This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come. ARTICLE HISTORY

An irrigation scheme, based on simulated soil moisture deficit, has been included in the variable infiltration capacity macroscale hydrologic model. Water withdrawals are taken from the nearest river, or, in periods of water scarcity, from reservoirs. Alternatively, water can be assumed freely available. The irrigation scheme successfully simulates crop consumptive water use in large river basins. In general, irrigation leads to decreased streamflow and increased evapotranspiration. The locally significant increases in evapotranspiration (or latent heat) results in lower surface temperatures, and hence decreased sensible heat flux. Simulations performed for a 20-year period for the Colorado and Mekong river basins indicate irrigation water requirements of 10 and 13.4 km 3 year K1 , respectively, corresponding to streamflow decreases of 37 and 2.3%. The increase in latent heat flux is accompanied by a decrease in annual averaged surface temperatures of 0.04 8C for both river basins. The maximum simulated increase in latent heat flux averaged over the three peak irrigation months for one grid cell is 63 W m K2 , where surface temperature decreases 2.1 8C. Simulated actual water use is somewhat less than simulated irrigation water requirements; 8.3 and 12.4 km 3 year K1 for the Colorado and Mekong river basin, respectively. q

Impacts of reservoirs and irrigation water withdrawals on continental surface water fluxes are studied within the framework of the Variable Infiltration Capacity (VIC) model for a part of North America, and for Asia. A reservoir model, designed for continental‐scale simulations, is developed and implemented in the VIC model. The model successfully simulates irrigation water requirements, and captures the main effects of reservoir operations and irrigation water withdrawals on surface water fluxes, although consumptive irrigation water use is somewhat underestimated. For the North American region, simulated irrigation water requirements and consumptive irrigation water uses are 191 and 98 km3year−1, while the corresponding numbers for the Asian region are 810 and 509 km3year−1, respectively. The consumptive uses represent a decrease in river discharge of 4.2 percent for the North American region, and 2.8 percent for the Asian region. The largest monthly decrease is about 30 percent, for the area draining the Western USA in June. The maximum monthly increase in streamflow (28 percent) is in March for the Asian Arctic region.

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successivecorrection schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at

Abstract. The spatial distribution of snow water equivalent (SWE) is modelled as a two parameter gamma distribution. The parameters of the distribution are dynamical in that they are functions of the number of accumulation and melting events and the temporal correlation of accumulation and melting events. The estimated spatial variability is compared to snow course observations from the alpine catchments Norefjell and Aursunden in Southern Norway. A fixed snow course at Norefjell was measured 26 times during the snow season and showed that the spatial coefficient of variation change during the snow season with a decreasing trend from the start of the accumulation period and a sharp increase in the melting period. The gamma distribution with dynamical parameters reproduced the observed spatial statistical features of SWE well both at Norefjell and Aursunden. Also the shape of simulated spatial distribution of SWE agreed well with the observed at Norefjell. The temporal correlation tends to be positive for both accumulation and melting events. However, at the start of melting, a better fit between modelled and observed spatial standard deviation of SWE is obtained by using negative correlation between SWE and melt.

This study presents results from an Airborne Laser Scanning (ALS) mapping survey of snow depth on the mountain plateau Hardangervidda, Norway, in 2008 and 2009 at the approximate time of maximum snow accumulation during the winter. The spatial extent of the survey area is >240 km2. Large variability is found for snow depth at a local scale (2 m2), and similar spatial patterns in accumulation are found between 2008 and 2009. The local snow-depth measurements were aggregated by averaging to produce new datasets at 10, 50, 100, 250 and 500 m2 and 1 km2 resolution. The measured values at 1 km2 were compared with simulated snow depth from the seNorge snow model (www.senorge.no), which is run on a 1 km2 grid resolution. Results show that the spatial variability decreases as the scale increases. At a scale of about 500 m2 to 1 km2 the variability of snow depth is somewhat larger than that modeled by seNorge. This analysis shows that (1) the regional-scale spatial pattern of snow distribution is well captured by the seNorge model and (2) relatively large differences in snow depth between the measured and modeled values are present.

Abstract:A guiding principle in hydrological modelling should be to keep the number of calibration parameters to a minimum. A reduced number of parameters to be calibrated, while maintaining the accuracy and detail required by modern hydrological models, will reduce parameter and model structure uncertainty and improve model diagnostics. In this study, the dynamics of runoff are derived from the distribution of distances from points in the catchments to the nearest stream. This distribution is unique for each catchment and can be determined from a geographical information system. The distribution of distances, will, when a celerity of (subsurface) flow is introduced, provide a distribution of travel times, or a unit hydrograph (UH). For spatially varying levels of saturation deficit, we have different celerities and, hence, different UHs. Runoff is derived from the superposition of the different UHs. This study shows how celerities can be estimated if we assume that recession events represent the combined UHs for different levels of saturation deficit. A new soil moisture routine which estimates saturated and unsaturated volumes of subsurface water and with only one parameter to calibrate is included in the new model. The performance of the new model is compared with that of the Swedish HBV model and is found to perform equally well for eight Norwegian catchments although the number of parameters to be calibrated in the module concerning soil moisture and runoff dynamics is reduced from seven in the HBV model to one in the new model. It is also shown that the new model has a more realistic representation of the subsurface hydrology.

In the mountains of Norway, snow depth (SD) is highly variable due to strong winds and open terrain. To investigate snow conditions on one of Europe's largest mountain plateaus, Hardangervidda, we conducted snow measurement campaigns in spring 2008 and 2009 using airborne lidar scanning at the approximate time of annual snow maximum (mid‐April). From 658 empirical distributions of SD at Hardangervidda, each comprised about 4,000 SD values sampled from a grid cell of 0.5 km2, quantitative tests have shown that the gamma distribution is a better fit for SD than the normal and log‐normal distributions. When aggregating snow and terrain data from 10 × 10 m to 0.5 km2, we find that the standard deviation of the terrain parameter squared slope, land cover, and the mean SD are highly correlated (0.7, 0.52, and 0.89) to the standard deviation of SD. A model for SD variance is proposed that, in addition to addressing the dependencies between the variability of SD and the terrain characteristics, also takes into account the observed nonlinear relationship between the mean and the standard deviation of SD. When validated against observed SD variance retrieved from the same area, the model explains 81–83% of the observed variance for spatial scales of 0.5 and 5.1 km2, which compares favorably to previous models. The model parameters can be determined from a GIS analysis of a detailed digital terrain and land cover model and will hence not increase the number of calibration parameters when implemented in environmental models.