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In cold and humid climates, warming temperatures will result in longer growing seasons, leading to land cover changes that could have long‐term impacts on groundwater recharge (GWR), in addition to the direct impacts of climate change. The objective of this study was therefore to investigate whether land cover (LC) changes need to be considered when simulating long‐term regional‐scale potential GWR in cold and humid climates by (1) quantifying how LC changes impact simulated GWR and (2) quantifying the combined impacts of LC and climate changes on the future GWR changes. Using the region of southern Quebec (Canada) as a case study and a water budget model, this work proposes an innovative coupling of land cover change scenarios and specific future climate conditions to simulate spatially distributed transient GWR over the 1951–2100 period. The results showed that including LC changes in long‐term GWR simulations produced statistically significant increases in GWR compared to using a constant LC through time (average of +13 mm). Massive afforestation taking place on agricultural lands simulated for one of the scenario chains (RCP4.5) increased GWR by reducing runoff during the snow‐dominated period (average − 17 mm). The results also showed that GWR was more sensitive to climate change for scenarios that included intense land cover changes. Additionally, the spatial distribution of the LC changes influenced their simulated impacts on GWR. Considering that the methodology was computationally feasible and entirely transferrable to the new CMIP6 ensemble, LC changes should be considered systematically in long‐term groundwater resources simulations. [ABSTRACT FROM AUTHOR]

The erosion impact of large coastal storm events typically occurs across broad (100s of km) sections of coastline and may include significant variability both alongshore and vertically between the berm and dunes. Identifying controls of variability in storm erosion is critical to understanding the response of coastlines to present and changing storminess. This contribution analyses immediate pre‐ and post‐storm Lidar data of over 1700 cross‐shore profile transects, determined at every 100 m alongshore and spanning 400km of the southeast Australian coastline. This unique dataset allowed for a data‐driven Bayesian network analysis of the key relationships between the measured storm erosion response and a range of variables describing the antecedent morphology and hydrodynamic forcing at the coastline. It was found that while erosion of the dune and berm was observed to increase with increased exposure of the local profile to incident storm waves, additional erosion controls were found to be different for these two different sections of the beach. Erosion of the berm was specifically linked to the pre‐storm berm volume, with more accreted berms experiencing a greater proportion of erosion of the overall berm, regardless of variability in forcing conditions. In contrast, dune erosion was equally controlled by the exceedance of wave runup above the antecedent dune toe elevation and the width of the beach immediately fronting the dune, with wider beaches resulting in reduced dune erosion. The results of this large, data‐driven analysis provide important affirmation and insights into the primary controls of berm and dune storm erosion. Plain Language Summary: The erosion caused by coastal storm events is often spatially‐variable ‐ some areas of the coast may experience severe erosion of the beach and dune, while adjacent areas may be relatively unaffected. Understanding the controls of this spatial variability in erosion is important to better manage storm erosion now and in the future. This research analyses an unprecedentedly detailed dataset of coastal storm erosion spanning a 400 km region of the southeast Australian coastline to identify controls of spatial variability in storm‐induced coastal erosion. It was found that the magnitude of erosion caused by the storm event at different locations along the coast was controlled by both the storm wave and water level conditions, as well as the pre‐storm state of the beach. In particular, erosion was controlled by 1) the exposure of the coastline to incident storm waves; 2) the elevation of wave runup with respect to dune toe elevation; 3) the pre‐storm volume of sand on the beach; and, 4) the pre‐storm width of the beach. The results provide useful insight into the controls of both local and regional‐scale coastal erosion and have implications for understanding and modelling coastal vulnerability to storms now and into the future. Key Points: Immediate pre‐ and post‐storm Lidar data of 400 km of coastline was analyzed to investigate key controls of variability in storm erosionDune erosion was equally controlled by the amount of runup that exceeded the dune toe and the width of the berm fronting the duneAccreted berms experienced a greater proportion of erosion of the overall berm, regardless of variability in forcing conditions [ABSTRACT FROM AUTHOR]

Groundwater vulnerability assessments of nonpoint source agrochemical contamination at regional scales are either qualitative in nature or require prohibitively costly computational efforts. By contrast, the type transfer function (TTF) modeling approach for vadose zone pesticide leaching presented here estimates solute concentrations at a depth of interest, only uses available soil survey, climatic, and irrigation information, and requires minimal computational cost for application. TTFs are soil texture based travel time probability density functions that describe a characteristic leaching behavior for soil profiles with similar soil hydraulic properties. Seven sets of TTFs, representing different levels of upscaling, were developed for six loam soil textural classes with the aid of simulated breakthrough curves from synthetic data sets. For each TTF set, TTFs were determined from a group or subgroup of breakthrough curves for each soil texture by identifying the effective parameters of the function that described the average leaching behavior of the group. The grouping of the breakthrough curves was based on the TTF index, a measure of the magnitude of the peak concentration, the peak arrival time, and the concentration spread. Comparison to process-based simulations show that the TTFs perform well with respect to mass balance, concentration magnitude, and the timing of concentration peaks. Sets of TTFs based on individual soil textures perform better for all the evaluation criteria than sets that span all textures. As prediction accuracy and computational cost increase with the number of TTFs in a set, the selection of a TTF set is determined by a given application. [ABSTRACT FROM AUTHOR]