Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm³ cm⁻³), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS‐2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance.

This study predicts summer (June–September) fire burned area across the western United States (U.S.) from 1984 to 2020 using ensembles of statistical models trained with pre-fire season climate conditions. Winter and spring climate conditions alone explain up to 53% of the interannual variability and 58% of the increasing trend of observed summer burned area, which suggests that climate conditions in antecedent seasons have been an important driver to broad-scale changes in summer fire activity in the western U.S. over the recent four decades. Relationships between antecedent climate conditions with summer burned area are found to be strongest over non-forested and middle-to-high elevation areas (1100–3300 m). Statistical models that predict summer burned area using both antecedent and fireseason climate conditions have improved performance, explaining 69% of the interannual variability and 83% of the increasing trend of observed burned area. Among the antecedent climate predictors, vapor pressure deficit averaged over winter and spring plays the most critical role in predicting summer fire burned area. Spring snow drought area is found to be an important antecedent predictor for summer burned area over snow-reliant regions in the nonlinear statistical modeling framework used in this analysis. Namely, spring snow drought memory is realized through dry anomalies in land (soil and fuel) and atmospheric moisture during summer, which favours fire activity. This study highlights the important role of snow drought in subseasonal-to-seasonal forecasts of summer burned area over snow-reliant areas.

The Biosphere‐Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land‐surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah‐MP LSM framework through evaluation and optimization of the Noah‐MP BATS ground snow albedo formulation using 2‐band (visible and near‐infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah‐MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh‐snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central‐southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh‐snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh‐snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh‐snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time‐varying formulation for fresh‐snow albedo.

Irrigation is not well represented in land surface, hydrological, and climate models. One way to account for irrigation is by assimilating satellite soil moisture data that contains irrigation signal with land surface models. In this study, the irrigation detection ability of SMAP enhanced 9 km and SMAP-Sentinel 1 (SMAP-S1), 3 km and 1 km soil moisture products are evaluated using the first moment (mean) and the second moment (variability) of soil moisture data. The SMAP enhanced 9 km soil moisture product lacks irrigation signals in an irrigated plain south of Urmia Lake, whereas SMAP-S1 products record irrigation signal in soil moisture variability. Despite observing higher variability over irrigated areas, there are only small and inconsistent wet biases observed over irrigated pixels relative to nearby nonirrigated pixels during the irrigation season. This is partly attributable to the climatology vegetation water content used in the SMAP-S1 soil moisture retrieval algorithm that is not accounting for crop rotation and land management. Thus, in the second part of this study, we updated the retrieval algorithm to use dynamic vegetation water content. The update increased vegetation water content up to 1 kg/m ² which corresponds with a 0.05 cm ³ /cm ³ increase in soil moisture during irrigation season. The update does not notably change soil moisture retrievals off season. This study shows that irrigation signals are present in both the first and second moment of soil moisture time series, and employing dynamic vegetation water content in the SMAP-S1 algorithm can enhance the irrigation signal over agricultural regions.

Accurate prediction of snowpack evolution and ablation is critical to supporting weather and hydrological applications. Convection‐permitting modeling has been shown to well capture observed snowpack evolution over many western United States (U.S.) mountain ranges, but some significant ablation biases still remain. In this study, we conduct process‐level snowpack analyses of a widely used convection‐permitting (4‐km) weather research and forecasting (WRF) modeling product (WRF4km) for the contiguous U.S. to understand the mechanisms causing its unobserved early‐spring snow ablation over Utah mountains. Analyses across Utah Snowpack Telemetry (SNOTEL) sites show that the unobserved snowpack ablation during mid‐February to late‐March in WRF4km is driven by multiple strong melting events. The melting results from the enhanced downward sensible heat flux to snowpack and enhanced ground solar radiation absorption, with generally larger contributions from the former before early March and from the latter after early March. The enhanced downward sensible heat flux to snowpack is mainly due to the enhanced surface heat exchange coefficient induced by high surface wind speeds. The enhanced ground solar radiation absorption is driven by both enhanced surface downward solar radiation and strong melting‐induced snow cover reduction that is caused by deficiencies in Noah‐MP snow‐related parameterizations used in WRF4km. The substantial snow cover reduction during melting decreases surface albedo and hence triggers a positive albedo feedback that further accelerates melting. Our analyses reveal possible deficiencies in WRF and Noah‐MP (e.g., canopy processes and snow albedo) and shed light on future directions for model improvements.

Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet irrigation water use (IWU) remains largely unknown in most regions. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. However, high‐resolution satellite soil moisture data is typically infrequent (e.g., 6–12 days) and thus may miss irrigation events. This study evaluates the ability to quantify IWU by assimilating high‐resolution (1 km) SMAP‐Sentinel 1 remotely sensed soil moisture with a physically based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experiments show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and a mean R² of 0.97, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance, resulting in an increased mean annual bias to 23% and decreased mean R² to 0.36. Adding random noise to synthetic observations does not significantly decrease model performance except for the experiments with low observation frequency (>12 days). In real‐world experiments, the PBS data assimilation approach underestimates observed IWU by 18.6% when the timing of IWU is known. IWU estimates are consistently significantly higher over irrigated pixels compared to the non‐irrigated pixels, indicating data assimilation skillfully conveys irrigation signals to the LSM.