scholarly journals Field Assessment of Soil Water Storage and Actual Evapotranspiration of Rainfed Maize (<i>Zea mays</i> L.) Genotypes in a Coastal Savannah Environment

2012 ◽  
Vol 02 (03) ◽  
pp. 213-222 ◽  
Author(s):  
Justice Okona Frimpong ◽  
Marcus Quaynor Addy ◽  
Emmanuel Ofori Ayeh ◽  
Harry Mensah Amoatey ◽  
Jacob Teye Kutufam ◽  
...  
Keyword(s):  
Zea Mays ◽  
Soil Water ◽  
Water Storage ◽  
Actual Evapotranspiration ◽  
Soil Water Storage ◽  
Field Assessment

Calculating Thornthwaite and Mather's actual evapotranspiration using an approximating function

1984 ◽  
Vol 14 (3) ◽  
pp. 466-467 ◽  
Author(s):  
J. Pastor ◽  
W. M. Post
Keyword(s):  
Soil Water ◽  
Exponential Function ◽  
Water Loss ◽  
Water Storage ◽  
Field Capacity ◽  
Actual Evapotranspiration ◽  
Soil Water Storage ◽  
Negative Exponential Function ◽  
Negative Exponential

A simple negative exponential function is presented which relates soil water storage to a maximum storage value (field capacity) and accumulated potential water loss. This formula summarizes 10 tables from Thornthwaite and Mather (Publications in Climatology, 10: 183–311, 1957) needed to calculate actual evapotranspiration (AET). Comparisons are presented for values predicted by this formula and Thornthwaite and Mather's tabulated values.

scholarly journals Ubiquitous Fractal Scaling and Filtering Behavior of Hydrologic Fluxes and Storages from A Mountain Headwater Catchment

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 613
Author(s):  
Ravindra Dwivedi ◽  
John F. Knowles ◽  
Christopher Eastoe ◽  
Rebecca Minor ◽  
Nathan Abramson ◽  
...  
Keyword(s):  
Water Balance ◽  
Soil Water ◽  
Water Storage ◽  
Actual Evapotranspiration ◽  
Soil Water Storage ◽  
Water Balance Components ◽  
Fractal Scaling ◽  
Wetting Properties ◽  
Soil Wetting

We used the weighted wavelet method to perform spectral analysis of observed long-term precipitation, streamflow, actual evapotranspiration, and soil water storage at a sub-humid mountain catchment near Tucson, Arizona, USA. Fractal scaling in precipitation and the daily change in soil water storage occurred up to a period of 14 days and corresponded to the typical duration of relatively wet and dry intervals. In contrast, fractal scaling could be observed up to a period of 0.5 years in streamflow and actual evapotranspiration. By considering long-term observations of hydrologic fluxes and storages, we show that, in contrast to previous findings, the phase relationships between water balance components changed with component period and were not perfectly in or out of phase at all periods. Self-averaging behavior was apparent, but the temporal scales over which this behavior was applicable differed among the various water balance components. Conservative tracer analysis showed that this catchment acted as a fractal filter by transforming white noise in the precipitation input signal to a 1/f flicker in the streamflow output signal by means of both spatial and temporal subsurface advection and dispersion processes and soil wetting properties. This study provides an improved understanding of hydrological filtering behavior in mountain critical zones that are critical sources of water and ecosystem services throughout the world.

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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