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Together with other "bottom‐up" methods, climate stress testing is becoming a prominent approach for climate change impact assessment of water systems. Compared with traditional approaches, stress testing is: (i) more focused on exploring the vulnerabilities of the system at hand; (ii) potentially more inclusive, being amenable to stakeholder involvement and (iii) well suited to identify robust policy options that better account for the deep uncertainty associated with multiple plausible futures. Stress testing is rapidly evolving and giving rise to new techniques and concepts, but few articles provide an accessible overview that can serve as an introduction to the field. Here, we review the underlying principles and concepts of climate stress testing, providing a guide to the main decisions involved in practical application. Topics include selection of stressors, characterizing and exploring the exposure space and data generation including the use of stochastic data. In a complex world where water decisions are made in the context of wider socio‐ecological systems, stress testing and other bottom‐up methods can support decisions that are not only robust to future uncertainty but also regarded as legitimate by affected communities. This article is categorized under:Engineering Water > Sustainable Engineering of WaterScience of Water > Water and Environmental ChangeWater and Life > Stresses and Pressures on Ecosystems [ABSTRACT FROM AUTHOR]

Hydrologic models are essential tools to understand and plan for the effect of changing climates; however, they underperform in transitory climate conditions. Existing research identifies models' inadequacy to perform during prolonged drought, but falls short on pinpointing which specific aspects of model performance are affected. We study five conceptual rainfall‐runoff models and their performance in 155 Australian catchments which recently experienced a 13‐year long drought. We use a wide range of performance metrics and a methodology based on ranked differences to a benchmark to fairly compare levels of degradation across metrics and periods. We show model performance degrading extensively during and after the drought, largely driven by overestimation of flow. Representation of shape and variability of hydrograph and flow‐duration curve are more resilient to the prolonged dry climate and rarely more degraded during the multi‐annual drought that on isolated dry years in the pre‐drought record. Conversely, volumetric error suffers from significant exacerbation over the multiple subsequent dry years. This indicates that catchment retention times and rates of storage depletion storage are significantly less affected by the drought than amounts of streamflow produced, pointing to a mismatch between reduction of influxes and out‐fluxes during the drought. We also identify a deficiency of models to delay and remove flow before it reaches the stream and keep track of moisture deficits over multiple dry seasons. By promoting rigorous investigation of models' shortcomings, we hope to foster the development of more robust model structures and/or calibration frameworks to improve applicability within climate change scenarios. Key Points: We compare aspects of model performance during and after multi‐annual drought against pre‐drought performancePerformance degradation is driven by bias in water balance estimates rather than errors in hydrograph shapeAccumulation and aggravation of errors over multiple dry years exacerbates performance degradation [ABSTRACT FROM AUTHOR]

The Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) is a flexible modelling framework reproducing the behaviour of 47 established hydrological models. MARRMoT can be used to calibrate and run models in a user-friendly and consistent way and is designed to facilitate the sharing of model code for reproducibility and to support intercomparison between hydrological models. Additionally, it allows users to create or modify models using components of existing ones. We present a new MARRMoT release (v2.1) designed for improved speed and ease of use. Whereas improved computational efficiency was the main driver for this redevelopment, MARRMoT v2.1 also succeeds in drastically reducing the verbosity and repetitiveness of the code, which improves readability and facilitates debugging. The process to create new models or modify existing ones within the toolbox is also simplified in this version, making MARRMoT v2.1 accessible for researchers and practitioners at all levels of expertise. These improvements were achieved by implementing an object-oriented structure and aggregating all common model operations into a single class definition that all models inherit from. The new modelling framework maintains and improves on several of the good practices built into the original MARRMoT and includes a number of new features such as the possibility of retrieving more output in different formats, which simplifies troubleshooting, and a new functionality that simplifies the calibration process. We compare outputs of 36 of the models in the framework to an earlier published analysis and demonstrate that MARRMoT v2.1 is highly consistent with the previous version of MARRMoT (v1.4), while achieving a 3.6-fold improvement in runtime on average. The new version of the toolbox and user manual, including several workflow examples for common application, are available from GitHub. [ABSTRACT FROM AUTHOR]