A tightly bound soil–water scheme within an atmosphere–land–surface model

2012 ◽  
Vol 452-453 ◽  
pp. 51-63 ◽  
Author(s):  
Rachel White ◽  
Ralf Toumi
Keyword(s):  
Soil Water ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model

Using satellite data in the Land Surface Model to estimate water availability of arid agricultural region

2020 ◽  
Author(s):  
Eugene Muzylev ◽  
Zoya Startseva ◽  
Elena Volkova ◽  
Eugene Vasilenko
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Availability ◽  
Land Surface ◽  
Land Surface Model ◽  
Soil Surface ◽  
Surface Model ◽  
Heat Regime ◽  
Vegetation Characteristics

<p>The water availability of agricultural arid regions can be assessed at presence using the physical-mathematical model of water and heat exchange between land surface and atmosphere LSM (Land Surface Model) adapted to satellite-derived estimates of meteorological and vegetation characteristics. The LSM is designed to calculate soil water content W, evapotranspiration Ev, vertical heat fluxes and other water and heat regime elements. Soil and vegetation characteristics were used in the LSM as parameters and meteorological characteristics were utilized as input variables.</p><p>The case study was carried out for the territory of the Saratov and Volgograd Trans-Volga region (the left-bank part of the Saratov and Volgograd regions) of 66600 km<sup>2</sup> for the vegetation seasons 2016-2018.</p><p>The satellite measurement data from radiometers AVHRR/NOAA, SEVIRI/Meteosat-10, -11, -8, and MSU-MR/Meteor-M No. 2 in visible and IR ranges were thematic processed to built estimates of vegetation index NDVI, emissivity E, vegetation cover fraction B, leaf index LAI, land surface temperature LST and precipitation.</p><p>LAI and B estimates were obtained using empirical dependencies on NDVI. The adequacy of the LAI and B estimates obtained from all sensor data was verified when comparing the LAI time behavior built for named vegetation seasons. Errors of determining B and LAI were 15 and 20%, respectively.</p><p>Satellite-derived estimates of daily, decadal and monthly precipitation sums for each pixel were obtained using the Multi Threshold Method (MTM) for detecting clouds, identifying its types allocating precipitation zones and determining their maximum intensity. The MTM is based on the developed algorithm of the transition from the assessment of precipitation intensity to the assessment of their daily amounts. Testing of the method was carried out when comparing these amounts with observed at meteorological stations. The probability of satellite-detected precipitation zones corresponded to the actual ones was ~ 80% for all radiometers.</p><p>Based on the MTM, computational algorithm to evaluate the LST was developed and verified on the study region data. Comparison of ground-measured and satellite-derived LST showed that the latter estimates for the overwhelming number of observation turned out to be comparable in accuracy with each other and with the ground-based data.</p><p>Calculations of water and heat regime elements (being the final products of the simulation) were carried out when replacing ground-based estimates of precipitation, LST, LAI and B in the LSM by satellite-derived ones at each time step in all nodes of the computational grid. The efficiency of such replacement procedures was confirmed by comparing measured and calculated values of W and Ev (the difference between them didn’t exceed 15% for W and 25% for Ev).</p><p>The possibility of using soil surface moisture estimates obtained from all-weather measurements by the scatterometer ASCAT/MetOp in the microwave range when simulating soil water content was also revealed. These estimates may use to set initial conditions for the vertical soil water transfer equation, as well as for calculating evaporation from the soil surface and the subsequent formation of the upper boundary condition for this equation.</p><p>As a summary, the described approach can be considered as a method for assessing the water availability for agricultural arid region.</p>

Evaluating Soil Water Content in a WRF-Noah Downscaling Experiment

2013 ◽  
Vol 52 (10) ◽  
pp. 2312-2327 ◽  
Author(s):  
Peter Greve ◽  
Kirsten Warrach-Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Root Zone ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Partitioning

AbstractSoil water content (SWC) depends on and affects the energy flux partitioning at the land–atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale.

scholarly journals Development of a Land Surface Model Including Evaporation and Adsorption Processes in the Soil for the Land–Air Exchange in Arid Regions

2007 ◽  
Vol 8 (6) ◽  
pp. 1307-1324 ◽  
Author(s):  
Genki Katata ◽  
Haruyasu Nagai ◽  
Hiromasa Ueda ◽  
Nurit Agam ◽  
Pedro R. Berliner
Keyword(s):  
Water Vapor ◽  
Soil Water ◽  
Land Surface ◽  
Soil Layer ◽  
Land Surface Model ◽  
Ground Surface ◽  
Surface Model ◽  
Water Vapor Flux ◽  
Vapor Flux ◽  
Adsorption Processes

