The consequences of climate change on food security in arid and semi-arid regions can be serious. Understanding climate change impacts on water balance is critical to assess future crop performance and develop sustainable adaptation strategies. This paper presents a climate change impact study on the water balance components of an agricultural watershed in the Mediterranean region. The restructured version of the Soil and Water Assessment Tool (SWAT+) model was used to simulate the hydrological components in the Sulcis watershed (Sardinia, Italy) for the baseline period and compared to future climate projections at the end of the 21st century. The model was forced using data from two Regional Climate Models under the representative concentration pathways RCP4.5 and RCP8.5 scenarios developed at a high resolution over the European domain. River discharge data were used to calibrate and validate the SWAT+ model for the baseline period, while the future hydrological response was evaluated for the mid-century (2006–2050) and late-century (2051–2098). The model simulations indicated a future increase in temperature, decrease in precipitation, and consequently increase in potential evapotranspiration in both RCP scenarios. Results show that these changes will significantly decrease water yield, surface runoff, groundwater recharge, and baseflow. These results highlight how hydrological components alteration by climate change can benefit from modelling high-resolution future scenarios that are useful for planning mitigation measures in agricultural semi-arid Mediterranean regions.

Understanding dominant processes influencing microorganism responses to storm events aids in the development of effective management controls on pathogen contamination in surface water so that they are suitable for water supply, recreation, and aquatic habitat. Despite the urgent needs at present, numerous facets of microbial transport and fate are still poorly understood. Using correlation and multiple regression combined with spatial analyses, this paper evaluates the relationship between antecedent precipitation and discharge, TSS, and E. coli concentrations, and examines correlations between E. coli and TSS, as well as whether and how those relationships change along an urban and rural gradient. The urban watershed exhibited a faster and stronger response of streamflow, TSS, and E. coli to precipitation mainly due to its higher degree of imperviousness. In general, TSS was significantly correlated with E. coli concentrations, which linearly decreased as % developed area increased, with large variation in regions with a high percentage of development, implying the more complex stormwater infrastructure and more variable pollutant sources of E. coli in the urban watershed. Seasonal differences for E. coli were noted. Specifically, summer showed a higher level of E. coli, which might be attributed to the higher temperature since E. coli is more likely to persist and grow in a warmer environment. Further multiple linear regression analyses showed the best E. coli prediction result for the largest, suburban watershed, using antecedent precipitation, TSS, and temperature as independent variables. The models are capable of explaining 60% and 50% of the variability in the E. coli concentration for the dry and wet season, respectively. The study not only provides more detailed and accurate characterization of the storm-period response of E. coli across an urban and rural gradient, but also lays a foundation for predicting the concentration of E. coli in practice, potentially suggesting effective watershed management decisions.

It is difficult to interpolate river channel topography due to complex anisotropy. As the anisotropy is often caused by river flow, especially the hydrodynamic and transport mechanisms, it is reasonable to incorporate flow velocity into topography interpolator for decreasing the effect of anisotropy. In this study, two new distance metrics defined as the time taken by water flow to travel between two locations are developed, and replace the spatial distance metric or Euclidean distance that is currently used to interpolate topography. One is a shortest temporal distance (STD) metric. The temporal distance (TD) of a path between two nodes is calculated by spatial distance divided by the tangent component of flow velocity along the path, and the STD is searched using the Dijkstra algorithm in all possible paths between two nodes. The other is a modified shortest temporal distance (MSTD) metric in which both the tangent and normal components of flow velocity were combined. They are used to construct the methods for the interpolation of river channel topography. The proposed methods are used to generate the topography of Wuhan Section of Changjiang River and compared with Universal Kriging (UK) and Inverse Distance Weighting (IDW). The results clearly showed that the STD and MSTD based on flow velocity were reliable spatial interpolators. The MSTD, followed by the STD, presents improvement in prediction accuracy relative to both UK and IDW