MOISTURE BALANCE IN SOILS OF THE EDMONTON AREA

1969 ◽  
Vol 49 (3) ◽  
pp. 403-407
Author(s):  
T. R. Verma ◽  
J. A. Toogood
Keyword(s):  
Soil Type ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Actual Evapotranspiration ◽  
Soil Types ◽  
Available Water ◽  
Wide Range ◽  
Moisture Balance

Estimated moisture deficits for the period from May to September were calculated for 22 soil types in the Edmonton area. These included the major soils and covered a wide range of textures. Total available water capacities to a depth of 122 cm ranged from 5 to 30 cm. Potential evapotranspiration values were calculated, using the Penman method, from available meteorological data. Actual evapotranspiration was estimated and the deficits determined. These were found to vary for any one particular soil type according to its location in the area. Coarse-textured soils had deficits in the 8- to 12-cm range, medium-textured soils in the 3.5- to 7-cm range, and fine-textured soils in the 2- to 5.5-cm range.

scholarly journals Evaluation of the Performance of CFSR Reanalysis Data Set for Estimating Potential Evapotranspiration (PET) in Turkey

2021 ◽  
Author(s):  
AHMET IRVEM ◽  
Mustafa OZBULDU
Keyword(s):  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Reanalysis Data ◽  
Actual Evapotranspiration ◽  
Observation Data ◽  
Reanalysis Dataset ◽  
Data Set ◽  
Climate Forecast System Reanalysis ◽  
Mountainous Regions ◽  
Weather Stations

Abstract Evapotranspiration is an important parameter for hydrological, meteorological and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, Potential Evapotranspiration (PET) is typically calculated using meteorological data to calculate actual evapotranspiration. However, it is very difficult to get complete and accurate data from meteorology stations in, rural and mountainous regions. This study examined the availability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations in the computation of potential annual and seasonal evapotranspiration. The PET calculations using the CFSR reanalysis dataset for the period 1987-2017 were compared to data observed at 259 weather stations observed in Turkey. As a result of the assessments, it was determined that the seasons in which the CFSR reanalysis data set had the best prediction performance were the winter (C'= 0.76 and PBias = -3.77) and the autumn (C' = 0.75 and PBias = -12.10). The worst performance was observed for the summer season. The performance of the annual prediction was determined as C'= 0.60 and PBias = -15.27. These findings indicate that the results of the PET calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used especially in the summer models.

Quality of potatoes in relation to soil and season II. The colour of the cooked potato

1946 ◽  
Vol 36 (3) ◽  
pp. 214-221 ◽  
Author(s):  
H. G. Wager
Keyword(s):  
Normal Distribution ◽  
Soil Type ◽  
Industrial Research ◽  
Yellow Pigment ◽  
Soil Types ◽  
Wide Range ◽  
Boulder Clay

Samples of potatoes were collected in three successive seasons from a wide range of soil types and their liability to stem-end blackening determined.Variety and season of growth affected the amount of stem-end blackening which developed.All soil types gave samples with a wide range of stem-end blackening, but the average amounts of blackening in samples from different soil types were shown to differ significantly. Samples from fen, blackland, sand, gravel, limestone and chalk blackened more than those from skirt, silt, warp, clay and boulder clay.The pH of the expressed sap of tubers was independent of the type of soil in which they were grown, but dependent on variety and locality of growth. No evidence that the pH of the tubers influenced the amount of stem-end blackening pigment was obtained.The yellowness of the flesh of tubers showed an approximately normal distribution. Slight evidence for an effect of soil type on the amount of yellow pigment was obtained.The work described above was carried out as part of the programme of the Food Investigation Board of the Department of Scientific and Industrial Research.

scholarly journals Calculation of meteorological water balance in Iraq

2020 ◽  
Vol 2 (1) ◽  
pp. 84-89
Author(s):  
Hussein Ilaibi Zamil Al-Sudani ◽  
Keyword(s):  
Water Balance ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Meteorological Station ◽  
Actual Evapotranspiration ◽  
North East ◽  
Crucial Component ◽  
Water Surplus ◽  
Main Components ◽  
The Impact

The hydrology section is divided into two main components, surface and groundwater. One of the most important outcomes in the water balance equation for any natural area or water body is Evapotranspiration and it is also a crucial component of the hydrologic cycle. Prediction of monthly evapotranspiration can be obtained depending on observed monthly average temperatures at a meteorological station in each year. Calculating of water balance in Iraq depending on meteorological data and Thornthwaite method was the aim of this research. Results of corrected potential evapotranspiration (PEc) obtained from applying Thornthwaite formula were compared with annual and monthly rainfall in thirty two meteorological station in order to estimate actual evapotranspiration (AE). The results showed that the annual summation of rainfall increased from south west towards north east according to the increasing ratio of rainfall due to the impact of Mediterranean climate condition on Iraq. Actual evapotranspiration depends directly on water excess during calculating water balance. Water surplus contour map indicates increased values towards north-east direction of Iraq, where water surplus depends directly on both rainfall and actual evapotranspiration.

scholarly journals Identification and predictability of soil quality factors and indicators from conventional soil and vegetation classifications

