Publications

High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States
Soil moisture (SM) space and time variability critically influences freshwater resources, agriculture, ecosystem dynamics, climate and land-atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Here, we present the first continental assessment of how SM varies at the local scales using SMAP-HydroBlocks. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non-linear SM-dependent hydrological, ecological, and biogeochemical processes using coarse-scale models and satellite estimates.
High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States
PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) …
PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
Satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). Our approach was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m resolution soil moisture estimates.
Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
Forecasting the Hydroclimatic Signature of the 2015/16 El Niño Event on the Western United States
Dry conditions in 2013–16 in much of the western United States were responsible for severe drought and led to an exceptional fire …
Forecasting the Hydroclimatic Signature of the 2015/16 El Niño Event on the Western United States