Spatial and temporal dynamics of stream water particulate and dissolved N, P and C forms along a catchment transect, NE Scotland

The dynamics of dissolved and particulate N, P and organic C were examined for field drains, through a headwater (4 km²), into a mesoscale stream (51 km²) and river (1844 km²) catchment. Distributions of N and P forms were similar in the agricultural headwater and field drains; annual P fluxes of particulate and dissolved forms were of equal magnitude, whilst N was dominated by NO₃–N. Across all scales organic P was an important, often dominant, component of the dissolved P. Temporal variation in nutrient concentrations and proportions was greatest in the headwater, where storms resulted in the generation of large concentrations of suspended particulate matter, particulate and dissolved P, particularly following dry periods. The data suggest that groundwater and minor point source inputs to the mesoscale catchment buffered the temporal variability in hydrochemistry relative to the headwater. Summer low flows were associated with large PO₄–P concentrations in the mesoscale catchment at a critical time of biological sensitivity. At the largest river catchment scale, organic forms of C, N and P dominated. Inorganic nutrient concentrations were kept small through dilution by runoff from upland areas and biological processes converted dissolved N and P to particulate forms. The different processes operating between the drain/headwater to the large river scale have implications for river basin management. Given the prevalence of organic and particulate P forms in our catchment transect, the bioavailability of these fractions needs to be better understood.     

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.     

Sensitivity and identifiability of stream flow generation parameters of the SWAT model

Implementation of sensitivity analysis (SA) procedures is helpful in calibration of models and also for their transposition to different watersheds. The reported studies on SA of Soil and Water Assessment Tool (SWAT) model were mostly focused on identifying parameters for pruning or modifying during the calibration process. This paper presents a sensitivity and identifiability analysis of model parameters that influence stream flow generation in SWAT. The analysis was focused on evaluating the sensitivity of the parameters in different climatic settings, temporal scales and flow regimes. The global sensitivity analysis (GSA) technique based on classical decomposition of variance, Sobol', was employed in this study. The results of the study indicate that modeled stream flow show varying sensitivity to parameters in different climatic settings. The results also suggest that the identifiability of a parameter for a given watershed is a major concern in calibrating the model for the specific watershed, as it might lead to equifinality of parameters. The SWAT model parameters show varying sensitivity in different years of simulation suggesting the requirement for dynamic updation of parameters during the simulation. The sensitivity of parameters during various flow regimes (low, medium and high flow) is also found to be uneven, which suggests the significance of a multi‐criteria approach for the calibration of models. Copyright © 2010 John Wiley & Sons, Ltd.     

Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models

It is crucial to analyze the sensitivity of watershed (rainfall-runoff) models to imperfect knowledge of rainfall input, in order to judge whether or not they are reliable and robust, especially if they are to be used for operational purposes. In this paper, a new approach to sensitivity analysis is proposed, based on a comparison between the efficiency ratings and parameter values of the models and the quality of rainfall input estimate (GORE and BALANCE indexes, assessing the quality of rainfall time distribution and the total depth respectively). Data from three watersheds of increasing size (71, 1120, and 10700 km²), are used to test three watershed models of varying complexity (three-parameter GR3J model and six-parameter modified versions of TOPMODEL and IHACRES).

These models are able to cope with imperfect rainfall input estimates, and react to improvements in rainfall input accuracy by better performance and reduced variability of efficiency. Two different types of model behavior were identified: the models either benefit from improved rainfall data by producing more consistent parameter values, or they are unable to take advantage of the improvements. Although the watershed size seems to be immaterial, the smaller watersheds appear to need more precise areal rainfall estimates (a higher concentration of raingages) to ensure good modeling results.     

Influence of rainfall spatial variability on flood prediction

This paper deals with the sensitivity of distributed hydrological models to different patterns that account for the spatial distribution of rainfall: spatially averaged rainfall or rainfall field. The rainfall data come from a dense network of recording rain gauges that cover approximately 2000 km² around Mexico City. The reference rain sample accounts for the 50 most significant events, whose mean duration is about 10 h and maximal point depth 170 mm. Three models were tested using different runoff production models: storm-runoff coefficient, complete or partial interception. These models were then applied to four fictitious homogeneous basins, whose sizes range from 20 to 1500 km². For each test, the sensitivity of the model is expressed as the relative differences between the empirical distribution of the peak flows (and runoff volumes), calculated according to the two patterns of rainfall input: uniform or non-uniform. Differences in flows range from 10 to 80%, depending on the type of runoff production model used, the size of the basin and the return period of the event. The differences are generally moderate for extreme events. In the local context, this means that uniform design rainfall combining point rainfall distribution and the probabilistic concept of the areal reduction factor could be sufficient to estimate major flood probability. Differences are more significant for more frequent events. This can generate problems in calibrating the hydrological model when spatial rainfall localization is not taken into account: a bias in the estimation of parameters makes their physical interpretation difficult and leads to overestimation of extreme flows.     

