scholarly journals Indications of Surface and Sub-Surface Hydrologic Properties from SMAP Soil Moisture Retrievals

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 36 ◽  
Author(s):  
Paul Dirmeyer ◽  
Holly Norton
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Soil Column ◽  
Remotely Sensed ◽  
Subsurface Geology ◽  
South Central ◽  
Significant Relationships ◽  
The Impact ◽  
Soil Moisture Variability ◽  
Moisture Variability

Variability and covariability of land properties (soil, vegetation and subsurface geology) and remotely sensed soil moisture over the southeast and south-central U.S. are assessed. The goal is to determine whether satellite soil moisture memory contains information regarding land properties, especially the distribution karst formations below the active soil column that have a bearing on land-atmosphere feedbacks. Local (within a few tens of km) statistics of land states and soil moisture are considered to minimize the impact of climatic variations, and the local statistics are then correlated across the domain to illuminate significant relationships. There is a clear correspondence between soil moisture memory and many land properties including karst distribution. This has implications for distributed land surface modeling, which has not considered preferential water flows through geologic formations. All correspondences are found to be strongest during spring and fall, and weak during summer, when atmospheric moisture demand appears to dominate soil moisture variability. While there are significant relationships between remotely-sensed soil moisture variability and land properties, it will be a challenge to use satellite data for terrestrial parameter estimation as there is often a great deal of correlation among soil, vegetation and karst property distributions.

scholarly journals The GLACE-Hydrology Experiment: Effects of Land–Atmosphere Coupling on Soil Moisture Variability and Predictability

2020 ◽  
Vol 33 (15) ◽  
pp. 6511-6529
Author(s):  
Sanjiv Kumar ◽  
Matthew Newman ◽  
David M. Lawrence ◽  
Min-Hui Lo ◽  
Sathish Akula ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Radiative Forcing ◽  
Climate Model ◽  
Signal To Noise Ratio ◽  
Boreal Summer ◽  
Runoff Variability ◽  
The Impact ◽  
Soil Moisture Variability ◽  
Moisture Variability

AbstractThe impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

scholarly journals Weak Land–Atmosphere Coupling Strength in HadAM3: The Role of Soil Moisture Variability

2005 ◽  
Vol 6 (5) ◽  
pp. 670-680 ◽  
Author(s):  
David M. Lawrence ◽  
Julia M. Slingo
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Coupling Strength ◽  
General Circulation ◽  
General Circulation Models ◽  
Soil Conditions ◽  
Wide Range ◽  
The Impact ◽  
Soil Moisture Variability ◽  
Moisture Variability

Abstract A recent model intercomparison, the Global Land–Atmosphere Coupling Experiment (GLACE), showed that there is a wide range of land–atmosphere coupling strengths, or the degree that soil moisture affects the generation of precipitation, amongst current atmospheric general circulation models (AGCMs). Coupling strength in the Hadley Centre atmosphere model (HadAM3) is among the weakest of all AGCMs considered in GLACE. Reasons for the weak HadAM3 coupling strength are sought here. In particular, the impact of pervasive saturated soil conditions and low soil moisture variability on coupling strength is assessed. It is found that when the soil model is modified to reduce the occurrence of soil moisture saturation and to encourage soil moisture variability, the soil moisture–precipitation feedback remains weak, even though the relationship between soil moisture and evaporation is strengthened. Composites of the diurnal cycle, constructed relative to soil moisture, indicate that the model can simulate key differences in boundary layer development over wet versus dry soils. In particular, the influence of wet or dry soil on the diurnal cycles of Bowen ratio, boundary layer height, and total heat flux are largely consistent with the observed influence of soil moisture on these properties. However, despite what appears to be successful simulation of these key aspects of the indirect soil moisture–precipitation feedback, the model does not capture observed differences for wet and dry soils in the daily accumulation of boundary layer moist static energy, a crucial feature of the feedback mechanism.

What’s the impact of improved soil representations in the ECMWF land surface model and how does it affect the extremes?

