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This paper presents an evaluation of the parameter sensitivity of a process-based model at the global scale using large-sample data. The analysis was carried out using the HYdrological Prediction of the Environment (HYPE) model, for which soil and snow parameters were evaluated using 187 river flow gauges spread worldwide. As a result, 6 out of 12 soil parameters and 7 out of 10 snow parameters were found to be sensitive. Taking advantage of the global dataset, an additional analysis was used to investigate links between catchment characteristics and parameter sensitivity. Different patterns of sensitivity were observed for different Köppen climate classes, which indicates that parameter regionalization would benefit from calibration based on climate zones. This numerical sensitivity method was compared with the judgement of a set of expert HYPE modellers to understand how numerical results compare with modellers' experience. [ABSTRACT FROM AUTHOR]

The effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection. [ABSTRACT FROM AUTHOR]

Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km 2), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (>10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE >0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge. [ABSTRACT FROM AUTHOR]

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes. [ABSTRACT FROM AUTHOR]

This study examines a method to improve a process-oriented hydrological model concept applied to another region than it was first developed for. In principle, we propose to analyse and refine each major hydrological process separately, sequentially, and iteratively. To test the method, the HYPE model concept (HYdrological Predictions for the Environment, originally developed for Sweden) was here applied to the data-sparse Niger River basin in West Africa. Errors in the baseline Niger-HYPE model were analysed to identify inadequately described processes. These process descriptions were subsequently isolated and refined through a set of experiments focusing on concept development, input data enhancement, and multivariable calibration. The refinements were guided by in situ discharge observations, earth observations, local expert knowledge, and previous studies. The results show that the original model concept could simulate the annual cycle of discharge, but not the magnitudes or daily dynamics (56-station average Nash-Sutcliffe Efficiency = −1). The main processes requiring improved descriptions were precipitation, evaporation, surface runoff, infiltration, soil storage, reservoir regulations, aquifer recharge, and flooding and river-atmosphere exchange in the Inner Niger Delta. Of these, evaporation, flooding and river-atmosphere exchange differ so much between Sweden and the Niger River that the model concept had to be refined. All refinements were synthesized in a new model version (Niger-HYPE2.0) performing significantly better across the basin (56-station average Nash-Sutcliffe Efficiency = 0.4). This study demonstrates the danger of applying a model off the shelf, and the obligation to carefully evaluate and revise process descriptions when applying a model concept to a new region. Moreover, the results indicate that our approach to separately, sequentially, and iteratively refine processes together with local experts can substantially improve process-oriented hydrological models. [ABSTRACT FROM AUTHOR]

This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures -- taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33% (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables. [ABSTRACT FROM AUTHOR]

The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30-40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model. A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows. It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51% of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model. In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its timedynamic behaviour after deforestation. [ABSTRACT FROM AUTHOR]

Open data make it possible to set up multi-basin models for large domains across environmental, climate and administrative boundaries. This study presents new methods for evaluating a number of aspects of multi-basin model performance, while exploring the performance of the E-HYPE_v2.1 model for several evaluation criteria in 181 independent river gauges across the European continent. Embedded model assumptions on dominant flow generating mechanisms are analysed by correlating physiographical characteristics to the flow regime. The results indicate that the model captures the spatial variability of flow and is therefore suitable for predictions in ungauged basins. The model shows good performance of long-term means and seasonality, while short-term daily variability is less well represented, especially for Mediterranean and mountainous areas. Major identified shortcomings refer to the resolution of precipitation patterns, aquifer exchanges, water extractions and regulation. This will guide the work with the next model version for which improvements in input data, processes and calibration have been identified to potentially contribute most to improved model performance. EDITOR D. Koutosyiannis ASSOCIATE EDITOR A. Efstratiadis [ABSTRACT FROM AUTHOR]

In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals. [ABSTRACT FROM AUTHOR]

