Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed

The increasing complexity of hydrological models results in a large number of parameters to be estimated. In order to better understand how these complex models work, efficient screening methods are required in order to identify the most important parameters. This is of particular importance for models that are used within an operational real‐time forecasting chain such as HQsim. The objectives of this investigation are to (i) identify the most sensitive parameters of the complex HQsim model applied in the Alpine Lech catchment and (ii) compare model parameter sensitivity rankings attained from three global sensitivity analysis techniques. The techniques presented are the (i) regional sensitivity analysis, (ii) Morris analysis and (iii) state‐dependent parameter modelling. The results indicate that parameters affecting snow melt as well as processes in the unsaturated soil zone reveal high significance in the analysed catchment. The snow melt parameters show clear temporal patterns in the sensitivity whereas most of the parameters affecting processes in the unsaturated soil zone do not vary in importance across the year. Overall, the maximum degree day factor (meltfunc_max) has been identified to play a key role within the HQsim model. Although the parameter sensitivity rankings are equivalent between methods for a number of parameters, for several key parameters differing results were obtained. An uncertainty analysis demonstrates that a parameter ranking attained from only one method is subjected to large uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes

When studying hydrological processes with a numerical model, global sensitivity analysis (GSA) is essential if one is to understand the impact of model parameters and model formulation on results. However, different definitions of sensitivity can lead to a difference in the ranking of importance of the different model factors. Here we combine a fuzzy performance function with different methods of calculating global sensitivity to perform a multi‐method global sensitivity analysis (MMGSA). We use an application of a finite element subsurface flow model (ESTEL‐2D) on a flood inundation event on a floodplain of the River Severn to illustrate this new methodology. We demonstrate the utility of the method for model understanding and show how the prediction of state variables, such as Darcian velocity vectors, can be affected by such a MMGSA. This paper is a first attempt to use GSA with a numerically intensive hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.     

A coupled hydrology–biogeochemistry model to simulate dissolved organic carbon exports from a permafrost‐influenced catchment

We outline the development of a simple, coupled hydrology–biogeochemistry model for simulating stream discharge and dissolved organic carbon (DOC) dynamics in data sparse, permafrost‐influenced catchments with large stores of soil organic carbon. The model incorporates the influence of active layer dynamics and slope aspect on hydrological flowpaths and resulting DOC mobilization. Calibration and evaluation of the model was undertaken using observations from Granger Basin within the Wolf Creek research basin, Yukon, northern Canada. Results show that the model was able to capture the dominant hydrological response and DOC dynamics of the catchment reasonably well. Simulated DOC was highly correlated with observed DOC (r² = 0.65) for the study period. During the snowmelt period, the model adequately captured the observed dynamics, with simulations generally reflecting the timing and magnitude of the observed DOC and stream discharge. The model was less successful over the later summer period although this partly reflected a lack of DOC observations for calibration. The developed model offers a valuable framework for investigating the interactions between hydrological and DOC processes in these highly dynamic systems, where data acquisition is often very difficult. © 2015 The Authors Hydrological Processes Published by John Wiley & Sons, Ltd.     

Biomechanical effects of trees in an old‐growth temperate forest

The role of biomechanical effects of trees (BETs) in ecosystem and landscape dynamics is poorly understood. In this study, we aim to (i) describe a widely applicable methodology for quantifying the main BETs in soil, and (ii) analyze the actual frequencies, areas and soil volumes associated with these effects in a mountain temperate old‐growth forest. The research took place in the Boubínský Primeval Forest in the Czech Republic; this forest reserve, predominated by Fagus sylvatica L. and Picea abies (L.) Karst., is among the oldest protected areas in Europe. We evaluated the effects of 4000 standing and lying trees in an area of 10.2 ha from the viewpoint of the following features: tree uprooting, root mounding, bioprotection, trunk baumsteins (rock fragments displaced by trunk growth), root baumsteins, stump hole infilling, trunk and root systems displacements, depressions formed after trunk fall, stemwash, and trunkwash. BETs were recorded in 59% of standing and 51% of lying dead trees (excluding the pervasive soil displacement by thickening trunks and roots and the infilling of decayed stumps). Approximately one tenth of the trees showed simultaneous bioprotective and bioerosion effects. Different tree species and size categories exhibited significantly different biomechanical effects. A bioprotective function was the most frequent phenomenon observed, while treethrows prevailed from the viewpoint of areas and soil volumes affected. The total area influenced by the BETs was 342 m² ha^?1. An additional 774 m² ha^?1 were occupied by older treethrow pit‐mounds with already decayed uprooted trunks. The total volume of soil associated with the studied phenomena was 322 m³ ha^?1, and apart from treethrows, volumes of the living and decaying root systems and bioprotective functions predominated. Other processes were not so frequent but still significant for biogeomorphology. Copyright © 2017 John Wiley & Sons, Ltd.     

