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A climatic anomaly can potentially affect the hydrological behaviour of a catchment for several years. This article presents a new approach to quantifying this multi-year hydrological memory, using exclusively streamflow and climate data. Rather than providing a single value of catchment memory, we aim to describe how this memory fades over time. The precipitation–runoff relationship is analyzed through the concept of elasticity. Elasticity quantifies the change in one quantity caused by the change in another quantity. We analyze the elasticity of the relation between the annual anomalies of runoff yield and humidity index. We identify Catchment Forgetting Curves (CFC) to quantify multi-year catchment memory, considering not only the current year's humidity anomaly but also the anomalies of the preceding years. The variability of CFCs is investigated on a set of 158 Swedish and 527 French catchments. As expected, French catchments overlying large aquifers exhibit a long memory, i.e., with the impact of climate anomalies detected over several years. In Sweden, the expected effect of the lakes is less clear. For both countries, a relatively strong relationship between the humidity index and memory is identified, with drier regions exhibiting longer memory. Taking into account the multi-year memory has significantly improved the elasticity analysis for 15 % of the catchments. Our work thus underlines the need to account for catchment memory in order to produce meaningful and geographically coherent elasticity indices.

Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE < 0.3. Furthermore, Kruskal–Wallis and Mann–Whitney statistical tests support a similar model performance for M3 and M4, suggesting that monthly PET-AET and daily streamflow (M3) represents an appropriate calibration sequence for regional modeling. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision-making at relatively high spatial and temporal resolution.

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 km2), 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.

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.

Our study develops and tests a geostatistical technique for locally enhancing macro-scale rainfall–runoff simulations on the basis of observed streamflow data that were not used in calibration. We consider Tyrol (Austria and Italy) and two different types of daily streamflow data: macro-scale rainfall–runoff simulations at 11 prediction nodes and observations at 46 gauged catchments. The technique consists of three main steps: (1) period-of-record flow–duration curves (FDCs) are geostatistically predicted at target ungauged basins, for which macro-scale model runs are available; (2) residuals between geostatistically predicted FDCs and FDCs constructed from simulated streamflow series are computed; (3) the relationship between duration and residuals is used for enhancing simulated time series at target basins. We apply the technique in cross-validation to 11 gauged catchments, for which simulated and observed streamflow series are available over the period 1980–2010. Our results show that (1) the procedure can significantly enhance macro-scale simulations (regional LNSE increases from nearly zero to ≈ 0.7) and (2) improvements are significant for low gauging network densities (i.e. 1 gauge per 2000 km2).

During the last 6 decades, forest biomass has increased in Sweden mainly due to forest management, with a possible increasing effect on evapotranspiration. However, increasing global CO2 concentrations may also trigger physiological water-saving responses in broadleaf tree species, and to a lesser degree in some needleleaf conifer species, inducing an opposite effect. Additionally, changes in other forest attributes may also affect evapotranspiration. In this study, we aimed to detect the dominating effect(s) of forest change on evapotranspiration by studying changes in the ratio of actual evapotranspiration to precipitation, known as the evaporative ratio, during the period 1961–2012. We first used the Budyko framework of water and energy availability at the basin scale to study the hydroclimatic movements in Budyko space of 65 temperate and boreal basins during this period. We found that movements in Budyko space could not be explained by climatic changes in precipitation and potential evapotranspiration in 60 % of these basins, suggesting the existence of other dominant drivers of hydroclimatic change. In both the temperate and boreal basin groups studied, a negative climatic effect on the evaporative ratio was counteracted by a positive residual effect. The positive residual effect occurred along with increasing standing forest biomass in the temperate and boreal basin groups, increasing forest cover in the temperate basin group and no apparent changes in forest species composition in any group. From the three forest attributes, standing forest biomass was the one that could explain most of the variance of the residual effect in both basin groups. These results further suggest that the water-saving response to increasing CO2 in these forests is either negligible or overridden by the opposite effect of the increasing forest biomass. Thus, we conclude that increasing standing forest biomass is the dominant driver of long-term and large-scale evapotranspiration changes in Swedish forests.

Global warming and hydropower regulations are major threats to future fresh-water availability and biodiversity. Here, the authors show that their impact on flow regime over a large landmass result in similar changes, but hydropower is more critical locally and may have potential for climate adaptation in floodplains.

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.

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 time-dynamic behaviour after deforestation.

The scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 by the IAHS) put considerable effort into improving the reliability of hydrological models to predict flow response in ungauged rivers. PUB's collective experience advanced hydrologic science and defined guidelines to make predictions in catchments without observed runoff data. At present, there is a raised interest in applying catchment models to large domains and large data samples in a multi-basin manner, to explore emerging spatial patterns or learn from comparative hydrology. However, such modelling involves additional sources of uncertainties caused by the inconsistency between input data sets, i.e. particularly regional and global databases. This may lead to inaccurate model parameterisation and erroneous process understanding. In order to bridge the gap between the best practices for flow predictions in single catchments and multi-basins at the large scale, we present a further developed and slightly modified version of the recommended best practices for PUB by Takeuchi et al. (2013). By using examples from a recent HYPE (Hydrological Predictions for the Environment) hydrological model set-up across 6000 subbasins for the Indian subcontinent, named India-HYPE v1.0, we explore the PUB recommendations, identify challenges and recommend ways to overcome them. We describe the work process related to (a) errors and inconsistencies in global databases, unknown human impacts, and poor data quality; (b) robust approaches to identify model parameters using a stepwise calibration approach, remote sensing data, expert knowledge, and catchment similarities; and (c) evaluation based on flow signatures and performance metrics, using both multiple criteria and multiple variables, and independent gauges for "blind tests". The results show that despite the strong physiographical gradient over the subcontinent, a single model can describe the spatial variability in dominant hydrological processes at the catchment scale. In addition, spatial model deficiencies are used to identify potential improvements of the model concept. Eventually, through simultaneous calibration using numerous gauges, the median Kling–Gupta efficiency for river flow increased from 0.14 to 0.64. We finally demonstrate the potential of multi-basin modelling for comparative hydrology using PUB, by grouping the 6000 subbasins based on similarities in flow signatures to gain insights into the spatial patterns of flow generating processes at the large scale.

The current work addresses one of the key building blocks towards an improved understanding of flood processes and associated changes in flood characteristics and regimes in Europe: the development of a comprehensive, extensive European flood database. The presented work results from ongoing cross-border research collaborations initiated with data collection and joint interpretation in mind. A detailed account of the current state, characteristics and spatial and temporal coverage of the European Flood Database, is presented. At this stage, the hydrological data collection is still growing and consists at this time of annual maximum and daily mean discharge series, from over 7000 hydrometric stations of various data series lengths. Moreover, the database currently comprises data from over 50 different data sources. The time series have been obtained from different national and regional data sources in a collaborative effort of a joint European flood research agreement based on the exchange of data, models and expertise, and from existing international data collections and open source websites. These ongoing efforts are contributing to advancing the understanding of regional flood processes beyond individual country boundaries and to a more coherent flood research in Europe.

Reproducibility and repeatability of experiments are the fundamental prerequisites that allow researchers to validate results and share hydrological knowledge, experience and expertise in the light of global water management problems. Virtual laboratories offer new opportunities to enable these prerequisites since they allow experimenters to share data, tools and pre-defined experimental procedures (i.e. protocols). Here we present the outcomes of a first collaborative numerical experiment undertaken by five different international research groups in a virtual laboratory to address the key issues of reproducibility and repeatability. Moving from the definition of accurate and detailed experimental protocols, a rainfall–runoff model was independently applied to 15 European catchments by the research groups and model results were collectively examined through a web-based discussion. We found that a detailed modelling protocol was crucial to ensure the comparability and reproducibility of the proposed experiment across groups. Our results suggest that sharing comprehensive and precise protocols and running the experiments within a controlled environment (e.g. virtual laboratory) is as fundamental as sharing data and tools for ensuring experiment repeatability and reproducibility across the broad scientific community and thus advancing hydrology in a more coherent way.

