MIKE SHE Model Research Articles

Modelling the hydrology of a catchment using a distributed and a semi‐distributed model

AbstractVarious hydrological models exist that describe the phases in the hydrologic cycle either in an empirical, semi‐mechanistic or fully mechanistic way. The way and level of detail for the different processes of the hydrologic cycle that needs to be described depends on the objective, the application and the availability of data. In this study the performance of two different models, the fully distributed MIKE SHE model and the semi‐distributed SWAT model, was assessed. The aim of the comparative study was to examine if both models are equally able to describe the different phases in the hydrologic cycle of a catchment, given the availability of hydrologic data in the catchment. For the comparison, historic data of the Jeker river basin, situated in the loamy belt region of Belgium, was used. The size of the catchment is 465 km2. The landscape is rolling, the dominant land use is farmland, and the soils vary from sandy‐loam to clay‐loam. The daily data of a continuous period of 6 years were used for the calibration and validation of both models. The results were obtained by comparing the performance of the two models using a qualitative (graphical) and quantitative (statistical) assessment, such as graphical representation of the observed and simulated river discharge, performance indices, the hydrograph maxima, the baseflow minima, the total accumulated volumes and the extreme value distribution of river flow data. The analysis revealed that both models are able to simulate the hydrology of the catchment in an acceptable way. The calibration results of the two tested models, although they differ in concept and spatial distribution, are quite similar. However, the MIKE SHE model predicts slightly better the overall variation of the river flow. Copyright © 2005 John Wiley & Sons, Ltd.

MODELING SEDIMENT TRAPPING IN A VEGETATIVE FILTER ACCOUNTING FOR CONVERGING OVERLAND FLOW

Vegetative filters (VF) are used to remove sediment and other pollutants from overland flow. When modeling thehydrology of VF, it is often assumed that overland flow is planar, but our research indicates that it can be two-dimensionalwith converging and diverging pathways. Our hypothesis is that flow convergence will negatively influence the sedimenttrapping capability of VF. The objectives were to develop a two-dimensional modeling approach for estimating sedimenttrapping in VF and to investigate the impact of converging overland flow on sediment trapping by VF. In this study, theperformance of a VF that has field-scale flow path lengths with uncontrolled flow direction was quantified using fieldexperiments and hydrologic modeling. Simulations of water flow processes were performed using the physically based,distributed model MIKE SHE. A modeling approach that predicts sediment trapping and accounts for converging anddiverging flow was developed based on the University of Kentucky sediment filtration model. The results revealed that as flowconvergence increases, filter performance decreases, and the impacts are greater at higher flow rates and shorter filterlengths. Convergence that occurs in the contributing field (in-field) upstream of the buffer had a slightly greater impact thanconvergence that occurred in the filter (in-filter). An area-based convergence ratio was defined that relates the actual flowarea in a VF to the theoretical flow area without flow convergence. When the convergence ratio was 0.70, in-filterconvergence caused the sediment trapping efficiency to be reduced from 80% for the planar flow condition to 64% for theconverging flow condition. When an equivalent convergence occurred in-field, the sediment trapping efficiency was reducedto 57%. Thus, not only is convergence important but the location where convergence occurs can also be important.

Application of the coupled MIKE SHE/MIKE 11 modelling system to a lowland wet grassland in southeast England

Hydrological modifications frequently result in wetland loss and degradation while wetland management, restoration and creation schemes rely upon further hydrological manipulations. These schemes can benefit from models which can accurately represent often complex wetland hydrological situations. Although the potential of the physically based, distributed model MIKE SHE to model wetlands has been demonstrated, a number of inadequacies in its channel flow component have been identified. These include difficulties in representing control structures and simulating inundation from channels. A coupling has been developed between MIKE SHE and the MIKE 11 hydraulic modelling system. This paper reports a coupled MIKE SHE/MIKE 11 model developed for a lowland wet grassland, the Elmley Marshes, in southeast England. Long term monitoring, supplemented by selected secondary sources, provided the necessary input, calibration and validation data. A procedure was developed to evaluate evaporation from ditch surfaces which could not be represented dynamically within MIKE 11. Two consecutive 18-month periods were used for model calibration and validation which were based upon comparisons of observed and simulated groundwater depths and ditch water levels. Model results were generally consistent with the observed data and reproduced the seasonal dynamics of groundwater and ditch water. The close association between flooding and both groundwater and ditch water levels was demonstrated. Topographic depressions are important for the initiation of flooding and are responsible for much of the shallow surface water in areas isolated from ditches. Deeper flooding occurs in areas which are inundated from these ditches. Results suggested that improvements could be made to the MIKE SHE bypass flow routine to enable it to more accurately represent macropore flow associated with soil cracking and swelling. Dynamic calculation of evaporation from ditch water surfaces would enhance the ability of the model to explore alternative water level management and climate change scenarios. The potential use of the model to investigate these scenarios is outlined.

