F. P. De Troch

Evaluation of cluster‐based rectangular pulses point process models for rainfall

This technical note presents a comparison of cluster-based point rainfall models using the historical hourly rainfall data observed between 1949 and 1976 at Denver, Colorado. The Denver data are used to analyze the performance of three classes of models, namely, the Bartlett-Lewis model, the geometric Neyman-Scott model and the Poisson Neyman-Scott model. The original formulation of the structure of each model, as well as the modified description developed in order to improve the zero depth probability, is considered in this study. Rodriguez-Iturbe et al.(1987a) concluded that it is unlikely that empirical analysis of rainfall data can be used to choose between the Bartlett-Lewis model and the Neyman-Scott model. In a subsequent paper, Rodriguez-Iturbe et al. (1987b) argued that the choice of the distribution of the number of cells per storm for the Neyman-Scott model, either geometric or Poisson, has no general bias effect on the stochastic structure. Some investigators (e.g., Burlando and Rosso, 1991), however, reported results contradictory to those of the previous authors. In light of these observations this note investigates the performance of the cluster-based models. For the Denver data the geometric Neyman-Scott model yields better results compared to the Poisson Neyman-Scott model. Moreover, the Bartlett-Lewis model is shown to be very sensitive to the sets of moment equations used in the parameter estimation. This sensitivity is not observed in the Neyman-Scott scheme and is believed to be a drawback for applying the Bartlett-Lewis model in hydrologic simulation studies.

Remote sensing of bare surface soil moisture using EMAC/ESAR data

Abstract In this paper we present first results of bare surface soil moisture retrieval using data from the European Multisensor Airborne Campaign/ Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on 9 April 1994 in the Zwalm catchment, Belgium. Data from EMAC Reflective Optics System Imaging Spectrometer (ROSIS) collected on 12 July 1994 over the same catchment were used to develop land use maps. Concurrent to the EMAC/ESAR overflights field data were collected in two subcatchments of the Zwalm catchment. The paper first presents the data processing procedures used for the radar images. Then we apply a theoretical backscattering model to investigate the sensitivity of EMAC/ESAR backscattering coefficients to surface parameters (topography, surface roughness, vegetation and soil moisture). By comparing the predicted backscattering coefficients to the observed ones, we can conclude that classical measurement techniques for surface roughness parameters in remote sensing campaigns are not accurate...

Application of remote sensing for hydrological modelling.

It has long been recognised that the results obtained by hydrological modelling of a river basin depend heavily on the quality of the input data used. The main problem in many hydrological studies is that there are not enough adequate data to describe quantitatively hydrological processes with sufficient accuracy. Studies on hydrological effects of land use and climate changes in large river basins are possible only if detailed information about topography, geology, soil, vegetation, and climate are available. With the advances of remote sensing techniques hydrological relevant information about large river basins can be derived from different sensors. A major problem facing the user of these data is how to effectively incorporate remotely sensed data into hydrological studies and models (Peck et al. , 1981; Rango, 1987; Schultz, 1988; Engman and Gurney, 1991).

Remote sensing of soil moisture using EMAC/ESAR data

Presents the first results of surface soil moisture retrieval using data from the European Multisensor Airborne Campaign/Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on April 9, 1994 in the Zwalm catchment, Belgium. The authors apply a theoretical backscattering model to investigate the sensitivity of EMAC/ESAR backscattering coefficients to surface parameters. By comparing the predicted backscattering coefficients to the observed ones, they conclude that current measurement techniques for surface roughness parameters in remote sensing campaigns are not accurate enough for retrieving soil moisture using theoretical models. A method based on simultaneous retrieval of surface roughness parameters and soil moisture using multiple ESAR measurements is hence proposed. Promising results on retrieved soil moisture confirm the validity of the proposed method.

Soil moisture inversion from ERS and SIR-C imagery at the Zwalm catchment, Belgium

Several models have been presented in the previously which should allow soil moisture inversion from bare soil radar backscattering. Three widely used models (i.e., the IEM (Fung et al. 1992), the models of Dubois et al. (1995) and of Oh et al. (1992)) have been applied to ERS and SIR-C data obtained over the Zwalm catchment in Belgium. Results show an inability of the models to estimate surface soil moisture accurately through direct inversion. However, applying the method of effective roughness parameters (Su et al. 1997) on the multi-temporal ERS tandem mission data resulted in a significant improvement of the soil moisture retrieval. The same method applied to multi-frequency SIR-C data was not able to improve the estimations. It is believed that the inversions suffer from the high soil roughness sensitivity of the backscattering at higher incidence angles.

