Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.

Infiltration excess overland flow is the dominant mechanism for runoff
generation in many dryland watersheds. Event-based rainfall-runoff models
therefore partition precipitation into two components: loss and excess
precipitation. The latter is then transformed into a runoff hydrograph.
Numerous loss models have been developed over the past century ranging
from simple empirical to sophisticated physically based methods. Complex
models can lead to equifinality and associated uncertainty at larger
spatial scales with varying soil and cover conditions. Simple models are
therefore widely used in hydrologic practice. In the absence of measured
data in many arid and semiarid regions, model parameters are often
estimated based on laboratory or field infiltrometer tests. Given the
documented importance of spatial scale on the runoff response in dryland
catchments, it is not clear how models parameterized at the point or soil
column scale will perform at the hillslope or catchment scale under
real-world conditions. In this study, we compared the performance of seven
simple loss models with three or less parameters: the Philip,
Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant
(IC) and the linear and constant (LC) models. The latter is a modification
of the IC model introduced in this study. We estimated parameters at the
plot scale (2.8 m2) using rainfall simulation and then tested
model performance at the hillslope (1.5–3.7 ha) and catchment
scale (2.4–2.8 km2) based on measured rainfall-runoff data at two
sites in New Mexico and Arizona, U.S. Results show that rainfall
simulation can be used successfully to parameterize loss models at the
hillslope scale. At the catchment scale, most models showed positive bias,
suggesting that other losses (such as channel or transmission losses) play
an important role in determining the catchment runoff response. Rainfall
intensity and temporal distribution were found to be crucial for accurate
runoff prediction. Models that are sensitive to rainfall intensity during
the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC)
therefore performed better than those with an initial abstraction term
(CN, IC). During intermittent rain, the best results were achieved by
methods expressing infiltration capacity as a function of cumulative
infiltration (LC, Smith-Parlange).