Including prior information in the estimation of effective soil parameters in unsaturated zone modelling

In this paper we propose a methodology to include prior information in the estimation of effective soil parameters for modelling the soil moisture content in the unsaturated zone. Laboratory measurements on undisturbed soil cores were used to estimate the moisture retention curve and hydraulic conductivity curve parameters. The soil moisture content was measured at 25 locations along three transects and at three different depths (surface, 30 and 60 cm) on an 80×20 m hillslope for the year 2001. Soil cores were collected in 84 locations situated in three profile pits along the hillslope. For the estimation of the effective soil hydraulic parameters the joint probability distribution of measured parameter values was used as prior information. A two-horizon single column 1D MIKE SHE model based on Richards' equation was set-up for nine soil moisture measurement locations along the middle transect of the hillslope. The goal of the model is to simulate the soil moisture profile at each location. The shuffled complex evolution (SCE) algorithm has been applied to estimate effective model parameters using either wide parameter ranges, referred to as the ‘no-prior’ case, or the joint probability distribution of measured parameter values as prior information (‘prior’ case). When the prior information is incorporated in the SCE optimisation the goodness-of-fit of the model predictions is only slightly worse compared to when no-prior information is incorporated. However, the effective parameter estimates are more realistic when the prior information is incorporated. For both the no-prior and prior case the generalised likelihood uncertainty estimation procedure (GLUE) was subsequently used to estimate the uncertainty bounds (UB) on the model predictions. When incorporating the prior information more parameter sets were accepted for the estimation of the predictive uncertainty and the parameter values were more realistic. Moreover, UB better enclosed the observations. Thus, incorporating prior information in GLUE reduces the amount of model evaluations needed to obtain sufficient behavioural parameter sets. The results indicate the importance of prior information in the SCE and GLUE parameter estimation strategies.

Use of sensitivity and uncertainty measures in distributed hydrological modeling with an application to the MIKE SHE model

A methodology to qualify and quantify uncertainty and sensitivity measures, mathematically related to a representative metamodel and correlation coefficients, using the Latin hypercube approach, is evaluated in the context of the spatially distributed hydrological model MIKE SHE. The characteristics of various outputs, such as cumulative catchment discharge, average soil water content, and groundwater elevation, are examined at different time and space scales. The soil hydraulic parameters make up the varying input. The input uncertainties and the corresponding Latin hypercube parameter perturbations are based on U.S. Department of Agriculture texture figures. Results indicate important differences between the measures, even in ranking, making combined interpretation of the measures necessary. The real‐world problem of correlation among parameters adds significant complexity to the assessment of uncertainty and sensitivity and cannot be disregarded without caution. The presented methodology, though still CPU intensive, allows the hydrological modeler to cope with this complexity.

Constraining soil hydraulic parameter and output uncertainty of the distributed hydrological MIKE SHE model using the GLUE framework

AbstractBoth calibration and uncertainty assessment are mandatory steps in today's modelling process. The former considers both the inputs (input variables and parameters) as well as model results. An exploratory investigation of the applicable parameter space results in a wide spectrum of values for a specific model output. By retaining only those model realizations that mimic reality in a sufficient way, inputs and associated response can be constrained, thereby quantifying the uncertainty involved. The generalized likelihood uncertainty estimation (GLUE) framework provides a structured methodology for this purpose.The study presented focuses on the applicability of the GLUE framework within the context of the distributed hydrological model MIKE SHE. Even though all significant processes involved are incorporated within the model, the problems of calibration and uncertainty assessment cannot be avoided. This has resulted in a quest for well‐delineated effective parameters crucial for sound mechanistic model application. Being a complex model, the number of possible realizations using MIKE SHE is fairly small due to computing time, and an in‐depth exploratory approach is impossible. On the other hand, several output variables are available aside from the hydrological river response, like water content in the soil profile or ground water level, which can be used for retaining realistic parameter sets.This study presents some preliminary results on the applicability of the GLUE–MIKE SHE framework and associated constrained preliminary parameter and output uncertainty values. Focus was given to the influence of soil hydraulic parameters on the hydrological behaviour of a small study basin. The soil hydraulic parameters were predicted using various pedo‐transfer‐function approaches and moisture retention measurements in the laboratory, and these distributions were then confronted with ranges of the constrained effective parameters. For the latter, the restrictions that were imposed based on behavioural acceptance were substantial. All the traditional methods show significant uncertainty, due to heterogeneity and model error, which cannot be disregarded. The first results suggest a reasonable match among effective parameters and laboratory measurements. The use of pedo‐transfer‐function distributions as effective parameters, however, may provide unacceptable results, even with liberal criteria. Copyright © 2002 John Wiley & Sons, Ltd.