There is an ongoing discussion whether floods occur more frequently today than in the past, and whether they will increase in number and magnitude in the future. To explore this issue in Sweden, we merged observed time series for the past century from 69 gauging sites throughout the country (450 000 km2) with high-resolution dynamic model projections of the upcoming century. The results show that the changes in annual maximum daily flows in Sweden oscillate between dry and wet periods but exhibit no significant trend over the past 100 years. Temperature was found to be the strongest climate driver of changes in river high flows, which are related primarily to snowmelt in Sweden. Annual daily high flows may decrease by on average −1% per decade in the future, mainly due to lower peaks from snowmelt in the spring (−2% per decade) as a result of higher temperatures and a shorter snow season. In contrast, autumn flows may increase by &plus;3% per decade due to more intense rainfall. This indicates a shift in flood-generating processes in the future, with greater influence of rain-fed floods. Changes in climate may have a more significant impact on some specific rivers than on the average for the whole country. Our results suggest that the temporal pattern in future daily high flow in some catchments will shift in time, with spring floods in the northern–central part of Sweden occurring about 1 month earlier than today. High flows in the southern part of the country may become more frequent. Moreover, the current boundary between snow-driven floods in northern–central Sweden and rain-driven floods in the south may move toward higher latitudes due to less snow accumulation in the south and at low altitudes. The findings also indicate a tendency in observations toward the modeled projections for timing of daily high flows over the last 25 years. Uncertainties related to both the observed data and the complex model chain of climate impact assessments in hydrology are discussed.

The most radical anthropogenic impact on water systems in Sweden originates from the years 1900–1970, when the electricity network was developed in the country and almost all rivers were regulated. The construction of dams and changes in water flow caused problems for ecosystems. Therefore, when implementing the EU Water Framework Directive (WFD) hydro-morphological indicators and targets were developed for rivers and lakes to achieve good ecological potential. The hydrological regime is one such indicator. To understand the change in flow regime we quantified the hydropower impact on river flow across Sweden by using the S-HYPE model and observations. The results show that the average redistribution of water during a year due to regulation is 19 % for the total discharge from Sweden. A distinct impact was found in seasonal flow patterns and flow duration curves. Moreover, we quantified the model skills in predicting hydropower impact on flow. The median NSE for simulating change in flow regime was 0.71 for eight dams studied. Results from the spatially distributed model are available for 37 000 sub-basins across the country, and will be used by the Swedish water authorities for reporting hydro-morphological indicators to the EU and for guiding the allocation of river restoration measures.

There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network.

The application of diffuse pollution models included in EUROHARP encompassed varying levels of parameterisation and approaches to the preparation of input data depending on the model and modelling team involved. Modellers consistently faced important decisions in relation to data interpretation, especially in those catchments with unfamiliar physical or climatic characteristics, where catchment conditions were beyond the range for which a particular model was originally developed, or where only limited input data were available. In addition to a broad discussion of data issues, this paper compares the performance of the four sub-annual output models tested in EUROHARP (EveNFlow, NL-CAT, SWAT and TRK) in three test catchments without the modelling teams having sight of measured flow and nitrate concentration data. Model performance in this “blind test” indicate that the range of predictions generated by any individual models pre and post calibration exceed the differences between the estimates yielded by all four models. Comparison of Analysis of Variance (ANOVA) statistics for simulated and observed flow, concentration and loads underscores the benefits of calibration for these intermediate and complex model formulations. Interpretation of input data (e.g.rainfall interpolation method and pedotransfer functions selected) appeared equally (or more) important than process representation. In the absence of calibration data, modeller unfamiliarity with a particular catchment and its environmental processes sometimes resulted in questionable assumptions and input errors which highlight the problems facing modellers charged with implementing policies under the Water Framework Directive (2000/60/EC) in poorly monitored catchments. Catchment data owners and modellers must therefore work more closely given that the output from diffuse pollution models is clearly modeller-limited as well as model-limited. [ABSTRACT FROM AUTHOR]

An ensemble of nutrient models was applied in 17 European catchments to analyse the variation that appears after simulation of net nutrient loads and partitioning of nutrient loads at catchment scale. Eight models for N and five models for P were applied in three core catchments covering European-wide gradients in climate, topography, soil types and land use (Vansjø-Hobøl (Norway), Ouse (Yorkshire, UK) and Enza (Italy)). Moreover, each of the models was applied in 3–14 other EUROHARP catchments in order to inter-compare the outcome of the nutrient load partitioning at a wider European scale. The results of the nutrient load partitioning show a variation in the computed average annual nitrogen and phosphorus loss from agricultural land within the 17 catchments between 19.1–34.6 kg N ha−1and 0.12–1.67 kg P ha−1. All the applied nutrient models show that the catchment specific variation (range and standard deviation) in the model results is lowest when simulating the net nutrient load and becomes increasingly higher for simulation of the gross nutrient loss from agricultural land and highest for the simulations of the gross nutrient loss from other diffuse sources in the core catchments. The average coefficient of variation for the model simulations of gross P loss from agricultural land is nearly twice as high (67%) as for the model simulations of gross N loss from agricultural land (40%). The variation involved in model simulations of net nutrient load and gross nutrient losses in European catchments was due to regional factors and the presence or absence of large lakes within the catchment. [ABSTRACT FROM AUTHOR]

