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Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments

Author

Listed:
  • Axel Bronstert
  • Benjamin Creutzfeldt
  • Thomas Graeff
  • Irena Hajnsek
  • Maik Heistermann
  • Sibylle Itzerott
  • Thomas Jagdhuber
  • David Kneis
  • Erika Lück
  • Dominik Reusser
  • Erwin Zehe

Abstract

Flood generation in mountainous headwater catchments is governed by rainfall intensities, by the spatial distribution of rainfall and by the state of the catchment prior to the rainfall, e.g. by the spatial pattern of the soil moisture, groundwater conditions and possibly snow. The work presented here explores the limits and potentials of measuring soil moisture with different methods and in different scales and their potential use for flood simulation. These measurements were obtained in 2007 and 2008 within a comprehensive multi-scale experiment in the Weisseritz headwater catchment in the Ore-Mountains, Germany. The following technologies have been applied jointly thermogravimetric method, frequency domain reflectometry (FDR) sensors, spatial time domain reflectometry (STDR) cluster, ground-penetrating radar (GPR), airborne polarimetric synthetic aperture radar (polarimetric SAR) and advanced synthetic aperture radar (ASAR) based on the satellite Envisat. We present exemplary soil measurement results, with spatial scales ranging from point scale, via hillslope and field scale, to the catchment scale. Only the spatial TDR cluster was able to record continuous data. The other methods are limited to the date of over-flights (airplane and satellite) or measurement campaigns on the ground. For possible use in flood simulation, the observation of soil moisture at multiple scales has to be combined with suitable hydrological modelling, using the hydrological model WaSiM-ETH. Therefore, several simulation experiments have been conducted in order to test both the usability of the recorded soil moisture data and the suitability of a distributed hydrological model to make use of this information. The measurement results show that airborne-based and satellite-based systems in particular provide information on the near-surface spatial distribution. However, there are still a variety of limitations, such as the need for parallel ground measurements (Envisat ASAR), uncertainties in polarimetric decomposition techniques (polarimetric SAR), very limited information from remote sensing methods about vegetated surfaces and the non-availability of continuous measurements. The model experiments showed the importance of soil moisture as an initial condition for physically based flood modelling. However, the observed moisture data reflect the surface or near-surface soil moisture only. Hence, only saturated overland flow might be related to these data. Other flood generation processes influenced by catchment wetness in the subsurface such as subsurface storm flow or quick groundwater drainage cannot be assessed by these data. One has to acknowledge that, in spite of innovative measuring techniques on all spatial scales, soil moisture data for entire vegetated catchments are still today not operationally available. Therefore, observations of soil moisture should primarily be used to improve the quality of continuous, distributed hydrological catchment models that simulate the spatial distribution of moisture internally. Thus, when and where soil moisture data are available, they should be compared with their simulated equivalents in order to improve the parameter estimates and possibly the structure of the hydrological model. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Axel Bronstert & Benjamin Creutzfeldt & Thomas Graeff & Irena Hajnsek & Maik Heistermann & Sibylle Itzerott & Thomas Jagdhuber & David Kneis & Erika Lück & Dominik Reusser & Erwin Zehe, 2012. "Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 879-914, February.
  • Handle: RePEc:spr:nathaz:v:60:y:2012:i:3:p:879-914
    DOI: 10.1007/s11069-011-9874-9
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    Cited by:

    1. Weitao Lv & Xiasong Hu & Xilai Li & Jimei Zhao & Changyi Liu & Shuaifei Li & Guorong Li & Haili Zhu, 2024. "Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 16(9), pages 1-21, April.

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