Use of Soil Survey Data for Regional Soil Water Simulation Models

1985 ◽  
Vol 49 (5) ◽  
pp. 1238-1244 ◽  
Author(s):  
J. H. M. Wösten ◽  
J. Bouma ◽  
G. H. Stoffelsen
1996 ◽  
Vol 76 (3) ◽  
pp. 263-273 ◽  
Author(s):  
R. de Jong ◽  
A. Bootsma

Soil water is of great importance to agricultural and hydrological systems, affecting crop yields, agricultural management practices and a wide range of physical and chemical processes in soils. Many models have been developed over the years to simulate soil water, ranging from simple water balance procedures to complex deterministic models. In this paper, some of the basic concepts, strengths, weaknesses and input requirements of soil water models are reviewed. Simple budget models which require only available water-holding capacity, based on the concepts of field capacity and wilting point, and monthly average values of precipitation and potential evapotranspiration (PET) are often adequate for climatic characterizations. However, detailed deterministic models are useful for investigating more complex processes such as solute movement, soil degradation and crop growth. These models are based on Richards’ equation, with an added term for root water extraction. They generally require daily or hourly meteorological data and detailed hydraulic conductivity and water potential functions for various soil layers.Meteorological data for input into models are widely available in Canada from a network of climate stations. However, the network density is sparse for those models which use the most accurate procedures for estimating PET, requiring parameters such as wind, solar radiation and/or humidity. Moreover, pedotransfer functions generally need to be used to estimate, from existing soil databases, the required soil inputs for sophisticated models.To demonstrate a practical application, a soil water model was used in conjunction with simple crop yield/water use relationships to characterize soil water regimes and crop production risks. The probability of obtaining break-even yields (PBEY) for continuous spring wheat was calculated and mapped for the Canadian prairie region. PBEY ranged from less than 20% in the most arid region of the prairies to between 60 and 100% in most humid areas. An estimate of PBEY based on modelled yields was in close agreement with that based on long-term measured yields in the sub-arid region. With increasing availability of integrated multi-process based models, this technique has potential for studying the effects of soil characteristics and crop management techniques on PBEY. Key words: Soil water, simulation models, review, spring wheat, break-even yield, Canadian prairies


2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;


Author(s):  
W. Thomas Walker ◽  
Scott H. Brady ◽  
Charles Taylor

The travel simulation models for many metropolitan areas were originally developed and calibrated with older large-sample travel surveys that can no longer be undertaken given today’s funding constraints. Small-sample travel surveys have been collected as part of model update activities required by the Intermodal Surface Transportation Efficiency Act and the Clean Air Act Amendments. Although providing useful information, these surveys are inadequate for calibrating elaborate simulation models by traditional techniques. Parameter transfer scaling based on small-sample surveys and other secondary source data can be a cost-effective alternative to large-sample surveys when existing models are being updated, particularly when the models tend to be robust and the required changes are relatively small. The use of parameter scaling methods to update the Delaware Valley Planning Commission’s existing travel simulation models is demonstrated. All available sources of data are incorporated into the update process including current survey data, census work trips from the Census Transportation Planning Package (CTPP), transit ridership checks, highway screenline counts, and Highway Performance Monitoring System travel estimates. A synopsis of experience with parameter scaling techniques including the model changes and resulting accuracy is provided. Overall, small-sample-based parameter scaling techniques were judged to be effective. The census CTPP data were evaluated versus the home interview and were found to be useful in the model recalibration effort as a source of small-area employment data by place of work and as a supplement to home interview data for model validation. However, a home interview survey is required as the primary source of travel data for both work and nonwork trips.


2011 ◽  
Vol 25 (11) ◽  
pp. 2823-2836 ◽  
Author(s):  
Jianqiang Deng ◽  
Xiaomin Chen ◽  
Zhenjie Du ◽  
Yong Zhang

Sign in / Sign up

Export Citation Format

Share Document