scholarly journals Soil Moisture, Snow, and Seasonal Streamflow Forecasts in the United States

2012 ◽  
Vol 13 (1) ◽  
pp. 189-203 ◽  
Author(s):  
Sarith Mahanama ◽  
Ben Livneh ◽  
Randal Koster ◽  
Dennis Lettenmaier ◽  
Rolf Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
The United States ◽  
Surface Model ◽  
Skill Levels ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization ◽  
Start Dates

Abstract Land surface model experiments are used to quantify, for a number of U.S. river basins, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Snow initialization has a major impact on skill during the spring melting season. Soil moisture initialization has a smaller but still statistically significant impact during this season, and in other seasons, its contribution to skill dominates. Realistic soil moisture initialization can contribute to skill at long leads (over 6 months) for certain basins and seasons. Skill levels in all seasons are found to be related to the ratio of initial total water storage (soil water plus snow) variance to the forecast period precipitation variance, allowing estimates of the potential for skill in areas outside the verification basins.

scholarly journals Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

2014 ◽  
Vol 15 (1) ◽  
pp. 69-88 ◽  
Author(s):  
Randal D. Koster ◽  
Gregory K. Walker ◽  
Sarith P. P. Mahanama ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Forecast Skill ◽  
Surface Model ◽  
Streamflow Forecast ◽  
Continental Scale ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow ◽  
Soil Moisture Initialization

Abstract Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

scholarly journals Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

2019 ◽  
Vol 20 (5) ◽  
pp. 793-819 ◽  
Author(s):  
Joseph A. Santanello Jr. ◽  
Patricia Lawston ◽  
Sujay Kumar ◽  
Eli Dennis
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Initial Conditions ◽  
Land Surface Model ◽  
Surface Model ◽  
Coupled Models ◽  
Wide Range ◽  
Soil Moisture Initialization

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

scholarly journals Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation

2016 ◽  
Vol 17 (2) ◽  
pp. 669-691 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
The United States ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Temperature

Abstract Multiangle and multipolarization L-band microwave observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated into the Goddard Earth Observing System Model, version 5 (GEOS-5), using a spatially distributed ensemble Kalman filter. A variant of this system is also used for the Soil Moisture Active Passive (SMAP) Level 4 soil moisture product. The assimilation involves a forward simulation of brightness temperatures (Tb) for various incidence angles and polarizations and an inversion of the differences between Tb forecasts and observations into updates to modeled surface and root-zone soil moisture, as well as surface soil temperature. With SMOS Tb assimilation, the unbiased root-mean-square difference between simulations and gridcell-scale in situ measurements in a few U.S. watersheds during the period from 1 July 2010 to 1 July 2014 is 0.034 m3 m−3 for both surface and root-zone soil moisture. A validation against gridcell-scale measurements and point-scale measurements from sparse networks in the United States, Australia, and Europe demonstrates that the assimilation improves both surface and root-zone soil moisture results over the open-loop (no assimilation) estimates in areas with limited vegetation and terrain complexity. At the global scale, the assimilation of SMOS Tb introduces mean absolute increments of 0.004 m3 m−3 to the profile soil moisture content and 0.7 K to the surface soil temperature. The updates induce changes to energy fluxes and runoff amounting to about 15% of their respective temporal standard deviation.

scholarly journals Impact of Irrigation Methods on Land Surface Model Spinup and Initialization of WRF Forecasts

2015 ◽  
Vol 16 (3) ◽  
pp. 1135-1154 ◽  
Author(s):  
Patricia M. Lawston ◽  
Joseph A. Santanello ◽  
Benjamin F. Zaitchik ◽  
Matthew Rodell
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Prediction Models ◽  
Land Surface Model ◽  
The United States ◽  
Time Of Day ◽  
Surface Model ◽  
Climate Conditions ◽  
Irrigation Methods ◽  
Time Space

Abstract In the United States, irrigation represents the largest consumptive use of freshwater and accounts for approximately one-third of total water usage. Irrigation impacts soil moisture and can ultimately influence clouds and precipitation through land–planetary boundary layer (PBL) coupling processes. This study utilizes NASA’s Land Information System (LIS) and the NASA Unified Weather Research and Forecasting Model (NU-WRF) framework to investigate the effects of drip, flood, and sprinkler irrigation methods on land–atmosphere interactions, including land–PBL coupling and feedbacks at the local scale. To initialize 2-day, 1-km WRF forecasts over the central Great Plains in a drier-than-normal (2006) and a wetter-than-normal year (2008), 5-yr irrigated LIS spinups were used. The offline and coupled simulation results show that regional irrigation impacts are sensitive to time, space, and method and that irrigation cools and moistens the surface over and downwind of irrigated areas, ultimately resulting in both positive and negative feedbacks on the PBL depending on the time of day and background climate conditions. Furthermore, the results portray the importance of both irrigation method physics and correct representation of several key components of land surface models, including accurate and timely land-cover and crop-type classification, phenology (greenness), and soil moisture anomalies (through a land surface model spinup) in coupled prediction models.

