scholarly journals Development of a Pilot Soil-Water Balance Model for Determination of Probability of a Working Day for Rice Harvesting

2021 ◽  
Vol 24 (2) ◽  
pp. 55-60
Author(s):  
Mahdi Khani ◽  
Seyyed Hossein Payman ◽  
Nader Pirmoradian
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Soil Water Balance ◽  
Balance Model ◽  
Limiting Factor ◽  
Evapotranspiration Rate ◽  
Feasibility Evaluation ◽  
Working Day ◽  
Factor Α

Abstract The probability of a working day is the ratio of workable days to the total available days in a working season for the intended operation. Soil moisture is the most important limiting factor in terms of rice harvesting. Therefore, to determine the PWD for this operation, a model was developed to estimate the soil moisture based on soil-water balance. To calibrate and validate the model, the soil moisture was monitored in the paddy field of the University of Guilan during the rice harvest seasons in 2017 and 2018. The calibration process was performed by applying a decreasing ratio, i.e. the senescence factor (α), at a daily evapotranspiration rate compared to that of previous day. At the validation stage, the simulated soil moisture contents were compared with measured values, which indicated a good model accuracy (NRMSE = 6.53%) of the soil moisture estimation. Therefore, this model can be used for the rice harvesting operation feasibility evaluation and PWD calculation.

scholarly journals Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

2014 ◽  
Vol 15 (3) ◽  
pp. 1117-1134 ◽  
Author(s):  
Eunjin Han ◽  
Wade T. Crow ◽  
Thomas Holmes ◽  
John Bolten
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Agricultural Drought ◽  
Soil Water Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
System Component

Abstract Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

The performance of Metop Advanced SCATterometer soil moisture data as a complementary source for the estimation of crop-soil water balance in Central Europe

2018 ◽  
Vol 156 (5) ◽  
pp. 577-598 ◽  
Author(s):  
S. Thaler ◽  
J. Eitzinger ◽  
M. Trnka ◽  
M. Možný ◽  
S. Hahn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Status ◽  
Soil Water Balance ◽  
Balance Model ◽  
Spatial Average ◽  
Soil Wetness

AbstractSimulation of the water balance in cropping systems is an essential tool, not only to monitor water status and determine drought but also to find ways in which soil water and irrigation water can be used more efficiently. However, besides the requirement that models are physically correct, the spatial representativeness of input data and, in particular, accurate precipitation data remain a challenge. In recent years, satellite-based soil moisture products have become an important data source for soil wetness information at various spatial-temporal scales. Four different study areas in the Czech Republic and Austria were selected representing Central European soil and climatic conditions. The performance of soil water content outputs from two different crop-water balance models and the Metop Advanced SCATterometer (ASCAT) soil moisture product was tested with field measurements from 2007 to 2011. The model output for soil water content shows that the crop model Decision Support System for Agrotechnology Transfer performs well during dry periods (<30% plant available soil moisture (ASM), whereas the soil water-balance model SoilClim presents the best results in humid months (>60% ASM). Moreover, the model performance is best in the early growing season and decreases later in the season due to biases in simulated crop-related above-ground biomass compared with the relatively stable grass canopy of the measurement sites. The Metop ASCAT soil moisture product, which presents a spatial average of soil surface moisture, shows the best performance under medium soil wetness conditions (30–50% ASM), which is related to low variation in precipitation frequency and under conditions of low-surface biomass (early vegetation season).

Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurementsat Nine Locations Across Europe

2013 ◽  
Vol 19 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Blaž Kurnik ◽  
Geertrui Louwagie ◽  
Markus Erhard ◽  
Andrej Ceglar ◽  
Lučka Bogataj Kajfež
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Balance Model ◽  
Seasonal Differences

scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Dry Season ◽  
Soil Water Balance ◽  
Rainfall Pattern ◽  
Site Specific ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

scholarly journals A Coupled Soil Water Balance Model for Simulating Depression-Focused Groundwater Recharge

2018 ◽  
Vol 17 (1) ◽  
pp. 170176 ◽  
Author(s):  
Saskia L. Noorduijn ◽  
Masaki Hayashi ◽  
Getachew A. Mohammed ◽  
Aaron A. Mohammed
Keyword(s):  
Water Balance ◽  
Groundwater Recharge ◽  
Soil Water ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Balance Model

scholarly journals High-Throughput Phenotyping of Crop Water Use Efficiency via Multispectral Drone Imagery and a Daily Soil Water Balance Model

2018 ◽  
Vol 10 (11) ◽  
pp. 1682 ◽  
Author(s):  
Kelly Thorp ◽  
Alison Thompson ◽  
Sara Harders ◽  
Andrew French ◽  
Richard Ward
Keyword(s):  
Water Balance ◽  
Water Use Efficiency ◽  
Soil Water ◽  
Water Use ◽  
High Throughput ◽  
Soil Water Balance ◽  
Balance Model ◽  
Crop Water ◽  
Crop Water Use ◽  
Use Efficiency

Improvement of crop water use efficiency (CWUE), defined as crop yield per volume of water used, is an important goal for both crop management and breeding. While many technologies have been developed for measuring crop water use in crop management studies, rarely have these techniques been applied at the scale of breeding plots. The objective was to develop a high-throughput methodology for quantifying water use in a cotton breeding trial at Maricopa, AZ, USA in 2016 and 2017, using evapotranspiration (ET) measurements from a co-located irrigation management trial to evaluate the approach. Approximately weekly overflights with an unmanned aerial system provided multispectral imagery from which plot-level fractional vegetation cover ( f c ) was computed. The f c data were used to drive a daily ET-based soil water balance model for seasonal crop water use quantification. A mixed model statistical analysis demonstrated that differences in ET and CWUE could be discriminated among eight cotton varieties ( p < 0 . 05 ), which were sown at two planting dates and managed with four irrigation levels. The results permitted breeders to identify cotton varieties with more favorable water use characteristics and higher CWUE, indicating that the methodology could become a useful tool for breeding selection.

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