Nitrogen and phosphorus retention estimates in streams and standing water bodies were compared for four European catchments by a series of catchment-scale modelling tools of different complexity, ranging from a simple, equilibrium input–output type to dynamic, physical-based models: source apportionment, MONERIS, EveNFlow, TRK, SWAT, and NL-CAT. The four catchments represent diverse climate, hydrology, and nutrient loads from diffuse and point sources in Norway, the UK, Italy, and the Czech Republic. The models' retention values varied largely, with tendencies towards higher scatters for phosphorus than for nitrogen, and for catchments with lakes (Vansjø-Hobøl, elivka) compared to mostly or entirely lakeless catchments (Ouse or Enza, respectively). A comparison of retention values with the size of nutrient sources showed that the modelled nutrient export from diffuse sources was directly proportional to retention estimates, hence implying that the uncertainty in quantification of diffuse catchment sources of nutrients was also related to the uncertainty in nutrient retention determination. This study demonstrates that realistic modelling of nutrient export from large catchments is very difficult without a certain level of measured data. In particular, even complex process oriented models require information on the retention capabilities of water bodies within the receiving surface water system and on the nutrient export from micro-catchments representing the major types of diffuse sources to surface waters. [ABSTRACT FROM AUTHOR]

Models' abilities to predict nutrient losses at subannual timesteps is highly significant for evaluating policy measures, as it enables trends and the frequency of exceedance of water quality thresholds to be predicted. Subannual predictions also permit assessments of seasonality in nutrient concentrations, which are necessary to determine susceptibility to eutrophic conditions and the impact of management practices on water quality. Predictions of subannual concentrations are pertinent to EC Directives, whereas load estimates are relevant to the 50% target reduction in nutrient loading to the maritime area under OSPAR. This article considers the ability of four models (ranging from conceptual to fully mechanistic), to predict river flows, concentrations and loads of nitrogen and phosphorus on a subannual basis in catchments in Norway, England, and Italy. Results demonstrate that model performance deemed satisfactory on an annual basis may conceal considerable divergence in performance when scrutinised on a weekly or monthly basis. In most cases the four models performed satisfactorily, and mismatches between measurements and model predictions were primarily ascribed to the limitations in input data (soils in the Norwegian catchment; weather in the Italian catchment). However, results identified limitations in model conceptualisation associated with the damping and lagging effect of a large lake leading to contrasts in model performance upstream and downstream of this feature in the Norwegian catchment. For SWAT applied to the Norwegian catchment, although flow predictions were reasonable, the large number of parameters requiring identification, and the lack of familiarity with this environment, led to poor predictions of river nutrient concentrations. [ABSTRACT FROM AUTHOR]

The capability of eight nutrient models to predict annual nutrient losses (nitrogen and phosphorus) at catchment scale have been studied in the EUROHARP project. The methodologies involved in these models differ profoundly in their complexity, level of process representation and data requirements. This evaluation is focused on model performance in three core catchments: the Vansjø-Hobøl (Norway), the Ouse (Yorkshire, UK) and the Enza (Italy). These three different model applications have been evaluated by comparing calculated annual nutrient loads (total N or nitrate and total P), based on observed flow and total nitrogen or nitrate and total phosphorus concentrations, and the annual nutrient loads that were simulated by the eight nutrient models. Four statistics have been applied for this purpose: the root mean squared error (RMSE), the mean absolute error (MAE), the mean error (ME), and Nash–Sutcliffe's model efficiency (NS). The results show that all model approaches can predict the calculated annual discharges. Depending on the observed statistics (RMSE, MAE, ME and NS) the scores of the model application differed, therefore no overall ‘best model’ could be identified. Although the water and nutrient loads from (sub)catchments can be predicted, the modelled pathways of nutrients within agricultural land and the nutrient losses to surface waters from agricultural land vary among the catchments and among those model approaches which are able to make this distinction. [ABSTRACT FROM AUTHOR]