scholarly journals Uncertainties, Correlations, and Optimal Blends of Drought Indices from the NLDAS Multiple Land Surface Model Ensemble

2014 ◽  
Vol 15 (4) ◽  
pp. 1636-1650 ◽  
Author(s):  
Youlong Xia ◽  
Michael B. Ek ◽  
David Mocko ◽  
Christa D. Peters-Lidard ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Pacific Northwest ◽  
Land Surface ◽  
Land Surface Model ◽  
The United States ◽  
Drought Indices ◽  
Surface Model ◽  
Total Runoff ◽  
Single Formula ◽  
Area Percentage

Abstract This study analyzed uncertainties and correlations over the United States among four ensemble-mean North American Land Data Assimilation System (NLDAS) percentile-based drought indices derived from monthly mean evapotranspiration ET, total runoff Q, top 1-m soil moisture SM1, and total column soil moisture SMT. The results show that the uncertainty is smallest for SM1, largest for SMT, and moderate for ET and Q. The strongest correlation is between SM1 and SMT, and the weakest correlation is between ET and Q. The correlation between ET and SM1 (SMT) is strongest in arid–semiarid regions, and the correlation between Q and SM1 (SMT) is strongest in more humid regions in the Pacific Northwest and the Southeast. Drought frequency analysis shows that SM1 has the most frequent drought occurrence, followed by SMT, Q, and ET. The study compared the NLDAS drought indices (a research product) with the U.S. Drought Monitor (USDM; an operational product) in terms of drought area percentage derived from each product. It proposes an optimal blend of NLDAS drought indices by searching for weights for each index that minimizes the RMSE between NLDAS and USDM drought area percentage for a 10-yr period (2000–09) with a cross validation. It reconstructed a 30-yr (1980–2009) Objective Blended NLDAS Drought Index (OBNDI) and monthly drought percentage. Overall, the OBNDI performs the best with the smallest RMSE, followed by SM1 and SMT. It should be noted that the contribution to OBNDI from different variables varies with region. So a single formula is probably not the best representation of a blended index. The representation of a blended index using the multiple formulas will be addressed in a future study.

scholarly journals AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land–Atmosphere Feedback

2005 ◽  
Vol 6 (5) ◽  
pp. 656-669 ◽  
Author(s):  
Sarith P. P. Mahanama ◽  
Randal D. Koster
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Circulation ◽  
Land Surface Model ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Surface Model ◽  
Negative Effects ◽  
Memory Time ◽  
Soil Moisture Initialization

Abstract Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM’s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). The LSM was first forced globally with a 15-yr observation-based dataset. The simulation was then repeated after imposing a representative set of GCM climate biases onto the forcings—the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1; NASA Global Modeling and Assimilation Office) AGCM over the same period. The AGCM’s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory time scales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America—parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models

2020 ◽  
Author(s):  
Hyunglok Kim ◽  
Venkataraman Lakshmi ◽  
Sujay Kumar ◽  
Yonghwan Kwon
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
The United States ◽  
Heat Fluxes ◽  
Surface Model ◽  
Data Sets ◽  
Surface Models ◽  
Band Radiometer

<p>Prediction of water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks on a regional-scale can benefit from the high-spatial-resolution soil moisture (SM) data of both satellite and modeled products. The reason is that the amount of surface SM controls in the partitioning of outgoing energy fluxes into latent and sensible heat fluxes.</p><p>Recently, NASA’s SMAP mission has been implemented, in order to provide 3-km and 1-km SM data from a combination of SMAP and Sentinel-1A/B observations along with 9- and 36-km SM data retrieved from an L-band radiometer brightness temperature (TB). The 3-km and 1-km SM products were produced by combining the Sentinel-1A/B C-band radar backscatter and SMAP radiometer TB observations.</p><p>In the present study, we assimilated SMAP-enhanced (9-km) and SMAP/Sentinel-1A/B SM (3-km and 1-km) products into a land surface model (LSM): SMAP-enhanced and SMAP/Sentinel-1A/B SM data were assimilated into Noah-MP3.6 LSM. Then, these products were evaluated against ground observations in the United States. Three DA products’ error characteristics were intercompared: (1) SMAP-enhanced 9-km DA, (2) SMAP/Sentinel-1A/B 3-km DA and (3) SMAP/Sentinel-1A/B 1-km DA.</p><p>When SMAP and SMAP/Sentinel SM data sets were assimilated into LSM, the R- and ubRMSE values for 9-, 3-, and 1-km SM data were greatly improved.</p>

scholarly journals Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

2016 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
The United States ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at 40° incidence angle from the Soil Moisture Active Passive mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations during the period 1 July 2010 to 1 May 2015 and for 187 sites across the United States. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g. increase in anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

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