Soil water storage, drainage, and leaching in four irrigated cotton-based cropping systems sown in a Vertosol with subsoil sodicity

Soil Research ◽  
2012 ◽  
Vol 50 (8) ◽  
pp. 652 ◽  
Author(s):  
N. R. Hulugalle ◽  
T. B. Weaver ◽  
L. A. Finlay
Keyword(s):  
Soil Water ◽  
Crop Residues ◽  
Cropping Systems ◽  
Groundwater Resources ◽  
Water Storage ◽  
Triticum Aestivum L ◽  
Soil Water Storage ◽  
Experimental Treatments ◽  
Winter Fallow

Comparative studies of drainage and leaching in irrigated cotton (Gossypium hirsutum L.) based cropping systems in Australian Vertosols are sparse. Our objective was to quantify soil water storage, drainage, and leaching in four cotton-based cropping systems sown on permanent beds in an irrigated Vertosol with subsoil sodicity. Drainage was inferred using the chloride mass-balance method, and soil water storage and leaching were measured with a neutron moisture meter and ceramic-cup water samplers, respectively, from September 2005 to May 2011 in an ongoing experiment. The experimental treatments were: CC, cotton monoculture, summer cotton with winter fallow; CV, cotton–vetch (Vicia benghalensis L.) rotation with vetch stubble retained as in-situ mulch; CW, cotton–wheat (Triticum aestivum L.), with wheat stubble incorporated and a summer–winter fallow; and CWV, cotton–wheat–vetch, with wheat and vetch stubbles retained as in-situ mulch and summer and spring fallows. Soil water storage was generally highest under CW and CWV and least under CV. An untilled short fallow (~3 months) when combined with retention of crop residues as surface mulch, as in CWV, was as effective in harvesting rainfall as a tilled long fallow (~11 months) with stubble incorporation, as in CW. Drainage under cotton was generally in the order CW ≥ CWV > CC = CV, all of which were considerably greater than drainage during fallows. Except for very wet and dry winters, drainage under wheat rotation crops was greater than that under vetch. During wet winters, saturated soil in the 0–0.6 m depth of treatments under fallow resulted in more drainage than in the drier, cropped plots. No definitive conclusions could be made with respect to the effects of cropping systems on salt and nutrient leaching. Leachate contained less nitrate-nitrogen, magnesium, and potassium, but leachate electrical conductivity was ~6 times higher than infiltrated water. The greater salinity of the leachate may pose a risk to groundwater resources.

Soil Water Storage in Dryland Cropping Systems: The Significance of Cropping Intensification

1998 ◽  
Vol 62 (4) ◽  
pp. 984-991 ◽  
Author(s):  
H. J. Farahani ◽  
G. A. Peterson ◽  
D. G. Westfall ◽  
L. A. Sherrod ◽  
L. R. Ahuja
Keyword(s):  
Soil Water ◽  
Cropping Systems ◽  
Water Storage ◽  
Soil Water Storage

Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations

2020 ◽  
Author(s):  
Nooshin Mehrnegar ◽  
Owen Jones ◽  
Michael B. Singer ◽  
Maike Schumacher ◽  
Thomas Jagdhuber ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Soil Water ◽  
Water Storage ◽  
Balance Model ◽  
Soil Water Storage ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Grace Data ◽  
The Usa

<p>Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.</p><p>The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.</p><p><strong>Keywords:</strong> USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.</p><p> </p>

scholarly journals Water storage change estimation from in situ shrinkage measurements of clay soils

2013 ◽  
Vol 17 (5) ◽  
pp. 1933-1949 ◽  
Author(s):  
B. te Brake ◽  
M. J. van der Ploeg ◽  
G. H. de Rooij
Keyword(s):  
Soil Water ◽  
Volume Change ◽  
Water Loss ◽  
Water Storage ◽  
Bulk Soil ◽  
Soil Water Storage ◽  
Dry Period ◽  
Soil Layers ◽  
Storage Change

Abstract. The objective of this study is to assess the applicability of clay soil elevation change measurements to estimate soil water storage changes, using a simplified approach. We measured moisture contents in aggregates by EC-5 sensors, and in multiple aggregate and inter-aggregate spaces (bulk soil) by CS616 sensors. In a long dry period, the assumption of constant isotropic shrinkage proved invalid and a soil moisture dependant geometry factor was applied. The relative overestimation made by assuming constant isotropic shrinkage in the linear (basic) shrinkage phase was 26.4% (17.5 mm) for the actively shrinking layer between 0 and 60 cm. Aggregate-scale water storage and volume change revealed a linear relation for layers ≥ 30 cm depth. The range of basic shrinkage in the bulk soil was limited by delayed drying of deep soil layers, and maximum water loss in the structural shrinkage phase was 40% of total water loss in the 0–60 cm layer, and over 60% in deeper layers. In the dry period, fitted slopes of the ΔV–ΔW relationship ranged from 0.41 to 0.56 (EC-5) and 0.42 to 0.55 (CS616). Under a dynamic drying and wetting regime, slopes ranged from 0.21 to 0.38 (EC-5) and 0.22 to 0.36 (CS616). Alternating shrinkage and incomplete swelling resulted in limited volume change relative to water storage change. The slope of the ΔV–ΔW relationship depended on the drying regime, measurement scale and combined effect of different soil layers. Therefore, solely relying on surface level elevation changes to infer soil water storage changes will lead to large underestimations. Recent and future developments might provide a basis for application of shrinkage relations to field situations, but in situ observations will be required to do so.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

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