Analysis of uncertainties associated with different methods to determine soil hydraulic properties and their propagation in the distributed hydrological MIKE SHE model

Complex hydrological models require a significant amount of data as input. The necessary measurement campaigns to determine input variables and parameters can be extremely expensive and time consuming, particularly at catchment scale. A subset of the inputs of hydrological models is the set of soil hydraulic parameters. Pedo-transfer-functions (PTFs), relating easily measurable soil properties to soil hydraulic parameters, can deliver candidate approximations for the required soil hydraulic properties. In the present study, uncertainties, resulting from four ways to obtain soil hydraulic parameters, are compared and evaluated with respect to their resulting uncertainties on different model outputs. These four methods are: (i) moisture retention lab measurements, (ii) prediction via PTFs using field texture measurements, (iii) prediction via PTFs using USDA texture classes, and (iv) prediction through the bootstrap-neural network approach using field texture measurements. The effect of parameter uncertainties on simulated catchment response was investigated using the spatially distributed, physically based hydrological MIKE SHE model in a joint deterministic-stochastic approach, based on the Latin Hypercube Sampling. As expected, different results are found for the different model outputs: discharge, ground water level, and soil water content. Including the PTF model as well as measurement fitting error, next to soil heterogeneity, when quantifying the input distributions, has a major impact, which cannot be neglected. Scaling issues were disregarded and parameters presumed to be grid-effective. The assumption of equal medians of the soil hydraulic functions, providing the input for the MIKE SHE model, generally cannot be rejected, but the uncertainties differed. The neural network approach consistently provides the smallest uncertainty, but exhibits different median values as well as uncertainty, and as such its application requires further research. No significant conclusions can be inferred for the ground water elevations — the model behaved differently for the separate methods, indicating even non-behavioural parameter sets. Soil water content and cumulative discharge uncertainty followed the iv, i, ii, iii order. K sat (ground water recharge, runoff-infiltration ratio) and θ s (soil water contents) can be established as influential parameters. Methods ii and i provide for similar in- and output, however their input distributions do not necessarily correspond to grid-effective values. Depending on the objective of the model application, approximation methods to assess soil hydraulic parameters can be a valid option.

Application of a distributed physically-based hydrological model to a medium size catchment

Abstract. Physically based distributed models are rarely calibrated and validated thoroughly because of lack of data. In practice, validation is limited to comparison of simulated and predicted discharges in a catchment, or of simulated and observed piezometric levels in some calibrated wells. Rarely, internal noncalibrated wells or discharge stations are included in model evaluation. In this study, the fully distributed physically based MIKE SHE model was applied to the 600-km2 catchment of the Grote and the Kleine Gete, Belgium. Firstly, the MIKE SHE model was calibrated against both daily discharge measurements and observed water levels and then validated using a simple split-sample test. The observed discharges were simulated successfully in both the calibration and the validation period, while results for the piezometric levels differed considerably among the wells. In addition, a multi-site validation test for 2 internal discharge stations and 6 observation wells showed inferior results for the discharge stations and comparable results for the water table wells. As in the calibration and the split-sample test validation, water table fluctuations were predicted well in some wells, but with little agreement in others. This may be due to scale effects and to the poor quality of the data in certain areas of the catchment. Mainly, the lack of data made it difficult to simulate time series of internal catchment variables with acceptable accuracy so that even the calibrated and validated model could not provide reliable predictions of the water table over the entire catchment. Keywords: integral hydrological modelling; distributed code; MIKE-SHE; model performance; model calibration; model validation

