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Modeling soil temperature, frost depth, and soil moisture redistribution in seasonally frozen agricultural soils.

机译:模拟季节性冻结的农业土壤中的土壤温度,霜冻深度和土壤水分的重新分布。

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Soil freezing and thawing processes and soil moisture redistribution play a critical role in the hydrology and microclimate of seasonally frozen agricultural soils. Accurate simulations of the depth and timing of frost and the redistribution of soil water are important for planning farm operations and choosing rotational crops. The Simultaneous Heat and Water (SHAW) model was used to predict soil temperature, frost depth, and soil moisture in agricultural soils near Carman, Manitoba. The model simulations were compared with three years of field data collected from summer 2005 to the summer 2007 in four cropping system treatments (oats with berseem clover cover crop, oats alone, canola, and fallow). The simulated soil temperatures compared well with the measured data in all the seasons (R2=0.96-0.99). The soil moisture simulations were better during the summer (RMSE=9.1-12.0% of the mean) compared to the winter seasons (RMSE=17.5-19.7% of the mean). During the winter, SHAW over-predicted by 0.02 to 0.10 m3 m-3 the amount of total soil moisture below the freeze front and under-predicted by 0.02 to 0.05 m3 m-3 the soil moisture in the upper frozen layers. The model was revised to account for the reduction in effective pore space resulting from frozen water to improve the winter soil moisture predictions. After this revision, the model performed well during the winter (RMSE=14.4% vs. 17.5%; R2=0.74 vs. 0.67 in vegetated treatments, and RMSE=12.9% vs. 19.7%; R2=0.73 vs. 0.52 in fallow treatments). The modified SHAW model is an enhanced tool for predicting the soil moisture status as a function of depth during spring thawing, and for assessing the availability of soil moisture at the beginning of the subsequent growing season.
机译:土壤的冻结和解冻过程以及土壤水分的重新分布在季节性冻结的农业土壤的水文和微气候中起着至关重要的作用。准确模拟霜冻的深度和时间以及土壤水分的重新分配对于规划农场运营和选择轮作作物非常重要。曼尼托巴省卡曼附近的农业土壤中的热与水同时(SHAW)模型用于预测土壤温度,霜冻深度和土壤湿度。将模型模拟与从2005年夏季至2007年夏季在四种种植系统处理(具有伯乐三叶草覆盖作物的燕麦,仅燕麦,低芥酸菜籽和休闲)的三年田间数据进行比较。在所有季节中,模拟的土壤温度均与实测数据进行了很好的比较(R 2 = 0.96-0.99)。与冬季(RMSE = 17.5-19.7%)相比,夏季的土壤水分模拟效果更好(RMSE = 9.1-12.0%的平均值)。在冬季,SHAW高估了0.02至0.10 m 3 m -3 低于冰冻锋面的土壤总水分,而低估了0.02至0.05 m 3 m -3 上部冻土层的土壤水分。对模型进行了修改,以考虑到冷冻水导致有效孔隙空间减少,从而改善了冬季土壤湿度的预测。修订后,该模型在冬季表现良好(RMSE = 14.4%对17.5%; R 2 = 0.74对0.67,在植被处理中,RMSE = 12.9%对19.7%; R 2 = 0.73与休闲治疗中的0.52)。修改后的SHAW模型是一种增强的工具,可用于在春季解冻过程中根据深度预测土壤湿度状态,并在随后的生长季节开始时评估土壤水分的有效性。

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