Carsten Montzka, Kathrina Rötzer, Harry Vereecken Research Centre Jülich,

Institute of Bio- and Geosciences: Agrosphere (IBG 3), Jülich, Germany

Spatio-temporal variability of global soil moisture products

• Soil moisture plays a key role for water and energy exchange between soil and atmosphere

• Soil moisture products from different sources (active/passive microwave RS, models) perform differently in specific regions

• For further use of different soil moisture products a more detailed analysis about advantages and drawbacks of specific methods is necessary

• Further use of soil moisture products can be: • Hydrological/weather forecast model calibration • Soil moisture data assimilation • Drought/flood monitoring/forecast • …

⇒Overall aim: Closing the catchment-scale water balance

Background

SMOS

soil moisture [m³/m³]

ERA ASCAT

overall mean soil moisture:

0.16 m³/m³ 0.21 m³/m³ 0.26 m³/m³

𝜃𝜃𝑛𝑛 = 1𝑇𝑇

�𝜃𝜃𝑛𝑛𝑛𝑛

𝑇𝑇

𝑛𝑛=1

𝜃𝜃𝑛𝑛: temporal mean of soil moisture for every grid point 𝑛𝑛 over all timesteps 𝑡𝑡

Rötzer et al. 2015

Evaluation: Temporal means (2010 - 2012)

• Investigation of spatial and temporal variability of SMOS and ASCAT soil moisture products

• Comparison to modeled ERA Interim soil moisture product from ECMWF (European Centre for Medium Range Weather Forecast)

• Objectives:

Comparison of the products, determination of influences on the soil moisture patterns of the products

Investigation of suitability of the soil moisture products for specific regions

Determination of influencing factors on „real“ soil moisture distribution

Spatio-temporal variability

1. Calculation of mean relative difference 𝛿𝛿𝑛𝑛 for every pixel 𝑛𝑛 through

𝛿𝛿𝑛𝑛 = 1𝑇𝑇�

𝜃𝜃𝑛𝑛𝑛𝑛 − 𝜃𝜃𝑛𝑛�̅�𝜃𝑛𝑛

𝑇𝑇

𝑛𝑛=1

2. Ranking of 𝛿𝛿𝑛𝑛 from lowest to highest

3. Comparison of ranks for the different products through correlation analysis

=> Provides information on the similarity of soil moisture distribution of the different products

Spatio-temporal variability

Af America Tropical Rainforest

Aw America / Africa Tropical Savannah

BW Asia Arid Desert

Adapted from Peel et al. (2007): Updated world Köppen-Geiger climate classification map. Hydrol. Earth Syst. Sci. 11, 1633-1644.

Koeppen-Geiger climate classification

Correlation of ranks of mean relative differences

Global correlation of ranks of MRDs of the three soil moisture products for different climate classes. The lines are just for increasing readability and do not imply any functional relationship.

Over the entire world, the products show reasonable correlation coefficients of 0.34 (ERA/SMOS), 0.44 (SMOS/ASCAT), and 0.79 (ERA/ASCAT) => the overall soil moisture patterns are similar.

Soil moisture variability across scales

Investigation of the variability across scales of global soil moisture products

Soil moisture variability across scales

Soil moisture variability across scales

Underestimation of soil moisture variance of global soil moisture products for sub-continental studies

Wüstebach China

Spatio-temporal variability: PC Analysis

Spatio-temporal variability: PC Analysis Mean sm=0.178 Mean std sm=0.050

Graf et al. 2014

Spatio-temporal variability: PC Analysis Wüstebach China

From small scale observations we know that the shape of the PC-SMmean curves is related to variability of hydraulic properties (Qu et al., 2015 ) => The aim is to predict the variability of hydraulic properties in larger catchments

Wüstebach Nile

Stockinger et al. 2014

Soil moisture–runoff relationship

Wüstebach Huang He

Stockinger et al. 2014

Soil moisture–runoff relationship

Wüstebach Yangtse

Stockinger et al. 2014

Soil moisture–runoff relationship

• Spatio-temporal variability – Structural differences between different global soil moisture products – Overall soil moisture patterns are similar

• Soil moisture variability across scales – Global soil moisture products underestimate soil moisture variability on sub-

continental scale – only continental-scale applications suggested for SMOS, ASCAT

• Principal component analysis – Potential to inversely estimate soil parameter heterogeneity – Potential to predict runoff by soil moisture analysis

• Soil moisture-runoff relationship – Evaluation of relationship for storage estimation of a basin

=> Closing the water cycle on catchment scale

Conclusions

• Graf, A., H. R. Bogena, C. Drue, H. Hardelauf, T. Putz, G. Heinemann, and H. Vereecken (2014), Spatiotemporal relations between water budget components and soil water content in a forested tributary catchment, Water Resources Research, 50(6), 4837-4857.

• Qu, W., H. R. Bogena, J. A. Huisman, J. Vanderborght, M. Schuh, E. Priesack, and H. Vereecken (2015), Predicting subgrid variability of soil water content from basic soil information, Geophysical Research Letters, 42(3), 789-796.

• Rötzer, K., C. Montzka, and H. Vereecken (2015), Spatio-temporal variability of global soil moisture products, Journal of Hydrology, 522, 187-202.

• Stockinger, M. P., H. R. Bogena, A. Lucke, B. Diekkruger, M. Weiler, and H. Vereecken (2014), Seasonal soil moisture patterns: Controlling transit time distributions in a forested headwater catchment, Water Resources Research, 50(6), 5270-5289.

References