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The increasing severity of hydrological droughts in the Mediterranean basin related to climate change raises the need to understand the processes sustaining low flow. The purpose of this paper is to evaluate simple mixing model approaches, first to identify and then to quantify streamflow contribution during low-water periods. An approach based on the coupling of geochemical data with hydrological data allows the quantification of flow contributions. In addition, monitoring during the low-water period was used to investigate the drying-up trajectory of each geological reservoir individually. Data were collected during the summers of 2018 and 2019 on a Mediterranean river (Gardon de Sainte-Croix). The identification of the end-members was performed after the identification of a groundwater geochemical signature clustered according to the geological nature of the reservoir. Two complementary methods validate further the characterisation: rock-leaching experiments and unsupervised classification (k -means). The use of the end-member mixture analysis (EMMA) coupled with a generalised likelihood uncertainty estimate (GLUE) (G-EMMA) mixing model coupled with hydrological monitoring of the main river discharge rate highlights major disparities in the contribution of the geological units, showing a reservoir with a minor contribution in high flow becoming preponderant during the low-flow period. This finding was revealed to be of the utmost importance for the management of water resources during the dry period. [ABSTRACT FROM AUTHOR]

Climate change and its possible effects on water resources has become an increasingly near threat. Therefore, the study of these impacts in highly regulated systems and those suffering extreme events is essential to deal with them effectively. This study responds to the need for an effective method to integrate climate change projections into water planning and management analysis in order to guide the decision-making, taking into account drought risk assessments. Therefore, this document presents a general and adaptive methodology based on a modeling chain and correction processes, whose main outcomes are the impacts on future natural inflows, a drought risk indicator, and the simulation of future water storage in the water resources system (WRS). This method was applied in the Júcar River basin (JRB) due to its complexity and the multiannual drought events it suffers recurrently. The results showed a worrying decrease in future inflows, as well as a high probability (≈80 %) of being under 50 % of total capacity of the WRS in the near future. However, the uncertainty of the results was considerable from the mid-century onwards, indicating that the skill of climate projections needs to be improved in order to obtain more reliable results. Consequently, this paper also highlights the difficulties of developing this type of method, taking partial decisions to adapt them as far as possible to the basin in an attempt to obtain clearer conclusions on climate change impact assessments. Despite the high uncertainty, the results of the JRB call for action and the tool developed can be considered as a feasible and robust method to facilitate and support decision-making in complex basins for future water planning and management. [ABSTRACT FROM AUTHOR]

Water resources management in Latin America and the Caribbean is particularly threatened by climatic, economic, and political pressures. To assess the region's ability to manage water resources, we conducted an unprecedented literature review of over 20 000 multilingual research articles using machine learning and an understanding of the socio-hydrologic landscape. Results reveal that the region's vulnerability to water-related stresses, and drivers such as climate change, is compounded by research blind spots in niche topics (reservoirs and risk assessment) and subregions (Caribbean nations), as well as by its reliance on an individual country (Brazil). A regional bright spot, Brazil, produces well-rounded water-related research, but its regional dominance suggests that funding cuts there would impede scientifically informed water management in the entire region. [ABSTRACT FROM AUTHOR]

Rainfall intensity–frequency–duration (IFD) relationships are commonly required for the design and planning of water supply and management systems around the world. Currently, IFD information is based on the "stationary climate assumption" that weather at any point in time will vary randomly and that the underlying climate statistics (including both averages and extremes) will remain constant irrespective of the period of record. However, the validity of this assumption has been questioned over the last 15 years, particularly in Australia, following an improved understanding of the significant impact of climate variability and change occurring on interannual to multidecadal timescales. This paper provides evidence of regime shifts in annual maximum rainfall time series (between 1913–2010) using 96 daily rainfall stations and 66 sub-daily rainfall stations across Australia. Furthermore, the effect of these regime shifts on the resulting IFD estimates are explored for three long-term (1913–2010) sub-daily rainfall records (Brisbane, Sydney, and Melbourne) utilizing insights into multidecadal climate variability. It is demonstrated that IFD relationships may under- or over-estimate the design rainfall depending on the length and time period spanned by the rainfall data used to develop the IFD information. It is recommended that regime shifts in annual maximum rainfall be explicitly considered and appropriately treated in the ongoing revisions of the Engineers Australia guide to estimating and utilizing IFD information, Australian Rainfall and Runoff (ARR), and that clear guidance needs to be provided on how to deal with the issue of regime shifts in extreme events (irrespective of whether this is due to natural or anthropogenic climate change). The findings of our study also have important implications for other regions of the world that exhibit considerable hydroclimatic variability and where IFD information is based on relatively short data sets. [ABSTRACT FROM AUTHOR]

