Conceptual Rainfall-runoff Model Research Articles

Quantifying sensitivity to droughts – an experimental modeling approach

Abstract. Meteorological droughts like those in summer 2003 or spring 2011 in Europe are expected to become more frequent in the future. Although the spatial extent of these drought events was large, not all regions were affected in the same way. Many catchments reacted strongly to the meteorological droughts showing low levels of streamflow and groundwater, while others hardly reacted. Also, the extent of the hydrological drought for specific catchments was different between these two historical events due to different initial conditions and drought propagation processes. This leads to the important question of how to detect and quantify the sensitivity of a catchment to meteorological droughts. To assess this question we designed hydrological model experiments using a conceptual rainfall-runoff model. Two drought scenarios were constructed by selecting precipitation and temperature observations based on certain criteria: one scenario was a modest but constant progression of drying based on sorting the years of observations according to annual precipitation amounts. The other scenario was a more extreme progression of drying based on selecting months from different years, forming a year with the wettest months through to a year with the driest months. Both scenarios retained the observed intra-annual seasonality for the region. We evaluated the sensitivity of 24 Swiss catchments to these scenarios by analyzing the simulated discharge time series and modeled storage. Mean catchment elevation, slope and area were the main controls on the sensitivity of catchment discharge to precipitation. Generally, catchments at higher elevation and with steeper slopes appeared less sensitive to meteorological droughts than catchments at lower elevations with less steep slopes.

The impact of projected changes in climate variability on the reliability of surface water supply in Scotland

Projected changes in precipitation and evapotranspiration under climate change and their impacts on the reliability of six water storage reservoirs and two river intake schemes in Scotland are examined. A conceptual rainfall–runoff model was used to simulate catchment runoff which, together with evapotranspiration, served as inputs into a reservoir model. Outputs from a regional climate model coupled with a weather generator indicate an increase in rainfall variability and evapotranspiration throughout the 21st century, resulting in a decrease in both the time-based and volumetric reliability of the reservoirs under the assumption of an unchanging demand, albeit with a less drastic reduction for the volumetric approach. It was found that the variability of rainfall had the greatest effect on reservoir reliability, outweighing the positive effect of an increase in total annual precipitation, while evapotranspiration had a lesser impact. A more drastic reduction in reliability was observed for the river intake schemes given their lack of storage capacity. The increase in water demand based on demographic projections further reduced reservoir reliability, especially when monthly variations in demand were taken into account. This paper concludes by suggesting adaptive strategies to deal with the projected changes in the supply and demand for water.

Assessment of the internal dynamics of the Australian Water Balance Model under different calibration regimes

Conceptual rainfall runoff models are used extensively in practice, as they provide a good balance between transparency and computational and data requirements. However, the degree to which they are able to represent underlying physical processes is poorly understood. This is because the performance of such models is generally assessed based on their ability to match total streamflow, rather than component processes. In this paper, the ability of the Australian Water Balance Model (AWBM) to represent baseflow and quickflow is assessed for 66 synthetic catchments with different physical characteristics and hydrological inputs under seven calibration regimes utilising a shuffled complex evolution (SCE) algorithm. The “observed” total-, base- and quick-flow hydrographs for these catchments are generated using HydroGeoSphere. The results indicate that while AWBM is generally able to match total streamflow well, the same does not apply to baseflow and quickflow, suggesting that these processes are not represented well by AWBM.

Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization

Parameter optimization for the conceptual rainfall-runoff (CRR) model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the Xinanjiang (XAJ) model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years) and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.

Predicting Surface Runoff from Catchment to Large Region

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1) modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2) parameterizing hydrological models in ungauged catchments, (3) improving hydrological model structure, and (4) using new remote sensing precipitation data.

