A.P.J. de Roo

A simple raster-based model for flood inundation simulation

In this paper the development of a new model for simulating flood inundation is outlined. The model is designed to operate with high-resolution raster Digital Elevation Models, which are becoming increasingly available for many lowland floodplain rivers and is based on what we hypothesise to be the simplest possible process representation capable of simulating dynamic flood inundation. This consists of a one-dimensional kinematic wave approximation for channel flow solved using an explicit finite difference scheme and a two-dimensional diffusion wave representation of floodplain flow. The model is applied to a 35 km reach of the River Meuse in The Netherlands using only published data sources and used to simulate a large flood event that occurred in January 1995. This event was chosen as air photo and Synthetic Aperture Radar (SAR) data for flood inundation extent are available to enable rigorous validation of the developed model. 100, 50 and 25 m resolution models were constructed and compared to two other inundation prediction techniques: a planar approximation to the free surface and a relatively coarse resolution two-dimensional finite element scheme. The model developed in this paper outperforms both the simpler and more complex process representations, with the best fit simulation correctly predicting 81.9% of inundated and non-inundated areas. This compares with 69.5% for the best fit planar surface and 63.8% for the best fit finite element code. However, when applied solely to the 7 km of river below the upstream gauging station at Borgharen the planar model performs almost as well (83.7% correct) as the raster model (85.5% correct). This is due to the proximity of the gauge, which acts as a control point for construction of the planar surface and the fact that here low-lying areas of the floodplain are hydraulically connected to the channel. Importantly though it is impossible to generalise such application rules and thus we cannot specify a priori where the planar approximation will work. Simulations also indicate that, for this event at least, dynamic effects are relatively unimportant for prediction of peak inundation. Lastly, consideration of errors in typically available gauging station and inundation extent data shows the raster-based model to be close to the current prediction limit for this class of problem. q 2000 Elsevier Science B.V. All rights reserved.

LISEM: a single-event physically based hydrological and soil erosion model for drainage basins; I: theory, input and output

The Limburg Soil Erosion Model (LISEM) is a physically based model incorporated in a raster geographical information system. This incorporation facilitates easy application in larger catchments, improves the user-friendliness by avoiding conversion routines and allows the use of remotely sensed data. Processes incorporated in this model are rainfall, interception, surface storage in microdepressions, infiltration and vertical movement of water in the soil, overland flow, channel flow, detachment by rainfall and through-fall, detachment by overland flow and transport capacity of the flow. Special attention has been paid to the influence of tractor wheeling, small roads and surface sealing.

A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment

Abstract Various regions in Europe have suffered from severe flooding over the last decennium. Earth observation techniques can contribute toward more accurate flood hazard modelling and they can be used to assess damage to residential properties, infrastructure and agricultural crops. For this study, detailed land cover maps were created by using IKONOS-2 high spatial resolution satellite imagery. The IKONOS-2 image was first divided into segments and the land cover was classified by using spectral, spatial and contextual information with an overall classification accuracy of 74%. In spite of the high spatial resolution of the image, classes such as residential areas and roads are still fairly difficult to identify. The IKONOS-2-derived land cover map was used as input for the flood simulation model LISFLOOD-FP to produce a Manning roughness factor map of inundated areas. This map provides a more accurate spatial distribution of Manning’s roughness factor than maps derived from land cover datasets such as the EU CORINE land cover dataset. CORINE-derived roughness maps provide only averaged, lumped values of roughness factors for each mapping unit and are hence less accurate. Next, a method to produce a property damage map after flooding is presented. The detailed land cover map, water depth estimates resulting from the LISFLOOD-FP model, and known relations between water depth and property damage yielded a map of estimated property damage for the 1995 flood which affected the villages of Itteren and Borgharen in the southern part of The Netherlands. Such a map is useful information for decision makers and insurance companies.

Physically-Based River Basin Modelling within a GIS: the LISFLOOD Model.

Although many geographical information systems (GISs) are very advanced in data processing and display, current GIS are not capable of physically based modelling. Especially, simulating transport of water and pollutants through landscapes is a problem in a GIS environment. A number of specific routing methods are needed in a GIS for hydrologic modelling, amongst these are the numerical solutions of the Saint-Venant equations, such as the kinematic wave approximation for transport of surface water in a landscape. The PCRaster Spatial Modelling language is a GIS capable of dynamic modelling. It has been extended recently with a kinematic wave approximation simulation tool to allow for physically based water flow modelling. The LISFLOOD model is an example of a physically based model written using the PCRaster GIS environment. The LISFLOOD model simulates river discharge in a drainage basin as a function of spatial data on topography, soils and land cover. Although hydrological modelling capabilities have largely increased, there is still a need for development of other routing methods, such as a diffusion wave. Copyright

Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data

The soil erosion model for Mediterranean regions SEMMED is presented and used to produce regional maps of simulated soil loss for two Mediterranean test sites: one in southern . France and one in Sicily. The model demonstrates the integrated use of 1 multi-temporal Landsat .

