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Mean biases (MAE) and their variance across catchments in the four groups of hydrological signatures (a-d) obtained with the four BA methods relative to raw CM output (gray background). Boxplots represent the range of MAE values (averaged over all 10 CMs) across the 50 catchments, circles on top represent the variance, with smaller circles depicting lower variance. The BA method with the largest variance is highlighted as a reference with a hollow circle (normalized to have same size across signatures), the other BA methods are scaled accordingly.
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Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biase...
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Citations
... In contrast, transport models are specifically designed for interpreting concentration data, conducting mass balances of contaminants, predicting the spread of pollutant plumes, designing pump and treat management strategies, planning monitoring approaches, and assessing risks associated with waste disposal [15,16]. These models are essential tools for evaluating the movement and impact of pollutants within groundwater systems, contributing to effective environmental management and decision-making [17,18]. Hence, a comprehensive understanding of general aspects related to groundwater flow and transport models is indispensable for their accurate application, reliable interpretation of results, and effective contribution to decision-making processes in various fields, including hydrogeology, environmental science, and water resource management. ...
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... To ensure that the subsequent hydrological modelling provided robust and reliable future streamflow simulations (Ehret et al., 2012;Muerth et al., 2013;Teutschbein and Seibert, 2013, 2010, biases (i.e., systematic errors) in these climate model simulations were adjusted using the distribution-scaling method (Boe et al., 2007;Déqué et al., 2007;Ines and Hansen, 2006). To date, this is one of the most commonly used and most reliable as well as cost-efficient bias-adjustment methods (Teutschbein and Seibert, 2013;Tootoonchi et al., 2022a, Tootoonchi et al., 2022b, which corrects biases in daily CM-simulated temperature and precipitation on a monthly basis. Distribution scaling ...
Droughts can affect a multitude of public and private sectors, with impacts developing slowly over time. While droughts are traditionally quantified in relation to the hydrological components of the water cycle that they affect, this manuscript demonstrates a novel approach to assess future drought conditions through the lens of the water-energy-food-ecosystem (WEFE) nexus concept. To this end, a set of standardized drought indices specifically designed to represent different nexus sectors across 50 catchments in Sweden was computed based on an ensemble of past and future climate model simulations. Different patterns in the response of the four nexus sectors water, energy, food and ecosystem services to future climate change emerged, with different response times and drought durations across the sectors. These results offer new insights into the propagation of drought through the WEFE nexus in cold climates. They further suggest that future drought projections can be better geared towards decision makers by basing them on standardized drought indices that were specifically tailored to represent particular nexus sectors.
