Physical Basin Characteristics Research Articles

Sediment storage and shallow groundwater response to beaver dam analogues in the Colorado Front Range, USA

AbstractEnthusiasm for using beaver dam analogues (BDAs) to restore incised channels and riparian corridors has been increasing. BDAs are expected to create a similar channel response to natural beaver dams by causing channel bed aggradation and overbank flow, which subsequently raise water tables and support vegetation growth. However, lack of funding for monitoring projects post‐restoration has limited research on whether BDAs actually cause expected channel change in the Front Range and elsewhere. Geomorphic and hydrologic response to BDAs was monitored in two watersheds 1 year post‐restoration. BDAs were studied at Fish Creek, a steep mountainous catchment, and Campbell Creek, a lower gradient piedmont catchment from May to October 2018. At each restoration site, the upstream‐ and downstream‐most BDAs were chosen for intensive study in comparison with unrestored reference reaches. Monitoring focused on quantifying sediment volumes in BDA ponds and recording changes to stream stage and riparian groundwater. Despite differences in physical basin characteristics, BDA pools at both sites stored similar volumes of sediment and stored more sediment than reference pools. Sediment storage is positively correlated to BDA height and pool surface area. However, BDAs did not have a significant influence on shallow groundwater. The lack of groundwater response proximal to BDAs could indicate that local watershed factors have a stronger influence on groundwater response than restoration design 1 year post‐restoration. Systematic, long‐term studies of channel and floodplain response to BDAs are needed to better understand how BDAs will influence geomorphology and hydrology.

Regional regression models for hydro‐climate change impact assessment

AbstractHydro‐climatic impacts in water resources systems are typically assessed by forcing a hydrologic model with outputs from general circulation models (GCMs) or regional climate models. The challenges of this approach include maintaining a consistent energy budget between climate and hydrologic models and also properly calibrating and verifying the hydrologic models. Subjective choices of loss, flow routing, snowmelt and evapotranspiration computation methods also increase watershed modelling uncertainty and thus complicate impact assessment. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to predict selected streamflow quantiles directly from meteorological variable output from climate models using regional regression models that also include physical basin characteristics. In this study, regional regression models are developed for the western Great Lakes states using ordinary least squares and weighted least squares techniques applied to selected Great Lakes watersheds. Model inputs include readily available downscaled GCM outputs from the Coupled Model Intercomparison Project Phase 3. The model results provide insights to potential model weaknesses, including comparatively low runoff predictions from continuous simulation models that estimate potential evapotranspiration using temperature proxy information and comparatively high runoff projections from regression models that do not include temperature as an explanatory variable. Copyright © 2014 John Wiley & Sons, Ltd.

Relating BTOPMC model parameters to physical features of MOPEX basins

This paper focuses on parameter regionalization for the block-wise use of the TOPMODEL with the Muskingum–Cunge routing method (BTOPMC). Pre-determined quantitative relationships with physical basin characteristics are used to reduce uncertainty in parameter estimation and enable prediction in ungauged basins. BTOPMC parameter values, which have previously resulted in efficient model performance, are applied to ten Model Parameter Estimation Experiment (MOPEX) basins without any parameter adjustment to test model performance using ‘blind’ a priori parameter estimates. It is quantitatively shown that parameters can not be satisfactorily estimated a priori without parameter adjustment, either through calibration or transfer functions linking parameters to physical basin features, referred to as ‘improved’ a priori parameter estimation. To compare model performance using different parameter estimation techniques, BTOPMC parameters are re-estimated by automatic optimization. As anticipated, model performance improves significantly when these optimized parameters are used. Good correlation of model performance is also observed between the 19-year validation and 20-year calibration results when optimized parameters are used. This indicates that BTOPMC's predictive accuracy depends strongly on calibration accuracy, implying that well-optimized parameter values can ensure the accuracy of model predictions. It appears reasonable to use optimized parameter values, selected from successful modelling applications, to make transfer functions which ‘improve’ a priori parameter estimates. The ‘improved’ a priori parameter estimation is achieved through multiple linear regression analysis relating BTOPMC parameters to soil and vegetation types within the basin being modelled. The results suggest that the preliminary parameter transfer functions are encouraging and that ‘improved’ a priori parameter estimation techniques have the potential to reduce parameter uncertainty and enable prediction in ungauged basins. To improve parameter estimation and predictive accuracy of the model, further research to determine more accurate values, and their relationship with physical basin characteristics, is needed.