DOI: 10.5061/dryad.tb2rbp01j
title: Data from: Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.

description: 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).
description: This dataset contains comparisons of observed and simulated
 runoff for seven loss models (Philip, Smith-Parlange, Horton, Kostiakov,
 curve number, initial and constant, linear and constant) at three
 different spatial scales: <strong>Plot
 Scale</strong> 19 runoff and infiltration time
 series were measured at seven test plots in central New Mexico. Tests were
 conducted using a portable rainfall simulator. A nozzle, suspended 3 m
 above ground, was used to spray water onto a
 2.8 m<sup>2</sup> test plot at a constant rate of 194.1 or
 58.4 mm/h. Below the nozzle, a sheet metal border was driven into the
 ground to a depth of approximately 5 cm. Any runoff originating inside the
 test plot was directed to a graduated container. Using a time lapse
 camera, runoff volume was recorded in one- or two-minute increments. For
 each time step, runoff rate was determined by dividing the incremental
 runoff volume by the plot size. Infiltration rate for each time step was
 calculated by subtracting the runoff rate from the rainfall rate. Each
 plot was tested under dry, intermediate and wet antecedent soil moisture
 conditions. Plot-scale data were used to optimize loss model
 parameters.  <strong>Hillslope
 Scale</strong> Optimized loss models were applied
 at the hillslope scale using data from the Walnut Gulch Experimental
 Watershed (https://doi.org/10.1029/2006WR005733). One example for each loss model is included in this dataset. The examples compare observed and simulated runoff from Walnut Gulch hillslope 102 resulting from the storm of August 4, 2002. Hillslope 102, with a contributing area of 1.5 ha, was modeled as a single basin. Precipitation data was obtained from a rain gage located on the hillslope. Excess precipitation was converted to runoff hydrographs by accounting for temporary storage effects using a linear reservoir model. <strong>Catchment Scale</strong> Optimized loss models were applied at the catchment scale using data from the Walnut Gulch Experimental Watershed (https://doi.org/10.1029/2006WR005733). One example for each loss model is included in this dataset. The examples compare observed and simulated runoff from Walnut Gulch catchment 4 resulting from the storm of July 25, 2008. The catchment was modeled using a 200 x 200 m grid. Precipitation for each grid cell was interpolated form adjacent rain gages. Excess precipitation was converted to runoff hydrographs by accounting for the translation time from each grid cell to the catchment outlet, and modeling temporary storage using a linear reservoir model.