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Standard quantile mapping (QM) performs well, as a bias adjustment method, in removing historical climate biases, but it can significantly alter a global climate model (GCM) signal. Methods that do incorporate GCM changes commonly consider mean changes only. Quantile delta mapping (QDM) is an exception, as it explicitly preserves relative changes in the quantiles, but it might present biases in preserving GCMs changes in standard deviation. In this work we propose the unbiased quantile mapping (UQM) method, which by construction preserves GCM changes of the mean and the standard deviation. Synthetic experiments and four Chilean locations are used to compare the performance of UQM against QDM, QM, detrended quantile mapping, and scale distribution mapping. All the methods outperform QM, but a tradeoff exists between preserving the GCM relative changes in the quantiles (QDM is recommended in this case), or changes in the GCM moments (UQM is recommended in this case). [ABSTRACT FROM AUTHOR]

Hydrological modeling tools can support collaborative decision processes by visually displaying hydrological systems connections, uncertainties and gaps, as well as conflicting preferences over water management strategies. Nevertheless, many challenges remain in the real application of these technical tools to successfully implement, capture, and communicate to non-experts the complexities of coupled human hydrological systems and effectively support science policy dialogues. The disappearance of a 12 km² lake in the Aculeo basin in Chile led to increasing socio environmental conflicts over the causes and effects of the water scarcity. A traditionally WEAP based hydrological model study aiming to explore the causes of the lake desiccation, was transformed into a multiple question driven socio hydrological modeling process to help answer the diversity of questions instigating conflict. The surface aquifer hydrological model tested a subset of socially accepted management strategies under two climate change scenarios, showing that combining more low impact, but socially acceptable adaptation measures such as using the out of season irrigation surplus (March to May), improving irrigation efficiency for agriculture industry and decreasing the grass surface in new urbanizations, would allow to recover up to half the Lake water volume even under a pessimistic climate change scenario. As presented in this article, flexible approaches and research agendas could better support the exploration of synergies towards collaboration and production of useful and socially acceptable hydrological models and water management strategies. [ABSTRACT FROM AUTHOR]

The spatial distribution of surface air temperatures is essential for understanding and modelling high‐relief environments. Good estimations of the surface temperature lapse rate (STLR) and the 0°C isotherm height (H0) are fundamental for hydrological modelling in mountainous basins. Although STLR changes in space and time, it is typically assumed to be constant leading to errors in the estimation of direct‐runoff volumes and flash‐floods risk assessment. This paper characterizes daily and seasonal temporal variations of the in‐situ STLR and H0 over the western slope of the subtropical Andes (central Chile). We use temperature data collected during 2 years every 10 min by a 16 sensors network in a small catchment with elevations ranging between 700 and 3,250 m. The catchment drains directly into Santiago, the Chilean capital with more than seven million inhabitants. Resulting values are compared against those obtained using off‐site, operational data sets. Significant intra‐ and inter‐day variations of the in‐situ STLR were found, likely reflecting changes in the low‐level temperature inversion during dry conditions. The annual average in‐situ STLR is −5.9°C/km during wet‐weather conditions. Furthermore, STLR and H0 estimations using off‐site gauges are extremely sensitive to the existence of gauging stations at high elevations. [ABSTRACT FROM AUTHOR]

Climate change is modifying the way we design and operate water infrastructure, including reservoirs. A particular issue is that current infrastructure and reservoir management rules will likely operate under changing conditions different to those used in their design. Thus, there is a big need to identify the obsolescence of current operation rules under climate change, without compromising the proper treatment of uncertainty. Acknowledging that decision making benefits from the scientific knowledge, mainly when presented in a simple and easy-to-understand manner, such identification—and the corresponding uncertainty—must be clearly described and communicated. This paper presents a methodology to identify, in a simple and useful way, the time when current reservoir operation rules fail under changing climate by properly treating and presenting its aleatory and epistemic uncertainties and showing its deep uncertainty. For this purpose, we use a reliability–resilience–vulnerability framework with a General Circulation Models (GCM) ensemble under the four Representative Concentration Pathways (RCP) scenarios to compare the historical and future long-term reservoir system performances under its current operation rule in the Limarí basin, Chile, as a case study. The results include percentiles that define the uncertainty range, showing that during the 21st century there are significant changes at the time-based reliability by the 2030s, resilience between the 2030s and 2040s, volume-based reliability by the 2080s, and the maximum failure by the 2070s. Overall, this approach allows the identification of the timing of systematic failures in the performance of water systems given a certain performance threshold, which contributes to the planning, prioritization and implementation timing of adaptation alternatives. [ABSTRACT FROM AUTHOR]