C. Loumagne

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Abstract Soil moisture is a key hydrological variable in flood forecasting: it largely influences the partition of rain between runoff and infiltration and thus controls the flow at the outlet of a catchment. The methodology developed in this paper aims at improving the commonly used hydrological tools in an operational forecasting context by introducing soil moisture data into streamflow modelling. A sequential assimilation procedure, based on an extended Kalman filter, is developed and coupled with a lumped conceptual rainfall–runoff model. It updates the internal states of the model (soil and routing reservoirs) by assimilating daily soil moisture and streamflow data in order to better fit these external observations. We present in this paper the results obtained on the Serein, a Seine sub-catchment (France), during a period of about 2 years and using Time Domain Reflectivity probe soil moisture measurements from 0–10 to 0–100 cm and stream gauged data. Streamflow prediction is improved by assimilation of both soil moisture and streamflow individually and by coupled assimilation. Assimilation of soil moisture data is particularly effective during flood events while assimilation of streamflow data is more effective for low flows. Combined assimilation is therefore more adequate on the entire forecasting period. Finally, we discuss the adequacy of this methodology coupled with Remote Sensing data.

Estimation of watershed soil moisture index from ERS/SAR data.

Abstract The aim of this article is to show that a watershed hydrological index could be derived from ERS/SAR measurements. Indeed, it is well known that, over bare soil, the SAR signal is a function of the geometric and dielectric surface properties. The problem to estimate soil moisture is to free from the effects of the space and time fluctuations of soil roughness and from the vegetation cover attenuation and scattering. The methodology presented here is based on the selection of land cover types or “targets,” for which the SAR signal is mainly sensitive to soil water content variations, and for which the vegetation and the roughness effects (in SAR signal) can be estimated and removed if needed. This method has been validated over an agricultural watershed in France. We show that the accuracy of the retrieved soil moisture is ±0.04–0.05 cm 3 /cm 3 , except during May and June, when vegetation cover is too dense to get reliable soil information.

Backscattering behavior and simulation comparison over bare soils using SIR-C/X-SAR and ERASME 1994 data over Orgeval☆

Abstract During April 1994, the three-frequency radar system flew on the Space Shuttle Endeavour, known as SIR-C/X-SAR mission (Shuttle Imaging Radar C/X-Synthetic Aperture Radar). Over the Orgeval watershed (France), the ground condition stayed very wet throughout the 5-day SAR mission. The SAR imagery allows a data collection over a range of roughness conditions on bare soils. Three classes were identified: very smooth sowings with crusted top layer, cloddy surfaces, and different ploughed fields for future crops. To complement the Shuttle Radar data (three frequencies L, C, X, incidence range from 44° to 57°), the helicopter-borne scatterometer ERASME (C- and X-bands, copolorized configurations) was used. Merging of the two databases was possible. As a result, incidence angles ranging from 25° to 50° are available in C- and X-bands for the copolarized cross sections. Then the major objective of the article is, over this available data collection, to begin the validation of current surface backscattering models to natural surfaces, the theoretical integral equation model (IEM) of Fung et al. (1992) and the empirical model of Oh et al. (1994). It shows adequacies and limits. The IEM model reproduces well radar scatter over smooth surfaces, but fails over rough surfaces, predicting a flatter response with incidence angle than the observed signals in C- and X-bands. Difference in backscatter response due to direction angles (perpendicular and parallel to the row direction) is difficult to reproduce over smooth surfaces by this model integrating anisotropic surface but may be due to the unadequacy of the surface representation. The Oh algorithm agrees well with the backscatter response over rough surfaces at medium incidence angle, but fails with a systematic underestimation over smooth conditions. As a conclusion, further developments are necessary on derivation of theoritical solutions over rough surfaces and on validation of semiempirical algorithms over data sets of various training sources (radar and natural conditions).

Land cover discrimination from multitemporal ERS images and multispectral Landsat images: A study case in an agricultural area in France

More and more remote sensing data corresponding to various wavelength domains is becoming available. Visible/infrared data were first used for land cover classification. However, radar data are becoming more widely used for hydrological and agricultural applications. This paper discusses the performance, for land cover type discrimination, of an optical image acquisition and a multitemporal radar series. For the majority of land cover types existing within the test site (representative of northern European agricultural areas), both ERS multitemporal SAR and Landsat multispectral visible/infrared classifications lead to good results, with the latter being more robust. For better identification of cultures that are less represented, the complementarity of the two datasets may be exploited using an efficient data fusion algorithm based on the Dempster-Shafer evidence theory. The performance of this combination was verified on two successive vegetation cycles.

Evaluation of the ERS 1/Synthetic Aperture Radar Capacity to Estimate Surface Soil Moisture: Two-Year Results Over the Naizin Watershed

One of the possible applications of satellite radar remote sensing is to estimate surface soil moisture. To evaluate the capacity of ERS 1/synthetic aperture radar (SAR), a European Space Agency (ESA) pilot project has been set up. The test site is a small agricultural watershed situated in the central part of French Brittany. During 1992 and 1993, almost all possible SAR images were acquired together with two types of ground truths: intensive ground measurements during 14 field campaigns and point automatic measurements over the entire period. From the comparison of those ground truth data with the ERS 1 images, the following results are obtained. On a field scale the relation between the radar signal and the surface soil moisture depends strongly on the type of culture: Correlation is poor for the different cultures except for wheat. On a basin scale, it is shown that during the period of low vegetation density, there is a linear correlation between the mean radar data and the point automatic measurements. This last result is very encouraging and could open the way to hydrological applications.

