MIKE SHE Code Research Articles

Impact of urban geology on model simulations of shallow groundwater levels and flow paths

Abstract. This study examines the impact of urban geology and spatial discretization on the simulation of shallow groundwater levels and flow paths at the city scale. The study uses an integrated hydrological model based on the MIKE SHE code that couples surface water and 3D groundwater simulations with a leaky sewer system. The effect of the geological configuration was analyzed by applying three geological models to an otherwise identical hydrological model. The effect of spatial discretization was examined by using two different horizontal discretizations for the hydrological models of 50 and 10 m, respectively. The impact of the geological configuration and spatial discretization was analyzed based on model calibration, simulations of high water levels, and particle tracking. The results show that a representation of the subsurface infrastructure, and near-terrain soil types, in the geological model impacts the simulation of the high water levels when the hydrological model is simulated in a 10 m discretization. This was detectable even though the difference between the geological models only occurs in 7 % of the volume of the geological models. When the hydrological model was run in a 50 m horizontal discretization, the impact of the urban geology on the high water levels was smoothed out. Results from particle tracking show that representing the subsurface infrastructure in the hydrological model changed the particles' flow paths and travel time to sinks in both the 50 and 10 m horizontal discretization of the hydrological model. It caused less recharge to deeper aquifers and increased the percentage of particles flowing to saturated-zone drains and leaky sewer pipes. In conclusion, the results indicate that even though the subsurface infrastructure and fill material only occupy a small fraction of the shallow geology, it affects the simulation of local water levels and substantially alters the flow paths. The comparison of the spatial discretization demonstrates that, to simulate this effect, the spatial discretization needs to be of a scale that represents the local variability in the shallow urban geology.

Analysis of Water Management Scenarios Using Coupled Hydrological and System Dynamics Modeling

The North China Plain (NCP) has been affected by severe water scarcity over the past several decades. To address this issue, several water management plans have already been launched. Among them, the new South-to-North Water Diversion Project (SNWDP) draws the most attention. However, the effectiveness of the SNWDP is yet to be evaluated. In the present study, we developed a coupled hydrological and system dynamics (SD) model to analyze water management scenarios for the NCP: S1: business as usual, S2: less agricultural water, S3: more domestic and industrial water reuse, S4: SNWDP, and S5: combined efforts. The impact of climate change has been accounted for in all scenarios. The SD model was based on VENSIM, and the hydrological model was based on the MIKE SHE code. The results indicated that the groundwater development in the NCP will not be sustainable without exploiting the full potential of the SNWDP resource. However, the SNWDP alone can only lessen the water crisis, it is not able to stop the groundwater storage from further declining. Solving the water scarcity problem in the NCP will require a combination of highly reduced water use and a continued water supply from the SNWDP. Our results also indicated that innovative water saving technologies are urgently needed in the restructuring of the industrial sector. Furthermore, the study illustrated an added challenge to water management since the uncertainty range on climate change impacts are shown to be of the same magnitude as the impacts of combined management measures. Therefore, it requires great caution when making the future water management strategies in the NCP.

Observational and predictive uncertainties for multiple variables in a spatially distributed hydrological model

AbstractIn this study, uncertainty in model input data (precipitation) and parameters is propagated through a physically based, spatially distributed hydrological model based on the MIKE SHE code. Precipitation uncertainty is accounted for using an ensemble of daily rainfall fields that incorporate four different sources of uncertainty, whereas parameter uncertainty is considered using Latin hypercube sampling. Model predictive uncertainty is assessed for multiple simulated hydrological variables (discharge, groundwater head, evapotranspiration, and soil moisture). Utilizing an extensive set of observational data, effective observational uncertainties for each hydrological variable are assessed. Considering not only model predictive uncertainty but also effective observational uncertainty leads to a notable increase in the number of instances, for which model simulation and observations are in good agreement (e.g., 47% vs. 91% for discharge and 0% vs. 98% for soil moisture). Effective observational uncertainty is in several cases larger than model predictive uncertainty. We conclude that the use of precipitation uncertainty with a realistic spatio‐temporal correlation structure, analyses of multiple variables with different spatial support, and the consideration of observational uncertainty are crucial for adequately evaluating the performance of physically based, spatially distributed hydrological models.

Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: the effect of preprocessing and post-processing on skill and statistical consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on the skill and statistical consistency of monthly streamflow and yearly minimum daily flow forecasts. Both raw and preprocessed meteorological seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface–subsurface hydrological model based on the MIKE SHE code. Streamflow predictions are then generated up to 7 months in advance. In addition to this, we post-process streamflow predictions using an empirical quantile mapping technique. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast-generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as ensemble streamflow prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by post-processing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not sufficient to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We discuss the importance of including a better estimation of the initial state of the catchment, which may increase the capability of the system to forecast streamflow at longer leads.

