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Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments. [ABSTRACT FROM AUTHOR]

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Amongst different aspects of a capital construction project, procurement is found to be the most important area and represents over 80% of the contract value. The selection of an appropriate procurement strategy is an important contributor to overall project success. Within several procurement methods, Early Contractor Involvement (ECI), a relatively new strategy to procure a construction project, is becoming more popular for infrastructure projects across Australia. However, it appears that ECI has been adopted as a preferred procurement option with little research or piloting, and decisions to select ECI for a project have been mostly judgmental, and subject to biases of the decision-makers. This paper focuses on this important issue and proposes a conceptual model for selecting ECI for a construction project. Grounded Theory research methodology is employed for this study that facilitates the generation of categories and contextualises theory. Validation of the theory was ensured by carefully practicing the theoretical coding procedures through 'open coding', 'axial coding', and 'selective coding'. The data is collected through individual interviews with experts within client organisations who held senior management level roles in their organisations and were involved in the selection process of ECI and could provide input into their experience in that area. The proposed selection model integrates the procurement selection criteria specifically related to the project characteristics, client's objectives, and internal and external project environments with alternative selection approaches and practices. This paper also discusses the notion of social, process, and output control by using ECI. [ABSTRACT FROM AUTHOR]

Objective: To examine whether personal psychological resources safeguard hospital nurses against adverse workplace consequences, particularly job burnout and the desire to leave the profession. Background: Nursing research has extensively documented the adverse effects of job burnout and staff turnover. With the current nursing shortage, it is imperative to identify resources and strategies that can mitigate adverse workplace outcomes. However, the role of personal psychological resources, or psychological capital, in aiding nurses to perform effectively in their work environment remains relatively unexplored. Study design and methods: This study adopted a cross-sectional survey design. The survey assessed nurses' experienced burnout (MBI-HSS), psychological capital (PCQ-24), and intentions to leave nursing. Hospital nurses (n= 258) from six states of Australia responded to an online anonymous survey between June and November 2022. Results: Respondents indicated a high degree of experienced burnout: 68.6% experienced high emotional exhaustion, 31.8% had high depersonalisation, and 31.8% had low personal accomplishment. Additionally, 38.8% had high intentions to leave the profession. Emotional exhaustion (p<.001, b=.56) and personal accomplishment (p=.006, b=-.15) were significant predictors of turnover intentions. Higher psychological capital was significantly associated with lower emotional exhaustion (p<.001, b=-.42), lower depersonalisation (p<.001, b=-.29), higher personal accomplishment (p<.001, b=.60), and lower turnover intentions (p<.001, b=.44). Discussion: Much of the nursing burnout and intent to leave literature focuses on negative rather than positive aspects of the work environment. Positive responses to workplace stimuli promote positive attitudes such as empowerment, job satisfaction, and organisational commitment that have a tangible impact on personal and occupational wellbeing. This may explain why nurses with stronger personal psychological resources experienced less burnout and voiced fewer intentions to leave the profession. Conclusion: The health and wellbeing of nurses should be a priority for healthcare organisations; the working conditions nurses face in Australian hospitals cause many to be negatively impacted by work stress. Implications for Research, Policy, and Practice: Nurses would benefit from initiatives to enhance their psychological resources. Targeted interventions to develop psychological capital should be examined in a nursing population. This can change policy, thereby benefitting the healthcare system. What is already known about the topic? • Australia is currently facing a shortage of qualified nurses. • Hospital nurses often experience job burnout and high levels of turnover due to the challenging nature of their work environment. • Personal psychological resources have been linked to positive workplace outcomes, such as job satisfaction and organisational commitment, in various settings. What this paper adds: • It demonstrates that nurses possessing greater personal psychological resources experience lower levels of burnout and are less likely to consider leaving the profession. • The paper suggests that implementing targeted interventions designed to enhance nurses' personal psychological resources could be a viable approach for mitigating burnout and turnover intentions (TI). [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a modified model for collaboration in BIM-based construction networks (BbCNs). Though BIM is increasingly adopted and implemented across the construction industry, the problems associated with the lack of collaboration among teams in BbCNs remain a major hindrance to reaping the full potential of BIM. Previous studies have been conceptual in nature. This paper, therefore, attempts to modify and validate existing conceptual models that describe collaboration in BbCNs. Design/methodology/approach: To modify the conceptual model for collaboration in BbCNs, qualitative data through semi-structured interviews with BIM experts in the industry were collected and analysed using qualitative methods including the use of NVivo software. Findings: The proposed model includes influential factors and their sub-factors to collaboration in BbCNs, as well as considering their indicators. Findings reveal that several overlooked concepts, particularly unfavourable BIM contractual arrangements, act as the root causes of the unwillingness of team members in BbCNs to engage in collaborative efforts. Research limitations/implications: The study's findings must be viewed in light of several limitations. First, the interviewees in this study were based in Australia hence their perceptions of BIM collaboration are reflective of the sociotechnical setting of BIM-enabled projects in this country. Also, the findings are based on the perception of experts in the field, rather than analysis of performance measures or quantitative assessment of associations among collaboration outcomes and various factors. This, however, provides the field with fertile grounds for future research. Practical implications: The study benefits researchers by shifting the collaboration discourse in BIM-enabled projects from technology-related issues to the people and contractual-related domains. Moreover, the developed qualitative model provides industry professionals with a point of reference to improve collaboration on BIM-enabled projects. Social implications: The study benefits researchers by shifting the collaboration discourse in BIM-enabled projects from technology-related issues to the people and contractual-related domains. Moreover, the developed qualitative model provides industry professionals with a point of reference to improve collaboration on BIM-enabled projects. Originality/value: Arguments provided in this study highlight the necessity of considering the contractual arrangement of BIM-related projects and foster the willingness of team members to collaborate. This can be addressed using clear and comprehensive BIM execution plans and clearly explaining the role of BIM managers in the process. [ABSTRACT FROM AUTHOR]

