scholarly journals Estimating Soil Evaporation Using Drying Rates Determined from Satellite-Based Soil Moisture Records

2018 ◽  
Vol 10 (12) ◽  
pp. 1945 ◽  
Author(s):  
Eric Small ◽  
Andrew Badger ◽  
Ronnie Abolafia-Rosenzweig ◽  
Ben Livneh
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Soil Layer ◽  
Soil Evaporation ◽  
Soil Drying ◽  
Model Calculations ◽  
Vertical Fluxes ◽  
Equivalent Water Thickness ◽  
Surface Soil Layer ◽  
Drying Rates

We describe an approach (ESMAP; Evaporation–Soil Moisture Active Passive) to estimate direct evaporation from soil, Esoil, by combining remotely-sensed soil drying rates with model calculations of the vertical fluxes in and out of the surface soil layer. Improved knowledge of Esoil can serve as a constraint in how total evapotranspiration is partitioned. The soil drying rates used here are based on SMAP data, but the method could be applied to data from other sensors. We present results corresponding to ten SMAP pixels in North America to evaluate the method. The ESMAP method was applied to intervals between successive SMAP overpasses with limited precipitation (<2 mm) to avoid uncertainty associated with precipitation, infiltration, and runoff. We used the Hydrus 1-D model to calculate the flux of water across the bottom boundary of the 0 to 50 mm soil layer sensed by SMAP, qbot. During dry intervals, qbot typically transfers water upwards into the surface soil layer from below, usually <0.5 mm day−1. Based on a standard formulation, transpiration from the surface soil layer, ET_s, is usually < 0.1 mm day−1, and, thus, generally not an important flux. Soil drying rates (converted to equivalent water thickness) are typically between 0 and 1 mm day−1. Evaporation is almost always greater than soil drying rates because qbot is typically a source of water to the surface soil and ET_s is negligible. Evaporation is typically between 0 and 1.5 mm day−1, with the highest values following rainfall. Soil evaporation summed over SMAP overpass intervals with precipitation <2 mm (60% of days) accounts for 15% of total precipitation. If evaporation rates are similar during overpasses with substantial precipitation, then the total evaporation flux would account for ~25% of precipitation. ESMAP could be used over spatially continuous domains to provide constraints on Esoil, but model-based Esoil would be required during intervals with substantial precipitation.

scholarly journals A continental-scale soil evaporation dataset derived from Soil Moisture Active Passive satellite drying rates

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ronnie Abolafia-Rosenzweig ◽  
Andrew M. Badger ◽  
Eric E. Small ◽  
Ben Livneh
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Model Development ◽  
Soil Evaporation ◽  
Soil Drying ◽  
Continental Scale ◽  
Time Period ◽  
Surface Models ◽  
Drying Rates

AbstractThis manuscript describes an observationally-based dataset of soil evaporation for the conterminous U.S. (CONUS), gridded to a 9 km resolution for the time-period of April 2015-March 2019. This product is termed E-SMAP (Evaporation-Soil Moisture Active Passive) in which soil evaporation is estimated from the surface layer, defined by the SMAP sensing depth of 50 mm, between SMAP overpass intervals that are screened on the basis of precipitation and SMAP quality control flags. Soil evaporation is estimated using a water balance of the surface soil that we show is largely dominated by SMAP-observed soil drying. E-SMAP soil evaporation is on average 0.72 mm day−1, which falls within the range of soil evaporation estimates (0.17–0.89 mm day−1) derived from operational land surface models and an alternative remote sensing product. E-SMAP is independent from existing soil evaporation estimates and therefore has the potential to improve understanding of evapotranspiration partitioning and model development.

scholarly journals Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

2018 ◽  
Vol 22 (3) ◽  
pp. 1649-1663 ◽  
Author(s):  
Peter J. Shellito ◽  
Eric E. Small ◽  
Ben Livneh
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Surface Soil ◽  
Soil Evaporation ◽  
Surface Model ◽  
Soil Drying ◽  
Potential Evaporation ◽  
Surface Soil Moisture ◽  
Drying Rates

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess controls on soil evaporation on a continental scale. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates, and our work suggests that SMAP-observed drying is also predominantly affected by direct soil evaporation. Data cover the domain of the North American Land Data Assimilation System Phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

scholarly journals Controls on surface soil drying rates observed by SMAP and simulated by the Noah land surface model

2017 ◽  
Author(s):  
Peter J. Shellito ◽  
Eric E. Small
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Vegetation Cover ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Model ◽  
Soil Drying ◽  
Potential Evaporation ◽  
Surface Soil Moisture ◽  
Drying Rates

