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.     

Sensitivity analyses of a distributed catchment model to verify the model structure

Sensitivity analyses are valuable tools for identifying important model parameters, testing the model conceptualization, and improving the model structure. They help to apply the model efficiently and to enable a focussed planning of future research and field measurement. Two different methods were used for sensitivity analyses of the complex process-oriented model TAC^D (tracer aided catchment model, distributed) that was applied to the meso-scale Brugga basin (40 km²) and the sub-basin St Wilhelmer Talbach (15.2 km²). Five simulations periods were investigated: two summer events, two snow melt induced events and one summer low flow period. The model was applied using 400 different parameter sets, which were generated by Monte Carlo simulations using latin hypercube sampling. The regional sensitivity analysis (RSA) allowed determining the most significant parameters for the complete simulation periods using a graphical method. The results of the regression-based sensitivity analysis were more detailed and complex. The temporal variability of the simulation sensitivity could be observed continuously and the significance of the parameters could be determined in a quantitative way. A dependency of the simulation sensitivity on initial- and boundary conditions and the temporal and spatial variability of the sensitivity to some model parameters was revealed by the regression-based sensitivity analysis. Thus, the difficulty of transferring the results to different time periods or model applications in other catchments became obvious. The analysis of the temporal course of the simulation sensitivity to parameter values in conjunction with simulated and measured additional data sets (precipitation, temperature, reservoir volumes etc.) gave further insight into the internal model behaviour and demonstrated the plausibility of the model structure and process conceptionalizations.     

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.     

On characterizing the temporal dominance patterns of model parameters and processes

Diagnostic analyses of hydrological models intend to improve the understanding of how processes and their dynamics are represented in models. Temporal patterns of parameter dominance could be precisely characterized with a temporally resolved parameter sensitivity analysis. In this way, the discharge conditions are characterized, that lead to a parameter dominance in the model. To achieve this, the analysis of temporal dynamics in parameter sensitivity is enhanced by including additional information in a three‐tiered framework on different aggregation levels. Firstly, temporal dynamics of parameter sensitivity provide daily time series of their sensitivities to detect variations in the dominance of model parameters. Secondly, the daily sensitivities are related to the flow duration curve (FDC) to emphasize high sensitivities of model parameters in relation to specific discharge magnitudes. Thirdly, parameter sensitivities are monthly averaged separately for five segments of the FDC to detect typical patterns of parameter dominances for different discharge magnitudes. The three methodical steps are applied on two contrasting catchments (upland and lowland catchment) to demonstrate how the temporal patterns of parameter dynamics represent different hydrological regimes. The discharge dynamic in the lowland catchment is controlled by groundwater parameters for all discharge magnitudes. In contrast, different processes are relevant in the upland catchment, because the dominances of parameters from fast and slow runoff components in the upland catchment are changing over the year for the different discharge magnitudes. The joined interpretation of these three diagnostic steps provides deeper insights of how model parameters represent hydrological dynamics in models for different discharge magnitudes. Thus, this diagnostic framework leads to a better characterization of model parameters and their temporal dynamics and helps to understand the process behaviour in hydrological models. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of spatial variability in snow water equivalent for a high mountain catchment

Multivariate statistical analysis was used to explore relationships between catchment topography and spatial variability in snow accumulation and melt processes in a small headwater catchment in the Spanish Pyrenees. Manual surveys of snow depth and density provided information on the spatial distribution of snow water equivalent (SWE) and its depletion over the course of the 1997 and 1998 melt seasons. A number of indices expressing the topographic control on snow processes were extracted from a detailed digital elevation model of the catchment. Bivariate screening was used to assess the relative importance of these topographic indices in controlling snow accumulation at the start of the melt season, average melt rates and the timing of snow disappearance. This suggested that topographic controls on the redistribution of snow by wind are the most important influence on snow distribution at the start of the melt season. Furthermore, it appeared that spatial patterns of snow disappearance were largely determined by the distribution of snow water equivalent (SWE) at the start of the melt season, rather than by spatial variability in melt rates during the melt season. Binary regression tree models relating snow depth and disappearance date to terrain indices were then constructed. These explained 70–80% of the variance in the observed data. As well as providing insights into the influence of topography on snow processes, it is suggested that the techniques presented herein could be used in the parameterization of distributed snowmelt models, or in the design of efficient stratified snow surveys. Copyright © 2003 John Wiley & Sons, Ltd.     

Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes

The National Weather Service (NWS) uses the SNOW17 model to forecast snow accumulation and ablation processes in snow-dominated watersheds nationwide. Successful application of the SNOW17 relies heavily on site-specific estimation of model parameters. The current study undertakes a comprehensive sensitivity and uncertainty analysis of SNOW17 model parameters using forcing and snow water equivalent (SWE) data from 12 sites with differing meteorological and geographic characteristics. The Generalized Sensitivity Analysis and the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm are utilized to explore the parameter space and assess model parametric and predictive uncertainty. Results indicate that SNOW17 parameter sensitivity and uncertainty generally varies between sites. Of the six hydroclimatic characteristics studied, only air temperature shows strong correlation with the sensitivity and uncertainty ranges of two parameters, while precipitation is highly correlated with the uncertainty of one parameter. Posterior marginal distributions of two parameters are also shown to be site-dependent in terms of distribution type. The SNOW17 prediction ensembles generated by the DREAM-derived posterior parameter sets contain most of the observed SWE. The proposed uncertainty analysis provides posterior parameter information on parameter uncertainty and distribution types that can serve as a foundation for a data assimilation framework for hydrologic models.     

A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin,East China

Parameter calibration and sensitivity analysis (SA) are usually not straightforward tasks for distributed hydrological models, owing to the complexity of models and the large number of parameters. A two-step SA approach is proposed for analysing hydrological signatures based on the distributed hydrology–soil–vegetation model (DHSVM) in the Jinhua River Basin, East China. A preliminary SA is conducted to obtain influential parameters via analysis of variance. These parameters are further analysed through a variance-based global sensitivity analysis method to achieve robust rankings and parameter contributions. Parallel computing is designed to reduce the computational burden. The results reveal that only a few parameters are significantly sensitive and that interactions between parameters cannot be ignored. When analysing hydrological signatures, it is found that water yield is simulated very well for most samples. Small and medium floods are simulated very well, while slight underestimations happen for large floods.     

Amplification of hydrological model uncertainties in projected climate simulations of the Upper Indus Basin: Does it matter where the water is coming from?

There is a growing appreciation of the uncertainties in the estimation of snow-melt and glacier-melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046–2075 compared to 1976–2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world. We suggest that modellers of glaciated catchments should present results from at least the book-ends: models with low sensitivity to ice-melt and models with high sensitivity to ice-melt. This would reduce confusion among decision makers when they are faced with similar contrasting results.     

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 multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments

We examine the value of additional information in multiple objective calibration in terms of model performance and parameter uncertainty. We calibrate and validate a semi‐distributed conceptual catchment model for two 11‐year periods in 320 Austrian catchments and test three approaches of parameter calibration: (a) traditional single objective calibration (SINGLE) on daily runoff; (b) multiple objective calibration (MULTI) using daily runoff and snow cover data; (c) multiple objective calibration (APRIORI) that incorporates an a priori expert guess about the parameter distribution as additional information to runoff and snow cover data. Results indicate that the MULTI approach performs slightly poorer than the SINGLE approach in terms of runoff simulations, but significantly better in terms of snow cover simulations. The APRIORI approach is essentially as good as the SINGLE approach in terms of runoff simulations but is slightly poorer than the MULTI approach in terms of snow cover simulations. An analysis of the parameter uncertainty indicates that the MULTI approach significantly decreases the uncertainty of the model parameters related to snow processes but does not decrease the uncertainty of other model parameters as compared to the SINGLE case. The APRIORI approach tends to decrease the uncertainty of all model parameters as compared to the SINGLE case. Copyright © 2006 John Wiley & Sons, Ltd.     