In EUROHARP, an EC Framework V project, which started in 2002 with 21 partners in 17 countries across Europe, a detailed intercomparison of contemporary catchment-scale modelling approaches was undertaken to characterise the relative importance of point and diffuse pollution of nutrients in surface freshwater systems. The study focused on the scientific evaluation of different modelling approaches, which were validated on three core catchments (the Ouse, UK; the Vansjo-Hobøl, Norway; and the Enza, Italy), and the application of each tool to three additional, randomly chosen catchments across Europe. The tools involved differ profoundly in their complexity, level of process representation and data requirements. The tools include simple loading models, statistical, conceptual and empirical model approaches, and physics-based (mechanistic) models. The results of a scientific intercomparison of the characteristics of these different model approaches are described. This includes an analysis of potential strengths and weaknesses of the nutrient models. [ABSTRACT FROM AUTHOR]

Conceptual hydrological models can move towards process-oriented modelling when addressing broader issues than discharge modelling alone. For instance, water quality modelling generally requires understanding of both pathways and travel times which might not be easily identified because observations at the outlet aggregate all processes at the catchment scale. In this study we tested if adding a second kind of observation, specifically sediment data, can help distinguish overland flow from total discharge. We applied a multi-objective calibration on both discharge and suspended sediment concentration simulation performance to the World-Wide Hydrological Predictions for the Environment (HYPE) model for 111 catchments spread over the USA. Results show that in comparison to two calibrations made one after the other, the multi-objective calibration leads to a significant improvement on the simulation performance of suspended sediments without a significant impact on the performance of discharge. New modelling hypotheses for overland flow calculations are proposed and resulted in similar discharge performances as the original one but with fewer parameters, which reduces equifinality and can prevent unwarranted model complexity in data-poor areas., (© 2022 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.)

The largest forest wildfire in Swedish modern history burnt 14,000 ha of Boreal forest in the Västmanland County (south-central Sweden) during the summer of 2014. Here, we assess the impacts of this wildfire on the hydrological regime during the three years after it happened. In the empirical experiment carried out, four catchments (two burnt and two non-burnt nearby) with similar character and climate and with mean area of 20 km 2 were compared. A total of 23 descriptors accounting for climate, land cover and flow signatures were defined and evaluated before and after the wildfire, using both remote sensing products and in situ streamflow observations. The results show three main changes in the hydrological behaviour of the burnt areas: (i) variation in duration and timing of snow season, with shorter and later beginning of the season; (ii) more dynamic behaviour of the streamflow, with smaller variation coefficient, a reduction in duration of high and low flows conditions and a more oscillating pattern; and, (iii) variations in catchment response (flashiness, runoff coefficient and actual evapotranspiration) from rainfall-events mainly during summer, but also in late autumn The study also reflects the usefulness of the combined analysis of flow signatures and remote sensing products to detect changes in catchment hydrology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea, and these loads might change in a changing climate. We show that the impact from climate change by mid-century is probably less than the direct impact of changing socioeconomic factors such as land use, agricultural practices, atmospheric deposition, and wastewater emissions. We compare results from dynamic modelling of nutrient loads to the Baltic Sea under projections of climate change and scenarios for shared socioeconomic pathways. Average nutrient loads are projected to increase by 8% and 14% for nitrogen and phosphorus, respectively, in response to climate change scenarios. In contrast, changes in the socioeconomic drivers can lead to a decrease of 13% and 6% or an increase of 11% and 9% in nitrogen and phosphorus loads, respectively, depending on the pathway. This indicates that policy decisions still play a major role in climate adaptation and in managing eutrophication in the Baltic Sea region.

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere 1 . These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe 2 . Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe 3 , because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results-arising from the most complete database of European flooding so far-suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century 4,5 , suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.

A warming climate is expected to have an impact on the magnitude and timing of river floods; however, no consistent large-scale climate change signal in observed flood magnitudes has been identified so far. We analyzed the timing of river floods in Europe over the past five decades, using a pan-European database from 4262 observational hydrometric stations, and found clear patterns of change in flood timing. Warmer temperatures have led to earlier spring snowmelt floods throughout northeastern Europe; delayed winter storms associated with polar warming have led to later winter floods around the North Sea and some sectors of the Mediterranean coast; and earlier soil moisture maxima have led to earlier winter floods in western Europe. Our results highlight the existence of a clear climate signal in flood observations at the continental scale., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