Given that the number of children and young people needing care keeps rising and fewer people are becoming foster carers, efforts to support carers and workers in foster caring are essential. This paper considers the experiences of carers and foster care agency workers involved in Australia's piloting of the Mockingbird Family. With a view understanding experience, data were collected via focus groups with carers and agency workers (n = 20) involved in piloting, implementation and evaluation. Deductive analysis applied the theory of experience to generate understanding of experience, as both intrinsic and extrinsic dimensions to capture strengths in the Mockingbird Family's foster caring networks. These dimensions of experience included collective passions of carers and workers; experiential change over time; collective experiences as a moving force; and experiences as transformational. Understanding of experience associated with the perceived strengths of the Mockingbird Family, including strategies to promote strong professional relationships between carers and workers, is an important element in strengthening environments of children and young people in care. Safe and stable environments are crucial for wellbeing. [ABSTRACT FROM AUTHOR]

BACKGROUND Cohort-component models incorporating directional migration are conceptually robust demographic models which are widely employed to forecast the populations of large subnational regions. However, they are difficult to apply at the local area scale. Simpler models, such as the Hamilton--Perry model, have modest input data requirements and are much quicker, cheaper, and easier to implement, but they offer less output detail, suffer from some conceptual and practical limitations, and can be less accurate. OBJECTIVE The aim of this paper is to describe and evaluate the synthetic migration cohort-component model -- an approach to implementing the bi-regional model for local area population forecasts without the need for any locally specific migration data. METHODS The new approach is evaluated by creating several sets of 'forecasts' for local areas of Australia over past periods. For comparison, forecasts from two types of Hamilton--Perry model are also evaluated. Error is measured primarily with an alternative Absolute Percentage Error measure for total population which takes into account how well or poorly the population age--sex structure is forecast. RESULTS In the evaluation for Australian local areas, the synthetic migration model generated more accurate forecasts that the two Hamilton--Perry models in terms of median, mean, and 90th percentile Absolute Percentage Errors. CONTRIBUTION The synthetic migration model combines the conceptual and practical advantages of the bi-regional cohort-component model with the light data requirements and ease of calculation of simpler cohort models. It allows the bi-regional model to be applied in circumstances where local area migration data are unavailable or unreliable. [ABSTRACT FROM AUTHOR]

