Sub-seasonal to seasonal (S2S) forecasts are critical for planning and management decisions in multiple sectors. This study shows results from dynamical downscaling using a regional climate model at a convection-permitting scale driven by boundary conditions from the global reanalysis of the Climate Forecast System Model (CFSR). Convection-permitting modeling (CPM) enhances the representation of regional climate by better resolving the regional forcings and processes, associated with topography and land cover, in response to variability in the large-scale atmospheric circulation. We performed dynamically downscaled simulations with the Weather Research and Forecasting (WRF) model over the Upper and Lower Colorado basin at 12 km and 3 km grid spacing from 2000 to 2010 to investigate the potential of dynamical downscaling to improved the modeled representation of precipitation the Southwestern United States. Employing a convection-permitting nested domain of 3 km resolution significantly reduces the bias in mean (∼2 mm/day) and extreme (∼4 mm/day) summer precipitation when compared to coarser domain of 12 km resolution and coarse resolution CFSR products. The convection-permitting modeling product also better represents eastward propagation of organized convection due to mesoscale convective systems at a subdaily scale, which largely account for extreme summer rainfall during the North American monsoon. In the cool season both coarse and high-resolution simulations perform well with limited bias of ∼1 mm/day for the mean and ∼2 mm/day for the extreme precipitation. Significant correlation was found (∼0.85 for summer and ∼0.65 for winter) for both coarse and high-resolution model with observed regionally and seasonally averaged precipitation. Our findings suggest that the use of CPM is necessary in a dynamical modeling system for S2S prediction in this region, especially during the warm season when precipitation is mostly convectively driven.

Machine learning (ML), as an artificial intelligence tool, has acquired significant progress in data-driven research in Earth sciences. Land Surface Models (LSMs) are important components of the climate models, which help to capture the water, energy, and momentum exchange between the land surface and the atmosphere, providing lower boundary conditions to the atmospheric models. The objectives of this review paper are to highlight the areas of improvement in land modeling using ML and discuss the crucial ML techniques in detail. Literature searches were conducted using the relevant key words to obtain an extensive list of articles. The bibliographic lists of these articles were also considered. To date, ML-based techniques have been able to upgrade the performance of LSMs and reduce uncertainties by improving evapotranspiration and heat fluxes estimation, parameter optimization, better crop yield prediction, and model benchmarking. Widely used ML techniques used for these purposes include Artificial Neural Networks and Random Forests. We conclude that further improvements in land modeling are possible in terms of high-resolution data preparation, parameter calibration, uncertainty reduction, efficient model performance, and data assimilation using ML. In addition to the traditional techniques, convolutional neural networks, long short-term memory, and other deep learning methods can be implemented.

Some of the most intense convective storms on earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November-December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were to (1) perform hydrological streamflow and meteorological observations in previously ungauged basins, and (2) build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage-discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5-6 hours from the peak of the rain event. Satellite observed rainfall product IMERG-Final showed a better representation of rain gauge estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-hour simulation of an extreme hydrometeorological event that occurred on November 27, 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region.

During the 21st century, Argentina has experienced one of the fastest agricultural expansion rates in the planet (Baldi & Paruelo, 2008;Graesser et al., 2015). In many Argentinian regions the past 60 years have seen a shift in agricultural production from one that had primarily perennial crops for livestock and grasses to one based on annual crops, largely dominated by soy, with confinement of livestock into feedlots. These changes came about due technological advances in agricultural production such as the introduction of transgenic varieties, no-till farming, and crop rotation which dramatically increased crop productivity in the region (Paruelo et al., 2005). Global economic shifts such as the increasing demand of soy-based and corn-based biofuels made it economically attractive for farmers to shift to soy and corn. As a result, in two decades (1995/96 to 2014/15), the cultivated area in regions such as Cordoba increased by 229%. Soy now dominates the landscape in the province of Cordoba accounting for nearly 60% of crops.How can these dramatic changes in land use affect the hydrologic cycle? Some effects could parallel those of other regions of the globe that have experienced similar land-use shifts, such as the Midwestern United States. In the US central region, European settlers arrived in the early to mid-19th century and by 1900 agriculture had become the dominant land use type, replacing the native grasses and forests of the region (Yaeger et al., 2013). Perennial and sod vegetation gave way to intensive corn and/or soybean crops with shorter summer growing seasons, which led to a decrease in evapotranspiration (ET). Decreased ET implied that more precipitation was going into groundwater recharge and routed into streams as baseflow (Zhang & Schilling, 2006). Several studies have attributed increased baseflow in the region to changes in land surface characteristics (

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