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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]

Documenting year-to-year variations in carbon storage potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO 2) emissions. However, the magnitude, pattern, and inner biomass partitioning of carbon storage potential and the effect of the changes in climate and CO 2 on inner carbon stocks remain poorly quantified. Herein, we use a spatially explicit individual-based dynamic global vegetation model to investigate the influences of the changes in climate and CO 2 on the enhanced carbon storage potential of vegetation. The modelling included a series of factorial simulations using the Climatic Research Unit (CRU) dataset from 1916 to 2015. The results show that CO 2 predominantly leads to a persistent and widespread increase in light-gathering vegetation biomass carbon stocks (LVBC) and water-gathering vegetation biomass carbon stocks (WVBC). Climate change appears to play a secondary role in carbon storage potential. Importantly, with the intensification of water stress, the magnitude of the light- and water-gathering responses in vegetation carbon stocks gradually decreases. Plants adjust carbon allocation to decrease the ratio between LVBC and WVBC for capturing more water. Changes in the pattern of vegetation carbon storage were linked to zonal limitations in water, which directly weaken and indirectly regulate the response of potential vegetation carbon stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrate that the long-term trend in increased vegetation biomass carbon stocks is driven by CO 2 fertilization and temperature effects that are controlled by water limitations. [ABSTRACT FROM AUTHOR]

Documenting year-to-year variations in carbon-sequestration potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO2) emissions. However, the magnitude, pattern and inner biomass partitioning of carbon-sequestration potential, and the effect of the changes in climate and CO2 on inner carbon stocks, remain poorly quantified. Herein, we use a spatially explicit individual based-dynamic global vegetation model to investigate the influences of the changes in climate and CO2 on the enhanced carbon-sequestration potential of vegetation. The modelling included a series of factorial simulations using the CRU dataset from 1916 to 2015. The results show that CO2 predominantly leads to a persistent and widespread increase in above-ground vegetation biomass carbon-stocks (AVBC) and below-ground vegetation biomass carbon-stocks (BVBC). Climate change appears to play a secondary role in carbon-sequestration potential. Importantly, with the mitigation of water stress, the magnitude of the above- and below-ground responses in vegetation carbon-stocks gradually increases, and the ratio between AVBC and BVBC increases to capture CO2 and sunlight. Changes in the pattern of vegetation carbon storage was linked to regional limitations in water, which directly weakens and indirectly regulates the response of potential vegetation carbon-stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrates that the long-term trend in increased vegetation biomass carbon-stocks is driven by CO2 fertilization and temperature effects that are controlled by water limitations. [ABSTRACT FROM AUTHOR]

We present an updated, spatially resolved estimate of 2003–2008 glacier surface elevation changes for the entire region of High Mountain Asia (HMA) from ICESat laser altimetry data. The results reveal a diverse pattern that is caused by spatially greatly varying glacier sensitivity, in particular to precipitation availability and changes. We introduce a spatially resolved zonation where ICESat samples are grouped into units of similar glacier behaviour, glacier type and topographic settings. In several regions, our new zonation reveals local differences and anomalies that have not been described previously. Glaciers in the Eastern Pamirs, Kunlun Shan and central TP were thickening by 0.1–0.7 m a -1 , and the thickening anomaly has a crisp boundary in the Eastern Pamirs that continues just north of the central Karakoram. Glaciers in the south and east of the TP were thinning, with increasing rates towards southeast. We attribute the glacier thickening signal to a stepwise increase in precipitation around ∼1997 –2000 on the Tibetan Plateau (TP). The precipitation change is reflected by growth of endorheic lakes in particular in the northern and eastern TP. We estimate lake volume changes through a combination of repeat lake extents from Landsat data and shoreline elevations from ICESat and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for over 1300 lakes. The rise in water volume contained in the lakes corresponds to 4–25 mm a -1 , when distributed over entire catchments, for the areas where we see glacier thickening. The precipitation increase is also visible in sparse in situ measurements and MERRA-2 climate reanalysis data but less visible in ERA-Interim reanalysis data. Taking into account evaporation loss, the difference between average annual precipitation during the 1990s and 2000s suggested by these datasets is 34–100 mm a -1 , depending on region, which can fully explain both lake growth and glacier thickening (Kunlun Shan) or glacier geometry changes such as thinning tongues while upper glacier areas were thickening or stable (eastern TP). The precipitation increase reflected in these glacier changes possibly extended to the northern slopes of the Tarim Basin, where glaciers were nearly in balance in 2003–2008. Along the entire Himalaya, glaciers on the first orographic ridge, which are exposed to abundant precipitation, were thinning less than glaciers in the dryer climate of the inner ranges. Thinning rates in the Tien Shan vary spatially but are rather stronger than in other parts of HMA. [ABSTRACT FROM AUTHOR]