A deep learning model designed for time series predictions, the long short-term memory (LSTM) architecture, is regularly producing reliable results in local and regional rainfall–runoff applications around the world. Recent large-sample hydrology studies in North America and Europe have shown the LSTM model to successfully match conceptual model performance at a daily time step over hundreds of catchments. Here we investigate how these models perform in producing monthly runoff predictions in the relatively dry and variable conditions of the Australian continent. The monthly time step matches historic data availability and is also important for future water resources planning; however, it provides significantly smaller training datasets than daily time series. In this study, a continental-scale comparison of monthly deep learning (LSTM) predictions to conceptual rainfall–runoff (WAPABA model) predictions is performed on almost 500 catchments across Australia with performance results aggregated over a variety of catchment sizes, flow conditions, and hydrological record lengths. The study period covers a wet phase followed by a prolonged drought, introducing challenges for making predictions outside of known conditions – challenges that will intensify as climate change progresses. The results show that LSTM models matched or exceeded WAPABA prediction performance for more than two-thirds of the study catchments, the largest performance gains of LSTM versus WAPABA occurred in large catchments, the LSTMs struggled less to generalise than the WAPABA models (e.g. making predictions under new conditions), and catchments with few training observations due to the monthly time step did not demonstrate a clear benefit with either WAPABA or LSTM. [ABSTRACT FROM AUTHOR]

We develop a method for computing Bayes' factors of conceptual rainfall-runoff models based on thermodynamic integration, gradient-based replica-exchange Markov Chain Monte Carlo algorithms and modern differentiable programming languages. We apply our approach to the problem of choosing from a set of conceptual bucket-type models with increasing dynamical complexity calibrated against both synthetically generated and real runoff data from Magela Creek, Australia. We show that using the proposed methodology the Bayes factor can be used to select a parsimonious model and can be computed robustly in a few hours on modern computing hardware. We introduce formal posterior predictive checks for the selected model. The prior calibrated posterior predictive p-value, which also tests for prior data conflict, is used for the posterior predictive checks. Prior data conflict is when the prior favours parameter values that are less likely given the data. [ABSTRACT FROM AUTHOR]

Invasive species cause severe threats to biodiversity and the economy, but often across multi‐actor landscapes. Therefore, collaboration among multiple actors across landscapes is often essential for managing invasive species. Deciding whether to collaborate depends on available resources and potential benefits. As such, it is vital to identify how different factors can influence the outcomes of collaborations.We developed and tested a conceptual model that integrates potential process factors and performance measures for outcomes of collaborative invasive species management. We surveyed professionals engaged in invasive species management in Queensland, Australia. We first assessed their perception of the presence of process factors and perception of the performance measures of collaborative invasive species management projects using descriptive methods. Then we tested the associations between process factors and performance measures using structural equation modelling.While confirming that a good collaboration of stakeholders (e.g. communication and trust) is essential to enhance performances, our results suggest that the way collaborative projects are structured (e.g. ability to participate in decision‐making, having shared goals) also significantly influences the performance of the projects, especially in achieving goals. Furthermore, results suggest that achieving long‐term collaborations and outcomes is directly influenced by the extent to which stakeholders support or oppose the collaboration. As such, our study contributes to a significant research gap by establishing a connection between the collaboration process and its outcomes.We conclude that decision‐makers should give equal importance to both designing favourable collaborative project structures and fostering collaborations with stakeholders through different means to enhance the benefits of collaboration in invasive species management. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Despite the considerable success of deep learning methods in modelling physical processes, they suffer from a variety of issues such as overfitting and lack of interpretability. In hydrology, conceptual rainfall-runoff models are simple yet fast and effective tools to represent the underlying physical processes through lumped storage components. Although conceptual rainfall-runoff models play a vital role in supporting decision-making in water resources management and urban planning, they have limited flexibility to take data into account for the development of robust region-wide models. The combination of deep learning and conceptual models has the potential to address some of the aforementioned limitations. This paper presents a sub-model hybridization of the GR4J rainfall-runoff model with deep learning architectures such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The results show that the hybrid models outperform both the base conceptual model as well as the canonical deep neural network architectures in terms of the Nash–Sutcliffe Efficiency (NSE) score across 223 catchments in Australia. We show that our hybrid model provides a significant improvement in predictive performance, particularly in arid catchments, and generalizing better across catchments. • Hybrid modelling features parsimony of data-driven and conceptual hydrologic models. • We present a deep learning-based hybridization for the GR4J rainfall-runoff model. • We present a hierarchical training approach consisting of gradient-based and evolutionary optimization of the respective models. • The results show that hybrid models outperform the canonical (base) hydrologic and deep learning model. • The addition of surface temperature and antecedent streamflow further improves the predictive performance. [ABSTRACT FROM AUTHOR]

