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Multi-scale assimilation of soil moisture data in land surface models

This is one of several projects concerned with improving land surface model predictions through data assimilation approaches. Land surface models try to predict exchanges of water, energy and trace gases between the land surface and the lower atmosphere, as well as storage and transport of water and energy on the land surface. Land surface models are used for modeling the lower boundary condition of regional and global circulation models and play therefore an essential role both for weather forecasting and climate predictions. Predictions with land surface models are affected by several sources of uncertainty:

  1. Uncertainty with respect to model forcings. Especially the uncertainty with regard to the precipitation amounts at a certain location plays an important role. Precipitation shows a strong spatio-temporal variability which is not captured well by rain gauge networks. The combination of rainfall radar and rain gauge data can improve the characterization of spatio-temporal variability, but uncertainty remains significant.
  2. Model parameter uncertainty. The uncertainty with respect to soil hydraulic parameters like porosity or saturated hydraulic conductivity and vegetation parameters like stomata resistance or rooting depth contributes significantly to the overall model prediction uncertainty. Given the fact that grid scales of land surface models are very large, it is very difficult to derive meaningful values for these parameters.
  3. Model structure uncertainty. Land surface models simplify the complex processes at the land surface: For example in CLM, the soil respiration is described with an empirical approach. Another example is neglecting lateral exchange fluxes of water and energy between grid cells. A third example is the fact that vegetation development is described with a static approach, neglecting the role of water stress or early/late phenology due to warm/cold conditions in spring.
  4. Uncertainty of initial conditions. The initial state of the system is not exactly known and subject to uncertainty as well.


These uncertainties can be reduced with help of measurement data. In an operational setting, measurement data can be assimilated with data assimilation methods to update states (for example soil moisture contents, soil temperature, leaf area index) and also model parameters (especially soil hydraulic and vegetation parameters). The updating of model parameters also has an impact for long-term climate predictions. An improved model parameterization should also improve the quality of the prediction of long-term exchange fluxes of water, energy and greenhouse gases between the land and the atmosphere.

The specific focus of this project is the multi-scale assimilation of soil moisture information. It is important to improve land surface models with soil moisture contents measured at different scales. Soil moisture contents vary at all different scales: even over small distances of a few meters large soil moisture contrasts can be observed, related to variability of soil hydraulic properties. On larger scales, the variability of soil moisture is also related to different land use types, topography and spatial variability of precipitation, to mention the most prominent contributions. Soil moisture is also measured at different scales. Point measurements (e.g., TDR, FDR) give very local and relatively precise information, but only for a small measurement volume (~ dm3). As soil moisture is also very heterogeneous on relatively small scales, the information gain from one local measurement might be limited. If many local measurements are gathered in an area, using sensor networks, high resolution and more precise information can be obtained at the field or small catchment scale. Remote sensing information provides exhaustive information, but for many remote sensing products the resolution is very coarse (e.g. for SMOS around 40 km) or revisiting time is very infrequent (e.g. for ALOS). Additional problems include the disturbing influence of vegetation, surface roughness and the limited penetration depth. Therefore, both local and remotely sensed measured soil moisture contents provide valuable information, but have also severe limitations. A further alternative is measuring soil moisture contents at the intermediate scale with hydrogeophysical methods (e.g., ground penetrating radar or electromagnetic induction methods) or with cosmic ray probes, which have recently been proposed as an alternative for collecting soil moisture information at intermediate scale. They could work as a tool to overcome the existing gap between point measurements and remote sensing data. Nevertheless, all methods will have limitations either related to significant measurement errors (with both a systematic and random component) and/or a lack of spatio-temporal resolution. The best way to proceed is to merge different data products.

Data assimilation methods are suited to merge model predictions and measurements made at different scales. However, in hydrology the experience with the assimilation of multi-scale data is limited and improved methods are needed.

This research project focuses on the following aspects:

  1. We test 10 cosmic ray probes which are installed in the Rur catchment (TERENO). Measurements are compared with measurements from a sensor network at two selected sites (Rollesbroich and Wüstebach), and for the other locations sporadic soil moisture measurements in the footprint of the cosmic ray probe are used for calibration/verification. We want to find out how cosmic ray probes perform in a humid climate (testing was nearly exclusively reported for dry conditions), and to deduce the role of different factors (e.g. biomass) that potentially influence the measurement. The end product of this task is the development of a measurement operator that links measured neutron counts with possible soil moisture distributions as function of time-varying hydrogen pools in the cosmic ray footprint.
  2. The assimilation of cosmic ray soil moisture data in the land surface model CLM, making use of the developed measurement operator.
  3. The development of different methodologies for the multi-scale assimilation of soil moisture measurements (point data, cosmic ray data and different remote sensing products) and testing them in synthetic experiments.
  4. The multi-scale assimilation of soil moisture information for the Rur catchment for a given period and an evaluation of its performance. The performance will especially be evaluated for the improvement as compared with open loop simulations, the assimilation of only one soil moisture data type, and the updating of model parameters (making use of a verification dataset).


This research is carried out in the context of the TR-32 project and financed by the DFG (German Science Foundation). We started April 2011 with work in the context of this project.

 Shown are soil moisture contents measured in Rollesbroich (Eifel, Germany) by cosmic ray probe and TDT for a one year period. Currently the fit between the measurements is still not very good, which might be related to the fact that certain hydrogen atom sources have not been taken into account properly.

Weblink to project:
tr32

Other relevant link:
Tereno

Contact persons:

Roland Baatz
Agrosphere (IBG-3)
Forschungszentrum Jülich GmbH
Leo Brandtstrasse
52425 Jülich
Germany
Tel. +49 02461 5931
E-mail: w.baatz@fz-juelich.de

Dr. Carsten Montzka
Agrosphere (IBG-3)
Forschungszentrum Jülich GmbH
Leo Brandtstrasse
52425 Jülich
Germany
Tel. +49 02461 3289
E-mail: c.montzka@fz-juelich.de

 

Alternative contact persons:

Prof. Dr. Harrie-Jan Hendricks Franssen
Agrosphere (IBG-3)
Forschungszentrum Jülich GmbH
Leo Brandtstrasse
52425 Jülich
Germany
Tel. +49 02461 4462
E-mail: h.hendricks-franssen@fz-juelich.de

Dr. Heye Bogena
Agrosphere (IBG-3)
Forschungszentrum Jülich GmbH
Leo Brandtstrasse
52425 Jülich
Germany
Tel. +49 02461 6752
E-mail: h.bogena@fz-juelich.de


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