CH₄ emissions from inland water are highly uncertain in the current global CH₄ budget, especially for rivers and streams due to sparse measurements and the uncertainty of measurements caused by turbulent water flow. A previous study has revealed that vertical hydrologic exchange flow (VHEF) is the main regulator of CH₄ emissions from riverbed sediments. However, to what extent the understanding obtained from the plot‐scale can be extended to the reach scale and basin scale remains unknown. To address this challenge, we developed a process‐based model to estimate CH₄ flux at the air‐water interface using the attributes available in the national hydrography data set. It calculates the annual mean flux of VHEF, CH₄ production in sediments, and CH₄ transport in the river channel in a sequential manner. Model performance is evaluated by CH₄ efflux observed at the Hanford reach of the Columbia River. We show that reach‐wise sediment hydrologic and biogeochemical conditions estimated from the national hydrography data set could serve as a good indicator of CH₄ emissions from rivers. Aerobic methane oxidation and export to the downstream are the dominant ways of total CH₄ loss for the large lowland river. The hotspots of CH₄ emissions are likely to be at the reaches with fine sediments and slow channel velocity. This study demonstrates the possibility of quantifying CH₄ emissions at the reach scale and the modeling framework has the potential to be extended to the basin scale to improve estimates of CH₄ emissions from lotic inland water.

Growing demands on water supply worldwide have resulted in aquifer overdraft in many regions, especially in alluvial basins under intensive irrigation. This further leads to serious deterioration of groundwater quality. Managed aquifer recharge (MAR) has been shown to mitigate groundwater overdraft, but whether MAR can actually stabilize or reverse the ongoing declines in regional groundwater quality caused by non‐point sources has not been demonstrated. This study was intended to address the question by investigating impacts of different MAR strategies on regional groundwater quality. A geostatistical model was first used to characterize a heterogeneous alluvial aquifer system in a portion of the Tulare Lake Basin. Three‐dimensional numerical models were then employed to simulate groundwater flow and mass transport. Next, MAR strategies were applied in locations with different geological conditions or joint with different irrigation activities, and their performances were evaluated. Results demonstrate the potential of significant, long‐term benefits for regional groundwater quality by applying strategic, high‐intensity recharge operations on geologically favorable subregions. Siting MAR above the incised valley fill (IVF) deposit, a near‐surface paleochannel containing unusually coarse, high‐conductivity hydrofacies, leads to more extensive improvement in the groundwater quality in terms of salinity due to significant vertical flow and lateral outward flow from the IVF. Overall, decades would be required to alleviate groundwater quality concerns in the studied 189 km² region. The simulations indicate that the deep concentrations remain below the secondary maximum contaminant level as the solute mass migrates downward with the prominent contribution from the attenuation via dispersion and matrix diffusion.

Accurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM _(ZS) ) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM _(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM _(ZS) and ES‐MDA perform better than IBOA does.

The hydrological and biogeochemical properties of the hyporheic zone in stream and riverine ecosystems have been extensively studied over the past two decades. Although it is widely acknowledged that sediment heterogeneity can influence biogeochemical reactions, little effort has been made to understand the role of heterogeneity on the spatiotemporal variability of riverbed redox conditions under changing flow dynamics at different timescales. Here we integrate a mechanistic model and field data to demonstrate that heterogeneity in permeability plays a vital role in modulating sediment redox conditions at both seasonal (annual) and event (daily‐to‐weekly) timescales, whereas heterogeneity in particulate organic carbon (POC) content only has a comparable influence on redox conditions at the seasonal timescale. These findings underscore the importance of accurately characterizing sediment heterogeneity, in terms of permeability and POC content, in quantifying biogeochemical dynamics in the riverbed and hyporheic zones of riverine ecosystems.

Hydrologically‐induced landslides are ubiquitous natural hazards in the Himalayas, posing severe threat to human life and infrastructure. Yet, landslide assessment in the Himalayas is extremely challenging partly due to complex and drastically changing climate conditions. Here we establish a mechanistic hydromechanical landslide modeling framework that incorporates the impacts of key water fluxes and stocks on landslide triggering and risk evolution in mountain systems, accounting for potential climate change conditions for the period 1991–2100. In the drainage basin of the largest river in the northern Himalayas– the Yarlung Zangbo River Basin (YZRB), we estimate that rainfall, glacier/snow melt and permafrost thaw contribute ∼38.4%, 28.8%, and 32.8% to landslides, respectively, for the period 1991–2019. Future climate change will likely exacerbate landslide triggering primarily due to increasing rainfall, whereas the contribution of glacier/snow melt decreases owing to deglaciation and snow cover loss. The total Gross Domestic Productivity risk is projected to increase continuously throughout the 21st century, while the risk to population shows a general declining trend. The results yield novel insights into the climatic controls on landslide evolution and provide useful guidance for disaster risk management and resilience building under future climate change in the Himalayas.