In gassy underground and spontaneous combustion prone coal mines, goaf management is a challenging task to reduce the risks of gas explosion and spontaneous combustion to acceptable levels. Goaf gas drainage via vertical surface goafholes is a primary control method to manage gas emissions in Australian coal mines. However, the operation of vertical surface goafholes maybe significantly impacted by insufficient understanding of goaf gas flow and coal self-heating behaviour, especially under different goaf gas drainage operating conditions. This paper focuses on analysing the field monitoring data collected from 161 goaf drainage holes at two coal mines in Australia. The data formed the basis to develop a conceptual model, which represents the changes of goaf atmosphere during the longwall retreat along with goaf drainage operations. The results indicate that the goaf drainage operating conditions may significantly affect goaf gas composition, which contributes to a different goaf atmosphere in comparison to existing models without goaf drainage. Within the studied longwall goaf, the 'oxidation zone' and 'inert zone' are identified with different carbon monoxide (CO) and oxygen (O 2) trends, and the 'oxidation zone' is conceptually further divided into the 'heat accumulation zone', 'elevated oxidation zone' and 'slow oxidation zone' based on the variation of gas composition and potential degree of oxidation of residual coal. The coal oxidation potential risk and corresponding goaf gas drainage strategies at these conceptual zones are also evaluated in this paper. The results of this study are expected to act as a guideline for future goaf gas drainage operations. • Gas composition analysis of goaf drainage production data was conducted. • A goaf environment conceptual model under various goaf gas drainage was proposed. • Coal oxidation risk at different goaf zones have been analysed. • Relevant goaf gas drainage plans have been proposed based on the conceptual model. [ABSTRACT FROM AUTHOR]

By examining a coastal zone with a long-term history of management strategies, the focus of this paper is on the evolution of a beach system on the updrift of a sequence of coastal interventions. Here, we review previous assessments of the natural morphodynamic adjustments of the Letitia Spit (New South Wales, Australia) in response to the construction of the Tweed River training walls (c. 1964) and a subsequent permanent artificial sand bypassing system (c. 2001) downdrift of our study site. In addition, the temporal and spatial scale of the changes on the sediment deposits is investigated for a period of 53 years (1967 to 2020). The results showed that while the immediate updrift sector (~1 km long) responds rapidly (from the first few months to two-three years) to the introduction of the coastal management structures, the extension of those impacts further updrift along the beach (~3 km) is gradual and takes decades. About two to three decades were necessary to reach a new equilibrium state after the accretionary period caused by the training wall obstruction of the littoral drift, whereas the erosional state derived from the commencement of the artificial bypassing continued for about one decade before the new equilibrium was reached. Overall, this paper contributes to the understanding of the evolution of an updrift beach in response to coastal protection works and highlights the importance of long-term morphological monitoring of the beach – from the dune to the depth of closure – for informing the planning of future coastal interventions and improving morphodynamic conceptual models focused on coastal structures and adjacent coastlines. [ABSTRACT FROM AUTHOR]

