This paper synthesizes our understanding of the Arctic's large‐scale freshwater cycle. It combines terrestrial and oceanic observations with insights gained from the ERA‐40 reanalysis and land surface and ice‐ocean models. Annual mean freshwater input to the Arctic Ocean is dominated by river discharge (38%), inflow through Bering Strait (30%), and net precipitation (24%). Total freshwater export from the Arctic Ocean to the North Atlantic is dominated by transports through the Canadian Arctic Archipelago (35%) and via Fram Strait as liquid (26%) and sea ice (25%). All terms are computed relative to a reference salinity of 34.8. Compared to earlier estimates, our budget features larger import of freshwater through Bering Strait and larger liquid phase export through Fram Strait. While there is no reason to expect a steady state, error analysis indicates that the difference between annual mean oceanic inflows and outflows (∼8% of the total inflow) is indistinguishable from zero. Freshwater in the Arctic Ocean has a mean residence time of about a decade. This is understood in that annual freshwater input, while large (∼8500 km3), is an order of magnitude smaller than oceanic freshwater storage of ∼84,000 km3. Freshwater in the atmosphere, as water vapor, has a residence time of about a week. Seasonality in Arctic Ocean freshwater storage is nevertheless highly uncertain, reflecting both sparse hydrographic data and insufficient information on sea ice volume. Uncertainties mask seasonal storage changes forced by freshwater fluxes. Of flux terms with sufficient data for analysis, Fram Strait ice outflow shows the largest interannual variability.

The formulation and implementation of LEAF-2, the Land Ecosystem-Atmosphere Feedback model, which comprises the representation of land-surface processes in the Regional Atmospheric Modeling System (RAMS), is described. LEAF-2 is a prognostic model for the temperature and water content of soil, snow cover, vegetation, and canopy air, and includes turbulent and radiative exchanges between these components and with the atmosphere. Subdivision of a RAMS surface grid cell into multiple areas of distinct land-use types is allowed, with each subgrid area, or patch, containing its own LEAF-2 model, and each patch interacts with the overlying atmospheric column with a weight proportional to its fractional area in the grid cell. A description is also given of TOPMODEL, a land hydrology model that represents surface and subsurface downslope lateral transport of groundwater. Details of the incorporation of a modified form of TOPMODEL into LEAF-2 are presented. Sensitivity tests of the coupled system are presented that demonstrate the potential importance of the patch representation and of lateral water transport in idealized model simulations. Independent studies that have applied LEAF-2 and verified its performance against observational data are cited. Linkage of RAMS and TOPMODEL through LEAF-2 creates a modeling system that can be used to explore the coupled atmosphere-biophysicalhydrologic response to altered climate forcing at local watershed and regional basin scales.

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The Arctic water cycle has experienced an unprecedented degree of change which may have planetary-scale impacts. The year 2007 in particular not only was unique in terms of minimum sea ice extent in the Arctic Ocean but also was a record breaking year for Eurasian river inflow to the Arctic Ocean. Over the observational period from 1936 to 2006, the mean annual river discharge for the six largest Russian rivers was 1796 km 3 y −1 , with the previous record high being 2080 km 3 y −1 , in 2002. The year 2007 showed a massive flux of fresh water from these six drainage basins of 2254 km 3 y −1 . We investigated the hydroclimatological conditions for such extreme river discharge and found that while that year's flow was unusually high, the overall spatial patterns were consistent with the hydroclimatic trends since 1980, indicating that 2007 was not an aberration but a part of the general trend.We wanted to extend our hydroclimatological analysis of river discharge anomalies to seasonal and monthly time steps; however, there were limits to such analyses due to the direct human impact on the river systems. Using reconstructions of the naturalized hydrographs over the Yenisey basin we defined the limits to analysis due to the effect of reservoirs on river discharge. For annual time steps the trends are less impacted by dam construction, whereas for seasonal and monthly time steps these data are confounded by the two sources of change, and the climate change signals were overwhelmed by the human-induced river impoundments. We offer two solutions to this problem; first, we recommend wider use of algorithms to 'naturalize' the river discharge data and, second, we suggest the identification of a network of existing and stable river monitoring sites to be used for climate change analysis.

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India is one of the world's largest food producers, making the sustainability of its agricultural system of global significance. Groundwater irrigation underpins India's agriculture, currently boosting crop production by enough to feed 170 million people. Groundwater overexploitation has led to drastic declines in groundwater levels, threatening to push this vital resource out of reach for millions of small-scale farmers who are the backbone of India's food security. Historically, losing access to groundwater has decreased agricultural production and increased poverty. We take a multidisciplinary approach to assess climate change challenges facing India's agricultural system, and to assess the effectiveness of large-scale water infrastructure projects designed to meet these challenges. We find that even in areas that experience climate change induced precipitation increases, expansion of irrigated agriculture will require increasing amounts of unsustainable groundwater. The large proposed national river linking project has limited capacity to alleviate groundwater stress. Thus, without intervention, poverty and food insecurity in rural India is likely to worsen.