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. Future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human land-use change can affect the climate system through bio-geophysical and bio-geochemical effects. To investigate these interrelationships, we developed MIROC-INTEG1 (MIROC INTEGrated terrestrial model version 1), an integrated model that combines the global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land use models. In this paper, we introduce the details and interconnections of the sub-models of MIROC-INTEG1, compare historical simulations with observations, and identify the various interactions between sub-models. MIROC-INTEG1 makes it possible to quantitatively evaluate the feedback processes or nexus between climate, water resources, crop production, land use, and ecosystem, and to assess the risks, trade-offs and co-benefits associated with future climate change and prospective mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

Water management throughout the western United States largely relies on the partitioning of cool season mountain precipitation into rain and snow, particularly snow as it maximizes available water for warm season use. Recent studies indicate a shift toward increased precipitation falling as rain, which is consistent with a warming climate. An approach is presented to estimate precipitation-phase partitioning across landscapes from 1948 to the present by combining fine-scale gridded precipitation data with coarse-scale freezing level and precipitation data from an atmospheric reanalysis. A marriage of these data sets allows for a new approach to estimate spatial patterns and trends in precipitation partitioning over elevational and latitudinal gradients in major water supply basins. This product is used in California as a diagnostic indicator of changing precipitation phase across mountain watersheds. Results show the largest increases in precipitation falling as rain during the past 70 years in lower elevation watersheds located within the climatological rain–snow transition regions of northern California during spring. Further development of the indicator can inform adaptive water management strategy development and implementation in the face of a changing climate. [ABSTRACT FROM AUTHOR]

As a typical alpine marsh wetland on the Qinghai-Tibet Plateau, the Zoige Wetland plays an important role in the water source recharge of Yellow River. Meanwhile, it is more sensitive to climate change. Therefore, it is necessary to study the hydrological cycle of Zoige Wetland and the impact of climate change, which will also provide important support and reference for the study of hydrological process, and protection of wetland water resources in Alpine areas. Based on the distributed hydrological model, this paper analyzed the runoff process in the Zoige wetland basin, and explored the trend of runoff variation in the Zoige wetland basin under different climate change scenarios in the next 30 years from 2020 to 2050. The impact of climate change on wetland runoff were also discussed. Results showed that the runoff in the Zoige Wetland basin decreased under the future climate change scenario. The rate of runoff reduction was the largest in Machu station, followed by Zoige station, and finally Tangke station. The reduction in non-flooding runoff is significantly higher than that in the flood season. The runoff during non-flood season in the Zoige Wetland basin from 2020 to 2050 will decrease by more than 25% in the future climate change scenario. The sharp decline in non-flooded runoff may have an impact on the protection of the Zoige Wetland and the management of water resources in the middle reaches of the Yellow River and downstream areas. This poses more stringent requirements for the protection of the Zoige Wetland in response to future climate change. Under future climate change scenarios, how to strengthen the management and protection of wetland water resources and prevent the degradation and atrophy of wetlands is the issue we need to further discuss and consider. [ABSTRACT FROM AUTHOR]

Human activities and climate change are two key factors influencing the variation of the total amount of available surface and groundwater, hereafter termed water resources. Quantitatively separating their impacts remains a challenge. To this end, we used time-varying Budyko-type equations and a hierarchical Bayesian model in this paper to separate their impacts in 31 provincial-level divisions of China. The time-varying Budyko-type equations treat the Budyko equation parameter w as a variable, which depends on human activities (represented by per capita gross regional production) and climate change (represented by temperature and precipitation). The hierarchical model quantifies the uncertainty of parameters and the interrelation between covariates across regions in China. The results show that the time-varying Budyko-type equation can improve the fitting capability for water resources in China. The hierarchical Bayesian model, which considered spatial dependence, reduced the uncertainty of the parameters compared to spatially independent counterparts. For most regions of China, human activities reduce water resources while climate change increases them. Southeastern China is the most influenced area, and its water resources decreased approximately 50 mm because of human activities. This study can provide a basis for water resource management under climate change and human activity constraints in China. [ABSTRACT FROM AUTHOR]

Climate change is a global stressor that can undermine water management policies developed with the assumption of stationary climate. While the response-surface-based assessments provided a new paradigm for formulating actionable adaptive solutions, the uncertainty associated with the stress tests poses challenges. To address the risks of unsatisfactory performances in a climate domain, this study proposed the incorporation of the logistic regression into a decision-centric framework. The proposed approach replaces the "response surfaces" of the performance metrics typically used for the decision-scaling framework with the "logistic surfaces" that describes the risk of system failures against predefined performance thresholds. As a case study, water supply and environmental reliabilities were assessed within the eco-engineering decision-scaling framework for a complex river basin in South Korea. Results showed that human-demand-only operations in the river basin could result in the water deficiency at a location requiring environmental flows. To reduce the environmental risks, the stakeholders could accept increasing risks of unsatisfactory water supply performance at the sub-basins with small water demands. This study suggests that the logistic surfaces could provide a computational efficiency to measure system robustness to climatic changes from multiple perspectives together with the risk information for decision-making processes. [ABSTRACT FROM AUTHOR]