Robustness analysis can support long-term planning, design and operation of large-scale water infrastructure projects confronting deeply uncertain futures. Diverse actors, contextual specificities, sectoral interests, and risk attitudes make it difficult to identify an acceptable and appropriate robustness metric to rank decision alternatives under deep uncertainty. Here, we contribute an exploratory framework to demonstrate how methodological choices affect robustness evaluation. The framework is applied to a multi-actor, multi-sector Inchampalli-Nagarjuna Sagar (INS) water transfer megaproject in Southern India. We evaluate a suite of dynamic adaptive water transfer strategies discovered using evolutionary multi-objective direct policy search (EMODPS), a status quo strategy of no water transfer, and a strategy proposed by regional authorities. We evaluate robustness across wide-ranging scenarios that capture key uncertainties in potential future changes in reservoir inflows and water demands in the basins. Results show that the priorities of different actors, sectoral perspectives, and risk attitude significantly affect robustness rankings of strategies. We found that compromise strategies obtained from EMODPS are better able to balance the diverse performance requirements across various actors and sectors when compared to the no-transfer and proposed transfer. We reveal a key robustness tradeoff between the donor basin’s ecological requirements and the recipient basin’s socio-economic requirements. While robustness analysis is central to water infrastructure planning, we show why exploratory robustness analyses that engage with conflicting stakeholder objectives is vital for long-term sustainability. Furthermore, the selection of compromise solutions should be guided by an explicit understanding of how assumed risk attitudes shape stakeholders’ understanding of consequential vulnerabilities.

Causal loop diagrams (CLDs) based on expert and/or stakeholder inputs inform the quantitative structure of socio-hydrological models (SHMs). However, a systematic exploration of the sensitivity of CLDs and SHMs to different levels of stakeholder inputs is lacking. For a large multi-purpose reservoir in southern India, we explore this sensitivity by developing three CLDs that integrate reservoir water balance, groundwater pumping, and consumer water use patterns. CLD1 is a conventional water balance-based reservoir model, while CLD2 additionally incorporates the reservoir operatorâ\euro™s judgment and groundwater pumping. CLD3 further incorporates the adaptive behavior of water users by adjusting demands in response to long-term (5-year) droughts. The correlation between observed and simulated monthly reservoir storage (2000-2013) for SHM1, SHM2, and SHM3 is 0.57, 0.85, and 0.87, respectively. SHM3 also outperforms SHM1 and SHM2 in simulating the relative use of surface and groundwater for irrigation purposes in the command area of the reservoir. Simulated demand deficits, command area groundwater levels, and minimum environmental flow satisfaction downstream of the reservoir for 1968-2013 using the three models exhibit substantial differences. SHM1 and SHM2 simulate deteriorating groundwater levels under the multi-year drought of 2001-2003 while SHM3 does not due to the consideration of adaptive farmer behavior. Thus, our understanding of water and food security during a multi-year drought can be significantly affected by the level of stakeholder inputs incorporated in the models. We highlight the importance of testing different SHMs structures to better understand human-water interactions under extreme conditions.

Over the last two decades, several datasets have been developed to assess flood risk at the global scale. In recent years, some of these datasets have become detailed enough to be informative at national scales. The use of these datasets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using global datasets and methodologies. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global datasets nationally. To assess national flood risk, we use 6 datasets of global flood hazard, 7 datasets of global population, and 3 different methods for calculating vulnerability that have been used in previous global studies of flood risk. We find that the datasets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global datasets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global datasets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.