Methodology for construction, calibration and validation of a national hydrological model for Denmark

An integrated groundwater/surface water hydrological model with a 1 km² grid has been constructed for Denmark covering 43,000 km². The model is composed of a relatively simple root zone component for estimating the net precipitation, a comprehensive three-dimensional groundwater component for estimating recharge to and hydraulic heads in different geological layers, and a river component for streamflow routing and calculating stream–aquifer interaction. The model was constructed on the basis of the MIKE SHE code and by utilising comprehensive national databases on geology, soil, topography, river systems, climate and hydrology. The present paper describes the modelling process for the 7330 km² island of Sjælland with emphasis on the problems experienced in combining the classical paradigms of groundwater modelling, such as inverse modelling of steady-state conditions, and catchment modelling, focussing on dynamic conditions and discharge simulation. Three model versions with different assumptions on input data and parameter values were required until the performance of the final, according to pre-defined accuracy criteria, model was evaluated as being satisfactory. The paper highlights the methodological issues related to establishment of performance criteria, parameterisation and assessment of parameter values from field data, calibration and validation test schemes. Most of the parameter values were assessed directly from field data, while about 10 ‘free’ parameters were subject to calibration using a combination of inverse steady-state groundwater modelling and manual trial-and-error dynamic groundwater/surface water modelling. Emphasising the importance of tests against independent data, the validation schemes included combinations of split-sample tests (another period) and proxy-basin tests (another area).     

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.     

Spatial contributions of diffuse inputs and within-channel processes to the form of stream water phosphorus over storm events

Concentrations of suspended particulate matter (SPM), NO₃-N and P fractions: PO₄-P, dissolved organic P (DOP), particulate P (PP) and bioavailable exchangeable P were examined over 5 storm events in two nested agricultural catchments in NE Scotland: a (51 km²) catchment and its headwater (4 km²). NO₃-N showed anticlockwise hysteresis for all storms in both catchments. In contrast, the headwater showed strong clockwise hysteresis of SPM, dissolved and particulate P concentrations, but which weakened through summer to spring. Less pronounced hysteresis of P forms in the larger catchment was attributed to a combination of factors: a less energetic system, nutrient leaching from the floodplain, a point source of a small sewage treatment works and the occurrence of coarser soil and sediment parent materials with less P adsorption and transport capacity. The headwater exhibited a strong ‘first flush’ effect of sediment and dissolved P, particularly following dry conditions, received a significant transfer of readily-solubilized organic P from the surrounding soils in late summer and after manure applications in winter, and was the likely cause of large sediment associated P signals observed in the 51 km² catchment. Our results suggest that steeper gradient headwaters should be targeted for riparian improvements to mitigate soil erosion from headwater fields. The efficiency of riparian erosion controls is also dependant on the size of the store of fine sediment material within the stream channel and this may be large.     

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.     

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.     

Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds

In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981–2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5–3.0 mm day⁻¹) against observations and an overestimation (10–60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications.     

Macrozoobenthic community of Fuxian Lake, the deepest lake of southwest China

Located in the Yunnan–Guizhou Plateau in southwest China, Fuxian Lake covers an area of 211 km², with maximum depth of 155 m. It is known to have a unique fauna, including 14 described endemic species. In order to describe the zoobenthic community of the lake more completely, the present study was conducted from August 2002 to August 2003. Altogether 62 benthic taxa, including 22 oligochaetes, 21 molluscs and 18 insects were identified, of which the dominant taxa belonged to Potamothrix, Procladius and Paraprososthenia. The standing stocks of benthos were much higher in the littoral (824 ind/m² in density, 3.72 g/m² in biomass) than in the profundal region (23 ind/m² in density, 0.10 g/m² in biomass). Species richness was greatest in summer and standing stocks were larger in spring and summer than in other seasons. Analyses of functional feeding groups indicated that collector-gatherers and scrapers were predominant in entire lake. Stepwise multiple regression analysis demonstrated that the water depth is the most important factor affecting the distribution of macrozoobenthos.     

