Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

Drought is widely known as a complex natural hazard, not just by its climatological features but also by human experiences and socio-economical impacts. Drought preparedness is the only way a society can mitigate effects and better cope with droughts. Here we present a methodological approach to guide the implementation of proactive drought plans, specially designed for hydrossystems and cities scales. We highlight strategies to engage local stakeholders in constructing such plans and build a participatory methodology. The preparedness drought plan methodology was developed and applied to two hydrosystems and two cities located in the Piranhas-Açu river basin, a drought-prone area of Brazilian Semi-arid. Our ndings suggest that participatory socio-technical methodologies, built only from the system operators' tacit knowledge, can achieve good results when data and resources are limited. Still, results can be enhanced by hydrologic and hydraulic modeling to assess vulnerability, scenarios and strategies. We illustrate and analyze the process by storytelling to develop a meaningful and convincing narrative that speaks to theory and practice, and we provide recommendations to facilitate this approach.

Uncertainty inherent in precipitation predictions from general circulation model (GCMs) may lead urban drainage systems to be underdesigned (or overdesigned) in the future. This issue can be mitigated with the use of risk analysis models. In this study, a decisionmaking tool, developed based on six models (minimin, minimax, expected value, Hurwicz, Savage, and scenario-based multiobjective robust optimization), was used to select GCM/representative concentration pathways (RCP) scenarios that would lead to robust designs of an urban drainage system located in Fortaleza, Brazil. The implementation costs of the studied drainage system were estimated using runoff derived from rainfall predictions from six GCMs and two RCPs. After applying the proposed decision-making tool, three GCM/RCP scenarios were selected for yielding the most resilient and reliable designs. The range of feasible GCM/RCP scenarios reflects the level of optimism or pessimism held by a decision maker. We strongly recommend that this method be incorporated in urban drainage system design in order to help municipal planners make better decisions in view of climate change uncertainty.

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