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A process-based coupled model of surface-subsurface flow is applied to the simulation of nonlinear hydrological dynamics for an experimental mountain headwater catchment in northeastern Italy. The comparison between measured and simulated responses, both distributed (water table and soil moisture) and integrated (streamflow at the outlet), shows that the model satisfactorily reproduces various nonlinear processes, in particular threshold behavior and hysteresis in the catchment storage-discharge relationship. We typically observe a clockwise loop in this relationship, i.e., streamflow response is faster than groundwater and soil moisture response, due to larger time scales for subsurface processes and to soil moisture persistence and redistribution. The model is based on a standard Richards equation representation of integrated saturated-unsaturated-runoff dynamics and needs no ad hoc parameterization (e.g., for macropores, pipe flow, or retention curve hysteresis) to capture observed hysteretic relationships between storage and discharge. Additional numerical experiments are carried out to investigate how heterogeneity (bedrock permeability and the distinction between riparian and hillslope areas) and aquifer thickness and topography affect this nonlinear dynamics. The results show that catchment topography and soil depth exert the main control on the hysteresis and threshold patterns. This is evident from a spatial analysis of streamflow and water table response times to storm events, where the threshold points correspond to changes in terrain slope. These findings are confirmed by a further set of analyses carried out on an idealized v-shaped catchment.

[1] This study aims to assess the impact of a class of radar rainfall errors on prediction uncertainty of a conceptual water balance model. Uncertainty assessment is carried out by means of the Generalized Likelihood Uncertainty Estimation procedure (GLUE). The effects of model input and structural error are separated, and the potential for compensating errors between them is investigated. It is shown that the radar rainfall bias term operates in a multiplicative sense on the model structural uncertainty, by either magnifying or reducing it according to the sign of the bias. The results show also that adjustment of radar rainfall, aimed to remove local biases and to reduce random errors, allows ensuring that a larger percentage of the observed flows are enclosed by the uncertainty bounds, with respect to nonadjusted radar input. However, this is obtained at the price of increasing the wideness of the uncertainty bounds. This effect is emphasized with increasing the radar beam elevation. As a second step, the issue of the impact of radar rainfall estimation error on model parameter distribution and parameter transferability across sites under the radar umbrella is examined. Radar data at different radar beam elevations are used to simulate radar estimation errors at different distances from the radar site and to analyze the impact of these errors on prediction uncertainty. The results show that distortion of parameter distribution due to radar error may be considerable and that adjustment of radar rainfall estimates improves the regionalization potential of radar-based precipitation estimates (at least for ranges less than 70 km).

[1] Road networks in mountainous forest landscapes have the potential to increase the susceptibility to shallow landsliding by altering subsurface flow paths. In this paper a road interception/rerouting grid-based model is developed and coupled with a hydrogeomechanical, threshold-based model for slope instability. Model results allow quantification of the influence of roads on shallow landsliding hazard across a landscape and generation of hypotheses about the broader geomorphic effect of roads. The model is applied to three sites in northeastern Italy to study the influence of three different procedures for upslope drainage area computation on model results. The three procedures are a single-flow direction procedure (D8), a multiple-flow direction procedure (MF), and an algorithm based on proportioning flow between two downslope pixels (DINF). Model results are highly sensitive to upslope drainage area algorithm choice. It is shown that D8 and MF are unsuitable for road impact evaluation due to grid bias (D8) and excessive dispersion (MF). Model implementation with the DINF procedure results in a reasonable compromise between the artifacts that characterize the other two algorithms. Observed landslide patterns are broadly consistent with model estimates, a finding that underscores the utility of this simple approach for predicting the geomorphic effects of forest roads constructed on steep slopes. The approach used in this study may be useful for defining criteria for road design that reduce the effects of roads on geomorphic processes.

[1] A comprehensive analysis of precipitation estimation by weather radar operated over hilly terrain is performed. The study is based on a nearly continuous 3-year database of weather radar observations and hourly rain accumulations from a dense rain gauge network located in southwest England. Radar rainfall processing scenarios associated with correction for systematic and range dependent errors are investigated. The focus of the study is in demonstrating the impact of radar range effects at short to medium (less than 70 km) distances and determining the significance of coupling mean-field and range-related bias adjustment in radar rainfall estimation. The investigation is performed on the basis of climatological radar rainfall statistics for selected radar sectors and radar-rain gauge comparisons over a densely instrumented medium size (135 km2) basin located at 35–40 km from the radar. The application of the combined adjustment is shown to reduce overall error over the catchment by 24%. The analysis suggests that gauge-based radar adjustment can be properly applied only when homogeneity in the accuracy of the radar rainfall estimates with respect to range and scanning elevation is ensured.