Measure plans are currently being developed for the Water Framework Directive (WFD) by European water authorities. In Sweden, such plans include measures for good ecological status in the coastal ecosystem. However, the effect of suggested measures is not yet known. We therefore experimented with different nutrient reduction measures on land and in the sea, using a model system of two coupled dynamic models for a semi-enclosed bay and its catchment. The science question was whether it is worthwhile to implement measures in the local catchment area to reach local environmental goals, or if the status of the Bay is more governed by the water exchange with the Sea. The results indicate that by combining several measures in the catchment, the nutrient load can be reduced by 15%-20%. To reach the same effect on nutrient concentrations in the Bay, the concentrations of the sea must be reduced by 80%. Hence, in this case, local measures have a stronger impact on coastal water quality. The experiment also show that the present targets for good ecological status set up by the Swedish water authorities may be unrealistic for this Bay. Finally, we discuss when and how to use hydro-ecological models for societal needs. [ABSTRACT FROM AUTHOR]

Water resource management is often based on numerical models, and large-scale models are sometimes used for international strategic agreements. Sometimes the modelled area entails several political entities and river basins. To avoid methodological bias in results, methods and databases should be homogenous across political and geophysical boundaries, but this may involve fewer details and more assumptions. This paper quantifies the uncertainty when the same model code is applied using two different input datasets; a more detailed one for the country of Sweden (S-HYPE) and a more general one for the entire Baltic Sea basin (Balt-HYPE). Results from the two model applications were compared for the Swedish landmass and for two specific Swedish river basins. The results show that both model applications may be useful in providing spatial information of water and nutrients at various scales. For water discharge, most relative errors are < 10% for S-HYPE and <25% for Balt-HYPE. Both applications reproduced the most mean concentration for nitrogen within 25% of the observed mean values, but phosphorus showed a larger scatter. Differences in model set-up were reflected in the simulation of both spatial and temporal dynamics. The most sensitive data were precipitation/temperature, agriculture and model parameter values. [ABSTRACT FROM AUTHOR]

A dynamic water quality model, HYPE, was applied to a large, data-sparse region to study whether reliable information on water quantity and water quality could be obtained for both gauged and ungauged waterbodies. The model (called S-HYPE) was set up for all of Sweden (∼450 000 km2), divided into sub-basins with an average area of 28 km2. Readily available national databases were used for physiographic data, emissions and agricultural practices, fixed values for representative years were used. Daily precipitation and temperature were used as the dynamic forcing of the model. Model evaluation was based on data from several hundred monitoring sites, of which approximately 90% had not been used in calibration on a daily scale. Results were evaluated using the Nash-Sutcliffe efficiency (NSE), correlation and relative errors: 92% of the spatial variation was explained for specific water discharge, and 88% and 59% for total nitrogen and total phosphorus concentrations, respectively. Day-to-day variations were modelled with satisfactory results for water discharge and the seasonal variation of nitrogen concentrations was also generally well captured. In 20 large, unregulated rivers the median NSE for water discharge was 0.84, and the corresponding number for 76 partly-regulated river basins was 0.52. In small basins, the NSE was typically above 0.6. These major achievements relative to previous similar experiments were ascribed to the step-wise calibration process using representative gauged basins and the use of a modelling concept, whereby coefficients are linked to physiographic variables rather than to specific sites. Editor D. Koutsoyiannis Citation Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C. and Lindström, G., 2012. Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale. Hydrological Sciences Journal, 57 (2), 229–247. [ABSTRACT FROM PUBLISHER]

Water quality in many stream catchments and river basins is severely impacted by nutrient enrichment as a result of agriculture. Water-resource managers worldwide are considering the potential role of riparian zones and floodplain wetlands in improving stream-water quality, as there is evidence at the site scale that such wetlands are efficient at removing nutrients from through-flowing water. However, recent studies have highlighted disadvantages of such use of wetlands, including emissions of greenhouse gases and losses of biodiversity that result from prolonged nutrient loading. Here, we discuss the water purification function of wetlands at the site and catchment scale and suggest ways in which these disadvantages could be overcome. [Copyright &y& Elsevier]

