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

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

doi:10.1029/2018gl077193

Cited by 75 publications

(59 citation statements)

References 28 publications

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“…L4_SM soil moisture estimates could also better predict the streamflow response to subsequent precipitation than model‐only estimates and L‐band (or C‐band) soil moisture retrievals by themselves (Crow et al, ). Finally, L4_SM data have been used successfully to evaluate and calibrate the coupling strength between soil moisture and the subsequent, storm‐scale runoff ratio in several different land surface models (Crow et al, , ).…”

Section: Introductionmentioning

confidence: 99%

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Reichle

Liu

Koster

et al. 2019

J Adv Model Earth Syst

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127

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The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 Soil Moisture (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 cm) soil moisture with a mean latency of ~2.5 days. The underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. In Version 4 of the L4_SM modeling system the upward recharge of surface soil moisture from below under nonequilibrium conditions was reduced, resulting in less bias and improved dynamic range of L4_SM surface soil moisture compared to earlier versions. This change and additional technical modifications to the system reduce the mean and standard deviation of the observation‐minus‐forecast Tb residuals and overall soil moisture analysis increments while maintaining the skill of the L4_SM soil moisture estimates versus independent in situ measurements; the average, bias‐adjusted root‐mean‐square error in Version 4 is 0.039 m3/m3 for surface and 0.026 m3/m3 for root zone soil moisture. Moreover, the coverage of assimilated SMAP observations in Version 4 is near global owing to the use of additional satellite Tb records for algorithm calibration. L4_SM soil moisture uncertainty estimates are biased low (by 0.01–0.02 m3/m3) against actual errors (computed versus in situ measurements). L4_SM runoff estimates, an additional product of the L4_SM algorithm, are biased low (by 35 mm/year) against streamflow measurements. Compared to Version 3, bias in Version 4 is reduced by 46% for surface soil moisture uncertainty estimates and by 33% for runoff estimates.

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Section: Introductionmentioning

confidence: 99%

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Reichle

Liu

Koster

et al. 2019

J Adv Model Earth Syst

Self Cite

150

127

View full text Add to dashboard Cite

show abstract

“…The variability of the near‐surface atmospheric conditions (e.g., humidity, dust particles, and temperature profiles) have demonstrated high correlation with extreme climate events such as droughts, dust outbreaks, floods, and wildfires. In addition, soil moisture plays an indirect but important role is that latent and sensible heat fluxes are controlled by surface soil moisture, which affects boundary layer stability and low‐altitude atmospheric conditions (Brocca et al, ; Crow et al, ; Delworth & Manabe, ; Haarsma et al, ; Kim et al, ). Even though surface soil moisture can be decoupled from root‐zone soil moisture over dry‐environment conditions (Hirschi et al, ), surface soil moisture can be a good indicator of the variability of deeper soil moisture in many cases (Choi & Jacobs, ; Dong & Crow, ; Qiu et al, ; Zohaib et al, ).…”

Section: Introductionmentioning

confidence: 99%

Global Dynamics of Stored Precipitation Water in the Topsoil Layer From Satellite and Reanalysis Data

Kim

Lakshmi

2019

Water Resources Research

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The amount of soil water in the topsoil layer (from 0 to 10 cm) has been regarded as a key factor in controlling land‐atmosphere interaction by determining the fraction of net radiation. In the present study, we investigate spatial trends of the stored precipitation fraction in the topsoil layer for varying vegetation and aridity indices by utilizing four satellites and two reanalysis data sets on a global scale. Using the Budyko framework, we relate climate regimes to the stored precipitation fraction on a global scale. A positive relation between the stored precipitation fraction with aridity index and a negative relation between the stored precipitation fraction and free parameter, vegetation optical depth, and isohydric slope are discovered. Even though the stored precipitation fraction values were calculated from different soil moisture and precipitation sources, they share an similar spatial trend: the drier and less vegetated the soil is, the more precipitation is retained in the top layer of the soil. Specifically, the topsoil retains 37% ± 11% of precipitated water three days after a rainfall event where the aridity index was greater than 5. Over wet and forest areas, due to large runoff fluxes and plants intercepting water before the precipitated water reached the ground, the topsoil retains 21% ± 2% of precipitated water three days after a rainfall event. Furthermore, by using the modeled data sets in the calculation of the stored precipitation fraction metric, we are able to conduct a sensitivity analysis of FP(f) metrics with respect to different sampling frequency values.

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“…Crow, Chen, et al () show that prestorm estimates of infiltration capacity inferred from SMAP Level‐4 surface soil moisture estimates (Reichle et al, ) can explain a substantial fraction of event‐to‐event variability in runoff coefficients (i.e., the fraction of rainfall accumulation depth converted into stormflow runoff during a storm event). This knowledge can then be used to evaluate the soil moisture/runoff coupling in land surface models (Crow et al, ). McColl, Wang, et al () used SMAP data to characterize drydown rates in the surface soil, rates that should be relevant to and captured by the large‐scale land model components of climate models.…”

Section: Introductionmentioning

confidence: 99%

Estimating Basin‐Scale Water Budgets With SMAP Soil Moisture Data

Koster

Crow

Reichle

et al. 2018

Water Resources Research

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Soil Moisture Active Passive (SMAP) Level‐2 soil moisture retrievals collected during 2015–2017 are used in isolation to estimate 10‐day warm season precipitation and streamflow totals within 145 medium‐sized (2,000–10,000 km2) unregulated watersheds in the conterminous United States. The precipitation estimation algorithm, derived from a well‐documented approach, includes a locally calibrated loss function component that significantly improves its performance. For the basin‐scale water budget analysis, the precipitation and streamflow algorithms are calibrated with 2 years of SMAP retrievals in conjunction with observed precipitation and streamflow data and are then applied to SMAP retrievals alone during a third year. While estimation accuracy (as measured by the square of the correlation coefficient, r2, between estimates and observations) varies by basin, the average r2 for the basins is 0.53 for precipitation and 0.22 for streamflow. For the subset of 22 basins that calibrate particularly well, the r2 increases to 0.63 for precipitation and to 0.51 for streamflow. The magnitudes of the estimated variables are also accurate, with sample pairs generally clustered about the 1:1 line. The chief limitation to the estimation involves large biases induced during periods of high rainfall; the accuracy of the estimates (in terms of r2 and root‐mean‐square error) increases significantly when periods of higher rainfall are not considered. The potential for transferability is also demonstrated by calibrating the streamflow estimation equation in one basin and then applying the equation in another. Overall, the study demonstrates that SMAP retrievals contain, all by themselves, information that can be used to estimate large‐scale water budgets.

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

Cited by 75 publications

References 28 publications

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Version 4 of the SMAP Level‐4 Soil Moisture Algorithm and Data Product

Global Dynamics of Stored Precipitation Water in the Topsoil Layer From Satellite and Reanalysis Data

Estimating Basin‐Scale Water Budgets With SMAP Soil Moisture Data

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