River flow is mainly controlled by climate, physiography and regulations, but their relative importance over large landmasses is poorly understood. Here we show from computational modelling that hydropower regulation is a key driver of flow regime change in snow-dominated regions and is more important than future climate changes. This implies that climate adaptation needs to include regulation schemes. The natural river regime in snowy regions has low flow when snow is stored and a pronounced peak flow when snow is melting. Global warming and hydropower regulation change this temporal pattern similarly, causing less difference in river flow between seasons. We conclude that in snow-fed rivers globally, the future climate change impact on flow regime is minor compared to regulation downstream of large reservoirs, and of similar magnitude over large landmasses. Our study not only highlights the impact of hydropower production but also that river regulation could be turned into a measure for climate adaptation to maintain biodiversity on floodplains under climate change.Global warming and hydropower regulations are major threats to future fresh-water availability and biodiversity. Here, the authors show that their impact on flow regime over a large landmass result in similar changes, but hydropower is more critical locally and may have potential for climate adaptation in floodplains.

We present a multi-model ensemble study for the Baltic Sea, and investigate the combined impact of changing climate, external nutrient supply, and fisheries on the marine ecosystem. The applied regional climate system model contains state-of-the-art component models for the atmosphere, sea ice, ocean, land surface, terrestrial and marine biogeochemistry, and marine food-web. Time-dependent scenario simulations for the period 1960-2100 are performed and uncertainties of future projections are estimated. In addition, reconstructions since 1850 are carried out to evaluate the models sensitivity to external stressors on long time scales. Information from scenario simulations are used to support decision-makers and stakeholders and to raise awareness of climate change, environmental problems, and possible abatement strategies among the general public using geovisualization. It is concluded that the study results are relevant for the Baltic Sea Action Plan of the Helsinki Commission.

To reduce eutrophication of the Baltic Sea, all nine surrounding countries have agreed upon reduction targets in the HELCOM Baltic Sea Action Plan (BSAP). Yet, monitoring sites and model concepts for decision support are few. To provide one more tool for analysis of water and nutrient fluxes in the Baltic Sea basin, the HYPE model has been applied to the region (called Balt-HYPE). It was used here for experimenting with land-based remedial measures and future climate projections to quantify the impacts of these on water and nutrient loads to the sea. The results suggest that there is a possibility to reach the BSAP nutrient reduction targets by 2100, and that climate change may both aggravate and help in some aspects. Uncertainties in the model results are large, mainly due to the spread of the climate model projections, but also due to the hydrological model.

An ensemble of nutrient models was applied in 17 European catchments to analyse the variation that appears after simulation of net nutrient loads and partitioning of nutrient loads at catchment scale. Eight models for N and five models for P were applied in three core catchments covering European-wide gradients in climate, topography, soil types and land use (Vansjø-Hobøl (Norway), Ouse (Yorkshire, UK) and Enza (Italy)). Moreover, each of the models was applied in 3-14 other EUROHARP catchments in order to inter-compare the outcome of the nutrient load partitioning at a wider European scale. The results of the nutrient load partitioning show a variation in the computed average annual nitrogen and phosphorus loss from agricultural land within the 17 catchments between 19.1-34.6 kg N ha(-1) and 0.12-1.67 kg P ha(-1). All the applied nutrient models show that the catchment specific variation (range and standard deviation) in the model results is lowest when simulating the net nutrient load and becomes increasingly higher for simulation of the gross nutrient loss from agricultural land and highest for the simulations of the gross nutrient loss from other diffuse sources in the core catchments. The average coefficient of variation for the model simulations of gross P loss from agricultural land is nearly twice as high (67%) as for the model simulations of gross N loss from agricultural land (40%). The variation involved in model simulations of net nutrient load and gross nutrient losses in European catchments was due to regional factors and the presence or absence of large lakes within the catchment.

The capability of eight nutrient models to predict annual nutrient losses (nitrogen and phosphorus) at catchment scale have been studied in the EUROHARP project. The methodologies involved in these models differ profoundly in their complexity, level of process representation and data requirements. This evaluation is focused on model performance in three core catchments: the Vansjø-Hobøl (Norway), the Ouse (Yorkshire, UK) and the Enza (Italy). These three different model applications have been evaluated by comparing calculated annual nutrient loads (total N or nitrate and total P), based on observed flow and total nitrogen or nitrate and total phosphorus concentrations, and the annual nutrient loads that were simulated by the eight nutrient models. Four statistics have been applied for this purpose: the root mean squared error (RMSE), the mean absolute error (MAE), the mean error (ME), and Nash-Sutcliffe's model efficiency (NS). The results show that all model approaches can predict the calculated annual discharges. Depending on the observed statistics (RMSE, MAE, ME and NS) the scores of the model application differed, therefore no overall 'best model' could be identified. Although the water and nutrient loads from (sub)catchments can be predicted, the modelled pathways of nutrients within agricultural land and the nutrient losses to surface waters from agricultural land vary among the catchments and among those model approaches which are able to make this distinction.