LISEM: A SINGLE-EVENT, PHYSICALLY BASED HYDROLOGICAL AND SOIL EROSION MODEL FOR DRAINAGE BASINS. II: SENSITIVITY ANALYSIS, VALIDATION AND APPLICATION

A new hydrological and soil erosion model has been developed and tested: LISEM, the Limburg soil erosion model. The model uses physically based equations to describe interception, infiltration and soil water transport, storage in surface depressions, splash and flow detachment, transport capacity and overland and channel flow. From the validation results it is clear that, although the model has several advantages over other models, the results of LISEM 1.0 are far from perfect. Based on the sensitivity analysis and field observations, the main reasons for these differences seems to be the spatial and temporal variability of the soil hydraulic conductivity and the initial pressure head at the basin scale. Another reason for the differences between measured and simulated results is our lack or understanding of the theory of hydrological and soil erosion processes.

Calibrating and validating the LISEM model for two data sets from the Netherlands and South Africa

Abstract Within the framework of the GCTE Soil Erosion Network the Limburg Soil Erosion Model (LISEM) has been tested and validated in two catchments in South-Limburg (the Netherlands) and Zululand (South Africa). The calibration and validation of the Green–Ampt version of the LISEM model using 10 storms from the Catsop catchment in the Netherlands shows that differences in measured and simulated hydrographs and sediment loads can be large. These differences may be caused by the sensitivity of the model to some of the input variables, such as saturated hydraulic conductivity and the initial soil moisture content. Given the uncertainty in the input maps of these variables and a limited number of point data that is used to create these maps, a large part of the differences between measured and simulated data can be explained by these uncertainties. Thus, it is clear that detailed process-based models such as LISEM require very detailed and high-resolution input data in order to produce quantitative reliable results. The runoff processes in the Zululand catchment appeared to be dominated by slow throughflow and groundwater flow, which are processes that are not incorporated in LISEM.

Modelling runoff and sediment transport in catchments using GIS

The application of geographical information systems (GIS) in modelling runoff and erosion in catchments offers considerable potential. Several examples illustrate simple GIS techniques to produce erosion hazard indices or erosion estimates using USLE-type models. Existing erosion models can also be loosely coupled to a GIS, such as the ANSWERS model. Furthermore, models can be fully integrated into a GIS by embedded coupling, such as the LISEM model. However, GIS raster-based erosion models do not necessarily produce better results than much simpler and partly lumped erosion models with ‘representative elements’, although they reproduce topography in more detail. The reasons for the disappointing results of spatial models must be sought in the uncertainty involved in estimating and measuring the large number of input variables at a catchment scale. There is a need for much simpler loosely coupled or embedded GIS erosion models simulating only the dominant processes operating in the catchment.

The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.

Effects of crust and cracks on simulated catchment discharge and soil loss

Sealing, crusting and cracking of crusts of the soil surface has been observed in many parts of the world in areas with sandy, silty and loamy soils. Sealing and crust formation occurs under the influence of rain storm and drying weather. With prolonged drying, surface crusts might crack, leading to complex situations with respect to infiltration and runoff generation. Cracking of crusted loamy soils appears to be a general process. This study aims to measure the hydraulic properties of fully crusted and cracked-crusted areas and to evaluate the effects of these measurements on catchment discharge and soil loss in a loess region of the Netherlands, using the LISEM soil erosion model. Samples with minimum infiltration rates (fully crusted) and with maximum infiltration rates (cracked-crusted surfaces) were taken from fields with bare soil or winter wheat and their soil hydraulic functions were measured. The results of these measurements were used as input in the LISEM soil erosion model. Simulations of discharge and soil loss were done for each of these two land-uses and for two rain events. Additionally, simulated discharge and soil loss under actual recorded land-use were calculated. In all cases, soils with no surface cracks produced higher figures for discharge and soil loss than those where 10% of the surface crust was cracked. For a good interpretation of the results for soil loss, the spatial distribution of cracked-crusted areas and fully crusted areas has to be investigated in detail. To deal with cracked-crusted and fully crusted areas in simulation modelling, care has to be taken to accurately measure the soil physical functions representing the maximum and minimum infiltration rates. An assignment of these functions to calculation grids has to be made. As the LISEM model is capable of assigning different soil physical functions to each calculation grid, an improved prediction of the soil physical behaviour of the catchment can be simulated.