Changes in environmental conditions within a river basin in South Central Sweden (1400 km2) and impacts on riverine nitrogen (N) transport were evaluated. A historical database was compiled and the process-based HBV-N model used to estimate flow normalised N loads in 1885, 1905, 1927, 1956, 1976, and 1994, using a standard climatological record (1985–1994). The study shows the value of process-based modelling in environmental impact assessment, by making it possible to assess and integrate the effect of a number of factors, both with regard to human impact and natural climatic variability. Factors taken into account include: the effects of land use, agricultural practices, atmospheric deposition, human dietary intake, use of flush toilets, lowering of lakes, building of dams, and climatic variability. For all years studied, agriculture was the overriding source of N, and changes in riverine N over time mainly reflected changes in land use and agricultural practices. In spite of decreasing N-leaching from agriculture, the net load remained fairly constant between 1885 and 1927, due to reduced N retention. Drainage of agricultural land had a dominating impact on reducing N retention, which increased the N loads, while the effects of the lowering of lake levels and dam building were less pronounced. Household N emission per capita was higher in 1994 than in 1927, as the increased consumption of meat and dairy products alone resulted in a higher increase of the emission than was compensated for with wastewater treatment improvement. In addition, introduction of flush toilets increased the emission from households. In total, the net load in 1976 was twofold higher than that in 1885, 1905 and 1927, due to increased leaching from agriculture, wastewater emission, and atmospheric deposition on lake surfaces. Finally, the impact of climatological variability was assessed, using a 110-yr climatological record. The choice of 10-yr period of climatological data was the factor that had the largest impact on calculated N load. [ABSTRACT FROM AUTHOR]

The HBV-N model was used for a scenario analysis of changes in nitrogen retention and transport caused by alterations of wetness due to land drainage, lowering of lakes, building of dams and climatic variability in a river basin in south-central Sweden (1885–1994). In general, dams were situated in locations more favourable for retention, compared to the lowered lakes. Rather modest conversions of water bodies only changed nitrogen transport by about 3%. The 180-times-larger increase of (mainly) tile-drained agricultural land had, according to simulations, increased the nitrogen transport by 17%, due to reduced retention. However, compared to human-induced alteration of the landscape N retention, the choice of 10-year periods of climatological data had the overriding effect on the calculated nitrogen transport. Weather-induced variations resulted in a 13% difference in nitrogen retention between various 10-year periods. When the model was driven by climatological data from the driest 10-year period (1905–1914), the estimated average annual load was only half of that obtained with climatological data from the wettest 10-year period (1975–1984). [ABSTRACT FROM AUTHOR]

Access to transparent and quality assured Open data is not only a constraint for scientists, but also for practical applications of new scientific knowledge. In this presentation we will demonstrate a web-based water service (http://hypeweb.smhi.se/), which bridges the gap between science and practice, and between scientific disciplines. We will also showcase a number of practical applications of real-world use cases on how new scientific data have been useful in decision-making.The water service HYPEWEB is using results from the Hydrological Predictions for the Environment (HYPE) model, which is applied for 130 000 catchments covering the world landmass and also for specific regions with higher resolution. The service is designed based on experience from numerous user interactions over the years from developing different web-based tools in various R&D projects. The current users are either so called knowledge purveyors (working with e.g. flood risk or climate change adaptation on behalf of authorities) or related research institutes (e.g. oceanographic or marine centers) or commercial companies (e.g. hydropower industry). The users can download open data manually, subscribe to automatic data deliveries or purchase customized data. The hydrological catchment model HYPE simulates water flow and substances on their way from precipitation through soil, river and lakes to the river outlet (Lindström et al., 2010). The catchment is divided into subbasins, which in turn are divided into classes (calculation units) depending on land use, soil type and elevation. In the large-scale modelling the numerical code calculates water volume and fluxes encompassing many river basins, cross regional and international boundaries, and a number of different geophysical and climatic zones (e.g. Andersson et al., 2017; Donnelly et al., 2016; Pechlivanidis and Arheimer, 2015). Thus, each geographical domain includes numerous coupled catchments and has a relatively high spatial resolution, although most basins are ungauged. The temporal resolution in the calculations is normally daily.From the web interface the users can explore maps & graphs of historical simulations, forecasts (1-10 days and monthly/seasonal), and projections of climate change impacts. The user can also check various criteria of model performance wherever gauges are available and get access to guidance in how to understand and use the data from exploring various info sheets, tutorials and showcases.References: Andersson J.C.M., Arheimer B., Traoré F., Gustafsson D., Ali A. 2017. Process refinements improve a hydrological model concept applied to the Niger River basin. Hydrological Processes pp.1-15. https://doi.org/10.1002/hyp.11376Donnelly, C, Andersson, J.C.M. and Arheimer, B., 2016. Using flow signatures and catchment similarities to evaluate a multi-basin model (E-HYPE) across Europe. Hydr. Sciences Journal 61(2):255-273, doi: 10.1080/02626667.2015.1027710Lindström, G., Pers, C.P., Rosberg, R., Strömqvist, J., and Arheimer, B. 2010. Development and test of the HYPE (Hydrological Predictions for the Environment) model – A water quality model for different spatial scales. Hydrology Research 41.3-4:295-319.Pechlivanidis, I. G. and Arheimer, B. 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case, Hydrol. Earth Syst. Sci., 19, 4559-4579, doi:10.5194/hess-19-4559-2015. [ABSTRACT FROM AUTHOR]