A method for retrieving soil moisture using active microwave data

Abstract In this paper we present a method of effective roughness parameters for soil moisture retrieval using SAR (Synthetic Aperture Radar) data. The method is based on simultaneous retrieval of surface roughness parameters and soil moisture by means of inverting a theoretical backscattering model using multiple SAR measurements as inputs. First results of surface soil moisture retrieval are presented, using data from the European Multisensor Airborne Campaign / Experimental Synthetic Aperture Radar (EMAC/ESAR) collected on April 9, 1994 in the Zwalm catchment, Belgium. Promising results for retrieved soil moisture confirm the validity of the proposed method.

Towards the upscaling of local surface flux models

Abstract Estimation of regional latent and sensible heat fluxes requires the upscaling of local models. One approach is to develop soil-vegetation-atmosphere transfer (SVAT) schemes at different scales (field, catchment and regional scale; e.g. Famiglietti and Wood (1994)). SVAT schemes at larger scales try to aggregate latent and sensible heat fluxes based on knowledge about the spatial distribution (GIS based) or the statistical-dynamic description of atmospheric forcing and internal state variables. Before one can approach this important problem of upscaling, it is necessary to study the impact of the parameters of the field scale SVAT model on the prediction of local fluxes. In this paper, we show results of a Monte Carlo sensitivity analysis performed on a local SVAT model, with data retrieved during the Autumn of 1995 in an experimental catchment in Belgium. During this field campaign, several parameters of the energy and water balance were measured by means of a Bowen ratio system, while soil moisture data were retrieved on a regular basis using an automatic TDR system. The results of the performed sensitivity analysis are presented and evaluated; conclusions are drawn with respect to the upscaling of local SVAT models to the larger scale.

ON THE USEFULNESS OF WEATHER RADAR DATA IN REAL-TIME HYDROLOGICAL FORECASTING IN BELGIUM

The hydrologic part of the forecasting model of the river Meuse in Belgium describes the rainfall-runoff relationships for the subcatchments. In this application, a linear stochastic black-box modelling technique is chosen, yielding what is known as a transfer function noise model. An important parameter in this representation of the real system is the dead time or concentration time of the system. This parameter directly influences the forecasting horizon. Efforts to extend this forecasting horizon are concentrated on the development of procedures to estimate future precipitation. Research can be directed along two main directions: statistical analysis (e.g. rainfall generators) and forecasting techniques based on meteorologic and weather radar information. Assuming that the error made in estimating precipitation intensities is acceptable, hydrological forecasting can benifit from this information. As a first attempt to incorporate radar information, one can try to develop some decision making procedure to choose between rainfall scenarios. In this paper, a simulation study is used to appreciate the usefulness of incorporating weather radar data in the flood forecasting model of the river Meuse.

THE LATERAL INFLOW INTO SUBMERGED DRAINS

ABSTRACT An attempt is made to explain the field measurements of piezometric height and discharge rate in a submerged drain system. The lateral inflow into the drain pipe is not necessarily uniformly distributed as is usually assumed. Hence, in analysing the hydrological performance of the drain pipe in the field, this fact must be considered. A general formula (equation (11)) is presented for calculating the actual distribution of the lateral inflow and a practical application is discussed.

Detecting variable source areas from temporal radar imagery using advanced image enhancement techniques

Recently, N. E. C. Verhoest et al. (1998) showed that it is possible to map variable source areas in a catchment using a principal component analysis. This technique, based on a temporal series of images, revealed the spatial soil moisture patterns from the vegetation and topographic effects introduced in a synthetic aperture radar (SAR) image. However, the obtained image is still corrupted with noise, which is partially related to the speckle observed within a SAR image. In order to get a noiseless image, which is more appropriate for hydrological modelling schemes, the authors apply a recently developed wavelet-based image denoising technique, A. Pizurica et al. (1999). The main advantage of this filtering technique is that it preserves the spatial patterns and observed edges, while it increases the signal to noise ratio significantly. The suitability of this denoising algorithm is investigated by comparing the hydrologic information included in these visually well-appearing images with the results obtained for their non-filtered counterparts.