Estimating Regional Scale Hydroclimatic Risk Conditions from the Soil Moisture Active-Passive (SMAP) Satellite

Champagne, Catherine; Zhang, Yinsuo; Cherneski, Patrick; Hadwen, Trevor

doi:10.3390/geosciences8040127

Cited by 5 publications

(5 citation statements)

References 42 publications

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“…Future work including spatial gridded comparison between these satellite products and reanalysis products, such as the recently released fifth generation of ECMWF reanalysis, ERA5 [75], could reveal more information about the spatial characteristics of these products and the consistency among these different products. Finally, we note that a more recent L-band product, the Soil Moisture Active and Passive (SMAP) [20], launched in 2015, has been shown to capture relative soil moisture trends well over Canada [76]. We anticipate that future work comparing IC and SMAP for a more recent period should evaluate which of these products performs best for monitoring SM variability across Canadian agricultural regions.…”

Section: Discussionmentioning

confidence: 94%

Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

2020

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Soil moisture is a critical indicator for climate change and agricultural drought, but its measurement is challenging due to large variability with land cover, soil type, time, space and depth. Satellite estimates of soil moisture are highly desirable and have become more widely available over the past decade. This study investigates and compares the performance of four surface soil moisture satellite datasets over Canada, namely, Soil Moisture and Ocean Salinity Level 3 (SMOS L3), versions 3.3 and 4.2 of European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a recent product called SMOS-INRA-CESBIO (SMOS-IC) that contains corrections designed to reduce several known sources of uncertainty in SMOS L3. These datasets were evaluated against in situ networks located in mostly agricultural regions of Canada for the period 2012 to 2014. Two statistical comparison methods were used, namely, metrics for mean soil moisture and median of metrics. The results suggest that, while both methods show similar comparisons for regional networks, over large networks, the median of metrics method is more representative of the overall correlation and variability and is therefore a more appropriate method for evaluating the performance of satellite products. Overall, the SMOS products have higher daily temporal correlations, but larger biases, against in situ soil moisture than the ESA CCI products, with SMOS-IC having higher correlations and smaller variability than SMOS L3. The SMOS products capture daily wetting and drying events better than the ESA CCI products, with the SMOS products capturing at least 75% of observed drying as compared to 55% for the ESA CCI products. Overall, for periods during which there are sufficient observations, both SMOS products are more suitable for agricultural applications over Canada than the ESA CCI products, even though SMOS-IC is able to capture soil moisture variability more accurately than SMOS L3.

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Section: Discussionmentioning

confidence: 94%

Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

2020

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“…This is likely because these data are satellite derived surface soil moisture and not root zone soil moisture that is needed to measure the effects of water deficits on vegetation. While these components of the soil matrix are correlated, surface soil moisture exhibits higher temporal variability due to land-atmosphere interactions that drive wetting and drying events (Champagne et al 2018). However, SM2 and to a lesser extent SM4 were important predictors of D3/D4 drought onset.…”

Section: ) Satellite Soil Moisture Percentilesmentioning

confidence: 97%

Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)

Mardian,

Champagne,

Bonsal

et al. 2023

Journal of Hydrometeorology

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Recent advances in Artificial Intelligence (AI) and Explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley Additive Explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based Evaporative Stress Index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere-ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.

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“…Another method uses satellite‐based vegetation parameters to monitor plant response to drought conditions (Klisch & Atzberger, 2016; Liu et al., 2021), sometimes in combination with satellite‐derived land surface temperatures (Karnieli et al., 2010; Son et al., 2012). Other effective earth observation techniques use the Gravity Recovery and Climate Experiment (GRACE) satellites to estimate terrestrial water storage anomalies (Houborg et al., 2012; K. S. Kumar et al., 2021; Thomas et al., 2014; Tian et al., 2021) or satellites operating in the microwave spectrum to estimate soil moisture anomalies (Champagne et al., 2018; Narasimhan & Srinivasan, 2005; Oozeer et al., 2020; Tao et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…S. Kumar et al, 2021;Thomas et al, 2014;Tian et al, 2021) or satellites operating in the microwave spectrum to estimate soil moisture anomalies (Champagne et al, 2018;Narasimhan & Srinivasan, 2005;Oozeer et al, 2020;Tao et al, 2021).…”

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confidence: 99%

A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor

Mardian

Champagne

Bonsal

et al. 2023

Water Resources Research

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Drought is a costly natural disaster that impacts economies and ecosystems worldwide, so monitoring drought and communicating its impacts to individuals, communities, industry, and governments is important for mitigation, adaptation, and decision‐making. This research describes a novel machine learning framework to predict and understand the Canadian Drought Monitor (CDM). This fully automated approach is trained on nearly two decades of expert analysis and would assist the comprehensive monitoring of drought impacts without the continued requirement of ground support, a benefit in many data‐limited areas across the country. The framework also integrates the Shapley Additive Explanation (SHAP) variable importance metric to provide insight into drought dynamics in near real‐time, demonstrating its usefulness for understanding the value of different data sets for drought assessments and dispelling the commonly held misconception that machine learning models are not useful for inference. The results demonstrate that the model can effectively predict the CDM maps and realistically capture the evolution of drought events over time. A SHAP analysis found that the Prairie drought of 2015 was related to a strong El Niño event that reduced water supply to a region already facing long‐term water deficits, and the subsequent reduction in groundwater availability was detected by the Gravity Recovery and Climate Experiment satellite. Overall, this research shows strong potential to streamline the CDM methodology, integrate scientific insight into operations in near real‐time using SHAP values, and provide an avenue to retrospectively extend the CDM for evaluating current and future drought events in a historical context.

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Estimating Regional Scale Hydroclimatic Risk Conditions from the Soil Moisture Active-Passive (SMAP) Satellite

Cited by 5 publications

References 42 publications

Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

Evaluation of Satellite-Derived Surface Soil Moisture Products over Agricultural Regions of Canada

Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)

A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor

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