Impact of grid resolution on the integrated and distributed response of a coupled surface–subsurface hydrological model for the des Anglais catchment,Quebec

Digital elevation models (DEMs) at different resolutions (180, 360, and 720 m) are used to examine the impact of different levels of landscape representation on the hydrological response of a 690‐km² catchment in southern Quebec. Frequency distributions of local slope, plan curvature, and drainage area are calculated for each grid size resolution. This landscape analysis reveals that DEM grid size significantly affects computed topographic attributes, which in turn explains some of the differences in the hydrological simulations. The simulations that are then carried out, using a coupled, process‐based model of surface and subsurface flow, examine the effects of grid size on both the integrated response of the catchment (discharge at the main outlet and at two internal points) and the distributed response (water table depth, surface saturation, and soil water storage). The results indicate that discharge volumes increase as the DEM is coarsened, and that coarser DEMs are also wetter overall in terms of water table depth and soil water storage. The reasons for these trends include an increase in the total drainage area of the catchment for larger DEM cell sizes, due to aggregation effects at the boundary cells of the catchment, and to a decrease in local slope and plan curvature variations, which in turn limits the capacity of the watershed to transmit water downslope and laterally. The results obtained also show that grid resolution effects are less pronounced during dry periods when soil moisture dynamics are mostly controlled by vertical fluxes of evaporation and percolation. Copyright © 2010 John Wiley & Sons, Ltd.     

Recognition method for mid‐ to long‐term runoff forecasting factors based on global sensitivity analysis in the Nenjiang River Basin

Mid‐ to long‐term runoff forecasting is important to China. Forecasting based on physical causes has become the trend of this field, and recognition of key factors is central to recent development. Here, global sensitivity analysis based on back‐propagation arithmetic was used to calculate the sensitivity of up to 24 factors that affect runoff in the Nenjiang River Basin. The following five indices were found to be key factors for mid‐ to long‐term runoff forecasting during flood season: Tibetan Plateau B, index of the strength of the East Asian trough, index of the area of the northern hemisphere polar vortex, zonal circulation index over the Eurasian continent and index of the strength of the subtropical high over the western Pacific. The hydrological climate of the study area and the rainfall–runoff laws were then analysed in conjunction with its geographical position and topographic condition. The rationality of the results can be demonstrated from the positive analysis point of view. The results of this study provide a general method for selection of mid‐ to long‐term runoff forecasting factors based on physical causes. Copyright © 2012 John Wiley & Sons, Ltd.     

Sensitivity and uncertainty analysis of mesoscale model downscaled hydro‐meteorological variables for discharge prediction

Precipitation and Reference Evapotranspiration (ETo) are the most important variables for rainfall–runoff modelling. However, it is not always possible to get access to them from ground‐based measurements, particularly in ungauged catchments. This study explores the performance of rainfall and ETo data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis data for the discharge prediction. The Weather Research and Forecasting (WRF) mesoscale model coupled with the NOAH Land Surface Model is used for the retrieval of hydro‐meteorological variables by downscaling ECMWF datasets. The conceptual Probability Distribution Model (PDM) is chosen for this study for the discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimations are taken into account for the PDM calibration and prediction in the case study catchment in England following the Generalized Likelihood Uncertainty Estimation approach. The goodness of calibration and prediction uncertainty is judged on the basis of the p‐factor (observations bracketed by the prediction uncertainty) and the r‐factor (achievement of small uncertainty band). The overall analysis suggests that the uncertainty estimates using WRF downscaled ETo have slightly smaller p and r values (p= 0.65; r= 0.58) as compared to ground‐based observation datasets (p= 0.71; r= 0.65) during the validation and hence promising for discharge prediction. On the contrary, WRF precipitation has the worst performance, and further research is needed for its improvement (p= 0.04; r= 0.10). Copyright © 2013 John Wiley & Sons, Ltd.     

