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Pardeep Pall, Lena M. Tallaksen, and Frode Stordal

Abstract

Rain-on-snow (ROS) events are multivariate hydrometeorological phenomena that require a combination of rain and snowpack, with complex processes occurring on and within the snowpack. Impacts include floods and landslides, and rain may freeze within the snowpack or on bare ground, potentially affecting vegetation, wildlife, and permafrost. ROS events occur mainly in high-latitude and mountainous areas, where sparse observational networks hinder accurate quantification—as does a scale mismatch between coarse-resolution (50–100 km) reanalysis products and localized events. Variability in the rain–snow temperature threshold and temperature sensitivity of snowmelt adds additional uncertainty. Here the high-resolution (1 km) seNorge hydrometeorological dataset, capturing complex topography and drainage networks, is utilized to produce the first large-scale climatology of ROS events for mainland Norway. For daily data spanning 1957–2016, suitable rain and snowpack thresholds for defining ROS events are applied to construct ROS climatologies for 1961–90 and 1981–2010 and to investigate trends. Differing ROS characteristics are found, reflecting Norway’s diverse climates. Relative to 1961–90, events in the 1981–2010 period decrease most in the southwest low elevations in winter, southeast in spring, and north in summer (consistent with less snow cover in a warming climate) and increase most in the southwest high elevations, central mountains, and north in winter–spring (consistent with increased precipitation and/or more snow falling as rain in a warming climate). Winter–spring events also broadly correlate with the North Atlantic Oscillation, and the Scandinavia pattern—and more so with the Arctic Oscillation, particularly in the southern mountain region where long-term ROS trends are significant (+0.50 and +0.33 daily ROS counts per kilometer squared per decade for winter and spring).

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Kerstin Stahl, Lena M. Tallaksen, Lukas Gudmundsson, and Jens H. Christensen

Abstract

Land surface models and large-scale hydrological models provide the basis for studying impacts of climate and anthropogenic changes on continental- to regional-scale hydrology. Hence, there is a need for comparison and validation of simulated characteristics of spatial and temporal dynamics with independent observations. This study introduces a novel validation framework that relates to common hydrological design measures. The framework is tested by comparing anomalies of runoff from a high-resolution climate-model simulation for Europe with a large number of streamflow observations from small near-natural basins. The regional climate simulation was performed as a “poor man’s reanalysis,” involving a dynamical downscaling of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) with the Danish “HIRHAM5” model. For 19 different anomaly levels, two indices evaluate the temporal agreement (i.e., the occurrence and frequency of dry and wet events based on daily anomalies), whereas two other indices compare the interannual variability and trends based on annual anomalies. Benchmarks on each index facilitated a comparison across indices, anomaly levels, and basins. The lowest agreement of observed and simulated anomalies was found for dry anomalies. Weak to moderately wet anomalies agreed best, but agreement dropped again for the wettest anomalies. The results could guide the decision on thresholds if this regional climate model were used for the assessment of climate change scenario impacts on flood and drought statistics. Indices vary across Europe, but a gradient with decreasing correspondence between observed and simulated runoff characteristics from west to east, from lower to higher elevations, and from fast to slowly responding basins can be distinguished. The suggested indices can easily be adapted to other study areas and model types to assist in assessing the reliability of predictions of hydrological change.

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Helene B. Erlandsen, Ingjerd Haddeland, Lena M. Tallaksen, and Jørn Kristiansen

Abstract

A seasonal snow cover, expansive forests, a long coast line, and a mountainous terrain are features of Norway’s geography. Forests, ground snow, and sea surface temperature (SST) vary on time scales relevant for weather forecasting and climate projections. The mapping and model parameterization of these features vary in novelty, accuracy, and complexity. This paper investigates how increasing the influence of each of these features affects southern Norway’s surface energy and water balance in a regional climate model (WRF). High-resolution (3.7 km) experimental runs have been conducted over two consecutive hydrological years, including 1) heightening the boreal forest line (the Veg experiment), 2) increasing ground snow by altering the snow/rain criterion (the Snow experiment), or 3) increasing the SST (the SST experiment). The Veg experiment led to an increase in annual net radiation in the study area (by 3 W m−2), largely balanced out by an increase in latent heat flux. Moisture recycling increased, leaving only a negligible decrease in annual runoff. Surface temperature increased by 0.1°C, and its seasonal variability was dampened. Significant changes were also found outside the area of vegetation change. Snow decreased by 1.5 W m−2, despite slight increases in downward shortwave and longwave radiation. Both sensible heat flux and surface temperature decreased (by 1.3 W m−2 and 0.2°C, respectively), but the annual water balance remained mostly unchanged. The SST experiment led to increased downward and upward longwave radiation. Surface temperature was raised by 0.2°C. Advected oceanic moisture and thus both precipitation and runoff increased (by 2.5% and 2.8%, respectively).

