Effect of root zone soil moisture on the SWAT model simulation of surface and subsurface hydrological fluxes

2021 ◽  
Vol 80 (18) ◽  
Author(s):  
Rajat Choudhary ◽  
P. Athira
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Model Simulation ◽  
Swat Model ◽  
Hydrological Fluxes

ВОДНЫЙ РЕЖИМ ОРОШАЕМЫХ СОЛОНЦОВЫХ КОМПЛЕКСНЫХ ПОЧВ И ОСОБЕННОСТИ ЕГО РЕГУЛИРОВАНИЯ

2020 ◽  
Author(s):  
Valery Yashin
Keyword(s):  
Soil Moisture ◽  
Ground Water ◽  
Irrigation Water ◽  
Surface Runoff ◽  
Root Zone ◽  
Irrigation Methods ◽  
Salt Flats

Представлены материалы исследований формирования режима влажности и динамики грунтовых вод орошаемых солонцовых комплексных почв при различных способах полива, проведенные в Волгоградском Заволжье. Установлена значительная неравномерность распределения влажности почвы при поливах дождеванием. Отмечается поверхностный сток по микрорельефу до 30% от поливной нормы, что приводит к недостаточности увлажнения корневой зоны на солонцах и переувлажнению почв в понижениях микрорельефа и потере оросительной воды на инфильтрационное питание грунтовых вод.The article presents the materials of research on the formation of the humidity regime and dynamics of ground water of irrigated saline complex soils under various irrigation methods, conducted in the Volgograd Zavolzhye. A significant unevenness in the distribution of soil moisture during irrigation with sprinkling has been established. There is a surface runoff on the microrelief of up to 30% of the irrigation norm, which leads to insufficient moisture of the root zone on the salt flats and waterlogging of the soil in the microrelief depressions and loss of irrigation water for infiltration feed of ground water.

scholarly journals Insulative effect of plastic mulch systems and comparison between the effects of different plant types

2020 ◽  
Vol 5 (1) ◽  
pp. 317-324
Author(s):  
Kayla Snyder ◽  
Christopher Murray ◽  
Bryon Wolff
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Plastic Mulch ◽  
Root Zone Temperature ◽  
Tomato Plants ◽  
Black Film ◽  
Black And White ◽  
Mulch Film ◽  
Mulch Films ◽  
Zone Temperature

AbstractTo address agricultural needs of the future, a better understanding of plastic mulch film effects on soil temperature and moisture is required. The effects of different plant type and mulch combinations were studied over a 3.5-month period to better grasp the consequence of mulch on root zone temperature (RZT) and moisture. Measurements of (RZT) and soil moisture for tomato (Solanum lycopersicum), pepper (Capsicum annuum) and carrot (Daucus carota) grown using polyolefin mulch films (black and white-on-black) were conducted in Ontario using a plot without mulch as a control. Black mulch films used in combination with pepper and carrot plants caused similar RZTs relative to uncovered soil, but black mulch film in combination with tomato plants caused a reduction in RZT relative to soil without mulch that increased as plants grew and provided more shade. White-on-black mulch film used in combination with tomatoes, peppers or carrots led to a reduction in RZT relative to soil without mulch that became greater than the temperature of soil without mulch. This insulative capability was similarly observed for black mulch films used with tomato plants. Apart from white-on-black film used in combination with tomatoes, all mulch film and plant combinations demonstrated an ability to stabilize soil moisture relative to soil without mulch. RZT and soil moisture were generally stabilized with mulch film, but some differences were seen among different plant types.

scholarly journals Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Saran Aadhar ◽  
Shanti Shwarup Mahto
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Crop Production ◽  
Monsoon Rainfall ◽  
Root Zone ◽  
Monsoon Season ◽  
Intraseasonal Variability ◽  
Positive Temperature ◽  
Groundwater Abstraction ◽  
Increased Risk

AbstractFlash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

scholarly journals Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5211
Author(s):  
Maedeh Farokhi ◽  
Farid Faridani ◽  
Rosa Lasaponara ◽  
Hossein Ansari ◽  
Alireza Faridhosseini
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Root Zone ◽  
Analytical Relationship ◽  
Moisture Profile ◽  
Essential Variable ◽  
Potential Benefits ◽  
Spatio Temporal ◽  
Hydrological Prediction

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

scholarly journals On the utility of land surface models for agricultural drought monitoring

2012 ◽  
Vol 16 (9) ◽  
pp. 3451-3460 ◽  
Author(s):  
W. T. Crow ◽  
S. V. Kumar ◽  
J. D. Bolten
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cross Correlation ◽  
Root Zone ◽  
Vegetation Indices ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Global Evaluation ◽  
Land Surface Models ◽  
Surface Models

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

&lt;p&gt;Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.&lt;br&gt;ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.&lt;/p&gt;

Soil moisture assessment based on Sentinel 1/2 and in-situ data: The vineyard case study

2021 ◽  
Author(s):  
Maria Paula Mendes ◽  
Ana Paula Falcão ◽  
Magda Matias ◽  
Rui Gomes
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Root Zone ◽  
Irrigation Management ◽  
Management Strategies ◽  
Winter Period ◽  
Classification Algorithms ◽  
Precipitation Regime ◽  
Terrain Attributes ◽  
Sentinel 2

&lt;p&gt;Vineyards are crops whose production has a major economic impact in the Portuguese economy (~750 million euros) being exported worldwide. As the climate models project a larger variability in precipitation regime, the water requirements of vineyards can change and drip irrigation can be responsible for salt accumulation in the root zone, especially when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production.&lt;/p&gt;&lt;p&gt;The aim of this work is to present a methodology to map surface soil moisture content (SMC) in a vineyard, (40 hectares) based on the application of two classification algorithms to satellite imagery (Sentinel 1 and Sentinel 2). Two vineyard plots were considered and three field campaigns (December 2017, January 2018 and May 2018) were conducted to measure soil moisture contents (SMC). A geostatistical method was used to estimate the SM class probabilities according to a threshold value, enlarging the training set (i.e., SMC data of the two plots) for the classification algorithms. Sentinel-1 and Sentinel-2 images and terrain attributes fed the classification algorithms. Both methods, Random Forest and Logistic Regression, classified the highest SMC areas, with probabilities above 14%, located close to a stream at the lower altitudes.&lt;/p&gt;&lt;p&gt;RF performed very well in classifying the topsoil zones with lower SMC during the autumn-winter period (F-measure=0.82).&lt;/p&gt;&lt;p&gt;This delineation allows the prevention of the occurrence of areas affected by salinization, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts.&lt;/p&gt;

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