Data from: Monitoring soil moisture at the catchment scale – A novel approach combining antecedent precipitation index and radar-derived rainfall data

Knowledge about soil moisture is important for event-based rainfall-runoff
models but monitoring conditions at the catchment scale is not a trivial
task. Soil moisture is highly variable in space and time, particularly in
dry climates with seasonal and spatially heterogeneous rainfall. Point
measurements are difficult to upscale, and remotely sensed (RS) data often
lack in spatial or temporal resolution for local or regional studies.
Longer latency periods – the time required before data becomes available –
of some RS data make them less applicable to time-sensitive analyses such
as flash flood forecasting. This study evaluated a novel approach for
estimating catchment-scale volumetric soil moisture using an antecedent
precipitation index (API) -based model. The model was trained and tested
using in-situ soil moisture measurements collected during a 3-month field
sampling campaign in a 142 km2 study area in central New
Mexico. The calibrated model was applied at the catchment scale to produce
soil moisture grids from radar-derived rainfall estimates. Model
performance, resolution and latency were compared to satellite-based soil
moisture estimates. Benefits of the proposed new method include high
spatial resolution (1 × 1 km or less depending
on the precipitation data source) and high prediction accuracy (root mean
square errors 0.014–0.018 m3/m3). Given the short latency period
for radar-derived rainfall data, the method has potential for use in
operational flood risk assessment and forecasting.