Current discourse on the transition to a decarbonized energy system future is dominated by renewable energy solutions. Initial conditions for this transition may vary across different regions and countries. There are, however, also opportunities for innovative solutions that utilize other low‐carbon energy sources and technology mix. Sustainable development is a contested concept and varies with priorities attached to social, economic, and environmental goals. Therefore, the one‐size‐fits‐all type of solution paradigm needs to be broadened, to accelerate action in the short to medium term. Our argument is that natural gas can be an important complementary transition fuel to support renewable energy in the short‐ and medium‐term transition phases. This means that the goal of zero fossil fuel as a short‐ and medium‐term solution needs to be reconsidered. This takes us to the next argument that innovation and upgraded technology in the low‐carbon fossil fuel sector will provide an important impetus for low‐carbon transition, which we see as a phase lasting until the middle of the century. However, the transition toward a sustainable energy future of gas‐fueled solutions has challenges from the social, technical, economic, geographical, and political points of view. Suitable local solutions should, however, also be assessed. These should take into consideration infrastructure, local demands, resources, and economic aspects as well as national energy policies. An analysis based on the experiences of four countries, both developed and developing, is presented in this study. The countries selected for this study can be placed in two categories: those with an abundance of natural gas reserves (Iran and Norway) and those that are import‐dependent (India and UK). The cross‐country analysis will help us to understand the realistic challenges and opportunities of natural gas as a transition fuel.

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a "calibration" procedure. Optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. Practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: modèle du Génie Ruralà 4 paramètres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that according to MS and SP, NSGA-II performs better than other techniques for calibrating GR4J using GRACE dTWS and in situ runoff data. Considering GD as a measure of efficiency, MPSO is found to be the best technique. CGA is found to be an efficient method, while considering the statistics of the GR4J's 4 calibrated parameters to rank the optimization techniques. The Nash-Sutcliffe model efficiency coefficient is also used to assess the predictive power of the calibrated hydrological models, for which our results indicate satisfactory performance of the assessed calibration experiments.

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