Temporal and Spatial Variability of Water Surplus in Ontario, Canada

Abstract

The temporal variability in estimated water surplus in 12 climatic regions of the province of Ontario, Canada, and its spatial distribution throughout most of the province are discussed in this paper. Surplus water is that which results from precipitation that runs off the land surface and that which drains through the soil profile to the water table and through subsurface drainage. A one-dimensional, deterministic model (DRAINMOD) that simulates soil water flow, including plant uptake, evapotranspiration, and freeze/thaw conditions, was used to estimate the water surplus. Simulations were performed using daily climatic data from January 1954 to December 2001 for each region. A reference corn crop and the predominant local soil conditions in each region, with the hydraulic properties for each layer in the soil profile, were used as model inputs. There was considerable year-to-year variability in annual water surplus in all regions caused by both precipitation and soil conditions. It was the least (~150 mm) in three regions and it exceeded 350 mm in another three regions, where winter snowfall is the greatest as a result of these regions being in the lea of one of the Great Lakes. The variability in water surplus generally increased as average water surplus increased.

Comparing soluble to controlled-release nitrogen fertilizers: storage cabbage yield, profit margins, and N use efficiency

“…In our study, the lack of in-season N losses was supported by N dissolution data. In our humid, temperate climate, although large rainfall events occur that may or may not lead to N losses, during May through September evapotranspiration exceeds precipitation in Ontario (Brown et al 2013), mitigating N losses. Others have observed weather conditions that delayed N release from CRF lowered crop yield with CRF compared with soluble N (Grant et al 2012;Farmaha and Sims 2013).…”

“…Likewise, considerable N remains in the field in crop residues (158-219 kg N ha −1 with 225 kg N ha −1 applied) after cabbage production (Congreves et al 2013(Congreves et al , 2014Smith et al 2016), which represents a potential environmental risk of N losses due to rapid decomposition and mineralization of low C to N ratio crop residues (Congreves and Van Eerd 2015). Moreover, during the non-growing season, precipitation exceeds evapotranspiration, thus, when the soil is not frozen, leaching events occur (Brown et al 2013). Clearly there is a need to identify best management practices that minimize N losses between growing seasons and (or) delay mineralization of cole crop residues until subsequent crop growth (Congreves and Van Eerd 2015).…”

Determining and Comparing Deep Drainage between Two Years – Dry (2012) and Wet (2013) – using Darcy’s Law

“…There was stronger potential for DD to occur in coarser-textured soil than finer-textured soil due to higher hydraulic conductivity (Brown et al, 2013). Thus, the mean annual DD was higher than the RO rate at sites with loamy or sandy soil; however, the RO rate was higher than DD at sites with clay or silty clay soils (Brown et al, 2013).…”

“…In the study, the average amount of evapotranspiration was used for both CT and NT treatments, thus it could have possibly overestimated the DD for CT and underestimated for NT treatments in the spring and fall (McCoy et al, 2006). Brown et al (2013) studied the temporal and spatial variability in estimated water surplus in 12 different regions in Ontario, Canada under different soil profile conditions. A deterministic hydrologic model (DRAINMOD), which estimates not only evapotranspiration (ET), runoff (RO), infiltration, seepage, and subsurface drainage, but also freeze and thaw conditions, was used in the study for estimation of the water surplus (Brown et al, 2013).…”

“…Brown et al (2013) studied the temporal and spatial variability in estimated water surplus in 12 different regions in Ontario, Canada under different soil profile conditions. A deterministic hydrologic model (DRAINMOD), which estimates not only evapotranspiration (ET), runoff (RO), infiltration, seepage, and subsurface drainage, but also freeze and thaw conditions, was used in the study for estimation of the water surplus (Brown et al, 2013). The positive linear relationship between DD and precipitation seemed to get stronger as the soil texture became coarser (sandy) and weaker as the soil texture became finer (clay).…”