Non-stationary hydrological sequences and rainfall-runoff relationship in a changingenvironment have brought new challenges to the application of hydrological models. Robusthydrological models are more reliable to evaluate the impact of changing environment onwater resources at the catchment scale. However, robustness differences in hydrologicalresponses to changing environment resulting from the use of different hydrological modelshave received much less attention. Therefore, this study compares the robustness of sixconceptual hydrological models (Xinanjiang, HBV, SIMHYD, IHACRES, GR4J andWASMOD) in different test scenarios in the Yellow River Basin and the Yangtze River Basinof China. The hierarchical scheme for systematic testing of hydrological models according tothe modelling tasks proposed by Klemes (1986) and 48-year long records of daily runoff andclimate are used in this study. Firstly, as the least challenging test in hierarchical scheme, thesimple split-sample test is performed for testing models’ ability to reproduce historicalrunoff under stationary conditions. Secondly, the proxy-basin test is carried out as abasic test for geographical transferability. In order to avoid the influence causedby the climate change, the single years of a basin are calibrated and the doubleyears of other basins are validated. Thirdly, the climate comparison periods (thedriest and wettest five years in a row) are selected as calibration and validationperiods. The differential split-sample test and proxy-basin differential split-sampletest are executed when calibration and validation occur in the same basin and indifferent basins respectively. By comparing the performance of different models in fourtests, more stable models can be selected and used in future studies. This study canprovide useful information for model selection and increase the credibility of modelsabout the research of hydrological impacts produced by changing environment. [ABSTRACT FROM AUTHOR]

Conventional frequency analyses are premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs). With evidence of hydrological impacts of climatic change, low frequency climate variability (e.g. El Nino and Southern Oscillation-ENSO) and other human related stressors such as land use changes, frequency analysis is now a key challenge as it has to incorporate (non-stationarity) or time-variation of the underlying distribution parameters. In this study, frequency analysis of extreme levels of Lake Malawi in Southern Malawi is performed incorporating non-stationarity. The lake is a critical transboundary water resource between Malawi, Mozambique, and Tanzania. The lakeshore areas are prone to extreme lake level variations which affects both infrastructure and life. With the lake undergoing considerable recession since 1980, there is a need to update the design extreme lake levels by incorporating non-stationarity. The study used observed mean lake levels from 1899 to 2017 from which annual maximum (AM) lake levels and partial duration series (PDS) were extracted. In the frequency analysis, the data area were firstly subjected to conventional analyses for evidence of non-stationarity using the Mann-Kendall (MK) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and the Augmented Dickey-Fuller Test (ADF). This was followed by the application of various routines in R software incorporating both stationary and non-stationary frequency analysis approaches such as the generalised additive model for location, scale and shape (GAMLSS) and Maximum likelihoods (ML). Goodness of fit was tested using the Akaike Information Criteria (AIC). The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore. [ABSTRACT FROM AUTHOR]

The statistical post-processing methods have been widely used to correct the biases of ensemble weather forecasts. However, most of the statistical post-processing methods are applicable to single weather variable at a single location, thus neglecting the spatiotemporal dependency structures of forecasting variables. This study extends the traditional single-site and single-variate (SSSV) method to multi-site and multivariate (MSMV) version by inducing the MSMV dependences based on the rank shuffle and Copula theory. Therefore, the study uses one SSSV method, Generator-based Post-Processing method (GPP), and three dependence reconstruction methods, i.e., Rank shuffle (RS), Gaussian Copula Coupling (GCC) and Empirical Copula Coupling (ECC). The dependence reconstructing methods are used to generate the spatially correlated random field for GPP to produce the MSMV ensembles (The pre-shuffling procedure) or to shuffle the generated GPP ensemble (The post-shuffling procedure). Therefore, a total of 6 MSMV methods (Pre-RS, Pre-GCC, Pre-ECC, Post-RS, Post-GCC, and Post-ECC) were evaluated in producing the skillful and correlated ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China based on the 11-ensemble forecasts from the Global Ensemble Forecasting System (GEFS) reforecasts. The results showed that the uncorrected GEFS forecasts tend to be biased, under-dispersive, and over-estimated the inter-site and inter-variable correlation for precipitation and temperature. GPP is skillful to remove bias in the ensemble mean and improve the skill of the ensemble spread but at the expense of losing the inter-site and inter-variable dependences. All the 6 MSMV methods tend to perform well in removing the bias in the inter-site dependencies for one fixed variable, but the post-shuffling methods (Post-RS, Post-GCC, and Post-ECC) are more effective in reconstructing the inter-site and inter-variate dependencies while preserving the univariate ensemble forecasts performance. [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]