Assessing the impacts of climate change and dams on floodplain inundation and wetland connectivity in the wet–dry tropics of northern Australia

Summary Floodplain wetlands and their hydrological connectivity with main river channels in the Australian wet–dry tropics are under increasing pressure from global climate change and water resource development, and there is a need for modelling tools to estimate the time dynamics of connectivity. This paper describes an integrated modelling framework combining conceptual rainfall–runoff modelling, river system modelling and hydrodynamic (HD) modelling to estimate hydrological connectivity between wetlands and rivers in the Flinders and Gilbert river catchments in northern Australia. Three historical flood events ranging from a mean annual flood to a 35-year return period flood were investigated using a two dimensional HD model (MIKE 21). Inflows from upstream catchments were estimated using a river network model. Local runoff within the HD modelling domain was simulated using the Sacramento rainfall–runoff model. The Shuttle Radar Topography Mission (SRTM) derived 30 m DEM was used to reproduce floodplain topography, stream networks and wetlands in the HD model. The HD model was calibrated using stream gauge data and inundation maps derived from satellite (MODIS: MODerate resolution Imaging Spectroradiometer) imagery. An algorithm was developed to combine the simulated water heights with the DEM to quantify inundation and flow connection between wetlands and rivers. The connectivity of 18 ecologically important wetlands on the Flinders floodplain and 7 on the Gilbert floodplain were quantified. The impacts of climate change and water resource development on connectivity to individual wetlands were assessed under a projected dry climate (2nd driest of 15 GCMs), wet climate (2nd wettest of 15 GCMs) and dam conditions. The results indicate that changes in rainfall under a wetter and drier future climate could have large impacts on area, duration and frequency of inundation and connectivity. Topographic relief, river bank elevation and flood magnitude were found to be the key factors contributing to the level of connectivity. Under a wetter future climate the average duration of connection of wetlands to the main river channel increased by 7% and under a drier climate it decreased by 18%. Construction of a 248 GL dam in the Flinders catchment and two (498 and 271 GL capacity) in the Gilbert catchment could reduce the average duration of connectivity by 1% and 2% in the Flinders and Gilbert catchments respectively. This information is potentially useful to future studies on the flood-dependent ecology in this region.

Combined Impact of Inland and Coastal Floods: Mapping Knowledge Base for Development of Planning Strategies

AbstractConcurrent incidence of heavy rainfall events and coastal storm surges can cause damages of exponential proportion to the coastal habitants and infrastructures. Super Storm Sandy and hurricane Irene were wake up calls that the floodplains and evacuation zones in New York City need major revision. Federal and State emergency agencies have been working on revising the appropriate maps and spatial tools for planning and management purposes. This paper presents an integrated scheme for floodplain delineation in coastal areas, considering the combined effect of inland and coastal flooding. The proposed scheme is applied to the Bronx River watershed in the Bronx section of New York City. Consideration of inland and coastal flooding is provided by developing a conceptual rainfall-runoff model and a sea level simulation model. Climate change has intensified the intensity and frequency of extreme rainfall events and storm surges in the region. In order to evaluate climate change impacts on floodplain, proj...

A constraint-based search algorithm for parameter identification of environmental models

Abstract. Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available (e.g., when making predictions in ungauged basins). In this study, we provide an alternative approach for parameter identification using constraints based on two types of restrictions derived from prior (or expert) knowledge. The first, called parameter constraints, restricts the solution space based on realistic relationships that must hold between the different model parameters while the second, called process constraints requires that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a search algorithm for finding parameter sets that simultaneously satisfy such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler's intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.

Hillslope-scale experiment demonstrates the role of convergence during two-step saturation

Abstract. Subsurface flow and storage dynamics at hillslope scale are difficult to ascertain, often in part due to a lack of sufficient high-resolution measurements and an incomplete understanding of boundary conditions, soil properties, and other environmental aspects. A continuous and extreme rainfall experiment on an artificial hillslope at Biosphere 2's Landscape Evolution Observatory (LEO) resulted in saturation excess overland flow and gully erosion in the convergent hillslope area. An array of 496 soil moisture sensors revealed a two-step saturation process. First, the downward movement of the wetting front brought soils to a relatively constant but still unsaturated moisture content. Second, soils were brought to saturated conditions from below in response to rising water tables. Convergent areas responded faster than upslope areas, due to contributions from lateral subsurface flow driven by the topography of the bottom boundary, which is comparable to impermeable bedrock in natural environments. This led to the formation of a groundwater ridge in the convergent area, triggering saturation excess runoff generation. This unique experiment demonstrates, at very high spatial and temporal resolution, the role of convergence on subsurface storage and flow dynamics. The results bring into question the representation of saturation excess overland flow in conceptual rainfall-runoff models and land-surface models, since flow is gravity-driven in many of these models and upper layers cannot become saturated from below. The results also provide a baseline to study the role of the co-evolution of ecological and hydrological processes in determining landscape water dynamics during future experiments in LEO.

