L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

Crow, Wade T.; Chen, F.; Reichle, Rolf H.; Liu, Q.

doi:10.1002/2017gl073642

Cited by 83 publications

(81 citation statements)

References 25 publications

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“…Following Crow et al (2005Crow et al ( , 2017, the Spearman rank correlation (R) between antecedent soil moisture and the event runoff coefficient is sampled across all available storm event periods within each basin between 31 March 2015 and 31 May 2017. Rank correlation is applied to minimize the confounding effect of potential nonlinearity in the relationship between antecedent soil moisture and event runoff coefficient.…”

Section: Storm Event Definition and Rank Correlation Metricmentioning

confidence: 99%

“…Sampling error bars for R in individual basins are estimated using a 5,000-member boot-strapping approach (where individual storm events are randomly sampled with replacement to preserve the underlying storm event sample size) and then combined to estimate uncertainty in R. Based on the auto-correlation analysis in Crow et al (2017), the 16 basins in Figure 1 are assumed to contain 7.4 spatially independent samples. This adjusted sample size is used to calculate the expected reduction in sampling uncertainty associated with averaging across all basins.…”

Section: Uncertainty Descriptionmentioning

confidence: 99%

“…However, diagnostic efforts involving these products have been hampered by the low quality of historically available, satellite-based soil moisture products . Crow et al (2017) demonstrates that the improved accuracy, complete spatio-temporal coverage, and subdaily frequency of the SMAP_L4 product make it uniquely suited for characterizing the relationship between prestorm soil moisture and storm-scale runoff response. The SMAP mission produces a Level-4 Surface and Root-zone Soil Moisture (SMAP_L4) product based on the assimilation of SMAP brightness temperature observations into an LSM (Reichle et al, 2016.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Crow¹,

Chen²,

Reichle

et al. 2018

Geophysical Research Letters

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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.

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Section: Storm Event Definition and Rank Correlation Metricmentioning

confidence: 99%

Section: Uncertainty Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Crow¹,

Chen²,

Reichle

et al. 2018

Geophysical Research Letters

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“…For example, Crow et al (2017) found that, among a range of competing soil moisture products, SMAP L4 surface soil moisture estimates provide the highest correlation with independently observed streamflow data for 16 basins in the south-central United States. For example, Crow et al (2017) found that, among a range of competing soil moisture products, SMAP L4 surface soil moisture estimates provide the highest correlation with independently observed streamflow data for 16 basins in the south-central United States.…”

Section: Introductionmentioning

confidence: 99%

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

Dong

Crow

Reichle

et al. 2019

Geophysical Research Letters

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The Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.

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“…One promising approach to improving SM estimation in turn improving rainfall-runoff modeling is to integrate the observed SM into the hydrological modeling process using data assimilation (DA) techniques [11][12][13][14][15][16]. In general, the SM data for integration can be obtained from field measurements and satellite observations.…”

Section: Introductionmentioning

confidence: 99%

ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

Wang

Liu

2018

Advances in Meteorology

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e assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. is study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. e results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. e less-effective performance on largeflood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. is study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

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L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting

Cited by 83 publications

References 25 publications

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model

ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

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