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Soil moisture plays a critical role in improving the weather and climate forecast and understanding terrestrial ecosystem processes. It is a key hydrologic variable in agricultural drought monitoring, flood forecasting, and irrigation management as well. Satellite retrievals can provide unprecedented soil moisture information at the global scale; however, the products are generally provided at coarse resolutions (25–50 km2). This often hampers their use in regional or local studies. The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) satellite mission was launched in January 2015 aiming to acquire soil moisture and freeze‐thaw states over the globe with 2 to 3 days revisit frequency. This work presents a new framework based on an ensemble learning method while using atmospheric and geophysical information derived from remote‐sensing and ground‐based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental United States at 1‐km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets are used, including soil texture and topography data among other information. The downscaled product was validated against in situ soil moisture measurements collected from two high density validation sites and 300 sparse soil moisture networks throughout the Continental United States. On average, the unbiased Root Mean Square Error between the downscaled SMAP soil moisture data and in‐situ soil moisture observations adequately met the SMAP soil moisture retrieval accuracy requirement of 0.04 m3/m3. In addition, other statistical measures, that is, Pearson correlation coefficient and bias, showed satisfactory results.

The recent bushfires (2019-2020) in New South Wales (NSW) Australia were catastrophic by claiming human and animal lives, affecting ecosystems, destroying infrastructure, and more. Recent studies have investigated relationships between hydroclimatic signals and past bushfires, and very recently, a few commentary papers claimed drought and fuel moisture content as the probable causes for the widespread 2019-2020 bushfires. Therefore, in this study, a novel work of encompassing a wide range of factors attributing to the recent bushfires is presented. Empirical evidence-based statistical methods are used to identify the hydroclimatic variables and geomorphic characteristics contributing to the 2019-2020 bushfires. The results highlight that ongoing drought, surface soil moisture (SSM), wind speed (WS10), relative humidity (RH), heat waves (HW), dead and live fuel moisture, and certain land cover types create favorable conditions for fire ignition and aid in fire propagation in different regions of the NSW state. The findings suggest that accounting for the above-identified variables in bushfire prediction and monitoring system are crucial in avoiding such catastrophes in the future. The overarching application of this study is developing robust and more versatile fire protection planning and management. Plain Language Summary Since the 2019-2020 Australian bushfires were catastrophic in terms of burnt area and severity, a detailed analysis of the primary causes is crucial. In this paper, several probable causes are tested statistically to establish their relationship with the burnt area. The results indicate that the ongoing drought, surface soil moisture, wind speed, relative humidity, heat waves, dead and live fuel moisture, and land cover with certain vegetation (particularly native eucalyptus and grazing land) are the primary causes of the widespread bushfire. These results are extremely critical in updating the current bushfire planning and management.

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This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States.