Loew, A.

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Geophysical Research AbstractsVol. 12, EGU2010-4752, 2010EGU General Assembly 2010© Author(s) 2010Combining precipitation and soil moisture observations: a way forimproved estimates of land surface water fluxes?Alexander LoewMax-Planck-Institute for Meteorology, Land in the Earth System, Hamburg, Germany (alexander.loew@zmaw.de)Simulations of the Earth water and energy fluxes are highly dependent on the reliability of model input data.Uncertainties of simulations of terrestrial water fluxes are hereby directly related to the accuracy of availableprecipitation data. As precipitation is characterized by small temporal and spatial correlation lengths, theuncertainties in precipitation data increase with decreasing density of available precipitation gauges.Active and passive satellite sensor systems allow for the derivation of quantitative precipitation informationfrom space. However, these precipitation estimates are prone to large uncertainties, especially over land, as themeasured signal is also influenced by the underlying land surface.However, as soil moisture directly depends on precipitation dynamics, its variation is likely to be used as aproxy for precipitation variability. Remote sensing techniques have been proven to allow for the monitoring ofsurface soil moisture dynamics at different spatio-temporal scales. Especially low frequency microwave data aremost sensitive to soil moisture dynamics.The present paper investigates the potential of combining microwave soil moisture information with (un-certain) precipitation estimates to partly compensate for uncertainties in precipitation data sets. The combined useof soil moisture and precipitation information within a data assimilation framework should therefore be favourablefor an estimation of surface water fluxes.The present paper investigates the general potential of a such combined approach by integrating L-band (1-2 GHz) microwave radiometer data into a simple model for soil wetness to compensate for uncertainties in apriori information of precipitation. A high correlation between the microwave signature and surface soil moisturewas found which is consistent with previous findings. An analytical data assimilation scheme for the integra-tion of that information into a soil wetness model, based on an antecedent precipitation index (API), was developed.The results reveal that the data assimilation filter adds or removes an amount of water partially compensat-ing for the actual precipitation error. The correlation coefficient between the filter update and actual precipitationerror was found to be 0.6 < r < 0.8 and the model simulations did show a better coincidence with in situ soilmoisture records, by integrating the microwave data. The results indicate a high potential to use L-band microwavedata to compensate for uncertainties in precipitation measurements in the absence of detailed ground basedmeasurements.

Combining precipitation and soil moisture observations: a way for improved estimates of land surface water fluxes?

https://pure.mpg.de/pubman/item/item_1855182_2/component/file_1855181/EGU2010-4752.pdf

Combining precipitation and soil moisture observations: a way for improved estimates of land surface water fluxes?

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