Simulating seasonal variations of tile drainage discharge in an agricultural catchment

AbstractSeasonal variations of tile drainage discharge were simulated in the 6 km2 Fensholt catchment, Denmark, with the coupled surface and subsurface HydroGeoSphere model. The catchment subsurface is represented in the model by 3 m of topsoil and clay, underlain by a heterogeneous distribution of sand and clay units. Two subsurface drainage networks were represented as nodal sinks. The spatial distribution of the heterogeneous units was generated stochastically and their hydraulic properties were calibrated to reproduce drainage discharge for one network and verified with drainage discharge for the other network. Simulated discharge was compared to that of another model for which the heterogeneous sand and clay units were replaced by a homogeneous unit, whose hydraulic conductivity was the mean value of the heterogeneous model. With the homogeneous model, drainage dynamics were correctly simulated but drainage discharge was less accurate compared to the heterogeneous model. Simulated discharge was also compared to that of a larger‐scale model created with the MIKE SHE code, built with the same heterogeneous model. HydroGeoSphere and MIKE SHE generated drainage discharge that was significantly different, with better simulated groundwater dynamics data produced by HydroGeoSphere. Nodal sinks in HydroGeoSphere reproduced drain flow peaks more accurately. Calibration against drainage discharge data suggests that drain flow is controlled primarily by geological heterogeneities included in the model and, to a lesser extent, by the nature of the soil units located between the drains and ground surface.

Integrated hydrological modeling of the North China Plain and implications for sustainable water management

Abstract. Groundwater overdraft has caused fast water level decline in the North China Plain (NCP) since the 1980s. Although many hydrological models have been developed for the NCP in the past few decades, most of them deal only with the groundwater component or only at local scales. In the present study, a coupled surface water–groundwater model using the MIKE SHE code has been developed for the entire alluvial plain of the NCP. All the major processes in the land phase of the hydrological cycle are considered in the integrated modeling approach. The most important parameters of the model are first identified by a sensitivity analysis process and then calibrated for the period 2000–2005. The calibrated model is validated for the period 2006–2008 against daily observations of groundwater heads. The simulation results compare well with the observations where acceptable values of root mean square error (RMSE) (most values lie below 4 m) and correlation coefficient (R) (0.36–0.97) are obtained. The simulated evapotranspiration (ET) is then compared with the remote sensing (RS)-based ET data to further validate the model simulation. The comparison result with a R2 value of 0.93 between the monthly averaged values of simulated actual evapotranspiration (AET) and RS AET for the entire NCP shows a good performance of the model. The water balance results indicate that more than 70% of water leaving the flow system is attributed to the ET component, of which about 0.25% is taken from the saturated zone (SZ); about 29% comes from pumping, including irrigation pumping and non-irrigation pumping (net pumping). Sustainable water management analysis of the NCP is conducted using the simulation results obtained from the integrated model. An effective approach to improve water use efficiency in the NCP is by reducing the actual ET, e.g. by introducing water-saving technologies and changes in cropping.

Evaluation of the value of radar QPE data and rain gauge data for hydrological modeling

[1] Weather radar-based quantitative precipitation estimation (QPE) is in principle superior to the areal precipitation estimated by using rain gauge data only, and therefore has become increasingly popular in applications such as hydrological modeling. The present study investigates the potential of using multiannual radar QPE data in coupled surface water—groundwater modeling with emphasis given to the groundwater component. Since the radar QPE is partly dependent on the rain gauge observations, it is necessary to evaluate the impact of rain gauge network density on the quality of the estimated rainfall and subsequently the simulated hydrological responses. A headwater catchment located in western Denmark is chosen as the study site. Two hydrological models are built using the MIKE SHE code, where they have identical model structures expect for the rainfall forcing: one model is based on rain gauge interpolated rainfall, while the other is based on radar QPE which is a combination of both radar and rain gauge information. The two hydrological models are inversely calibrated and then validated against field observations. The model results show that the improvement introduced by using radar QPE data is in fact more obvious to groundwater than to surface water at daily scale. Moreover, substantial negative impact on the simulated hydrological responses is observed due to the cut down in operational rain gauge network between 2006 and 2010. The radar QPE based model demonstrates the added value of the extra information from radar when rain gauge density decreases; however it is not able to sustain the level of model performance preceding the reduction in number of rain gauges.

Hydrological modeling in the karst area, Rižana spring catchment, Slovenia

Karst aquifers are known for their heterogeneity and irregular complex flow patterns which make them more difficult to model and demand specific modeling approaches. This paper presents one such approach which is based on a conceptual model. The model was applied in a karst area of the catchment of Rižana spring (200 km²). It is based on the MIKE SHE code and incorporates the main hydrological processes and geological features of the karst aquifer (diffuse and concentrated infiltration, allogenic recharge, quick and slow groundwater flow, shifting groundwater divides and groundwater outflow from the catchment area). Modeling of evapotranspiration and flow in the upper part of the unsaturated zone is more detailed. For the modeling of groundwater flow in the karst aquifer, a conceptual model was applied which uses drainage function for the simulation of groundwater flow through large conduits (karst channels and large fissures). The model was calibrated and validated against the observed Rižana spring discharge which represents a measured response of the aquifer. The results of validation show that the model is able to adequately simulate temporal evolution of the spring discharge, measured by Nash–Sutcliffe coefficient (0.82) as well as overall water balance.

