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Detection and attribution of precipitation variability are fundamentally challenging, especially in the presence of complex nonlinear relationships between precipitation variability and large‐scale teleconnections. The aim of this study is twofold. First, we identify abrupt changes and the trend and periodicity characteristics of long‐term (1950–2019) annual precipitation series over the Norway mainland by using the Multiple Comparison Procedures, the Mann‐Kendall test, and the Wavelet Analysis. Second, we interpret the variability characteristics found over five regions of Norway by exploring their relationships with teleconnections through the Maximal Information Coefficient. The results indicate that significant abrupt changes appeared in the mean (variance) value of annual precipitation series at 117 (49) out of 159 rainfall stations of five regions in Norway at a significance level of 0.05. The occurrences of change points varied from 1979 to 1984 in five regions of Norway. The mean and variance of the annual precipitation series increased by 32% and 16% at most, respectively, compared with those values before the change points. The first periodicity (spanning four to five decades) was the dominant periodic component and could be used to best characterize the Norwegian precipitation variability. Because the subtropical Azores High (subpolar Icelandic Low) moves toward (away from) Europe, the relationship between the annual precipitation series and the Scandinavian pattern (Atlantic Multidecadal Oscillation) series tended to be of an upward (downward) hook structure. The association of precipitation variability and teleconnections found in this study can pave the way for new possibilities with regard to detection and attribution of precipitation variability. Key Points: Significant abrupt changes, increases and periodicities (40–50 years) constitute the Norwegian annual precipitation variabilityThe maximal information coefficient adequately associates the Norwegian precipitation variability with teleconnectionsThe maximal information coefficient captures hook structures of relationships between precipitation and teleconnections [ABSTRACT FROM AUTHOR]

Over 50 percent of the world's population live in urban areas and the percentage is steadily increasing. Oslo, the capital city of Norway, is projected to grow by 20 percent by 2030 according to Statistics Norway. Urbanization has strong impacts on quality and quantity of local runoff as well as erosion and sediment transport. The urban hydrological system is very complex due to interaction of overland runoff and groundwater with the sewer system and man-made changes of the land surface. The water balance in urban catchments is significantly changed due to human activities. Here we study the water balance in an urban catchment in Oslo by three independent methods, data analysis, MIKE Urban and Storm Water Management Model (SWMM). By using the three methods, we quantify each water balance component, i.e. precipitation, evapotranspiration, base flow from sewage system, base flow from groundwater and runoff from precipitation. The study contributes not only to the understanding of urban water system, but also to the ongoing project at the Oslo Municipality to implement low impact development (LID) infrastructure, such as green roof and rain gardens, to reduce and delay overland flow. The catchment studied is a suburban residential area that drains into the Akerselva River, which is the largest river in the Oslo.Acknowledgements: This work is supported by the Research Council of Norway (New Water Ways: 270742). [ABSTRACT FROM AUTHOR]

Humans are "urban species". In Norway, more than 80 percent of the population are living in cities and towns. Urbanization has strong impacts on quality and quantity of local runoff as well as erosion and sediment transport. Flooding of urban areas poses great challenges to human safety and sustained economic growth. Together with urban development, increase of intensive rainfall in terms of magnitude and frequency due to climate change makes the problem of urban floods more challenging. Oslo, the capital of Norway is continually developing rapidly, uses the Stormwater tree step approach (S3SA) for an overland flood protection strategy. The two first steps are infiltration and detention of rain. The third step is to use safe floodways to drain water. Maps of floodways are crucial in urban flood mitigation and city planning. In this study, we use four methods to identify floodways. These methods are accumulation analysis based on single flow direction multiple flow direction algorithm, SIMulated Water Erosion (SIMWE) and fully dynamic surface flow modelling. The study compares the differences between the methods and highlights their advantages and disadvantages for various applications.Acknowledgements: This work is supported by the Research Council of Norway (New Water Ways: 270742). [ABSTRACT FROM AUTHOR]

Quantifying hydrologic uncertainty associated with the nonlinear and heteroscedastic dependence structure of multi-step-ahead deterministic flood forecasts is fundamentally challenging. A hydrologic uncertainty processor (HUP) can effectively measure hydrologic uncertainty raised in deterministic forecast models under the hypothesis that there is no input uncertainty. This study explored a HUP embedded with a copula function for configuring the probabilistic flood forecast model. The outputs of the HUP formed a posterior distribution of the process, conditional upon the deterministic forecasts. The main merit of the copula-based HUP lies in capturing the features of the nonlinear and heteroscedastic dependence structure among hydrologic variables as well as in alleviating the uncertainty encountered in operational flood forecasts. To demonstrate reliability and effectiveness of the proposed method, the copula-based HUP was implemented to generate multi-step-ahead forecasts from horizons t+1 up to t+4 at a time scale of 6-hours for a flood-induced inflow series of the Three Gorges Reservoir (TGR) in China. The back propagation neural network and the nonlinear autoregressive with exogenous inputs neural network were investigated to offer a better deterministic forecast result for further probabilistic forecasting. For comparison purpose, the meta-Gaussian HUP was also conducted. The results indicated that the proposed method not only outperformed the comparative HUP but also significantly enhanced the accuracy of probabilistic flood forecasts for the TGR inflows, where effective quantification of hydrologic uncertainty was achieved. We demonstrated that the copula-based HUP could suitably extract the complex nonlinear and heteroscedastic dependence structure relating to the outputs of the deterministic forecast model. Consequently, hydrologic uncertainty could be alleviated and model reliability as well as forecast accuracy for future horizons could be significantly improved.Acknowledgements: This work is supported by the Research Council of Norway (FRINATEK Project 274310). [ABSTRACT FROM AUTHOR]