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Daniel G. Kingston, James H. Stagge, Lena M. Tallaksen, and David M. Hannah

Abstract

Quantification of large-scale climate drivers of drought is necessary to understand better and manage these spatially extensive and often prolonged natural hazards. Here, this issue is advanced at the continental scale for Europe. Drought events are identified using two indices—the 6-month cumulative standardized precipitation and standardized precipitation evapotranspiration indices (SPI-6 and SPEI-6, respectively)—both calculated using the gridded Water and Global Change (WATCH) Forcing Dataset for 1958–2001. Correlation of monthly time series of the percentage of European area in drought with geopotential height for 1958–2001 indicates that a weakening of the prevailing westerly circulation is associated with drought onset. Such conditions are linked to variations in the eastern Atlantic/western Russia (EA/WR) and North Atlantic Oscillation (NAO) atmospheric circulation patterns. Event-based analysis of the most widespread European droughts reveals that a higher number are identified by the SPEI-6 than the SPI-6, with SPEI-6 drought events showing a greater variety of spatial locations and start dates. Atmospheric circulation drivers also vary between the two types of events, with EA/WR-type variation associated most frequently with SPEI-6 drought, and the NAO associated with SPI-6. This distinction reflects the sensitivity of these drought indices to the underlying drought type (meteorological water balance versus precipitation, respectively) and associated differences in their timing and location (Europe-wide year round versus northern Europe winter). As such, this study provides new insight into both the identification of Europe-wide drought and patterns of large-scale climate variation associated with two different drought indices.

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Daniel G. Kingston, Anne K. Fleig, Lena M. Tallaksen, and David M. Hannah

Abstract

Droughts are high-impact events that have substantial implications for both human and natural systems. As such, improved understanding of the hydroclimatological processes involved in drought development is a major scientific imperative of direct practical relevance. To address this research need, this paper investigates the chain of processes linking antecedent ocean–atmosphere variation to summer streamflow drought in Great Britain. Analyses are structured around four distinct drought regions (defined using hierarchical cluster analysis) for the period 1964–2001. Droughts were identified using a novel regional drought area index. Composite analysis of monthly sea surface temperature (SST) prior to drought onset reveals a horseshoe- or tripole-shaped pattern of North Atlantic SST anomalies that is similar to patterns of SST anomalies associated with the North Atlantic Oscillation (NAO). Patterns in geopotential height, wind, moisture vapor flux, and precipitation prior to drought onset support the influence of the NAO but also demonstrate that the atmospheric bridge linking North Atlantic SST to drought development is too complex to be described solely by indices of the NAO. In revealing new information on the chain of processes leading to the development of hydrological drought in Great Britain, this paper has the potential to inform drought-forecasting research and so improve drought preparedness and management.

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Lukas Gudmundsson, Lena M. Tallaksen, Kerstin Stahl, Douglas B. Clark, Egon Dumont, Stefan Hagemann, Nathalie Bertrand, Dieter Gerten, Jens Heinke, Naota Hanasaki, Frank Voss, and Sujan Koirala

Abstract

Large-scale hydrological models describing the terrestrial water balance at continental and global scales are increasingly being used in earth system modeling and climate impact assessments. However, because of incomplete process understanding and limits of the forcing data, model simulations remain uncertain. To quantify this uncertainty a multimodel ensemble of nine large-scale hydrological models was compared to observed runoff from 426 small catchments in Europe. The ensemble was built within the framework of the European Union Water and Global Change (WATCH) project. The models were driven with the same atmospheric forcing data. Models were evaluated with respect to their ability to capture the interannual variability of spatially aggregated annual time series of five runoff percentiles—derived from daily time series—including annual low and high flows. Overall, the models capture the interannual variability of low, mean, and high flows well. However, errors in the mean and standard deviation, as well as differences in performance between the models, became increasingly pronounced for low runoff percentiles, reflecting the uncertainty associated with the representation of hydrological processes, such as the depletion of soil moisture stores. The large spread in model performance implies that any single model should be applied with caution as there is a great risk of biased conclusions. However, this large spread is contrasted by the good overall performance of the ensemble mean. It is concluded that the ensemble mean is a pragmatic and reliable estimator of spatially aggregated time series of annual low, mean, and high flows across Europe.

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Richard Harding, Martin Best, Eleanor Blyth, Stefan Hagemann, Pavel Kabat, Lena M. Tallaksen, Tanya Warnaars, David Wiberg, Graham P. Weedon, Henny van Lanen, Fulco Ludwig, and Ingjerd Haddeland

Abstract

Water-related impacts are among the most important consequences of increasing greenhouse gas concentrations. Changes in the global water cycle will also impact the carbon and nutrient cycles and vegetation patterns. There is already some evidence of increasing severity of floods and droughts and increasing water scarcity linked to increasing greenhouse gases. So far, however, the most important impacts on water resources are the direct interventions by humans, such as dams, water extractions, and river channel modifications. The Water and Global Change (WATCH) project is a major international initiative to bring together climate and water scientists to better understand the current and future water cycle. This paper summarizes the underlying motivation for the WATCH project and the major results from a series of papers published or soon to be published in the Journal of Hydrometeorology WATCH special collection. At its core is the Water Model Intercomparison Project (WaterMIP), which brings together a wide range of global hydrological and land surface models run with consistent driving data. It is clear that we still have considerable uncertainties in the future climate drivers and in how the river systems will respond to these changes. There is a grand challenge to the hydrological and climate communities to both reduce these uncertainties and communicate them to a wider society.

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