Global hydrological models (GHMs) have been developed to calculate the water balance on global or continental scales which is essential for water resources management. The hydrological processes are complex, heterogeneous, and hardly characterized by laboratory and field measurements, especially for the evapotranspiration (ET) rate because of the difficulties of directly measuring atmospheric vapor flux. As a result, practical theories of potential ET (PET) and their impact on runoff generation remain elusive. PET/ET methods in GHM can behave differently because of different assumptions and input requirements. In this study, we assess the variations of the water balance components in Europe based on six PET methods, i.e., Penman-Monteith (PM), radiation-based Priestley-Taylor (PT), radiation-based Turc, temperature-based Hargreaves, an empirical temperature-based (empirical-T) method, and a PET product of Global Land Evaporation Amsterdam Model (GLEAM). Besides, a global Macro-scale Water and Snow Balance Modeling System (WASMOD-M) is calibrated at daily time step based on 66 gauged watersheds in Europe and applied with two regionalization methods (i.e., the Global mean method and the Koppen method) at the 0.5°×0.5° grid scale for the 1997-2008 period. Results show that (1) the Turc and empirical-T methods derive similar results of water balance with that of GLEAM while largely different from that of PT and PM; (2) the empirical-T and Turc methods show a slight decrease, while others show a slightly increase trend in Europe on average; as for the seasonal PET, all methods show a slight increase in spring and autumn and a slight decrease in winter; (3) all methods indicate that the increase of PET and Actual ET (AET) in central Europe is more obvious than other parts of Europe; and (4) different methods derive a similar spatial distribution of PET and runoff in Europe although they vary considerably in the quantity. Further study is in progress to characterize surface processes and to investigate the differences of water balance component regarding the assumptions of PET methods. [ABSTRACT FROM AUTHOR]

The ability of hydrological models to forecast runoff depends largely on the quality model forcing datasets. A number of global and regional distributed datasets hold great promise for hydrologic prediction by providing a more comprehensive spatial and temporal coverage. While these datasets are now more commonly used for hydrologic simulation, a significant degree of sensitivity to precipitation amount and timing is observed. In this study, Statkraft's Hydrologic Forecasting Toolbox (Shyft) is used to evaluate simulations forced with observations and three different distributed forcing datasets: I) ERA Interim, ii) Water and Global Change (WATCH) Forcing Data ERA-interim (WFDEI), and iii) COordinated Regional Climate Downscaling EXperiment with contributing institute Rosssy Centre, Swedish Meteorological and Hydrological Institute (CORDEX-SMHI)) over the Narayani catchment, Nepal. Not only does this provide an opportunity to evaluate variability and uncertainty resulting from the ensemble of forcing data, but it also demonstrates the capability of these data in data sparse regions. The fidelity of streamflow simulation is greatest when using observed and WFDEI forcing datasets. Results demonstrate the successful application of global gridded datasets in regional watershed-scale modeling. The distributed hydrologic simulations show good streamflow simulation performance based on statistical model evaluation techniques. Results are also promising for inter-annual variability. This study shows that WFDEI datasets hold great potential for understanding the hydrology of data-sparse Himalayan regions and indicate that CORDEX-SMHI and ERA-I derived precipitation products will require further validation, bias correction, and algorithm improvements, particularly over the high mountain regions. [ABSTRACT FROM AUTHOR]

Remote sensing data has been increasingly used to get improved estimates of spatially distributed hydrological variables. Although the relative informational value of time-series observations varies in response to multiple factors, the commonly used ensemble based smoothing schemes assume crisp and equal informational value for all observations of the assimilated data. The particle batch smoother (Pbs) is one of these schemes that has been gaining interest as a data assimilation (DA) scheme in hydrological models due to its distribution free likelihood and its capability to estimate the state vector directly at a relatively low computational cost. However, one of the main challenges in using Pbs and other Bayesian-based DA schemes is, that most of the weights are assigned to one or very few ensemble members and this may lead to degeneration of the statistical information in the ensembles. Here we consider an alternative approach that ensures fair distribution of weights among ensemble members based on the limits of acceptability concept (LoA). In this study, LoA was adopted as a DA scheme by introducing a methodology for relaxing the strict requirement of its original formulation as a rejectionist framework. A fuzzy coefficient was also introduced in both ensemble-based DA schemes to take into account for the variability in informational value of the assimilated observations.The fuzzy-logic based DA schemes were applied to a case study focused on the assimilation of remote sensing fractional snow cover area (fSCA) into a hydrological model. Timing of the more informative period was assumed to vary both spatially and temporally in response to various climatic and physiographic factors. Accordingly, the assimilation period was partitioned into three timing windows based on the critical points in the time axis where the mean snow cover changes and where the melt-out period starts. An automatic detection approach was used to locate these critical points. The parametric (likelihood-based) and non-parametric change point detection schemes employed to locate the change point in each grid cell and year yielded reasonable results. Similarly, the DA scheme based on LoA has yielded an encouraging result. An improved estimate of SWE was also obtained in most of the analysis years as a result of introducing the fuzzy coefficients in both DA schemes. The most significant improvement was obtained in the correlation coefficient between the predicted and observed SWE values; with an increase by 8% and 16% after introducing the fuzzy coefficient in Pbs and LoA, respectively. Results from this study suggest that, the concept of variable informational value of the assimilated observations is consistent with the notion that many of the variables that we usually consider to be crisp quantity and deterministic are actually fuzzy that carry considerable amount of uncertainty. [ABSTRACT FROM AUTHOR]