The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions. [ABSTRACT FROM AUTHOR]

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]

Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models' ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process. • Flood–Duration–Frequency (QDF) models produce flood statistics at multiple durations. • We extend QDF models so that different quantiles can have different slopes. • This improves estimates and allows for a wider range of durations to be modeled. • Additionally, we introduce a Bayesian framework for QDF estimation. [ABSTRACT FROM AUTHOR]

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes. • Gridded climate input data simulated using stochastic weather generators. • Significance of changes in runoff regime assessed using a statistical test. • Runoff variability due to input variables quantified using ANOVA. • Results in line with findings based on large-scale internal climate variability. • Runoff projections need to consider local-scale internal variability of climate. [ABSTRACT FROM AUTHOR]

• All GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments. • No significant change of flood characteristics during the last millennium from ensemble-mean results. • In future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway. • Climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. With the recent warming trend over Europe and the Arctic, the Nordic regions have experienced more frequent and damaging extreme hydrological events which are anticipated to increase towards the end of the 21st century. Despite explicit trends, large variations have been observed across basins and regions when it comes to precipitation and floods hinting at a strong natural hydroclimatic variability that further complicates any assessment of potential future changes. In this study, we aim to better understand how climate variability links with the current extremes and future projections of floods in Norway in the context of the last millennium and the future. Specifically, we simulate over 1000 years (850–2099) daily discharge and floods at 34 catchments over five regions of Norway from the last millennium (including a warm period and a cold period; 850–1849) to the end of the 21st century by an ensemble model-chain method including four global climate models (GCMs), two bias-correction methods and two hydrological models. The modelling results show (i) all GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments, (ii) no significant change of flood characteristics during the last millennium from ensemble-mean results, (iii) in future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway, and (iv) climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. [ABSTRACT FROM AUTHOR]

• The study evaluated the dependence of regionalization methods on hydrological models. • The performance of regionalization methods assessed in varied climate conditions. • Output averaging option works better than parameter averaging option. • Difference between output and parameter options increases with model complexity. • Simpler models are not superior to complex models in regionalization study. Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). Great progress has been achieved in the field of regionalization in previous studies, in which different hydrological models have been coupled with various regionalization methods. However, different conclusions have been drawn due to the use of different hydrological models, regionalization methods, and study regions. In this study, we assessed the performance of the five most widely used regionalization methods (spatial proximity with parameter averaging option (SP-par), spatial proximity with output averaging option (SP-out), physical similarity with parameter averaging option (Phy-par), physical similarity with output averaging option (Phy-out), and regression methods (PCR)) and four daily rainfall-runoff models (GR4J, WASMOD, HBV and XAJ, with 6, 8, 13, and 17 parameters, respectively) at the same time. Our aim was to evaluate how the performance of the regionalization methods depends on (a) the selection of hydrological models, (b) nonstationary climate conditions, and (c) different climatic regions. This investigation used data from 86 independent catchments evenly distributed throughout Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results showed that (a) the SP-out and Phy-out methods performed better than the SP-par and Phy-par for all the hydrological models, and the regression method performed worst in most cases; (b) the difference between the parameter averaging option and the output averaging option is positively related to the number of hydrological model parameters, i.e. the greater the number of parameters, the larger the difference between the two options; (c) the XAJ model with the greatest number of parameters produced the best results in most cases, and models with fewer parameters tend to produce similar performance for the different regionalization methods; (d) models with more parameters displayed larger declines in performance than those with fewer parameters for nonstationary conditions; and (e) clear differences in the performance of the regionalization methods exist among the three climatic regions. This study provides insight into the relationship between the complexity of hydrological models and regionalization methods in cold and seasonally snow-covered regions. [ABSTRACT FROM AUTHOR]

Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003-2012). Great progress has been achieved in the field of regionalization in a large number of studies, in which different hydrological models have been coupled with various regionalization methods. However, most of these studies have concentrated their evaluations on either one hydrological model or one regionalization method. In our study, we assess the performance of three regionalization methods (spatial proximity, physical similarity and regression methods) and four daily lumped rainfall-runoff models (GR4J, WASMOD, HBV and XAJ) at the same time. Our aim is to evaluate how the performance depends on the inter-relationship between the hydrological model and regionalization method. This investigation uses data from 86 evenly distributed catchments in Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results show that (a) the output averaging option for distance-based methods performs better for all hydrological models and the regression method performs worst in most cases; (b) the difference between the parameter averaging option and output averaging option is positively related to the number of hydrological model parameters, i.e. the more the number of parameters, the larger the difference between the two options; (c) models with fewer parameters tend to produce similar performance for the different regionalization methods; and (d) clear differences in the performance of regionalization methods exist between the three climate classes. This study provides insight to the influence from hydrological models on the performance of regionalization methods in various climate regions. [ABSTRACT FROM AUTHOR]