scholarly journals Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas

1999 ◽  
Vol 17 (1) ◽  
pp. 149-157 ◽  
Author(s):  
F. Fécan ◽  
B. Marticorena ◽  
G. Bergametti
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Wind Erosion ◽  
Large Scale ◽  
Empirical Relationship ◽  
Capillary Forces ◽  
Atmospheric Composition ◽  
Soil Water Retention ◽  
Erosion Threshold ◽  
Semi Arid

Abstract. Large-scale simulation of the soil-derived dust emission in semi-arid regions needs to account for the influence of the soil moisture on the wind erosion threshold. Soil water retention consists of molecular adsorption on the soil grain surface and capillary forces between the grain. Interparticle capillary forces (characterized by the moisture tension) are the main factor responsible for the increase of the wind erosion threshold observed when the soil moisture increases. When the soil moisture content is close to but smaller than the maximum amount of adsorbed water, w' (depending on the soil texture), these capillary forces are considered as not strong enough to significantly increase the erosion threshold. An expression of the moisture tension as a function of soil moisture and w' is derived from retention curves. From this expression, a parametrization of the ratio of the wet to dry erosion thresholds has been developed as a function of soil moisture and soil texture. The coefficients of this parametrization have been determined by using experimental data from the literature. An empirical relationship between w' and soil clay content has been established. The erosion threshold ratios simulated for different soil textures were found to be in good agreement with the experimental data.Key words. Atmospheric composition and structure (Aerosols and particles) · Hydrology (soil moisture)

scholarly journals Quantifying the impact of groundwater depth on evapotranspiration in a semi-arid grassland region

2011 ◽  
Vol 15 (3) ◽  
pp. 787-806 ◽  
Author(s):  
M. E. Soylu ◽  
E. Istanbulluoglu ◽  
J. D. Lenters ◽  
T. Wang
Keyword(s):  
Soil Moisture ◽  
Water Table ◽  
Soil Texture ◽  
Land Surface ◽  
Surface Interactions ◽  
Water Table Depth ◽  
Richards Equation ◽  
Hydraulic Parameter ◽  
Soil Hydraulic ◽  
Semi Arid

Abstract. Interactions between shallow groundwater and land surface processes play an important role in the ecohydrology of riparian zones. Some recent land surface models (LSMs) incorporate groundwater-land surface interactions using parameterizations at varying levels of detail. In this paper, we examine the sensitivity of land surface evapotranspiration (ET) to water table depth, soil texture, and two commonly used soil hydraulic parameter datasets using four models with varying levels of complexity. The selected models are Hydrus-1D, which solves the pressure-based Richards equation, the Integrated Biosphere Simulator (IBIS), which simulates interactions among multiple soil layers using a (water-content) variant of the Richards equation, and two forms of a steady-state capillary flux model coupled with a single-bucket soil moisture model. These models are first evaluated using field observations of climate, soil moisture, and groundwater levels at a semi-arid site in south-central Nebraska, USA. All four models are found to compare reasonably well with observations, particularly when the effects of groundwater are included. We then examine the sensitivity of modelled ET to water table depth for various model formulations, node spacings, and soil textures (using soil hydraulic parameter values from two different sources, namely Rawls and Clapp-Hornberger). The results indicate a strong influence of soil texture and water table depth on groundwater contributions to ET. Furthermore, differences in texture-specific, class-averaged soil parameters obtained from the two literature sources lead to large differences in the simulated depth and thickness of the "critical zone" (i.e., the zone within which variations in water table depth strongly impact surface ET). Depending on the depth-to-groundwater, this can also lead to large discrepancies in simulated ET (in some cases by more than a factor of two). When the Clapp-Hornberger soil parameter dataset is used, the critical zone becomes significantly deeper, and surface ET rates become much higher, resulting in a stronger influence of deep groundwater on the land surface energy and water balance. In general, we find that the simulated sensitivity of ET to the choice of soil hydraulic parameter dataset is greater than the sensitivity to soil texture defined within each dataset, or even to the choice of model formulation. Thus, our findings underscore the need for future modelling and field-based studies to improve the predictability of groundwater-land surface interactions in numerical models, particularly as it relates to the parameterization of soil hydraulic properties.

scholarly journals Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

2007 ◽  
Vol 110 (1) ◽  
pp. 79-97 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Matthew E. Garcia ◽  
David M. Mocko ◽  
Michael A. Tischler ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Remotely Sensed ◽  
Semi Arid

scholarly journals Evaporation from cultivated and semi-wild Sudanian Savanna in west Africa

2017 ◽  
Vol 21 (8) ◽  
pp. 4149-4167 ◽  
Author(s):  
Natalie C. Ceperley ◽  
Theophile Mande ◽  
Nick van de Giesen ◽  
Scott Tyler ◽  
Hamma Yacouba ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
West Africa ◽  
Large Scale ◽  
Vegetation Index ◽  
Cultural Beliefs ◽  
Heat Fluxes ◽  
Remotely Sensed Data ◽  
Evaporative Fraction ◽  
Semi Arid