Comparison of different distributed hydrological models for characterization of catchment spatial variability

Most physically-based hydrological models rely on either spatially distributed or lumped characterizations of topography and other spatial variations. Three disturbed hydrological models with different treatments of topography, namely the MIKE SHE, TOPMODEL and GB model (geomorphology-based hydrological model) are discussed in this paper. A regular square grid system is used by MIKE SHE for representation of catchment spatial variability and as the fundamental computation units. TOPMODEL characterizes the topography using a distributed topographic index, ln(a/tan β). The areas with the same ln(a/tan β) are assumed to be hydrologically similar. Then, the catchment is divided into a number of segments corresponding to the topographic index intervals. The GB model employs the catchment area function and width function to lump the topography and divide the catchment into a series of flow interval-hillslopes. The catchment spatial variations are averaged over each flow interval and represented by one-dimensional distribution functions with respect to the flow distance from the catchment outlet. The hillslope elements are the fundamental computational units in the GB model. The above three models are applied to the Seki River in Japan. The model structure and performance are compared and discussed in the paper. Copyright © 2000 John Wiley & Sons, Ltd.

Simulation of water flow on irrigation bay scale with MIKE-SHE

This study focuses on water flow of a 9-ha experimental irrigation site in a shallow water table area in the Tragowel Plains, Australia, with the aim of quantifying the processes affecting surface drainage and groundwater levels. The irrigation site was intensively monitored to provide the required input and calibration data for modelling the physical processes. Simulation of flow processes, including infiltration, capillary rise, evapotranspiration, overland flow, groundwater exfiltration and groundwater flow into the drain, was performed using the physically-based, distributed model MIKE-SHE. The model was calibrated against the observed piezometric levels, drain flow and soil moisture data collected over a 19-month period, that represented different field conditions under seasonal changes. Model simulations were generally consistent with the observed data. However, the results highlighted inadequacies of the model, particularly in representing variations in rapid flow through macropores that occur due to the cracking and swelling properties of the soil. Despite this deficiency, the modelling study allowed quantification of the effective flow processes and provided an insight into their implications in determining the quality (i.e. salinity) and quantity of drain flow and groundwater levels.

Towards a distributed physically based model description of the urban aquatic environment

MIKE SHE, a distributed physically based 3D modelling package, has recently been applied in the urban modelling area through a research project in Sweden, funded by the Swedish Water and Wastewater Works Association. The overall goal was to test if it is possible to describe the surrounding geohydrological processes and their interaction with the sewer network, similar to the way dynamic pipe flow modelling can give a detailed description of the hydraulics. The project was carried out in Vittskövle, a village outside the City of Kristianstad, Sweden. The village has extreme inflows to its treatment plant due to large amounts of groundwater infiltration into the sewer network. A MIKE SHE model was built and verified successfully for the catchment. Simulations were carried out in order to evaluate the effects from historical measures and alternative future alleviation schemes. The results indicate among others, that the construction of a new alternative drainage scheme would make it possible to reduce the inflow to the plant by as much as 75% without risk of increased groundwater levels. The application of MIKE SHE in Vittskövle shows expressively the possibilities and applicability of the model in urban areas. Compared with more simple conceptual models, MIKE SHE provides the possibility to analyse the effects from future changes in the geohydrological system.

Towards a distributed physically based model description of the urban aquatic environment

MIKE SHE, a distributed physically based 3D modelling package, has recently been applied in the urban modelling area through a research project in Sweden, funded by the Swedish Water and Wastewater Works Association. The overall goal was to test if it is possible to describe the surrounding geohydrological processes and their interaction with the sewer network, similar to the way dynamic pipe flow modelling can give a detailed description of the hydraulics. The project was carried out in Vittskövle, a village outside the City of Kristianstad, Sweden. The village has extreme inflows to its treatment plant due to large amounts of groundwater infiltration into the sewer network. A MIKE SHE model was built and verified successfully for the catchment Simulations were carried out in order to evaluate the effects from historical measures and alternative future alleviation schemes. The results indicate among others, that the construction of a new alternative drainage scheme would make it possible to reduce the inflow to the plant by as much as 75% without risk of increased groundwater levels. The application of MIKE SHE in Vittskövle shows expressively the possibilities and applicability of the model in urban areas. Compared with more simple conceptual models, MIKE SHE provides the possibility to analyse the effects from future changes in the geohydrological system.