Predicting water yield from a mountain ash forest catchment using a terrain analysis based catchment model

The structure, capabilities and performance of a distributed parameter hydrologic model are described. The model, called Topog-Yield, permits a transient analysis of unsaturated-saturated flow and evapotranspiration to be performed across complex terrain using a one-dimensional framework. It is applied to a 0.32 km² mountain ash (Eucalyptus regnans) forest catchment in the central Victorian highlands, Australia. We compare observed and predicted daily runoff values for the site over a continuous 12 year period (1972–1983) when the catchment vegetation was in an undisturbed climax condition. All input parameter values were based on published or measured data, although some variables were adjusted within the range of known variability to yield a best fit between predicted and observed streamflow in the first year of simulation, 1972. Although the model was ‘calibrated’ for the first year, all variables other than climatic inputs remained fixed for the following 11 years. Modelled and observed daily runoff values compare well throughout the period of simulation, despite a wide range of climatic conditions. When modelled daily runoff values were lumped on a monthly basis, the model was able to explain 87% of the variation in observed monthly streamflows over the 12 year period. Modelled annual runoff was within ±5% of observed values for 6 of the 12 years of record. Annual runoff prediction errors exceeded ±10% of observed values in only 2 of the 12 years. By the end of the 12 year simulation, the model had over-predicted runoff by less than 5%. Input data requirements and model results are discussed in the light of a preliminary sensitivity analysis.     

Soil moisture variation in relation to topography and land use in a hillslope catchment of the Loess Plateau, China

The profile characteristics and the temporal dynamics of soil moisture variation were studied at 26 locations in Da Nangou catchment (3.5 km²) in the loess area of China. Soil moisture measurements were performed biweekly at five depths in the soil profile (0–5, 10–15, 20–25, 40–45 and 70–75 cm) from May to October 1998 using Delta-T theta probe. Soil moisture profile type and temporal variation type and their relationship to topography and land use were identified by detrended canonical correspondence analysis (DCCA) and correlation analysis. The profile distribution of time-averaged soil moisture content can be classified into three types i.e. decreasing-type, waving-type and increasing-type. The profile features of soil moisture (e.g. profile gradient and profile variability) are influenced by different environmental factors. The profile type of soil moisture is only attributed to land use while profile gradient and profile variability of soil moisture is mainly related to land use and topography (e.g. landform type and slope). The temporal dynamics of layer-averaged soil moisture content is grouped into three types including three-peak type, synchro-four-peak type and lagged-four-peak type. These types are controlled by topography rather than by land use. The temporal dynamic type of soil moisture shows significant correlation with relative elevation, slope, aspect, while temporal variance displays significant relation with slope shape. The mean soil moisture is related to both the profile and dynamics features of soil moisture and is controlled by both land use and topography (e.g. aspect, position, slope and relative elevation). The spatial variability of soil moisture across landscape varies with both soil depths and temporal evolution.     

PSYCHIC – A process-based model of phosphorus and sediment transfers within agricultural catchments. Part 2. A preliminary evaluation

This paper describes the preliminary evaluation of the PSYCHIC catchment scale (Tier 1) model for predicting the mobilisation and delivery of phosphorus (P) and suspended sediment (SS) in the Hampshire Avon (1715 km²) and Herefordshire Wye (4017 km²) drainage basins, in the UK, using empirical data. Phosphorus and SS transfers to watercourses in the Wye were predicted to be greater than corresponding delivery in the Avon; SS, 249 vs 33 kg ha⁻¹ yr⁻¹; DP, 2.57 vs 1.26 kg ha⁻¹ yr⁻¹; PP, 2.20 vs 0.56 kg ha⁻¹ yr⁻¹. The spatial pattern of the predicted transfers was relatively uniform across the Wye drainage basin, whilst in the Avon, delivery to watercourses was largely confined to the river corridors and small areas of drained land. Statistical performance in relation to predicted exports of P and SS, using criteria for relative error (RE) and root mean square error (RMSE), reflected the potential shortcomings associated with using longer-term climate data for predicting shorter-term (2002–2004) catchment response and the need to refine calculations of point source contributions and to incorporate additional river basin processes such as channel bank erosion and in-stream geochemical processing. PSYCHIC is therefore best suited to characterising longer-term catchment response.     

Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

Green roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration-validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling-Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration-validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other non-calibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof. Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity. Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.     