Nitrogen and phosphorus retention estimates in streams and standing water bodies were compared for four European catchments by a series of catchment-scale modelling tools of different complexity, ranging from a simple, equilibrium input-output type to dynamic, physical-based models: source apportionment, MONERIS, EveNFlow, TRK, SWAT, and NL-CAT. The four catchments represent diverse climate, hydrology, and nutrient loads from diffuse and point sources in Norway, the UK, Italy, and the Czech Republic. The models' retention values varied largely, with tendencies towards higher scatters for phosphorus than for nitrogen, and for catchments with lakes (Vansjø-Hobøl, Zelivka) compared to mostly or entirely lakeless catchments (Ouse or Enza, respectively). A comparison of retention values with the size of nutrient sources showed that the modelled nutrient export from diffuse sources was directly proportional to retention estimates, hence implying that the uncertainty in quantification of diffuse catchment sources of nutrients was also related to the uncertainty in nutrient retention determination. This study demonstrates that realistic modelling of nutrient export from large catchments is very difficult without a certain level of measured data. In particular, even complex process oriented models require information on the retention capabilities of water bodies within the receiving surface water system and on the nutrient export from micro-catchments representing the major types of diffuse sources to surface waters.

In EUROHARP, an EC Framework V project, which started in 2002 with 21 partners in 17 countries across Europe, a detailed intercomparison of contemporary catchment-scale modelling approaches was undertaken to characterise the relative importance of point and diffuse pollution of nutrients in surface freshwater systems. The study focused on the scientific evaluation of different modelling approaches, which were validated on three core catchments (the Ouse, UK; the Vansjo-Hobøl, Norway; and the Enza, Italy), and the application of each tool to three additional, randomly chosen catchments across Europe. The tools involved differ profoundly in their complexity, level of process representation and data requirements. The tools include simple loading models, statistical, conceptual and empirical model approaches, and physics-based (mechanistic) models. The results of a scientific intercomparison of the characteristics of these different model approaches are described. This includes an analysis of potential strengths and weaknesses of the nutrient models.

Models' abilities to predict nutrient losses at subannual timesteps is highly significant for evaluating policy measures, as it enables trends and the frequency of exceedance of water quality thresholds to be predicted. Subannual predictions also permit assessments of seasonality in nutrient concentrations, which are necessary to determine susceptibility to eutrophic conditions and the impact of management practices on water quality. Predictions of subannual concentrations are pertinent to EC Directives, whereas load estimates are relevant to the 50% target reduction in nutrient loading to the maritime area under OSPAR. This article considers the ability of four models (ranging from conceptual to fully mechanistic), to predict river flows, concentrations and loads of nitrogen and phosphorus on a subannual basis in catchments in Norway, England, and Italy. Results demonstrate that model performance deemed satisfactory on an annual basis may conceal considerable divergence in performance when scrutinised on a weekly or monthly basis. In most cases the four models performed satisfactorily, and mismatches between measurements and model predictions were primarily ascribed to the limitations in input data (soils in the Norwegian catchment; weather in the Italian catchment). However, results identified limitations in model conceptualisation associated with the damping and lagging effect of a large lake leading to contrasts in model performance upstream and downstream of this feature in the Norwegian catchment. For SWAT applied to the Norwegian catchment, although flow predictions were reasonable, the large number of parameters requiring identification, and the lack of familiarity with this environment, led to poor predictions of river nutrient concentrations.

