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The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions - may it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.

Four weather generators, namely, R-package version of the Generalised Linear Model for daily Climate time series (RGLIMCLIM), Stochastic Climate Library (SCL), R-package multi-site precipitation generator (RGENERATRPREC) and R-package Multi-site Auto-regressive Weather GENerator (RMAWGEN), were used to generate multi-sites stochastic daily rainfall for a small catchment in Australia. The results show the following: (1) All four models produced reasonable results in terms of annual, monthly and daily rainfall occurrence and amount, as well as daily extreme, multi-day extremes and dry/wet spell length. However, they also simulated a large range of variability, which not only demonstrates the advantages of multiple weather generators rather than a single model but also is more suitable for climate change and variability impact studies. (2) Every model has its own advantages and disadvantages due to their different theories and principles. This enhances the benefits of using multiple models. (3) The models can be further calibrated/improved to have a “better” performance in comparison with observations. However, it was chosen not to do so in this case study for two reasons: to obtain a full range of climate variability and to acknowledge the uncertainties associated with observation data. The latter are interpolated from limited stations and therefore have high pairwise correlations—ranging from 0.69 to 0.99 with a median and mean value of 0.87 and 0.88, respectively, for daily rainfall. These conclusions were drawn from a case study in Australia, but could be extended to general guidelines of using weather generators for climate change and variability studies.

Drought, generally characterized by below-average water supply, propagates through thehydrologic system with consequent ecological and societal impacts. Compared with other drought aspects,the recovery of drought especially in the hydrological components, which directly relates to the recovery ofwater resources for agricultural, ecological and human needs, is less-understood. Here, taking the Millen-nium drought in southeast Australia ( 1997–2009) as an illustrating case, we comprehensively examinedmultiple aspects of the meteorological (i.e., precipitation) and hydrological (i.e., streamflow and base flow)droughts across 130 unimpaired catchments using long-term hydro-meteorological observations. Resultsshow that the duration and intensity of the meteorological drought are both lengthened and amplified inthe hydrological drought, suggesting a nonstationarity in the rainfall-runoff relationship during a prolongeddrought. Additionally, we find a time lag commonly exists between the end of the meteorological droughtsand the end of the hydrological drought, with the recovery of base flow showing a longer lag than therecovery of streamflow. The recovery rate of precipitation after drought was found to be the dominant fac-tor that controls the recovery of hydrological droughts while catchment landscape (i.e., valley bottom flat-ness) plays an important but secondary role in controlling the lags in the hydrological recovery. Otherhydro-climatic factors and catchment properties appear to have only minor influences governing hydrologi-cal drought recovery. Our findings highlight a delayed response in the terrestrial components of the hydro-logical cycle to precipitation after prolonged drought, and provide valuable scientific guidance to waterresources management and water security assessment in regions facing future droughts

Traditional sensitivity assessment (SA) methods have limitations which motivate a new approach, the subject of a new project at ANU and the Universities of Adelaide and Melbourne, with the Murray-Darling Basin Commission and the South Australia Dept. of Water, Land and Biodiversity Conservation as partners. The limitations include high computing load, restricted scope and validity of the results, excessive volume of results and failure to distinguish SA from uncertainty assessment. The new approach has three main aims: (i) to investigate sensitivity of a wide range of model outcomes, not only the values of individual output variables; (ii) to examine sensitivity to changes which are not small; (iii) to find efficiently features such as critical or near-redundant parameter combinations. Requirements such as output ranges, credible behaviour or given rank order of scenario outcomes define an acceptable outcome set. SA then explores the feasible set of parameter values producing acceptable outcomes. This inverts the mapping by the model from parameters to outcomes. Existing techniques for inverting an output set through a non-linear model work only on small numbers of parameters and outputs, and assume that the output set is bounded by either a box (pairs of bounds on individual variables) or an ellipsoid. Consequently it is proposed to simplify SA by set inversion by two tactics. First, the model is split into simpler sections, e.g. with linear dynamics, to allow use of efficient, approximate inversion methods such as ellipsoidal, orthotopic or parallelotopic bounding. Second, attention is confined to features of the feasible set which can answer specific questions, such as largest or smallest diameter, indicating the least and most critical linear parameter combinations. Numerical search from approximate bounds, computed with the help of standard bounding algorithms, is contemplated to find such features. Even with these tactics, SA by set inversion faces several dif