大型浅水湖泊藻类模型参数敏感性分析

选取太湖作为典型湖泊在之前研究基础上建立藻类模型,对模型中与藻类有关的40个参数进行拉丁超立方抽样,并使用区域敏感性分析方法和普适似然不确定性分析方法进行敏感性分析.结果表明:在所选的40个参数中,有7个参数是敏感的参数,对模拟的结果影响较大.在藻类生长、基础代谢、牧食和沉降4个藻类变化过程中藻类生长的敏感参数最多,影响最大;在藻类生长项中,叶绿素的消光系数是藻类生长光照限制中的最敏感参数,而最低适宜生长温度及其对藻类生长的影响系数则是温度限制中的敏感参数;并且不同湖区的不确定性在不同时间差异明显,对于藻类低浓度湖区和藻类暴发期的模拟需要加以关注.     

Global sensitivity analysis of hydrologic processes in major snow‐dominated mountainous river basins in Colorado

The performance of watershed models in simulating stream discharge depends on the adequate representation of important watershed processes. In snow‐dominated systems, snow, surface and subsurface hydrologic processes comprise a complex network of nonlinear interactions that influence the magnitude and timing of discharge. This study aims to identify critical processes and interactions that control discharge hydrographs in five major mountainous snow‐dominated river basins in Colorado, USA. A comprehensive watershed model (Soil and Water Assessment Tool) and a variance‐based global sensitivity analysis technique (Fourier Amplitude Sensitivity Test) were used in conjunction to identify critical models parameters and processes that they represent. Average monthly streamflow and streamflow root mean square error over a period of 20 years were used as two separate objective functions in this analysis. Examination of the sensitivity of monthly streamflow revealed the influence of parameters on flow volume, whereas the sensitivity of streamflow root mean square error also exposed the influence of parameters on the timing of the hydrographs. A stability analysis was performed to investigate the computational requirements for a robust sensitivity analysis. Results show that streamflow volume is mostly influenced by shallow subsurface processes, whereas interactions between groundwater and snow processes were the key in the timing of streamflows. A large majority of important parameters were common among all study watersheds, which underlies the prospect for regionalization of process‐based hydrologic modelling in headwater river basins in Colorado. Copyright © 2013 John Wiley & Sons, Ltd.     

A simulation‐based framework for risk assessment and probabilistic sensitivity analysis of base‐isolated structures

A versatile, simulation‐based framework for risk assessment and probabilistic sensitivity analysis of base‐isolated structures is discussed in this work. A probabilistic foundation is used to address the various sources of uncertainties, either excitation or structural, and to characterize seismic risk. This risk is given, in this stochastic setting, by some statistics of the system response over the adopted probability models and stochastic simulation is implemented for its evaluation. An efficient, sampling‐based approach is also introduced for establishing a probabilistic sensitivity analysis to identify the importance of each of the uncertain model parameters in affecting the overall risk. This framework facilitates use of complex models for the structural system and the excitation. The adopted structural model explicitly addresses nonlinear characteristics of the isolators and of any supplemental dampers, and the effect of seismic pounding of the base to the surrounding retaining walls. An efficient stochastic ground motion model is also discussed for characterizing future near‐fault ground motions and relating them to the seismic hazard for the structural site. An illustrative example is presented that emphasizes the results from the novel probabilistic sensitivity analysis and their dependence on seismic pounding occurrences and on addition of supplemental dampers. Copyright © 2011 John Wiley & Sons, Ltd.     

How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance

Model diagnostic analyses help to improve the understanding of hydrological processes and their representation in hydrological models. A detailed temporal analysis detects periods of poor model performance and model components with potential for model improvements, which cannot be found by analysing the whole discharge time series. In this study, we aim to improve the understanding of hydrological processes by investigating the temporal dynamics of parameter sensitivity and of model performance for the Soil and Water Assessment Tool model applied to the Treene lowland catchment in Northern Germany. The temporal analysis shows that the parameter sensitivity varies temporally with high sensitivity for three groundwater parameters (groundwater time delay, baseflow recession constant and aquifer fraction coefficient) and one evaporation parameter (soil evaporation compensation factor). Whereas the soil evaporation compensation factor dominates in baseflow and resaturation periods, groundwater time delay, baseflow recession constant and aquifer fraction coefficient are dominant in the peak and recession phases. The temporal analysis of model performance identifies three clusters with different model performances, which can be related to different phases of the hydrograph. The lowest performance, when comparing six performance measures, is detected for the baseflow cluster. A spatially distributed analysis for six hydrological stations within the Treene catchment shows similar results for all stations. The linkage of periods with poor model performance to the dominant model components in these phases and with the related hydrological processes shows that the groundwater module has the highest potential for improvement. This temporal diagnostic analysis enhances the understanding of the Soil and Water Assessment Tool model and of the dominant hydrological processes in the lowland catchment. Copyright © 2013 John Wiley & Sons, Ltd.     