Bondage/discipline, dominance/submission, and sadism/masochism (BDSM; "kink") are frequently pathologized as derivatives of abuse. Although the link is unsubstantiated, some kink-identified people who happen to be survivors of trauma may engage in kink, or trauma play, to heal from, cope with, and transform childhood abuse or adolescent maltreatment. The present study sought a thematic model (Braun & Clarke, Qualitative Research in Psychology, 3(2), 77–101, 2006) of trauma recovery through kink using a critical realist, inductive approach to inquiry. Participants were eligible if they had experienced early abuse, were adults, and practiced kink. Six superordinate themes were generated from semi-structured interviews with 20 participants from five countries: cultural context of healing (e.g. using BDSM norms and previous therapy to reframe kink and trauma), restructuring the self-concept (e.g. strengthening internal characteristics which had been harmed or distorted), liberation through relationship (e.g. learning to be valued by intimate others), reclaiming power (e.g. setting and maintaining personal boundaries), repurposing behaviors (e.g. engaging in aspects of prolonged exposure), and redefining pain (e.g. transcending painful memories through masochism). Notably, participants only reported retraumatizing experiences prior to learning about the structural safeguards of BDSM. Research and clinical implications are discussed by drawing on general models of trauma recovery. [ABSTRACT FROM AUTHOR]

Earth's tropical savannas typically support high biomass of diverse grazing herbivores that depend on a highly fluctuating resource: high-quality forage. An annual wet–dry cycle, fire and herbivory combine to influence forage quality and availability throughout the year. In the savannas of northern Australia, a depauperate suite of large native (marsupial) herbivores (wallaroos [Osphranter spp.] and the agile wallaby [Notamacropus agilis]) compete for resources with non-native large herbivores introduced in the late nineteenth century, particularly bovines (feral and managed cattle [Bos spp.] and feral water buffalo [Bubalus bubalis]) that now dominate the landscape. Anecdotal reports of recent population declines of large macropods and negative impacts of bovines highlight the need to better understand the complex relationship between forage, fire and abundance of native and introduced large herbivores. The pyric herbivory conceptual model, which posits complex feedbacks between fire and herbivory and was developed outside Australia, predicts that native and introduced large herbivores will both respond positively to post-fire forage production in Australian savannas where they co-occur. We used grazing exclosures, forage biomass and nutrient analyses and motion-sensor camera-trapping to evaluate the overall robustness of the pyric herbivory model in the Australian context, specifically whether forage quantity and quality are impacted by herbivory, season and fire activity, and which forage attributes most influence large grazing herbivore abundance. Forage quantity, as measured by live, dead and total herbaceous biomass and proportion of biomass alive, was higher inside herbivore exclosures, even at relatively low densities of herbivores. Forage quality, as measured by fibre content, was not affected by herbivory, however, crude protein content of live herbaceous biomass was greater outside herbivore exclosures. Recent fire was an important predictor of all measures of forage quantity and quality. Recent fire occurrence decreased overall quantity (biomass) but increased quality (decreased fibre content and increased crude protein content); late dry season fires resulted in forage with the highest crude protein content. The predictions of the pyric herbivory conceptual model are consistent with observations of the feeding behaviour of introduced bovines and some large macropods in northern Australian savannas, lending support to the global generality of pyric herbivory in fire-prone grassy biomes. [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]