Simulating water temperatures and stratification of a pre‐alpine lake with a hydrodynamic model: calibration and sensitivity analysis of climatic input parameters

We report on the calibration of the one‐dimensional hydrodynamic lake model Dynamic Reservoir Simulation Model to simulate the water temperature conditions of the pre‐alpine Lake Ammersee (southeast Germany) that is a representative of deep and large lakes in this region. Special focus is given to the calibration in order to reproduce the correct thermal distribution and stratification including the time of onset and duration of summer stratification. To ensure the application of the model to investigate the impact of climate change on lakes, an analysis of the model sensitivity under stepwise modification of meteorological input parameters (air temperature, wind speed, precipitation, global radiation, cloud cover, vapour pressure and tributary water temperature) was conducted. The total mean error of the calibration results is ?0.23 °C, the root mean square error amounts to 1.012 °C. All characteristics of the annual stratification cycle were reproduced accurately by the model. Additionally, the simulated deviations for all applied modifications of the input parameters for the sensitivity analysis can be differentiated in the high temporal resolution of monthly values for each specific depth. The smallest applied alteration to each modified input parameter caused a maximum deviation in the simulation results of at least 0.26 °C. The most sensitive reactions of the model can be observed through modifications of the input parameters air temperature and wind speed. Hence, the results show that further investigations at Lake Ammersee, such as coupling the hydrodynamic model with chemo‐dynamic models to assess the impact of changing climate on biochemical conditions within lakes, can be carried out using Dynamic Reservoir Simulation Model. Copyright © 2012 John Wiley & Sons, Ltd.     

Uncertainty in water resource model parameters used for climate change impact assessment

Despite their acknowledged limitations, lumped conceptual models continue to be used widely for climate‐change impact assessments. Therefore, it is important to understand the relative magnitude of uncertainties in water resource projections arising from the choice of model calibration period, model structure, and non‐uniqueness of model parameter sets. In addition, external sources of uncertainty linked to choice of emission scenario, climate model ensemble member, downscaling technique(s), and so on, should be acknowledged. To this end, the CATCHMOD conceptual water balance model was used to project changes in daily flows for the River Thames at Kingston using parameter sets derived from different subsets of training data, including the full record. Monte Carlo sampling was also used to explore parameter stability and identifiability in the context of historic climate variability. Parameters reflecting rainfall acceptance at the soil surface in simpler model structures were found to be highly sensitive to the training period, implying that climatic variability does lead to variability in the hydrologic behaviour of the Thames basin. Non‐uniqueness of parameters for more complex model structures results in relatively small variations in projected annual mean flow quantiles for different training periods compared with the choice of emission scenario. However, this was not the case for subannual flow statistics, where uncertainty in flow changes due to equifinality was higher in winter than summer, and comparable in magnitude to the uncertainty of the emission scenario. Therefore, it is recommended that climate‐change impact assessments using conceptual water balance models should routinely undertake sensitivity analyses to quantify uncertainties due to parameter instability, identifiability and non‐uniqueness. Copyright © 2005 John Wiley & Sons, Ltd.     

Sensitivity of the Weather Research and Forecasting (WRF) model to downscaling ratios and storm types in rainfall simulation

Accurate information of rainfall is needed for sustainable water management and more reliable flood forecasting. The advances in mesoscale numerical weather modelling and modern computing technologies make it possible to provide rainfall simulations and forecasts at increasingly higher resolutions in space and time. However, being one of the most difficult variables to be modelled, the quality of the rainfall products from the numerical weather model remains unsatisfactory for hydrological applications. In this study, the sensitivity of the Weather Research and Forecasting (WRF) model is investigated using different domain settings and various storm types to improve the model performance of rainfall simulation. Eight 24‐h storm events are selected from the Brue catchment, southwest England, with different spatial and temporal distributions of the rainfall intensity. Five domain configuration scenarios designed with gradually changing downscaling ratios are used to run the WRF model with the ECMWF 40‐year reanalysis data for the periods of the eight events. A two‐dimensional verification scheme is proposed to evaluate the amounts and distributions of simulated rainfall in both spatial and temporal dimensions. The verification scheme consists of both categorical and continuous indices for a first‐level assessment and a more quantitative evaluation of the simulated rainfall. The results reveal a general improvement of the model performance as we downscale from the outermost to the innermost domain. Moderate downscaling ratios of 1:7, 1:5 and 1:3 are found to perform better with the WRF model in giving more reasonable results than smaller ratios. For the sensitivity study on different storm types, the model shows the best performance in reproducing the storm events with spatial and temporal evenness of the observed rainfall, whereas the type of events with highly concentrated rainfall in space and time are found to be the trickiest case for WRF to handle. Finally, the efficiencies of several variability indices are verified in categorising the storm events on the basis of the two‐dimensional rainfall evenness, which could provide a more quantitative way for the event classification that facilitates further studies. It is important that similar studies with various storm events are carried out in other catchments with different geographic and climatic conditions, so that more general error patterns can be found and further improvements can be made to the rainfall products from mesoscale numerical weather models. Copyright © 2011 John Wiley & Sons, Ltd.     

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.