Assimilation of soil moisture into hydrological models for flood forecasting: a variational approach

The objectives of this paper are to present a procedure for parameter updating that can be combined with any conceptual rainfall–runoff model for flood-forecasting purposes and to study the improvements in flood forecasting induced by the assimilation of soil moisture information into rainfall–runoff models. The main feature of this methodology is that it carries out updating by reference to not only recent streamflow observations, as in the case of classic procedures, but also soil moisture measurements, which can be retrieved either from time domain reflectometry (TDR) probes or from satellite remote sensing using synthetic aperture radar (SAR) systems. The aim of the research was to assess the usefulness of this additional soil moisture information. To this end, an approach has been suggested that gradually introduces the additional information and can detect a threshold above which this information benefits the whole flood-forecasting procedure. This methodology was put forward for use in the European AIMWATER project on four catchments within the Seine River basin upstream from Paris and on the Arade catchment in southern Portugal.

Integration of remote sensing data into hydrological models for reservoir management

Abstract The purpose of this paper is to present the methodology set up to derive catchment soil moisture from Earth Observation (EO) data using microwave spaceborne Synthetic Aperture Radar (SAR) images from ERS satellites and to study the improvements brought about by an assimilation of this information into hydrological models. The methodology used to derive EO data is based on the appropriate selection of land cover types for which the radar signal is mainly sensitive to soil moisture variations. Then a hydrological model is chosen, which can take advantage of the new information brought by remote sensing. The assimilation of soil moisture deduced from EO data into hydrological models is based principally on model parameter updating. The main assumption of this method is that the better the model simulates the current hydrological system, the better the following forecast will be. Another methodology used is a sequential one based on Kalman filtering. These methods have been put forward for use in the European AIMWATER project on the Seine catchment upstream of Paris (France) where dams are operated to alleviate floods in the Paris area.

Etat hydrique du sol et prevision des debits

Abstract The present work intends to show to what extent direct measurements of soil-moisture conditions, which up to now were not included in simulation processes, can improve discharge forecasting on a small catchment. In most storage models, it has been shown that because of an inadequate representation of the water yield function at the basin scale, the simulation of discharges was made difficult, particularly during transition periods. A comparison was drawn, based on a very simple model applied to the Orgeval catchment, between the classical method calculating with indirect methods the soil storage state and the proposed method which replaces these calculations by measurements of the soil-moisture conditions. The results of the comparison show the superiority of the second method, the first one being unable to translate completely the complex reality of the rainfall-runoff transformation processes. The results show the interest of including in a forecasting model a soil-moisture condition index which is measured locally at a small time-step during the hydrological cycle.

Assimilation of soil moisture into hydrological models: the sequential method

Improving the accuracy of rainfall–runoff models, and in particular their performance in flood prediction, is a key point of continental hydrology. This paper presents a new approach to these problems by the use of soil moisture data (remote sensing or in situ measurements). A first step is to correct past modeling errors, especially errors in simulated runoff, to obtain a more accurate forecast of runoff. Other significant parameters are the interactions occurring within the soil–vegetation–atmosphere interface, which are dominating factors in the processes of the transformation of rainfall into flow at a catchment-area scale. These phenomena can be integrated in hydrological modeling by introducing soil moisture measurements and thus explicitly taking into account the hydric state of the soil. The hydrological models used are global conceptual models. The methodology that we used is a sequential assimilation procedure, which permits step by step control of the evolution of the model and limits its divergence from the available data (soil moisture and flow). The efficiency of the assimilation procedure in flood prediction is discussed, with a particular focus on the contribution of soil moisture data. The question of the time repetitivity of measurements and its influence on the performance of the modeling is also tackled.

How significant are quadratic criteria? Part 2. On the relative contribution of large flood events to the value of a quadratic criterion

Abstract Quadratic criteria (i.e. based on squared residuals) are widely used to assess the performance of hydrological models. However, the largest errors have a relatively strong influence on the final criterion values, which may be considered a drawback for a complete assessment. This paper studies the case of updated models used for real-time forecasting. It is shown that the fraction of the data series actually impacting the final criterion value is small on many catchments and corresponds to the time steps characterised by the greatest runoff variations. In fact, model updating makes the error distribution more peak-shaped, giving even more relative importance to the time steps with the largest errors. Therefore, assessing the performance of an updated model with a quadratic criterion emphasises that these criteria focus more on the most difficult time steps to model (and the most interesting ones in the case of short-term flood forecasting). Citation Berthet. L., Andréassian, V., Perrin, C., & Loumagne, C. (2010) How significant are quadratic criteria? Part 2. On the relative contribution of large flood events to the value of a quadratic criterion. Hydrol. Sci. J. 55(6), 1063–1073.