Dataset Information

Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.

ABSTRACT: Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between pre-storm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation depth converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration-excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent sub-surface stormflow or saturation-excess runoff generation processes.

SUBMITTER: Crow WT

PROVIDER: S-EPMC6140354 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.

Crow W T WT Chen F F Reichle R H RH Xia Y Y Liu Q Q

Geophysical research letters 20180504 10

Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in pre-storm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the NASA Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonst ...[more]

PMID: 30237639

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Project description:This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m-3 and 0.001 m3 m-3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m-3, but increased the root zone bias by 0.014 m3 m-3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.

Dataset Information

Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.

Publications

Exploiting soil moisture, precipitation and streamflow observations to evaluate soil moisture/runoff coupling in land surface models.

Similar Datasets

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