Integration of earth observation data in distributed hydrological models: the Senegal River basin

This study investigates how remotely sensed data, with a focus on the optical satellite sensors NOAA AVHRR and METEOSAT, can be used in distributed hydrological modeling. The ultimate goal of the study was to develop methods to integrate satellite remote sensing data in large-scale hydrological models by applying state-of-the-art methods to extract relevant hydrological data from the satellite data and by developing new algorithms. A physically based, distributed hydrological model, based on a modified version of the MIKE SHE code, was applied to the Senegal River basin in West Africa, and the effect on the model performance by using remotely sensed data was examined. Results of daily rainfall estimates from METEOSAT data and vegetation dynamics from NOAA AVHRR data used as input to the model and the potential of using a remotely sensed dryness index for validation of the model are reported. The performance of simulated discharge was improved using remotely sensed vegetation dynamics, and marginal improvement in model performance was obtained using remotely sensed precipitation.

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

An integrated groundwater/surface water hydrological model with a 1 km 2 grid has been constructed for Denmark covering 43,000 km 2. 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 km 2 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).

Transient modeling of regional groundwater flow using parameter estimates from steady-state automatic calibration

Use of automatic calibration of large physically based hydrological models running in a transient mode is severely constrained by the long simulation time. Here, the possibility of using a steady-state model, with fast model execution, as the basis for automatic calibration of the involved parameters is examined. It is tested if the parameters estimated with the steady-state model can be used for the subsequent transient modeling of the same hydrological system. The problem is analysed for the 5900 km 2 South Jutland area, being one of the 11 model areas of the National Water Resource Model of Denmark. The area is modelled using the MIKE SHE code while UCODE is used for optimization. Hydraulic head data and stream discharge data are available for calibration. It is examined how the estimated parameters are affected by the choice of (a) recharge input to the steady-state model, and (b) target data used for calibration. The reliability of the parameters is evaluated and the resulting ability of the optimized models to simulate the transient response of the system is assessed. The results show that the estimated parameters are highly sensitive to the way that the steady-state model is conceptualized. The study suggests how the steady-state model should be conceptualized in order to obtain reliable parameter estimates that produce acceptable transient model results.

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

AbstractIn a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km2 Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time‐series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response.In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high‐resolution temporal Meteosat Second Generation data. Copyright © 2002 John Wiley & Sons, Ltd.

A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status

A simplified land surface dryness index (Temperature–Vegetation Dryness Index, TVDI) based on an empirical parameterisation of the relationship between surface temperature ( T s) and vegetation index (NDVI) is suggested. The index is related to soil moisture and, in comparison to existing interpretations of the T s/NDVI space, the index is conceptually and computationally straightforward. It is based on satellite derived information only, and the potential for operational application of the index is therefore large. The spatial pattern and temporal evolution in TVDI has been analysed using 37 NOAA-AVHRR images from 1990 covering part of the Ferlo region of northern, semiarid Senegal in West Africa. The spatial pattern in TVDI has been compared with simulations of soil moisture from a distributed hydrological model based on the MIKE SHE code. The spatial variation in TVDI reflects the variation in moisture on a finer scale than can be derived from the hydrological model in this case.

Hydrological modelling of a drained grazing marsh under agricultural land use and the simulation of restoration management scenarios

The capability of the spatially-distributed, physically-based, rainfall-runoff modelling system, MIKE SHE, to simulate the hydrological behaviour of the natural and drained parts of the North Kent Grazing Marshes, UK, is investigated. The MIKE SHE code is applied to Bells Creek, a small, underdrained, agricultural catchment located within the marshes. The model is used to both provide insights into the essential parameters that control the hydrological processes in the catchment, and predict the influence of various, hypothetical, water management strategies (land use and drainage) on pumped discharge and soil moisture storage in the catchment. The water table model predictions arising from these hypothetical scenarios are also compared against field data obtained from on-going hydrological research on the neighbouring, natural, Elmley Marshes. The comparison is found to be favourable. The results of this study indicate the potential of the MIKE SHE system to simulate the hydrological regime of these wetlands, and hence to play an important role as a tool that can assist environmental and conservation agencies in the sound management of wetland resources.

Parameterisation, calibration and validation of distributed hydrological models

This paper emphasizes the different requirements for calibration and validation of lumped and distributed models. On the basis of a theoretically founded modelling protocol, the different steps in distributed hydrological modelling are illustrated through a case study based on the MIKE SHE code and the 440 km 2 Karup catchment in Denmark. The importance of a rigorous and purposeful parameterisation is emphasized in order to get as few “free” parameters as possible for which assessments through calibration are required. Calibration and validation using a split-sample procedure were carried out for catchment discharge and piezometric heads at seven selected observation wells. The validated model was then used for two further validation tests. Firstly, model simulations were compared with observations from three additional discharge sites and four additional wells located within the catchment. This internal validation showed significantly poorer results compared to the calibration/validation sites. Secondly, the validated model based on a 500 m model grid was used to generate three additional models with 1000 m, 2000 m and 4000 m grids through interpolation of model parameters. The results from the multi-scale validation suggested that a maximum grid size of 1000 m should be used for simulations of discharge and ground-water heads, while the results deteriorated with coarser model grids.