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1. Integrating optical and microwave satellite observations for high res-olution soil moisture estimate and applications in CONUS drought analyses

   https://doi.org/10.24294/jgc.v4i1.1313  

   Sun, Donglian ; Li, Yu ; Zhan, Xiwu ; Yang, Chaowei ; Yang, Ruixin ( June 2019 , Journal of Geography and Cartography)

   In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible. 

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2. Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations

   https://doi.org/10.3390/rs11141704  

   Sun, Donglian ; Li, Yu ; Zhan, Xiwu ; Houser, Paul ; Yang, Chaowei ; Chiu, Long ; Yang, Ruixin ( July 2019 , Remote Sensing)

   Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring. 

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3. Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

   https://doi.org/10.1175/JHM-D-20-0057.1  

   Gavahi, Keyhan ; Abbaszadeh, Peyman ; Moradkhani, Hamid ; Zhan, Xiwu ; Hain, Christopher ( October 2020 , Journal of Hydrometeorology)

   null (Ed.)

   Abstract Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition. 

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4. Trends, Variability, and Drivers of Flash Droughts in the Contiguous United States

   https://doi.org/10.1029/2022WR032186  

   Lesinger, Kyle ; Tian, Di ( September 2022 , Water Resources Research)

   Flash droughts are recently recognized subseasonal extreme climate phenomena, which develop with rapid onset and intensification and have significant socio‐environmental impacts. However, their historical trends and variability remain unclear largely due to the uncertainty associated with existing approaches. Here we comprehensively assessed trends, spatiotemporal variability, and drivers of soil moisture (SM) and evaporative demand (ED) flash droughts over the contiguous United States (CONUS) during 1981–2018 using hierarchical clustering, wavelet analysis, and bootstrapping conditional probability approaches. Results show that flash droughts occur in all regions in CONUS with Central and portions of the Eastern US showing the highest percentage of weeks in flash drought. ED flash drought trends are significantly increasing in all regions, while SM flash drought trends were relatively weaker across CONUS, with small significant increasing trends in the South and West regions and a decreasing trend in the Northeast. Rising ED flash drought trends are related to increasing temperature trends, while SM flash drought trends are strongly related to trends in weekly precipitation intensity besides weekly average precipitation and evapotranspiration. In terms of temporal variability, high severity flash droughts occurred every 2–7 years, corresponding with ENSO periods. For most CONUS regions, severe flash droughts occurred most often during La Niña and when the American Multidecadal Oscillation was in a positive phase. Pacific Decadal Oscillation negative phases and Artic Oscillation positive phases were also associated with increased flash drought occurrences in several regions. These findings may have implications for informing long‐term flash drought predictions and adaptations.

    

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5. Dry-to-Wet Soil Gradients Enhance Convection and Rainfall over Subtropical South America

   https://doi.org/10.1175/JHM-D-23-0031.1  

   Chug, Divyansh ; Dominguez, Francina ; Taylor, Christopher M. ; Klein, Cornelia ; Nesbitt, Stephen W. ( September 2023 , Journal of Hydrometeorology)

   Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s⁻¹), high convective available potential energy (>1500 J kg⁻¹), and low convective inhibition (<250 J kg⁻¹) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.

    

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