2021 ◽  
Author(s):  
Paul Simfukwe ◽  
Paul W Hill ◽  
Davey L Jones ◽  
Bridget Emmett ◽  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Quality ◽  
Soil Type ◽  
Soil Classification ◽  
Biological Indicators ◽  
Soil Types ◽  
Quality Factors ◽  
Soil Attributes ◽  
Wide Range

Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter , Organic matter humification , Soluble nitrogen , Microbial biomass , Reduced nitrogen and Soil humification index . Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.

scholarly journals Modelling Actual Evapotranspiration Seasonal Variability by Meteorological Data-Based Models

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 50 ◽  
Author(s):  
Mirka Mobilia ◽  
Marius Schmidt ◽  
Antonia Longobardi
Keyword(s):  
Prediction Models ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Threshold Value ◽  
Heat Fluxes ◽  
Actual Evapotranspiration ◽  
Net Radiation ◽  
Antecedent Precipitation ◽  
Annual Dynamic ◽  
Eddy Covariance Measurements

This study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80% in the case of the Sister site, of about 30% in the case of Rollesbroich, and close to 60% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.

Root penetration rate - a benchmark to identify soil and plant limitations to rooting depth in wheat

2007 ◽  
Vol 47 (5) ◽  
pp. 590 ◽  
Author(s):  
J. A. Kirkegaard ◽  
J. M. Lilley
Keyword(s):  
Soil Type ◽  
Penetration Rate ◽  
Sowing Date ◽  
Thermal Time ◽  
Soil Types ◽  
Rooting Depth ◽  
Root Penetration ◽  
Available Water ◽  
Time Basis ◽  
Highly Correlated

Data on wheat rooting depth was compiled from 36 agronomic experiments conducted in southern NSW from 1990 to 2004. Rooting depth was measured by direct soil coring and observation of roots using core-break or root washing techniques. Maximum rooting depth varied from 80 to 180 cm and was influenced by the depth of soil wetting, soil type and the duration of the vegetative phase (sowing to anthesis) as determined by interactions of sowing date, variety and seasonal conditions. The root penetration rate (RPR cm/day), defined as (maximum root depth measured at or after anthesis) / (days from sowing to anthesis), emerged as a simple but unifying parameter which could be used to estimate the potential rooting depth of wheat on different soils. RPR, expressed on a thermal time basis, was highly correlated with that expressed on a simpler time basis (r = 0.92). Incomplete wetting of the soil profile reduced maximum rooting depth and RPR in 12 of the 36 crops studied, and root penetration in the subsoil was clearly restricted in soil layers with less than 45 to 50% plant available water. Soil type influenced the RPR. The average RPR for wheat was 1.13 ± 0.04 cm/day on Red Kandosols (n = 11), 1.01 ± 0.07 cm/day on a Red Sodosol (n = 3) and 0.79 ± 0.03 cm/day on Red Chromosols (n = 10). The RPR was relatively constant across cultivars and sowing dates within these soil types, although there was some evidence for a reduction in RPR with later sowing independent of time or thermal time. We suggest that the RPR (cm/day) established for wheat on various soil types provides a useful tool for wheat growers to estimate the rooting depth and available water and nutrients in-season. It also provides a benchmark to indicate potential subsoil limitations to crop growth, and for researchers investigating opportunities to increase the maximum rooting depth of wheat through management or breeding.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Properties of humic acids depending on the land use in different parts of Slovakia

2021 ◽  
Author(s):  
Magdalena Banach-Szott ◽  
Bozena Debska ◽  
Erika Tobiasova
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Size Distribution ◽  
Humic Acids ◽  
Forest Ecosystem ◽  
Soil Type ◽  
Soil Types ◽  
Organic Matter Quality ◽  
Degree Of Maturity ◽  
Effects Of Land Use

AbstractMany studies report organic carbon stabilization by clay minerals, but the effects of land use and soil type on the properties of humic acids (HAs) are missing. The aim of the paper is to determine the effects of land use and soil types on the characteristics of HAs, which have a considerable influence on organic matter quality. It was hypothesised that the effect of the land use on HAs properties depends on the particular size distribution. The research was performed in three ecosystems: agricultural, forest, and meadow, located in Slovakia. From each of them, the samples of 4 soil types were taken: Chernozem, Luvisol, Planosol, and Cambisol. The soil samples were assayed for the content of total organic carbon (TOC) and the particle size distribution. HAs were extracted with the Schnitzer method and analysed for the elemental composition, spectrometric parameters in the UV-VIS range, and hydrophilic and hydrophobic properties, and the infrared spectra were produced. The research results have shown that the properties of HAs can be modified by the land use and the scope and that the direction of changes depends on the soil type. The HAs of Chernozem and Luvisol in the agri-ecosystem were identified with a higher “degree of maturity”, as reflected by atomic ratios (H/C, O/C, O/H), absorbance coefficients, and the FT-IR spectra, as compared with the HAs of the meadow and forest ecosystem. However, as for the HAs of Cambisol, a higher “degree of maturity” was demonstrated for the meadow ecosystem, as compared with the HAs of the agri- and forest ecosystem. The present research has clearly identified that the content of clay is the factor determining the HAs properties. Soils with a higher content of the clay fraction contain HAs with a higher “degree of maturity”.

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