Abstract A one-dimensional soil model has been developed to better predict heat and water exchanges in arid and semiarid regions. New schemes to calculate evaporation and adsorption in the soil were incorporated in the model. High performance of the model was confirmed by comparison of predicted surface fluxes, soil temperature, and volumetric soil water content with those measured in the Negev Desert, Israel. Evaporation and adsorption processes in the soil have a large impact on the heat and water exchange between the atmosphere and land surface and are necessary to accurately predict them. Numerical experiments concerning the drying process of soil are performed using the presented model and a commonly used land surface model. The results indicated that, when the dry soil layer (DSL) develops, water vapor flux to the atmosphere is caused by evaporation in the soil rather than evaporation at the ground surface. Moreover, the adsorption process has some impact on the water and heat balance at the ground surface. The upward water vapor flux during the daytime is due to evaporation of soil water in the DSL, which is stored during the night due to adsorption. When the DSL progresses sufficiently, almost the same amounts of water are exchanged between the air and the soil surface by daytime evaporation and nighttime adsorption. In such conditions, latent heat due to evaporation and adsorption in the soil also work to reduce the diurnal variation of surface temperature.

Effects of Gravel on Energy and Water Transport in the Soil of Qinghai-Tibet Plateau

2020 ◽  
Author(s):  
shihua lyu
Keyword(s):  
Soil Temperature ◽  
Soil Water ◽  
Land Surface ◽  
Land Surface Model ◽  
Thermal Inertia ◽  
Tibet Plateau ◽  
Surface Model ◽  
Gravel Content ◽  
Water Move ◽  
The Mean

<p>According to the shortcomings of the land surface model, the new scheme is developed and applied to the simulating soil process at Madoi and Nagqu. Simulations show that gravel tend to reduce soil water holding capacity and enhance soil hydraulic conductivity, surface infiltration and drainage. As a result, the upper layer of soil mixed with gravel tends to drier due to the soil water move to deeper layer. The mean biases of soil moisture between the simulation and observation reduced by 25- 48% at two sites. Soil thermal conductivity is increased with gravel content and the soil thermal inertia was decreased with gravel content increasing. Therefore the deeper layer temperature of soil containing gravel is rapid response to air temperature change. The mean biases of soil temperature between the simulation and observation reduced by 9.1-25% at two sites. From the simulation results at Madoi and Nagqu, we find that the new scheme performed better than the original scheme in simulating soil temperature and water content and the land model implemented the new scheme is suitable for simulating land process in the QTP.</p>

Towards long-term satellite root-zone soil moisture: 40-year Soil Water Index dataset from ESA CCI COMBINED Soil Moisture product.

2020 ◽  
Author(s):  
Adam Pasik ◽  
Bernhard Bauer-Marschallinger ◽  
Wolfgang Preimesberger ◽  
Tracy Scanlon ◽  
Wouter Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Water Index ◽  
Global Coverage ◽  
T Values

<p>Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling. <br>Here we present the first long-term SWI dataset from ESA CCI Soil Moisture v04.5 COMBINED product, covering a 40-year period between 1978 and 2018. The ESA CCI dataset is unique because of its long-term global coverage based on merged observations from both active and passive sensors. The SWI is calculated for eight T-values (1, 5, 10, 15, 20, 40, 60, 100), where T-value is a temporal length ruling the infiltration; depending on the soil characteristics it translates into different soil depths.<br>Primary results show promise for pursuing development of an operational SWI product. Here, we present the results of SWI validation against data from the International Soil Moisture Network (ISMN) using the QA4SM framework, as well as results of the attempt to establish relationship between T-values and particular soil depths.</p>

scholarly journals Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

2021 ◽  
Vol 22 (5) ◽  
pp. 1065-1084
Author(s):  
Anne Felsberg ◽  
Gabriëlle J. M. De Lannoy ◽  
Manuela Girotto ◽  
Jean Poesen ◽  
Rolf H. Reichle ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Water ◽  
Land Surface ◽  
Complex Terrain ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Probability Estimates ◽  
Terrestrial Water

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

scholarly journals Weak sensitivity of the terrestrial water budget to global soil texture maps in the ORCHIDEE land surface model

2019 ◽  
Author(s):  
Salma Tafasca ◽  
Agnès Ducharne ◽  
Christian Valentin
Keyword(s):  
Soil Water ◽  
Soil Texture ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Model ◽  
Global Scale ◽  
Added Value ◽  
Surface Model ◽  
Total Runoff ◽  
The Impact

Abstract. Soil physical properties play an important role for estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at global scale using the ORCHIDEE LSM, forced by several complex or globally-uniform soil texture maps. The model shows a realistic sensitivity of runoff processes and soil moisture to soil texture, and reveals that medium textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps being rather similar by construction, especially when upscaled at the 0.5° resolution used here, they result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling. The added-value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.

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