The application of diffuse pollution models included in EUROHARP encompassed varying levels of parameterisation and approaches to the preparation of input data depending on the model and modelling team involved. Modellers consistently faced important decisions in relation to data interpretation, especially in those catchments with unfamiliar physical or climatic characteristics, where catchment conditions were beyond the range for which a particular model was originally developed, or where only limited input data were available. In addition to a broad discussion of data issues, this paper compares the performance of the four sub-annual output models tested in EUROHARP (EveNFlow, NL-CAT, SWAT and TRK) in three test catchments without the modelling teams having sight of measured flow and nitrate concentration data. Model performance in this "blind test" indicate that the range of predictions generated by any individual models pre and post calibration exceed the differences between the estimates yielded by all four models. Comparison of Analysis of Variance (ANOVA) statistics for simulated and observed flow, concentration and loads underscores the benefits of calibration for these intermediate and complex model formulations. Interpretation of input data (e.g. rainfall interpolation method and pedotransfer functions selected) appeared equally (or more) important than process representation. In the absence of calibration data, modeller unfamiliarity with a particular catchment and its environmental processes sometimes resulted in questionable assumptions and input errors which highlight the problems facing modellers charged with implementing policies under the Water Framework Directive (2000/60/EC) in poorly monitored catchments. Catchment data owners and modellers must therefore work more closely given that the output from diffuse pollution models is clearly modeller-limited as well as model-limited.

A participatory modelling process (DEMO) has been developed and applied in a 350 km2 catchment in southern Sweden. The overall goal is to improve the dialogues between experts and local stakeholders by using numerical models as a platform for discussions. The study is focused on reducing nutrient load and on the development of a locally established measure plan, which is requested by the European Water Framework Directive. The HBV-NP model was chosen as it can calculate effects and costs for different allocations of several combined measures in a catchment. This paper shows the impact of including local data in the modelling process vs. using more general data. It was found that modelled diffuse nutrient pollution was highly modified when including local know-how, soft information and more detailed field investigations. Leaching from arable land was found to be 35% higher using more detailed information on for instance, agricultural practices, crop and soil distribution. Moreover, the stakeholders' acceptance of model results and reliance on experts was increased by applying the participatory process and involving stakeholders in the modelling procedure.

The authors are involved in a project aiming at the development of a methodology for participatory modeling as a tool for public participation in water resource management. In this paper, some examples of different degrees of stakeholder influence in six key dimensions of participatory modeling are identified and discussed. Arnstein's (A ladder of citizen participation. Journal of the American Institute of Planners, 1969, 4, 216-224) critical discussion of different degrees of "real" decision-making power is taken as a point of departure to assess possible degrees of stakeholder influence. Can we as participatory modelers be sure that we are really inviting our research objects to an equal communicative relationship where local perspectives, knowledge and priorities are respected to the same extent as central and/or expert perspectives? This paper presents an approach that could be used as a tool for structured reflection to avoid unreflective tendencies towards expert knowledge dominance and low degree of stakeholders' real influence over the process.

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.

The dynamic catchment model HBV-N has been further developed by adding routines for phosphorus transport and is now called the HBV-NP model. The model was shown to satisfactorily simulate nutrient dynamics in the Rönneå catchment (1,900 km2). Its sensitivity to input data was tested, and results demonstrated the increased sensitivity to the selection of input data on a subcatchment scale when compared with the catchment scale. Selection of soil and land use databases was found to be critical in some subcatchments but did not have a significant impact on a catchment scale. Although acceptable on a catchment scale, using templates and generalization, with regards to emissions from point sources and rural households, significantly decreased model performance in certain subcatchments when compared with using more detailed local information. A division into 64 subcatchments resulted in similar model performance at the catchment outlet when compared with a lumped approach. Adjusting the imported matrixes of the regional leaching of nitrogen, from agricultural land, against mean subcatchment water percolation did not have a significant impact on the model performance.

The HBV-NP model is a newly developed water quality model that describes the turnover and fluxes of both nitrogen and phosphorous. It is based on the conceptual precipitation/runoff HBV model. The HBV-NP model was applied for simulation of nitrogen for the Rönneå catchment in southern Sweden. The catchment was divided into 64 subcatchments in the model. Discharge measurements from six stations and nitrogen measurements from 12 stations were used in the calibration of parameters in the model. Eight automatic calibrations were performed with different combinations of time periods, objective functions, and levels of the nitrogen load in the model. A regionally extended interpretation of the Nash-Sutcliffe R2 criterion was used in the calibration. In the evaluation of the criterion, the errors were summed over both time steps and sampling points. Scenario simulations of combined measures for reduction of nitrogen load into the sea by 30% were thereafter performed with the eight sets of parameters established by calibration. The model parameters were not uniquely defined by the calibration. However, the simulated relative reduction of nitrogen load into the sea was relatively insensitive to the choice of parameter set, given the available input sources, variables, and data.