be linked to the SST warming in the Indo-western Pacific since the 1970s. The internal variability in cold-season NA AR occurrence frequency is then quantified by means of the signal-to-noise ratio. The calculations show that the internal variability is relatively weak over the Great Antilles and central-to-eastern US but extremely strong over Northwestern Europe, which can be attributed to the strong SST control associated with ENSO and the chaotic variations of the NAO, respectively.
Read the Climate Dynamics paper HERE.