Dynamics of snow ablation in a small Alpine catchment observed by repeated terrestrial laser scans

The spatio‐temporal distribution of snow in a catchment during ablation reflects changes in the total amount of snow water equivalent and is thus a key parameter for the estimation of melt water run‐off. This study explores possible rules behind the spatial variability of snow depth during the ablation season in a small Alpine catchment with complex topography. The snow depth observations are based on more than 160 000 terrestrial laser scanner data points with a spatial resolution of 1 m, which were obtained from 11 scanning campaigns of two consecutive ablation seasons. The analysis suggests that for estimating cumulative snow melt dynamics from the catchment investigated, assessing the initial snow distribution prior to the melt season is more important than addressing spatial differences in the melt behaviour. Snow volume and snow‐covered area could be predicted well using a conceptual melt model assuming spatially uniform melt rates. However, accurate results were only obtained if the model was initialized with a pre‐melt snow distribution that reflected measured mean and standard deviation. Using stratified melt rates on the other hand did not improve the model results. At least for sites with similar meteorological and topographical conditions, the model approach presented here comprises an efficient way to estimate snow depletion dynamics, especially if persistent snow accumulation pattern between years facilitate the characterization of the initial snow distribution prior to the melt. Copyright © 2011 John Wiley & Sons, Ltd.     

Delineation of flood generating processes and their hydrological response

Knowledge about flood generating processes can be beneficial for numerous applications. Especially in the context of climate change impact assessment, daily patterns of meteorological and catchment state conditions leading to flood events (i.e., storylines) may be of value. Here, we propose an approach to identify storylines of flood generation using daily weather and snow cover observations. The approach is tested for and applied to a typical pre‐Alpine catchment in the period between 1961 and 2014. Five precipitation parameters were determined that describe temporal and spatial characteristics of the flood associated precipitation events. The catchment's snow coverage was derived using statistical relationships between a satellite‐derived snow cover climatology and station snow measurements. Moreover, (pre‐) event snow melt sums were estimated using a temperature‐index model. Based on the precipitation and catchment state parameters, 5 storylines were identified with a cluster analysis: These are (a) long duration, low intensity precipitation events with high precipitation depths, (b) long duration precipitation events with high precipitation depths and episodes of high intensities, (c) shorter duration events with high or (d) low precipitation intensity, respectively, and (e) rain‐on‐snow events. The event groups have distinct hydrological characteristics that can largely be explained by the storylines' respective properties. The long duration, high intensity storyline leads to the most adverse hydrological response, namely, a combination of high peak magnitudes, high volumes, and long durations of threshold exceedance. The results show that flood generating processes in mesoscale catchments can be distinguished on the basis of daily meteorological and catchment state parameters and that these process types can explain the hydrological flood properties in a qualitative way. Hydrological simulations of daily resolution can thus be analysed with respect to flood generating processes.     

Sensitivity and uncertainty analysis of a sediment transport model: a global approach

Computerized sediment transport models are frequently employed to quantitatively simulate the movement of sediment materials in rivers. In spite of the deterministic nature of the models, the outputs are subject to uncertainty due to the inherent variability of many input parameters in time and in space, along with the lack of complete understanding of the involved processes. The commonly used first-order method for sensitivity and uncertainty analyses is to approximate a model by linear expansion at a selected point. Conclusions from the first-order method could be of limited use if the model responses drastically vary at different points in parameter space. To obtain the global sensitivity and uncertainty features of a sediment transport model over a larger input parameter space, the Latin hypercubic sampling technique along with regression procedures were employed. For the purpose of illustrating the methodologies, the computer model HEC2-SR was selected in this study. Through an example application, the results about the parameters sensitivity and uncertainty of water surface, bed elevation and sediment discharge were discussed.     

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km²), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R ²) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters.     

Sensitivity analysis of extended TOPMODEL for agricultural watersheds equipped with tile drains

An extension of TOPMODEL was developed for rainfall–runoff simulation in agricultural watersheds equipped with tile drains. Tile drain functions are incorporated into the framework of TOPMODEL. Nine possible flow generation scenarios are suggested for tile-drained watersheds and applied in the modelling procedure. In the model development, two methods of simulation of the flow in the unsaturated zone were compared: the traditional, physically based storage approach and a new approach using a transfer function. A regionalized sensitivity analysis was used to determine the sensitivity of parameters and to compare the behaviour of the transfer function with that of the simple storage-related formulation. The number of accepted combinations of parameter values, on average, was higher for the transfer function approach than when using a Monte Carlo method of parameter estimation. Since the rainfall–runoff response pattern tends to vary seasonally, seven events distributed throughout a year were used in the sensitivity analysis to investigate the seasonal variation of the hydrological characteristics. © 1997 John Wiley & Sons, Ltd.     

A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

Hydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.     