Flooding is a growing concern in West Africa. Improved quantification of discharge extremes and associated uncertainties is needed to improve infrastructure design, and operational forecasting is needed to provide timely warnings. In this study, we use discharge observations, a hydrological model (Niger-HYPE) and extreme value analysis to estimate peak river flow statistics (e.g. the discharge magnitude with a 100-year return period) across the Niger River basin. To test the model's capacity of predicting peak flows, we compared 30-year maximum discharge and peak flow statistics derived from the model vs. derived from nine observation stations. The results indicate that the model simulates peak discharge reasonably well (on average + 20%). However, the peak flow statistics have a large uncertainty range, which ought to be considered in infrastructure design. We then applied the methodology to derive basin-wide maps of peak flow statistics and their associated uncertainty. The results indicate that the method is applicable across the hydrologically active part of the river basin, and that the uncertainty varies substantially depending on location. Subsequently, we used the most recent bias-corrected climate projections to analyze potential changes in peak flow statistics in a changed climate. The results are generally ambiguous, with consistent changes only in very few areas. To test the forecasting capacity, we ran Niger-HYPE with a combination of meteorological data sets for the 2008 high-flow season and compared with observations. The results indicate reasonable forecasting capacity (on average 17% deviation), but additional years should also be evaluated. We finish by presenting a strategy and pilot project which will develop an operational flood monitoring and forecasting system based in-situ data, earth observations, modelling, and extreme statistics. In this way we aim to build capacity to ultimately improve resilience toward floods, protecting lives and infrastructure in the region. [ABSTRACT FROM AUTHOR]

The Arctic-HYCOS observing system provides daily and monthly gauged river discharge data from a selection of stations operated by the national hydrological services in the Arctic Council member states. The set of stations have been selected to provide a basis for monitoring fresh water flow to the Arctic Ocean and for monitoring changes in the hydrological regime. We have performed an assessment of the observation system with respect to these objectives, focusing on spatial and temporal coverage, as well as metadata and data management aspects. The current list includes 427 stations of which 72 are listed as flow-to-ocean stations, representing the most reliable downstream station in the river basins. The flow-to-ocean network represent about 60% of the drainage basin area of the Arctic Ocean and related northern seas (excluding Greenland) – and about the same fraction of the total flow-to-ocean estimated by the Arctic-HYPE hydrological model (http://hypeweb.smhi.se/). The largest gaps in spatial coverage was found in Greenland, Svalbard, and northern coastal areas of Eurasia and North America. Scandinavia was also rather poorly represented in the selected station network mainly due to a spatial limit of the basin area to be included. Metadata on station location were assessed and improved by cross-checking information from several databases and aligning stations to flow accumulation data from the GWD-LR database. A method was developed for estimating effective upstream area using high resolution elevation data and applied using the Arctic-DEM dataset. The impact of the improved hydrological data is assessed using the Arctic-HYPE model. This assessment was carried out under the H2020 project "Integrated Arctic Observation Systems (INTAROS)". [ABSTRACT FROM AUTHOR]

• Hydrology of the entire Greater Mekong region was modeled with HYPE. • Regionalization was obtained by similarity in physiography and climate. • Regionalization reached 80% of performance of local calibration at ungauged basins. • Water level based flow correlation helped evaluate model at ungauged basins. • Flow correlation framework outperformed the current metrics of modeled water level. A novel approach of combining regionalization and satellite observations of various hydrological variables were employed to significantly improve prediction of streamflow signatures at "geopolitically ungauged" basins. Using the proposed step-wise physiography and climate-based regionalization approach, the model performance at ungauged basins reached 80% of performance of locally calibrated parameters and significantly outperformed the global regionalization parameters. The proposed water level based flow correlation was found to help diagnose models and outperform the existing performance metrics of simulated water levels at ungauged basins. The study also set up the first multi-national, multi-catchment hydrological model in the Greater Mekong region, the top global biodiversity and major disaster risk hotspot in the world through sequential and iterative refinement of the existing global hydrological model. New model setup or existing models in the poorly-gauged and ungauged basins could benefit from the proposed approach to predict and evaluate models at ungauged basins. [ABSTRACT FROM AUTHOR]

