Soil spatial heterogeneity poses a challenge to accurate soil moisture determination. Remote sensing, in particular, using sensors that acquire data at microwave frequencies, is being used to overcome this challenge. In situ soil moisture monitoring can be used to validate remotely sensed surface soil moisture estimates and as inputs for agronomic and hydrologic models. Nine in situ soil moisture stations were established in Manitoba (Canada) and instrumented with Stevens Hydra Probes. The sensors were installed in triplicate with vertical orientation at the surface and with horizontal orientation at the 5-, 20-, 50-, and 100-cm depths. To ensure accuracy of the measured soil moisture, both laboratory and ield calibrations were conducted. These calibrated soil moisture values were compared with the probe default values and those generated using published calibrations. Overall, the results showed that the ield calibration was superior (coeficient of determination r 2 of 0.95) to the laboratory calibration (r 2 of 0.89). In addition, coarse-textured sites generally performed better than the ine-textured, high cation exchange capacity (CEC) sites. At the Kelburn site with high clay and CEC, the use of ield calibration reduced the root mean square error from 0.188 to 0.026 m 3 m −3 . However, at the low clay and CEC Treherne site, gains in accuracy were minimal, about 0.005 m 3 m −3 . The laboratory calibration consistently underestimated soil moisture at all the evaluation sites, whereas both Topp and Logsdon calibrations overestimated soil moisture.
Soil moisture from Soil Moisture Ocean Salinity (SMOS) passive microwave satellite data was assessed as an information source for identifying regions experiencing climate-related agricultural risk for a period from 2010 to 2013. Both absolute soil moisture and soil moisture anomalies compared to a 4-yr SMOS satellite baseline were used in the assessment. The 4-yr operational period of SMOS was wetter than the 30-yr climate normal in many locations, particularly in the late summer for most regions and in the spring for the province of Manitoba. This leads to a somewhat unrepresentative baseline that skews anomaly measures at different parts of the growing season. SMOS soil moisture does, however, show a clear trend where extremes are present, with drier-than-average conditions during periods that drought and dry soil risks were identified and wetter-than-average conditions when flooding and excess moisture were present. Areas where extreme weather events caused crop losses were identifiable using SMOS soil moisture, both at the provincial and regional scales. The variability in soil moisture between at-risk areas and normal areas is very small but consistent, both geographically and over time, making SMOS a good real-time indicator for risk assessment.
The National Drought Model (NDM) is an amalgamation of the atmospheric component of the original Palmer Drought and Versatile Soil Moisture Budget (VSMB) models. The NDM uses locally derived coefficients from the station or gridded climate data to calculate a calibration factor for comparing locations in time and space. A modular approach is used to model major processes such as evapotranspiration, biometeorological time, snowmelt, and the cascading of soil moisture down to the root zone. The modular approach allows modifications to be made to specific sections without making structural changes to the entire model or the data inputs. The NDM is an operational tool, integrating data from the climate, soil, and plant sciences to monitor agroclimatic risks such as drought and excess moisture. In this paper, the capacity of the NDM to monitor extreme agroclimatic risks, such as drought and flooding of agricultural soils, was assessed. Using the Palmer Drought Severity Index component of the NDM, the mapping of the spatial extent and severity of the 2001 and 2002 droughts across Canada and the excess moisture conditions on the Canadian Prairies in 2010 agreed with other assessments. The validation study of soil moisture at two Alberta locations (Lethbridge and Beaverlodge) showed that the VSMB tracked the soil moisture flux in the root zone successfully in response to changing environmental conditions. The VSMB explained about 70 and 60% of the variance in observed soil moisture at the two respective locations.RÉSUMÉ [Traduit par la rédaction] Le modèle national de sécheresse (NDM) est un amalgame de la composante atmosphérique du modèle original de Palmer et du modèle adaptatif de bilan d'humidité du sol (VSMB). Le NDM se sert de coefficients dérivés localement des données aux stations ou d'une grille de données climatiques pour calculer un facteur d'étalonnage servant à comparer des endroits dans le temps et dans l'espace. Une approche modulaire est utilisée pour modéliser les processus principaux, comme l'évapotranspiration, le temps biométéorologique, la fonte de la neige et la progression de l'humidité du sol vers le bas jusque dans la zone des racines. L'approche modulaire permet de faire des modifications dans des sections particulières sans faire de changements structuraux dans l'ensemble du modèle ou dans les données d'entrée. Le NDM est un outil opérationnel qui intègre les données des sciences climatologique, pédologique et botanique pour surveiller les risques agroclimatiques, comme la sécheresse ou l'excès d'humidité. Dans cet article, nous avons évalué la capacité du NDM de surveiller les risques agroclimatiques extrêmes comme la sécheresse ou l'inondation des sols agricoles. En utilisant la composante
Passive microwave derived satellite soil moisture data was evaluated over in situ monitoring sites in Canada from two L-Band sensors. Soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) and the Aquarius mission were used, which collect data at different spatial resolutions and using different retrieval models. Both sensors tend to underestimate soil moisture, with the underestimation from SMOS much more pronounced. Correlation coefficients show a reasonably good correspondence with in situ data, and this correlation tends to be better at sites where sub-grid soil moisture variability is represented in the in situ measured data. This highlights the importance of distributed in situ networks.
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