Projecting climate change impacts on stream flow regimes with tracer‐aided runoff models ‐ preliminary assessment of heterogeneity at the mesoscale

The northern mid‐high latitudes form a region that is sensitive to climate change, and many areas already have seen – or are projected to see – marked changes in hydroclimatic drivers on catchment hydrological function. In this paper, we use tracer‐aided conceptual runoff models to investigate such impacts in a mesoscale (749 km²) catchment in northern Scotland. The catchment encompasses both sub‐arctic montane sub‐catchments with high precipitation and significant snow influence and drier, warmer lowland sub‐catchments. We used downscaled HadCM3 General Circulation Model outputs through the UKCP09 stochastic weather generator to project the future climate. This was based on synthetic precipitation and temperature time series generated from three climate change scenarios under low, medium and high greenhouse gas emissions. Within an uncertainty framework, we examined the impact of climate change at the monthly, seasonal and annual scales and projected impacts on flow regimes in upland and lowland sub‐catchments using hydrological models with appropriate process conceptualization for each landscape unit. The results reveal landscape‐specific sensitivity to climate change. In the uplands, higher temperatures result in diminishing snow influence which increases winter flows, with a concomitant decline in spring flows as melt reduces. In the lowlands, increases in air temperatures and re‐distribution of precipitation towards autumn and winter lead to strongly reduced summer flows despite increasing annual precipitation. The integration at the catchment outlet moderates these seasonal extremes expected in the headwaters. This highlights the intimate connection between hydrological dynamics and catchment characteristics which reflect landscape evolution. It also indicates that spatial variability of changes in climatic forcing combined with differential landscape sensitivity in large heterogeneous catchments can lead to higher resilience of the integrated runoff response. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Weighing the importance of model uncertainty against parameter uncertainty in earthquake loss assessments

Epistemic uncertainties can be classified into two major categories: parameter and model. While the first one stems from the difficulties in estimating the values of input model parameters, the second comes from the difficulties in selecting the appropriate type of model. Investigating their combined effects and ranking each of them in terms of their influence on the predicted losses can be useful in guiding future investigations. In this context, we propose a strategy relying on variance-based global sensitivity analysis, which is demonstrated using an earthquake loss assessment for Pointe-à-Pitre (Guadeloupe, France). For the considered assumptions, we show: that uncertainty of losses would be greatly reduced if all the models could be unambiguously selected; and that the most influential source of uncertainty (whether of parameter or model type) corresponds to the seismic activity group. Finally, a sampling strategy was proposed to test the influence of the experts’ weights on models and on the assumed coefficients of variation of parameter uncertainty. The former influenced the sensitivity measures of the model uncertainties, whereas the latter could completely change the importance rank of the uncertainties associated to the vulnerability assessment step.     

Catchment‐scale sensitivity and uncertainty in water quality modelling

In this paper, we assess the performance of the catchment model SIMulated CATchment model (SIMCAT), to predict nitrate and soluble reactive phosphorus concentrations against four monitoring regimes with different spatial and temporal sampling frequencies. The Generalised Likelihood Uncertainty Estimation (GLUE) uncertainty framework is used, along with a general sensitivity analysis to understand relative parameter sensitivity. Improvements to model calibration are explored by introducing more detailed process representation using the Integrated Catchments model (INCA) water quality model, driven by the European hydrological predictions for the environment model. The results show how targeted sampling of headwater watercourses upstream of point discharges is essential for calibrating diffuse loads and can exert a strong influence on the whole‐catchment model performance. Further downstream, if the point discharges and loads are accurately represented, then the improvement in the catchment‐scale model performance is relatively small as more calibration points are added or frequency is increased. The higher‐order, dynamic model integrated catchments model of phosphorus dynamics, which incorporates sediment and biotic interaction, resulted in improved whole‐catchment performance over SIMCAT, although there are still large epistemic uncertainties from land‐phase export coefficients and runoff. However, the very large sampling errors in routine monitoring make it difficult to invest confidence in the modelling, especially because we know phosphorous transport to be very episodic and driven by high flow conditions for which there are few samples. The environmental modelling community seems to have been stuck in this position for some time, and whilst it is useful to use an uncertainty framework to highlight these issues, it has not widely been adopted, perhaps because there is no clear mechanism to allow uncertainties to influence investment decisions. This raises the question as to whether it might better place a cost on uncertainty and use this to drive more data collection or improved models, before making investment decisions concerning, for example, mitigation strategies. Copyright © 2016 John Wiley & Sons, Ltd.