2021 ◽  
Author(s):  
Souhail Boussetta ◽  
Gabriele Arduini ◽  
Gianpaolo Balsamo ◽  
Emanuel Dutra ◽  
Anna Agusti-Panareda ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Soil Depth ◽  
Soil Column ◽  
Carbon Fluxes ◽  
Moisture Stress ◽  
Rooting Depth ◽  
Surface Model ◽  
Soil Moisture Stress ◽  
The Impact

<p>With increasingly higher spatial resolution and a broader applications, the importance of soil representation (e.g. soil depth, vertical discretisation, vegetation rooting) within land surface models is enhanced. Those modelling choices actually affects the way land surfaces store and regulate water, energy and also carbon fluxes. Heat and water vapour fluxes towards the atmosphere and deeper soil, exhibit variations spanning a range of time scales from minutes to months in the coupled land-atmosphere system. This is further modulated by the vertical roots' distribution, and soil moisture stress function, which control evapotranspiration under soil moisture stress conditions. Currently in the ECMWF land Surface Scheme the soil column is represented by a fixed 4 layers configuration with a total of approximately 3m depth.</p><p>In the present study we explore new configurations with increased soil depth (up to 8m) and higher vertical discretisation (up to 10 layers) including a dissociation between the treatment of water and heat fluxes. Associated with the soil vertical resolution, the vertical distribution of roots is also investigated. A new scheme that assumes a uniform root distribution with an associated maximum rooting depth is explored. The impact of these new configurations is assessed through surface offline simulations driven by the ERA5 meteorological forcing against in-situ and global products of energy, water and carbon fluxes with a particular focus on the diurnal cycle and extreme events in recent years.</p>

Evaluation of the impact of rainfall on soil moisture variability

1998 ◽  
Vol 21 (5) ◽  
pp. 375-384 ◽  
Author(s):  
Chulsang Yoo ◽  
Juan B. Valdés ◽  
Gerald R. North
Keyword(s):  
Soil Moisture ◽  
The Impact ◽  
Soil Moisture Variability ◽  
Moisture Variability

scholarly journals The Physics of Drought in the U.S. Central Great Plains

2016 ◽  
Vol 29 (18) ◽  
pp. 6783-6804 ◽  
Author(s):  
Ben Livneh ◽  
Martin P. Hoerling
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
Growing Season ◽  
Surface Model ◽  
Moisture Conditions ◽  
Soil Moisture Variability ◽  
The U.S ◽  
Rainfall Deficits ◽  
Moisture Variability

Abstract The semiarid U.S. Great Plains is prone to severe droughts having major consequences for agricultural production, livestock health, and river navigation. The recent 2012 event was accompanied by record deficits in precipitation and high temperatures during the May–August growing season. Here the physics of Great Plains drought are explored by addressing how meteorological drivers induce soil moisture deficits during the growing season. Land surface model (LSM) simulations driven by daily observed meteorological forcing from 1950 to 2013 compare favorably with satellite-derived terrestrial water anomalies and reproduce key features found in the U.S. Drought Monitor. Results from simulations by two LSMs reveal that precipitation was directly responsible for between 72% and 80% of the soil moisture depletion during 2012, and likewise has accounted for the majority of Great Plains soil moisture variability since 1950. Energy balance considerations indicate that a large fraction of the growing season temperature variability is itself driven by precipitation, pointing toward an even larger net contribution of precipitation to soil moisture variability. To assess robustness across a larger sample of drought events, daily meteorological output from 1050 years of climate simulations, representative of conditions in 1979–2013, are used to drive two LSMs. Growing season droughts, and low soil moisture conditions especially, are confirmed to result principally from rainfall deficits. Antecedent meteorological and soil moisture conditions are shown to affect growing season soil moisture, but their effects are secondary to forcing by contemporaneous rainfall deficits. This understanding of the physics of growing season droughts is used to comment on plausible Great Plains soil moisture changes in a warmer world.

scholarly journals Impacts of soil-aquifer heat and water fluxes on simulated global climate

2013 ◽  
Vol 10 (1) ◽  
pp. 1185-1212 ◽  
Author(s):  
N. Y. Krakauer ◽  
M. J. Puma ◽  
B. I. Cook
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Circulation ◽  
Soil Column ◽  
Circulation Model ◽  
The Arctic ◽  
Water Fluxes ◽  
Limited Impact ◽  
Mean Climate ◽  
The Impact

Abstract. Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50 m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1 K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and land-surface temperature, and decrease the soil moisture memory of the land surface on annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

2020 ◽  
Vol 21 (9) ◽  
pp. 2041-2060
Author(s):  
Peter J. Shellito ◽  
Sujay V. Kumar ◽  
Joseph A. Santanello ◽  
Patricia Lawston-Parker ◽  
John D. Bolten ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Soil Column ◽  
Soil Layer ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
L Band ◽  
The Impact

AbstractThe utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common. This study assesses the degree to which model agreement with observations is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequate or inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth is notably different from the 5-cm (or less) sensing depth of L-band radiometers such as NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model (RTM) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Agreement between models and observations is quantified using Kolmogorov–Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Results show that agreement of SSM and Tb with observations depends primarily on systematic biases, and the sign of those biases depends on the particular subspace being analyzed (SSM or Tb). This study concludes that the role of increased soil layer discretization on simulated soil moisture and Tb is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations.

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