Responses of surface water quality to future land cover and climate changes in the Neka River basin, Northern Iran.

The spatial and temporal dimensions of environmental impacts of climate and land cover changes are two significant factors altering hydrological processes. Studying the effects of these factors on water quality, provides important insight for water resource management and optimizing land planning given increasing water scarcity and water pollution. The impact of land cover and climate changes on surface water quality was assessed for the Neka River basin in Northern Iran. The widely used Soil and Water Assessment Tool (SWAT) was applied for pollutant modeling, and was calibrated using the Sequential Uncertainty Fitting (SUFI-2) algorithm. An ensemble of 17 CMIP5 climate models under two IPCC greenhouse gas emission scenarios were selected, and future land cover change (LCC) was modeled based on the evolution that occurred in the last decades. We simulated the impacts of climate change (CC) and LCC on sediment, nitrate, and phosphate for the 2035-2065 time slice. The annual loads of sediment, phosphate, and nitrate are projected to decrease under both CC scenarios based on the inter-model average, and generally follow a pattern similar to the change in river discharge. Nitrate concentrations show an increase across all seasons, while the sediment and phosphate concentrations increase in winter and autumn under CC conditions. Results indicate that pollutants are expected to increase under LCC alone, mainly due to the expansion of the cultivated areas. Overall, it seems CC has a greater impact than LCC on the variation of water quality variables in the Neka River basin. With a combined change in climate and land cover, the annual nitrate concentrations are expected to increase by + 19.7% and + 17.9%, under RCP 4.5 and RCP 8.5, respectively. The combined impacts of the CC and LCC caused a decline in the annual sediment and phosphate concentrations by -10.1% and -2.2% under RCP 4.5 and -9%, and -3.2% under RCP 8.5, respectively.

Model-based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments.

Ungauged headwater basins are an abundant part of the river network, but dominant influences on headwater hydrologic response remain difficult to predict. To address this gap, we investigated the ability of a physically based watershed model (the Distributed Hydrology-Soil-Vegetation Model) to represent controls on metrics of hydrologic partitioning across five adjacent headwater subcatchments. The five study subcatchments, located in Tenderfoot Creek Experimental Forest in central Montana, have similar climate but variable topography and vegetation distribution. This facilitated a comparative hydrology approach to interpret how parameters that influence partitioning, detected via global sensitivity analysis, differ across catchments. Model parameters were constrained a priori using existing regional information and expert knowledge. Influential parameters were compared to perceptions of catchment functioning and its variability across subcatchments. Despite between-catchment differences in topography and vegetation, hydrologic partitioning across all metrics and all subcatchments was sensitive to a similar subset of snow, vegetation, and soil parameters. Results also highlighted one subcatchment with low certainty in parameter sensitivity, indicating that the model poorly represented some complexities in this subcatchment likely because an important process is missing or poorly characterized in the mechanistic model. For use in other basins, this method can assess parameter sensitivities as a function of the specific ungauged system to which it is applied. Overall, this approach can be employed to identify dominant modeled controls on catchment response and their agreement with system understanding.

Development of a dual permeability model within a hydrological catchment modeling framework: 1D application.

Preferential flow contributes significantly to pesticide fast transfer from surface to groundwater. Modeling this process at several scales is an important challenge for improving the representation of this process which is often neglected. In this study, we developed a dual permeability approach in a hydrological modeling framework, CMF, which is a collaborative environment for developing spatially-integrated models of water fluxes. In the development we propose here, infiltration in macropores which are connected to the surface is activated when the first matrix layer reaches saturation. A transfer function is used to represent water fluxes from macropores to matrix. This approach is tested in 1D by comparison with the dual permeability approach included in Hydrus1D, on 4 typical soil-types (sandy-loam, silty-loam, clay-loam and sandy-clay-loam). The results showed an underestimation of the flux infiltrated in the matrix surface and important infiltration in macropores with the new model, for most of soil-types, comparing to Hydrus1D. Similarities are observed for fluxes transferred from macropores to matrix. Solute transport is then coupled to CMF-DP model considering a convection transport and a linear adsorption to represent pesticides behavior in macroporous soils. The approach we developed is similar to Hydrus though having the advantage to need less input parameters, especially for the exchange between the two porous media. In the future, it could be applied for predicting pesticides transfer in macroporous soils at different scales for operational applications.