Abstract. Drydown periods that follow precipitation events provide an opportunity to assess the mechanisms by which soil moisture dissipates from the land surface. We use SMAP (Soil Moisture Active Passive) observations and Noah simulations from drydown periods to quantify the role of soil moisture, potential evaporation, vegetation cover, and soil texture on soil drying rates. Rates are determined using finite differences over intervals of 1 to 3 days. In the Noah model, the drying rates are a good approximation of direct soil evaporation rates. Data cover the domain of the North American Land Data Assimilation System phase 2 and span the first 1.8 years of SMAP's operation. Drying of surface soil moisture observed by SMAP is faster than that simulated by Noah. SMAP drying is fastest when surface soil moisture levels are high, potential evaporation is high, and when vegetation cover is low. Soil texture plays a minor role in SMAP drying rates. Noah simulations show similar responses to soil moisture and potential evaporation, but vegetation has a minimal effect and soil texture has a much larger effect compared to SMAP. When drying rates are normalized by potential evaporation, SMAP observations and Noah simulations both show that increases in vegetation cover lead to decreases in evaporative efficiency from the surface soil. However, the magnitude of this effect simulated by Noah is much weaker than that determined from SMAP observations.

Outflows of Organic Halide Precursors from Forest Regions

1989 ◽  
Vol 21 (12) ◽  
pp. 1877-1880 ◽  
Author(s):  
S. Saito ◽  
K. Hattori ◽  
T. Okumura
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Surface Soil ◽  
Soil Layer ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Deep Soil ◽  
Organic Halide ◽  
Forest Regions ◽  
Surface Soil Layer

Outflows of organic halide precursors (OXPs) from forest regions were studied in relation to water quality monitoring in the Yodo River basin. Firstly, the contribution of outflows from forest regions relative to the total was roughly estimated. Then equations for flows of these substances were formulated, divided into four different subflow categories: precipitation; throughfall; surface soil layer; and, deep soil layer. Finally, annual outflow loads were calculated for a test forest area.

scholarly journals Biochar induced improvement in root system architecture enhances nutrient assimilation by cotton plant seedlings

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Feng ◽  
Wanli Xu ◽  
Guangmu Tang ◽  
Meiying Gu ◽  
Zengchao Geng
Keyword(s):  
Root System ◽  
System Architecture ◽  
Fine Roots ◽  
Nitrogen Assimilation ◽  
Surface Soil ◽  
Soil Layer ◽  
Root System Architecture ◽  
Seedling Stage ◽  
Agronomic Efficiency ◽  
Surface Soil Layer

Abstract Background Raising nitrogen use efficiency of crops by improving root system architecture is highly essential not only to reduce costs of agricultural production but also to mitigate climate change. The physiological mechanisms of how biochar affects nitrogen assimilation by crop seedlings have not been well elucidated. Results Here, we report changes in root system architecture, activities of the key enzymes involved in nitrogen assimilation, and cytokinin (CTK) at the seedling stage of cotton with reduced urea usage and biochar application at different soil layers (0–10 cm and 10–20 cm). Active root absorption area, fresh weight, and nitrogen agronomic efficiency increased significantly when urea usage was reduced by 25% and biochar was applied in the surface soil layer. Glutamine oxoglutarate amino transferase (GOGAT) activity was closely related to the application depth of urea/biochar, and it increased when urea/biochar was applied in the 0–10 cm layer. Glutamic-pyruvic transaminase activity (GPT) increased significantly as well. Nitrate reductase (NR) activity was stimulated by CTK in the very fine roots but inhibited in the fine roots. In addition, AMT1;1, gdh3, and gdh2 were significantly up-regulated in the very fine roots when urea usage was reduced by 25% and biochar was applied. Conclusion Nitrogen assimilation efficiency was significantly affected when urea usage was reduced by 25% and biochar was applied in the surface soil layer at the seedling stage of cotton. The co-expression of gdh3 and gdh2 in the fine roots increased nitrogen agronomic efficiency. The synergistic expression of the ammonium transporter gene and gdh3 suggests that biochar may be beneficial to amino acid metabolism.

scholarly journals Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

2019 ◽  
pp. 77-86
Author(s):  
A. G. Buevich ◽  
I. E. Subbotina ◽  
A. V. Shichkin ◽  
A. P. Sergeev ◽  
E. M. Baglaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Surface Soil ◽  
Soil Layer ◽  
Generalized Regression Neural Network ◽  
Residual Kriging ◽  
Surface Soil Layer ◽  
Artificial Neural ◽  
Generalized Regression

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

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