The Copernicus Climate Change Service (C3S) aims to provide various information of past, present and future climate relevant to support adaption and mitigation strategies. The C3S Climate Data Store (CDS) provides a "one stop shop" for users to discover and process quality-assured climate data of different origins (e.g. in-situ observations, reanalysis, climate projections, and seasonal forecasts). The CDS toolbox was implemented to facilitate the transformation of data into climate information and products that are tailored to user needs. This toolbox provides functions that allow users to develop their own web-based applications for their specific requirements. All data and processing is open and free, and can be used by anyone for any purpose in downstream applications from the C3S website (https://climate.copernicus.eu/). Here we present user examples and experiences from 19 different showcases on six continents from across the globe on existing and future potential of user uptake using C3S. The aim is to inspire more users addressing sectors such as health, water security, transport, biodiversity, tourism, agriculture, and food production to make use of the high relevant climate and water information of the C3S global datasets for site-specific impact analysis. In addition to technical training and guidance, specific datasets of climate impact indicators (related to temperature, precipitation and water) as well as seasonal forecasts of water-related variables have been produced in co-design with users for the implementation in the CDS. These new global datasets are based on bias adjusted CMIP5 data on a 50 km grid. Water-related climate impact indicators are based on hydrological impact modelling using the HYPE model (http://hypeweb.smhi.se/) and are provided for catchments. Continuous time-series of essential climate variables were made available at a daily temporal resolution whereas the climate impact indicators are presented in 30 years means of annual or monthly values. The experience from user uptake in the global community show that climate science is difficult and that science communication and training is needed for the data to be fully transparent and useful. We found that there is a need of information at various levels of complexity, for instance: (1) short and clear key-messages and summarizing maps and graphs for policy-makers and global companies; (2) confidence figures and tools for transferring the climate signal to local applications for consultant engineers; (3) workflows and scripts for indicator production in new applications for developers of information systems. Particularly, users from across the globe found an added value in quick visualization, zooming and easy downloading facilities of ensemble data for their region. User guidance, tutorials and info sheets were also found very effective for better understanding and applications in regional or local showcases. [ABSTRACT FROM AUTHOR]

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea. These freshwater inflows vary in magnitude from year to year as well as within each year due to e.g. natural variability, weather patterns, and seasonal human activities. Changing climate and socio-economics contribute additional uncertainty to estimation of the freshwater inflows to the Baltic Sea that needs to be taken into account when planning management practices and mitigation measures. Continental to global scale hydrological models have emerged in recent years as tools e.g. for flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. E-HYPE is a pan-European application of the HYPE model developed as a multi-purpose tool for large-scale hydrological analyses. HYPE is a process-based combined rainfall-runoff and water quality model. We compared current freshwater inflows from land with those from dynamic modelling with E-HYPE using projections of climate change (RCP8.5) and scenarios of shared socioeconomic pathways (SSPs) for 2050s. We selected a range of climate models and socioeconomic assumptions to evaluate possible effects and to estimate the uncertainty and then evaluated impacts of selected nutrient mitigation measures under this range of conditions. We show that while climate change affects nutrient loads to the Baltic Sea, these impacts can be overshadowed by the impacts of changing socioeconomic factors such as land use, agricultural practices, population changes, dietary changes, atmospheric deposition, and waste-water collection and treatment by mid-century. Here, we discuss our analyses and experiences with flows and nutrient generation, transport, and transformation in the Baltic Sea Drainage Basin under changing climate and socio-economic conditions. Our findings indicate that average nutrient loads are expected to increase by 8% and 14% for nitrogen and phosphorus, respectively, as a response to climate change on average. However, changes in the socioeconomic drivers can lead to significant changes in the loads, ranging from a decrease by 13% and 6% to an increase by 11% and 9% in nitrogen and phosphorus loads, respectively, depending on the socioeconomic pathway to be followed. This means that policy decisions still play a major role in climate adaptation and in managing eutrophication in the Baltic Sea region. [ABSTRACT FROM AUTHOR]