Climate change scenarios and its impact on water resources of Langtang Khola Basin, Nepal

Abstract. General Circulation Models (GCMs) successfully simulate future climate variability and climate change on a global scale; however, poor spatial resolution constrains their application for impact studies at a regional or a local level. The dynamically downscaled precipitation and temperature data were used for the future climate scenarios prediction for the period 2000–2050s, under the Special Report on Emissions Scenarios (SRES) A2 and A1B scenarios. In addition, rating equation was developed from measured discharge and gauge (stage) height data. The generated precipitation and temperature data from downscale and rating equation was used to run the HBV-Light 3.0 conceptual rainfall–runoff model for the calibration and validation of the model, gauge height was taken in the reference period (1988–2009). In the HBV-Light 3.0, a GAP optimization approach was used to calibrate the observed streamflow. From the precipitation scenarios with SRES A2 and A1B emissions at Kyanging, an increase of precipitation during summer and spring and a decrease during winter and autumn seasons was shown. The model projected annual precipitation for the 2050s of both the A2 and A1B scenarios are 716.4 mm and 703.6 mm, respectively. Such precipitation projections indicate the future increase of precipitation in all seasons except the summer. By the end of the 2050s simulation projects an increase maximum (minimum) discharge of 37.8 m3/s (13.9 m3/s) for A1B scenario and 36.2 m3/s (14.3 m3/s) for A2 scenario. A maximum projected discharge will increase for all seasons except for spring, whereas the minimum will decrease in summer.

The importance of hydrological uncertainty assessment methods in climate change impact studies

Abstract. Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting

This study compares sequential and variational streamflow assimilation techniques for short-term hydrological forecasting based on a lumped conceptual rainfall-runoff model and two dissimilar watersheds (Canada and Germany). The assessment targets the Ensemble Kalman filter (EnKF) and variational data assimilation (VDA). Deterministic streamflow forecasts are computed on a daily time step over a 10-day forecast horizon, using meteorological observations as inputs to the model. Results show that the EnKF leads to the highest performance for all forecast horizons while the optimal set-up for the VDA, which often competes with the EnKF, varies from one watershed to the other. EnKF surpasses forecasts without assimilation for all horizons and for both watersheds where the NSE varies between 0.88 and 0.79 on the au Saumon watershed in Canada and between 0.92 and 0.87 on the Schlehdorf watershed in Germany on a 10-day horizon, which is not always true for the VDA. The naïve output assimilation is also assessed and is only helpful for the first two days of the forecasts.

Parameter uncertainty and identifiability of a conceptual semi-distributed model to simulate hydrological processes in a small headwater catchment in Northwest China

Abstract Introduction Conceptual hydrological models are useful tools to support catchment water management. However, the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task. Here, we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model, HBV-light, with emphasis on parameter identifiability, uncertainty, and model structural validity. Results The results of a regional sensitivity analysis (RSA) show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used. Results based on the generalized likelihood uncertainty estimation (GLUE) method further show that most of the model parameters are well constrained, showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used. Finally, the dynamic identifiability analysis (DYNIA) shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff. Conclusions The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy. Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis. More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and, specifically, improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff. Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.

Analysing the Sub-processes of a Conceptual Rainfall-Runoff Model Using Information About the Parameter Sensitivity and Variance

Sensitivity analyses are often carried out for the main model output and using a predefined evaluation period. It is possible, however, to get much more knowledge about how the system works when estimating the sensitivity for the output of individual modules for each time step (time-varying sensitivity analysis). This is shown here using a variance-based sensitivity analysis for a conceptual rainfall-runoff model applied to a mountainous catchment. The first-order and total sensitivities were computed using Sobol’s method. Since the parameter ranges used in the sensitivity analysis were obtained through a Markov chain Monte Carlo (MCMC) sampling, the sensitivity indices reflect the parameter uncertainty and make a good use of the previous available information. As a first step, the variance of each flow component was calculated. The flow component with the highest variance at each time step can be regarded as the ‘dominant physical control’, which has been defined as the parameter to which the model output reacts in a highly sensitive way when the parameter varies within realistic ranges. This information about the dominant processes can be used for facilitating model calibration by identifying the periods on which to focus when calibrating different parameters. It also can be useful for estimating the amount of data available for calibrating each process. The second part presents the total sensitivity indices and interactions for individual flow components considering a 2-year period. The results show large differences in the time-varying sensitivity patterns of the flow components. It is concluded that such a high-resolution sensitivity analysis for each flow component is a good complement to a sensitivity analysis of the total discharge, increasing our understanding about the internal functioning of individual modules which can be helpful when comparing different model formulations.

Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China

Abstract. Although elevation data are globally available and used in many existing hydrological models, their information content is still underexploited. Topography is closely related to geology, soil, climate and land cover. As a result, it may reflect the dominant hydrological processes in a catchment. In this study, we evaluated this hypothesis through four progressively more complex conceptual rainfall-runoff models. The first model (FLEXL) is lumped, and it does not make use of elevation data. The second model (FLEXD) is semi-distributed with different parameter sets for different units. This model uses elevation data indirectly, taking spatially variable drivers into account. The third model (FLEXT0), also semi-distributed, makes explicit use of topography information. The structure of FLEXT0 consists of four parallel components representing the distinct hydrological function of different landscape elements. These elements were determined based on a topography-based landscape classification approach. The fourth model (FLEXT) has the same model structure and parameterization as FLEXT0 but uses realism constraints on parameters and fluxes. All models have been calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two sub-catchments. It was found that FLEXT0 and FLEXT perform better than the other models in nested sub-catchment validation and they are therefore better spatially transferable. Among these two models, FLEXT performs better than FLEXT0 in transferability. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish between landscape elements with different hydrological functions; (2) FLEXT0 and FLEXT are much better equipped to represent the heterogeneity of hydrological functions than a lumped or semi-distributed model, and hence they have a more realistic model structure and parameterization; (3) the soft data used to constrain the model parameters and fluxes in FLEXT are useful for improving model transferability. Most of the precipitation on the forested hillslopes evaporates, thus generating relatively little runoff.

A test of how coupling of vegetation to the atmosphere and climate spatial variation affects water yield modelling in mountainous catchments

Evapotranspiration (ET) is a major component of the water balance in water-limited ecosystems in south-east Australia. ET is spatially variable, especially in steeper catchments, due to variations in land cover and topography. In hydrological models, ET is most often simulated as supply-limited, i.e. depending on the soil moisture availability. In reality, transpiration is also strongly controlled by atmospheric demand, particularly during demand-limited periods. In this study we used a simple conceptual rainfall-runoff model (HBV) to test the hypothesis that atmospheric constraints to transpiration, especially during conditions when soil moisture is not limiting, determine the variations in catchment water yield in the Australian Alps. This is tested by including demand limitations of transpiration in the macroscopic ET function. A second hypothesis was that model performance can be improved by using spatial climate surfaces, derived using physiographic co-variables, rather than point (gauge) measurements. Both hypotheses were tested on a medium-sized catchment (148 km2) in south-east Australia, under a forest cover composed of mostly native eucalypts. Spatial surfaces of the model inputs (air temperature, rainfall, vapour pressure deficit and potential ET) were generated, taking into account the topographic influence of the forcing meteorological variables. Spatial variability in meteorological variables and potential ET was greatest during winter. Inclusion of atmospheric-induced limitations to transpiration into the ET sub model improved streamflow simulation, especially during demand-limited periods. This was expected given that canopies of eucalypt forests are well-coupled to the atmosphere – changes in atmospheric demand have a large influence on transpiration. In addition, high resolution (30 m) surfaces of potential ET, temperature, and vapour pressure deficit, developed by including the influence of topography on forcing variables, improved model performance compared to point-based inputs.

Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach

Practical experience has demonstrated that single objective functions, no matter how carefully chosen, prove to be inadequate in providing proper measurements for all of the characteristics of the observed data. One strategy to circumvent this problem is to define multiple fitting criteria that measure different aspects of system behavior, and to use multi-criteria optimization to identify non-dominated optimal solutions. Unfortunately, these analyses require running original simulation models thousands of times. As such, they demand prohibitively large computational budgets. As a result, surrogate models have been used in combination with a variety of multi-objective optimization algorithms to approximate the true Pareto-front within limited evaluations for the original model. In this study, multi-objective optimization based on surrogate modeling (multivariate adaptive regression splines, MARS) for a conceptual rainfall-runoff model (Xin’anjiang model, XAJ) was proposed. Taking the Yanduhe basin of Three Gorges in the upper stream of the Yangtze River in China as a case study, three evaluation criteria were selected to quantify the goodness-of-fit of observations against calculated values from the simulation model. The three criteria chosen were the Nash-Sutcliffe efficiency coefficient, the relative error of peak flow, and runoff volume (REPF and RERV). The efficacy of this method is demonstrated on the calibration of the XAJ model. Compared to the single objective optimization results, it was indicated that the multi-objective optimization method can infer the most probable parameter set. The results also demonstrate that the use of surrogate-modeling enables optimization that is much more efficient; and the total computational cost is reduced by about 92.5%, compared to optimization without using surrogate modeling. The results obtained with the proposed method support the feasibility of applying parameter optimization to computationally intensive simulation models, via reducing the number of simulation runs required in the numerical model considerably.

Quantifying uncertainty in rainfall–runoff models due to design losses using Monte Carlo simulation: a case study in New South Wales, Australia

With the potentially devastating consequences of flooding, it is crucial that uncertainties in the modelling process are quantified in flood simulations. In this paper, the impact of uncertainties in design losses on peak flow estimates is investigated. Simulations were carried out using a conceptual rainfall–runoff model called RORB in four catchments along the east coast of New South Wales, Australia. Monte Carlo simulation was used to evaluate parameter uncertainty in design losses, associated with three loss models (initial loss–continuing loss, initial loss–proportional loss and soil water balance model). The results show that the uncertainty originating from each loss model differs and can be quite significant in some cases. The uncertainty in the initial loss–proportional loss model was found to be the highest, with estimates up to 2.2 times the peak flow, whilst the uncertainty in the soil water balance model was significantly less, with up to 60 % variability in peak flows for an annual exceedance probability of 0.02. Through applying Monte Carlo simulation a better understanding of the predicted flows is achieved, thus providing further support for planning and managing river systems.

Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes

Abstract. Investigation was made on the climate change signal for hydrometeorological and hydrological variables along the Paute River basin, in the southern Ecuador Andes. An adjusted quantile perturbation approach was used for climate downscaling, and the impact of climate change on runoff was studied for two nested catchments within the basin. The analysis was done making use of long daily series of seven representative rainfall and temperature sites along the study area and considering climate change signals of global and regional climate models for IPCC SRES scenarios A1B, A2 and B1. The determination of runoff was carried out using a lumped conceptual rainfall–runoff model. The study found that the range of changes in temperature is homogeneous for almost the entire region with an average annual increase of approximately +2.0 &degC. However, the warmest periods of the year show lower changes than the colder periods. For rainfall, downscaled results project increases in the mean annual rainfall depth and the extreme daily rainfall intensities along the basin for all sites and all scenarios. Higher changes in extreme rainfall intensities are for the wetter region. These lead to changes in catchment runoff flows, with increasing high peak flows and decreasing low peak flows. The changes in high peak flows are related to the changes in rainfall extremes, whereas the decreases in the low peak flows are due to the increase in temperature and potential evapotranspiration together with the reduction in the number of wet days.

Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes – Part 2: Intercomparison of models and calibration approaches

An intercomparison of different approaches for the construction and calibration of lumped conceptual rainfall-runoff models is made based on two case studies with unrelated meteorological and hydrological characteristics located in two regions, Belgium and Kenya. While a model with pre-fixed “one-size-fits-all” model structure is traditionally used in lumped conceptual rainfall-runoff modeling, this paper shows the advantages of model structure inference from data or field evidence in a case-specific and step-wise way using non-commensurable measures derived from observed series. The step-wise model structure identification method does not lead to higher accuracy than the traditional approach when evaluated using common statistical criteria like the Nash–Sutcliffe efficiency. The method is, however, favorable to produce a well-balanced calibration obtaining accurate results for a wide range of runoff properties: total flows, quick and slow subflows, cumulative volumes, peak flows, low flows, frequency distributions of peak and low flows, changes in quick flows for given changes in rainfall. It furthermore is shown that model performance evaluation procedures that account for the flow residual serial dependency and homoscedasticity are preferred. Explicit evaluation of model results for peak and/or low flow extremes and changes in these extremes make the models useful for impact investigations on such hydrological extremes.