Rapid development of underground space necessitates the efficient management of underground areas. Data modelling plays an underpinning role in integrating and managing underground physical and legal data. The physical data refers to semantic and spatial data of underground assets such as utilities, tunnels, and basements, while the legal data comprises the ownership information and the extent of underground legal spaces and the semantic and spatial relationships between legal spaces. Current Underground Land Administration (ULA) practices mainly focus on representing only either legal spaces or the physical reality of subsurface objects using fragmented and isolated 2D drawings, leading to ineffective ULA. A complete and accurate 3D representation of underground legal spaces integrated with the 3D model of their physical counterparts can support different use cases of ULA beyond underground land registration, such as planning, design and construction of underground assets (e.g. tunnels and train stations), utility management and excavation. CityGML is a prominent semantic data model to represent 3D urban objects at a city scale, making it a good choice for underground because underground assets such as tunnels and utilities are often modelled at city scales. However, CityGML, in its current version, does not support legal information. This research aims to develop an Application Domain Extension (ADE) for CityGML to support 3D ULA based on the requirements defined in the Victorian state of Australia. These requirements include primary underground parcels and secondary underground interests. This work extends CityGML 3.0, which is the new version of this model. In CityGML 3.0, UML conceptual models as platform-independent models are suggested to express ADEs. Thus, the ADE proposed in this study will be based on UML. The findings of this study show that extending CityGML to support legal information can be a viable solution to meet the requirements of a 3D integrated model for ULA. The CityGML ADE proposed in this study can potentially provide a new solution for 3D digital management of underground ownership rights in Victoria, and it can be used to implement an integrated 3D digital data environment for ULA. [ABSTRACT FROM AUTHOR]

Purpose: In Australia, it is estimated that volunteers provide over $14.6 billion of unpaid labour to not-for-profit organisations. Much of the work that volunteers undertake is within a project context, yet the impact of a project and its environment on volunteer commitment is unknown. A conceptual model proposes three categories of factors that impact volunteer commitment to a project: purposeful, emotional and contextual. The purpose of this research is to empirically explore the conceptual model of volunteer commitment with volunteers working on projects in order to seek support for, and refine, the model. Design/methodology/approach: The research design was exploratory and Q methodology was used as a framework to collect and analyse data. Forty-one Australian volunteers engaged in project-based tasks participated in the study. Findings: Refinement of the initial conceptual model of volunteer commitment is required. The three categories that influence volunteer commitment to a project should be labelled "my contribution", "relationships" and "the project". The revised model shows early evidence that the project itself is the main commitment building factor. Originality/value: Whilst volunteer retention is not a new field of knowledge, research such as this further informs not-for-profit organisations in their volunteer recruitment and retention practices. [ABSTRACT FROM AUTHOR]

A method is presented to address model state uncertainty in hydrologic model simulation. This is achieved by introducing tuneable parameters that allow adjustments to the model states. Excessive dimensionality is avoided by introducing only a limited number of parameters that control the index (timing) and size of the state adjustments. The method is designed to compensate for issues with hydrologic model structures, particularly those relevant to the soil moisture state in a rainfall‐runoff model. In the context of water resource planning and management, errors in the model states have often been overlooked as an important source of uncertainty and have the potential to significantly degrade model simulations. A synthetic study shows that a classical parameter estimation approach will produce biased distributions when state errors exist, and that the proposed state and parameter uncertainty estimation (SPUE) can remove the bias in parameter estimates for improved model simulations. In a real case study, SPUE and the classical approach are implemented in 46 sites around Australia. The results show that hydrologic parameter distributions for a selected conceptual model can be significantly different when accounting for state uncertainty. This has large implications for scenario modeling since it puts into dispute how to determine appropriate parameters for such studies. SPUE outperforms the classical approach in a range of calibration and validation metrics, particularly for sites that contain zero flows. Future work involves testing SPUE with different hydrologic models and likelihood formulations, and enhancing rigor by explicitly accounting for observational data uncertainty. Key Points: A method is presented that allows calibration of the timing and size of a limited number of hydrologic state adjustmentsThe method aims to obtain improved simulations for scenario modeling in water resource planning and managementThe method outperformed a classical parameter estimation approach in synthetic and real case settings [ABSTRACT FROM AUTHOR]