Subsurface pipe failures in cold regions are generally believed to be exacerbated by differential strain in shallow soils induced by seasonal freeze and thaw cycles. The transient stress–strain fields resulting from soil water phase change may influence the occurrence of local buried pipe breaks including those related to urban water mains. This work proposes that freezing-induced frost loading results in uneven stress–strain distributions along the buried water mains placing them at risk of bending, breaking, and/or leaking. A coupled thermal-hydraulic-mechanical (THM) model was developed to illustrate the interactions among moisture, temperature, and stress–strain fields within variably saturated freezing soils. Several typical cases involving highly frost-susceptible and lower frost-susceptible soils underlying roadbeds were examined. Results show that the magnitude of frost-induced compressive stress and strain changes between different frost-susceptible soils can vary significantly. Such substantial differences in stress–strain fields would increase the breakage risk of water mains buried within different types of soils. Furthermore, even water mains buried within soils with low frost-susceptibility are at risk when additional sources of soil water exist and are available to migrate to the freezing front. To reduce the risk of damage to buried pipe-like infrastructure, such as municipal water mains, from soil freezing phenomena, the selected backfill material should have fairly consistent frost susceptibility or a broad zone of transition should be considered between materials with significantly different frost susceptibility. In addition, buried pipes should be kept as far away from external sources of subsurface water as possible considering the potential for the water source to exacerbate the level of risk to the pipe.

Wetlands are important ecosystems—they provide vital hydrological and ecological services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extents and hydrological processes is important for valuing wetlands' function. The purpose of this study is to dynamically represent the spatial extents and hydrological processes of wetlands and investigate their feedback to regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP land surface model with two major modifications: (a) modifying the subgrid saturation fraction for spatial wetland extent and (b) incorporating a dynamic wetland storage to simulate hydrological processes. This scheme was evaluated at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while suppressing sensible heat and runoff in the wetland scheme. Finally, the dynamic wetland scheme was applied in the Weather Research and Forecasting (WRF) model. The wetlands scheme not only modifies the surface energy balance but also interacts with the lower atmosphere, shallowing the planetary boundary layer height and promoting cloud formation. A cooling effect of 1–3°C in summer temperature is evident where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for >10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling and wetland conservation, especially in mitigating extreme heatwaves under climate change.

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split‐sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large‐sample SST assessment study empirically assesses how different data splitting methods influence post‐validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub‐periods (CSPs). A full‐period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35‐year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split‐sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split‐sample approach in hydrological modeling.