Uncertainty and sensitivity analysis of a travel-time distribution-based stream water electrical conductivity model

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration C_rain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.     

A macro‐element model for inelastic building analysis

A three‐dimensional model for approximate inelastic analysis of buildings is presented herein. The model is based on a single macro‐element per building storey. The inelastic properties of the model are characterized by the so‐called ultimate storey shear and torque (USST) surfaces. Different algorithms for the construction of these surfaces, as well as their applications in building modelling, are presented and discussed. Two alternative procedures are developed to integrate the force‐deformation constitutive relationship of the macro‐elements. The first one follows the exact trajectory of the load path of the structure on the USST, and the second uses linear programming without ever forming the USST surface. The accuracy of the model and integration procedure is evaluated by means of the earthquake response of single‐storey systems. The model and integration procedure developed is finally used to compute the inelastic response of a seven‐storey R/C building. The results of this investigation show that the model proposed, although approximate, can be effective in estimating the inelastic deformation demand of a building. It also enables the engineer to capture and interpret important features of the three‐dimensional inelastic response of a structure even before performing any inelastic dynamic analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Hydrological field data from a modeller's perspective: Part 2: process‐based evaluation of model hypotheses

The current generation of hydrological models has been widely criticized for their inability to adequately simulate hydrological processes. In this study, we evaluate competing model representations of hydrological processes with respect to their capability to simulate observed processes in the Mahurangi River basin in Northland, New Zealand. In the first part of this two‐part series, the precipitation, soil moisture, and flow data in the Mahurangi were used to estimate the dominant hydrological processes and explore several options for their suitable mathematical representation. In this paper, diagnostic tests are applied to gain several insights for model selection. The analysis highlights dominant hydrological processes (e.g. the importance of vertical drainage and baseflow compared to sub‐surface stormflow), provides guidance for the choice of modelling approaches (e.g. implicitly representing sub‐grid heterogeneity in soils), and helps infer appropriate values for model parameters. The approach used in this paper demonstrates the benefits of flexible model structures in the context of hypothesis testing, in particular, supporting a more systematic exploration of current ambiguities in hydrological process representation. The challenge for the hydrological community is to make better use of the available data, not only to estimate parameter values but also to diagnostically identify more scientifically defensible model structures. Copyright © 2010 John Wiley & Sons, Ltd.     

Projected risks to groundwater‐dependent terrestrial vegetation caused by changing climate and groundwater abstraction in the Central Perth Basin,Western Australia

The effect of potential climate change on groundwater‐dependent vegetation largely depends on the nature of the climate change (drying or wetting) and the level of current ecosystem dependence on groundwater resources. In south‐western Australia, climate projections suggest a high likelihood of a warmer and drier climate. The paper examines the potential environmental impacts by 2030 at the regional scale on groundwater‐dependent terrestrial vegetation (GDTV) adapted to various watertable depths, on the basis of the combined consideration of groundwater modelling results and the framework for GDTV risk assessment. The methodology was tested for the historical period from 1984 to 2007, allowing validation of the groundwater model results' applicability to such an assessment. Climate change effects on GDTV were evaluated using nine global climate models under three greenhouse gas emission scenarios by applying the climate projections to groundwater models. It was estimated that under dry climate scenarios, GDTV is likely to be under high and severe risk over more than 20% of its current habitat area. The risk is also likely to be higher under an increase in groundwater abstraction above current volumes. The significance of climate change risk varied across the region, depending on both the intensity of the change in water regime and the sensitivity of the GDTV to such change. Greater effects were projected for terrestrial vegetation dependent on deeper groundwater (6–10 m). Copyright © 2013 John Wiley & Sons, Ltd.     

Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

We investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AIC_C] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.     

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.     

A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon,USA

High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R² = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R² = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.