Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well as on the fate of pollutants. Therefore, snow and frost need to be central inherent elements of risk assessment and management schemes. However, snow and frost are often neglected or treated summarily or in a simplistic way by groundwater modellers. Snow deposition is uneven, and the snow is likely to sublimate, be redistributed and partly melt during the winter influencing the mass and spatial distribution of snow storage available for infiltration, the presence of ice layers within and under the snowpack and, therefore, also the spatial distribution of depths and permeability of the soil frost. In steep terrain, snowmelt may travel downhill tens of metres in hours along snow layers. The permeability of frozen soil is mainly influenced by soil type, its water and organic matter content, and the timing of the first snow in relation to the timing of sub-zero temperatures. The aim with this paper is to review the literature on snow and frost processes, modelling approaches with the purpose to visualize and emphasize the need to include these processes when modelling, managing and predicting groundwater recharge for areas exposed to seasonal snow and frost.

The European Groundwater Directive could be improved by limiting the scopes of the Annexes I and II to the manmade and natural substances, respectively, and by defining a common monitoring protocol. The changes in the European landuse patterns, in particular the urban sprawl phenomena, obscure the distinction between the point and diffuse sources of contamination. In the future more importance will be given to the household contamination. Moreover, the agricultural environment could be used for developing new conceptual models related to the pharmaceuticals.

14Saline lakes have diminished considerably due to large-scale irrigation projects throughout the 15 world. Environmental flow (EF) release from upstream reservoirs could help conserve and 16 restore these lakes. However, experiences from regions lacking environmental legislation or 17 with insufficient water resources management show that, despite EF allocation, farmers tend 18 to use all available water for agriculture. In this study, we employed a new method for 19 designing environmental flow release strategies to restore desiccated terminal lakes in arid and 20 semi-arid regions with intensive cultivation within the catchment. As a novelty, the method 21 takes into account farmers' water use behavior, return flow from irrigation, interaction with 22 groundwater, evaporation together without considering any detail of each and natural flow 23 regime in upstream systems to design an optimum monthly EF release strategy for reservoirs. 24 We applied the method to the water resource system of Lake Urmia, once the largest saline 25 lake in the Middle East and now one of the most endangered saline lakes in the world. The 26 analysis showed that the EF released is exploited by lowland farmers before reaching Lake 27 Urmia and that inflow to the lake from some rivers has decreased by up to 80%. We propose a 28 new EF release strategy that requires a considerable change in practice whereby water is 29 released in the shortest possible time (according to reservoir outlet capacity) during the period 30 of lowest irrigation demand in winter. Restoring the lake to minimum ecological level would 31 require 2.4-3.4 km 3 EF allocation by different methods of release based on the recent condition 32 (2002-2011) of the lake.33 34

Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction. Despite advances in remote sensing technology and enhanced satellite observations, the estimation of snow depth at local scale still requires improved accuracy and flexibility. The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge. In this paper, a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward. Significantly, the results of Random Forest classifier showed an accuracy of 94%, indicating a promising alternative in snow depth measurement compared to in situ, LiDAR, or expensive large-scale wireless sensor network, which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.