The analysis of transit/residence time distributions (TTDs and RTDs) provides important insights into the dynamics of stream‐water ages and subsurface mixing. These insights have significant implications for water quality. For a small agricultural catchment in central Germany, we use a 3D fully coupled surface‐subsurface hydrological model to simulate water flow and perform particle tracking to determine flow paths and transit times. The TTDs of discharge, RTDs of storage and fractional StorAge Selection (fSAS) functions are computed and analyzed on daily basis for a period of 10 years. Results show strong seasonal fluctuations of the median transit time of discharge and the median residence time, with the former being strongly related to the catchment wetness. Computed fSAS functions suggest systematic shifts of the discharge selection preference over four main periods: In the wet period, the youngest water in storage is preferentially selected, and this preference shifts gradually toward older ages of stored water when the catchment transitions into the drying, dry and wetting periods. These changes are driven by distinct shifts in the dominance of deeper flow paths and fast shallow flow paths. Changes in the shape of the fSAS functions can be captured by changes in the two parameters of the approximating Beta distributions, allowing the generation of continuous fSAS functions representing the general catchment behavior. These results improve our understanding of the seasonal dynamics of TTDs and fSAS functions for a complex real‐world catchment and are important for interpreting solute export to the stream in a spatially implicit manner.

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Sea‐level rise and increases in the frequency and intensity of ocean surges caused by climate change are likely to exacerbate adverse effects on low‐lying coastal areas. The landward flow of water during ocean surges introduces salt to surficial coastal aquifers and threatens groundwater resources. Coastal topographic features (e.g., ponds, dunes, barrier islands, and channels) likely have a strong impact on overwash and salinization processes, but are generally highly simplified in modeling studies. To understand topographic impacts on groundwater salinization, we modeled a theoretical overwash event and variable‐density groundwater flow and salt transport in 3‐D using the fully coupled surface and subsurface numerical simulator, HydroGeoSphere. The model simulates the coastal aquifer as an integrated system considering overland flow, coupled surface and subsurface exchange, variably saturated flow, and variable‐density groundwater flow. To represent various coastal landscape types, we simulated both synthetic fields and real‐world coastal topography from Delaware, USA. The groundwater salinization assessment suggested that the topographic connectivity promoting overland flow controls the volume of aquifer that is salinized. In contrast, the amount of water that can be stored in surface depressions determines the amount of seawater that infiltrates the subsurface and the time for seawater to flush from the aquifer. Our study suggests that topography has a significant impact on groundwater salinization due to ocean surge overwash, with important implications for coastal land management and groundwater vulnerability assessment.

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MicroRNAs (miRNAs) are a group of small, non-coding RNAs that play important roles in plant growth, development and stress response. There have been an increasing number of investigations aimed at discovering miRNAs and analyzing their functions in model plants (such as Arabidopsis thaliana and rice). In this research, we constructed small RNA libraries from both polyethylene glycol (PEG 6,000) treated and control potato samples, and a large number of known and novel miRNAs were identified. Differential expression analysis showed that 100 of the known miRNAs were down-regulated and 99 were up-regulated as a result of PEG stress, while 119 of the novel miRNAs were up-regulated and 151 were down-regulated. Based on target prediction, annotation and expression analysis of the miRNAs and their putative target genes, 4 miRNAs were identified as regulating drought-related genes (miR811, miR814, miR835, miR4398). Their target genes were MYB transcription factor (CV431094), hydroxyproline-rich glycoprotein (TC225721), quaporin (TC223412) and WRKY transcription factor (TC199112), respectively. Relative expression trends of those miRNAs were the same as that predicted by Solexa sequencing and they showed a negative correlation with the expression of the target genes. The results provide molecular evidence for the possible involvement of miRNAs in the process of drought response and/or tolerance in the potato plant.

StorAge Selection (SAS) functions describe how catchments selectively remove water of different ages in storage via discharge, thus controlling the transit time distribution (TTD) and solute composition of discharge. SAS‐based models have been emerging as promising tools for quantifying catchment‐scale solute export, providing a coherent framework for describing both velocity‐driven and celerity‐driven transport. Due to their application in headwaters only, the spatial heterogeneity of catchment physiographic characteristics, land use management practices, and large‐scale validation have not been adequately addressed with SAS‐based models. Here, we integrated SAS functions into the grid‐based mHM‐Nitrate model (mesoscale Hydrological Model) at both grid scale (distributed model) and catchment scale (lumped model). The proposed model provides a spatially distributed representation of nitrogen dynamics within the soil zone and a unified approach for representing both velocity‐driven and celerity‐driven subsurface transport below the soil zone. The model was tested in a heterogeneous mesoscale catchment. Simulated results show a strong spatial heterogeneity in nitrogen dynamics within the soil zone, highlighting the necessity of a spatially explicit approach for describing near‐surface nitrogen processing. The lumped model could well capture instream nitrate concentration dynamics and the concentration–discharge relationship at the catchment outlet. In addition, the model could provide insights into the relations between subsurface storage, mixing scheme, solute export, and the TTDs of discharge. The distributed model shows results that are comparable to the lumped model. Overall, the results reveal the potential for large‐scale applications of SAS‐based transport models, contributing to the understanding of water quality‐related issues in agricultural landscapes.

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