Abstract. Rain-fed farming is the primary livelihood of semi-arid west Africa. Changes in land cover have the potential to affect precipitation, the critical resource for production. Turbulent flux measurements from two eddy-covariance towers and additional observations from a dense network of small, wireless meteorological stations combine to relate land cover (savanna forest and agriculture) to evaporation in a small (3.5 km2) catchment in Burkina Faso, west Africa. We observe larger sensible and latent heat fluxes over the savanna forest in the headwater area relative to the agricultural section of the watershed all year. Higher fluxes above the savanna forest are attributed to the greater number of exposed rocks and trees and the higher productivity of the forest compared to rain-fed, hand-farmed agricultural fields. Vegetation cover and soil moisture are found to be primary controls of the evaporative fraction. Satellite-derived vegetation index (NDVI) and soil moisture are determined to be good predictors of evaporative fraction, as indicators of the physical basis of evaporation. Our measurements provide an estimator that can be used to derive evaporative fraction when only NDVI is available. Such large-scale estimates of evaporative fraction from remotely sensed data are valuable where ground-based measurements are lacking, which is the case across the African continent and many other semi-arid areas. Evaporative fraction estimates can be combined, for example, with sensible heat from measurements of temperature variance, to provide an estimate of evaporation when only minimal meteorological measurements are available in remote regions of the world. These findings reinforce local cultural beliefs of the importance of forest fragments for climate regulation and may provide support to local decision makers and rural farmers in the maintenance of the forest areas.

scholarly journals Influence of Soil Moisture and Crust Formation on Soil Evaporation Rate: A Wind Tunnel Experiment in Hungary

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 935
Author(s):  
Gábor Négyesi ◽  
Szilárd Szabó ◽  
Botond Buró ◽  
Safwan Mohammed ◽  
József Lóki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Wind Tunnel ◽  
Wind Velocity ◽  
Soil Texture ◽  
Wind Erosion ◽  
Sandy Loam ◽  
Sandy Soils ◽  
Wind Tunnel Experiment ◽  
Crust Formation ◽  
Four Levels

In both arid and semiarid regions, erosion by wind is a significant threat against sustainability of natural resources. The objective of this work was to investigate the direct impact of various soil moisture levels with soil texture and organic matter on soil crust formation and evaporation. Eighty soil samples with different texture (sand: 19, loamy sand: 21, sandy loam: 26, loam: 8, and silty loam: 6 samples) were collected from the Nyírség region (Eastern Hungary). A wind tunnel experiment was conducted on four simulated irrigation rates (0.5, l.0, 2.0, and 5.0 mm) and four levels of wind speeds (4.5, 7.8, 9.2, and 15.5 m s−1). Results showed that watering with a quantity equal to 5 mm rainfall, with the exception of sandy soils, provided about 5–6 h protection against wind erosion, even in case of a wind velocity as high as 15.5 m s−1. An exponential connection was revealed between wind velocities and the times of evaporation (R2 = 0.88–0.99). Notably, a two-way ANOVA test revealed that both wind velocity (p < 0.001) and soil texture (p < 0.01) had a significant effect on the rate of evaporation, but their interaction was not significant (p = 0.26). In terms of surface crusts, silty loamy soils resulted in harder and more solid crusts in comparison with other textures. In contrast, crust formation in sandy soils was almost negligible, increasing their susceptibility to wind erosion risk. These results can support local municipalities in the development of a local plan against wind erosion phenomena in agricultural areas.

scholarly journals Energy and mass transfer parameters in a brazilian semi-arid ecosystem under different thermohydrological conditions

2015 ◽  
Vol 30 (4) ◽  
pp. 381-393 ◽  
Author(s):  
Antônio Heriberto de Castro Teixeira ◽  
Ricardo Guimarães Andrade ◽  
Janice Freitas Leivas
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Large Scale ◽  
Sensible Heat Flux ◽  
Vapour Pressure Deficit ◽  
Lower Atmosphere ◽  
Irrigated Agriculture ◽  
Natural Ecosystem ◽  
Transfer Parameters ◽  
Semi Arid

ABSTRACT In the Brazilian semi-arid region, the natural vegetation ("Caatinga") has been replaced by irrigated agriculture, emphasising the importance for quantification of the energy and mass exchanges. Eddy covariance and micro-climatic measurements in this natural ecosystem, were analysed for two years under different thermohydrological conditions. Sensible heat flux (H) accounted for 49 and 64% of the net radiation (Rn), respectively, during the wetter and the drier conditions of 2004 and 2005. The corresponding fractions of Rn partitioned as latent heat flux (LE) were 40% and 25%. Evapotranspiration (ET) in 2004, with 693 mm, represented 96% of precipitation (P), while in 2005 (399 mm), it was 18% higher than P, which evidenced the use of the remaining soil moisture from the previous wetter year. All the soil-water-vegetation-atmosphere transfer parameters were influenced by the rainfall amounts. However, the surface resistance (rs) was the most strongly affected by the soil moisture status, dropping with increases of the ratio of ET to reference evapotranspiration (ET0). On the other hand, the highest rs values were related to increases in both vapour pressure deficit (De) and aerodynamic temperature (T0). The current research aimed to quantify the energy and mass exchange between the "Caatinga" and the lower atmosphere, testing in which circumstances the biophysical controlling parameters can be reasonably predicted from agrometeorological data, throughout parameterizations, to incorporate in large-scale models.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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