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This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

This paper starts from the point that our current political‐economic‐climate conjuncture demands new engagements at the dynamic interface of climate capitalism. Using two cases of climate capitalist responses to climate challenges, I demonstrate the reparative potentials that emerge from the tensions and ambiguities that typify that conjuncture. In the first case, I examine financialised climate infrastructure in Jakarta, Indonesia, that promises to protect the city from flooding while enriching city elites, but against which diverse social movements and collectives have organised. The second case is about a cooperative energy provider in Australia, operating on the terrain of a neoliberalised electricity market and climate change, and working towards multifaceted repair by collectivising and redistributing surplus and modelling democratic engagement. Those involved in these vastly different cases both pursue repair and reparations through climate capitalist projects by reckoning with historical and present climate debts while constructing forward‐looking programs. As such, they chart the first steps towards reparative climate futures. This paper identifies the political and socioecological potentials from two key climate capitalist projects: flood protection infrastructures in Jakarta and cooperative energy in Australia. From this, it builds a framework of repair and reparations for engaging with climate capitalism and argues that the emerging tensions and ambiguities facilitate steps towards reparative climate futures. [ABSTRACT FROM AUTHOR]

Summary: Natural resource management in Australia is beset with 'wicked' problems: diminishing biodiversity, increasing soil erosion, spreading soil salinity and global climate change all impact private landholders across rural Australia. These problems highlight the complexity of biodiversity conservation, and the need for inclusive, respectful approaches that enable the participation of rural communities, private landholders and government agencies to effectively manage biodiversity on public and private lands. To address these problems, some landholders, rural communities and other stakeholders are seeking evidence‐based solutions through directly applicable research. In this paper, we identify current barriers to such research and highlight principles and processes for co‐designing and managing research based on mutual trust, respect, power sharing and acceptance of various knowledge systems, as embodied in the theory of reconciliation ecology. We suggest that a 'community of practice' approach could assist in establishing effective deliberative relations as a basis for active collaboration in natural resource management research to address these complex problems. [ABSTRACT FROM AUTHOR]

Future water availability is threatened by changes in both climate and water demand. Water rights with differing priorities are an important foundation of demand‐side tools (e.g., buyback, water pricing, and water market) to improve water use efficiency and reduce water scarcity, especially in highly regulated river systems. This paper assesses the impact of climate change on water yields from carryover storage with dual‐priority (high/low) water rights allocation systems using a simple and rapid analytical method. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and Cv). We evaluate this method against a water resource simulation model in the Goulburn River basin, Australia. In general, our analytical "dual‐priority" Gould‐Dincer model reproduces water allocation estimates from the simulation model. We further demonstrate this method across 12 Australian catchments to investigate the climate change impact on "dual‐priority" water rights yield at the catchment scale. The hydrological projections show decreasing mean annual runoff and increasing annual runoff variability, except for some catchments in northern Australia. Water yield for high‐priority water rights (HPWRs) and low‐priority water rights (LPWRs) decreases for most catchments except for some catchments in northern Australia. South Dandalup in the 2070s (RCP8.5) shows the largest percentage decrease in HPWR and LPWR yield (about −53.53% and −56.81%, respectively). Our results show that changes in mean annual inflow have a more significant influence on water yield of HPWR and LPWR than Cv. Overall, the simple method provides a rapid assessment of water yields with "dual‐priority" water rights which is applicable across multiple sites at regional or even global scale. Key Points: A simple analytical method is used to evaluate the impact of climate change on the yield of "dual‐priority" water rightsWater yield for both high‐ and low‐priority water rights generally decreases under future climate in AustraliaFuture change in mean annual inflow has a more significant influence on water yield than change in annual flow variability [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding, yet knowledge gaps constrain their conservation and further utilization. To address these gaps, the potential distributions of 22 RWR taxa were modeled, and their conservation statuses were assessed. Most taxa were identified as medium priority for further conservation. Further ex‐situ collecting hotspots are in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas, while habitat protection lies in Southeast and South Asia, Northern Australia, and West Africa. Climate change may shift equatorial habitats to higher latitudes and mountain habitats to higher altitudes. Summary: Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding. However, knowledge gaps on their geographic distributions and conservation status constrain their conservation and further utilization.To fill these critical gaps, we modeled the potential distributions of 22 RWR taxa under current climate scenarios, assessed their conservation status, both ex situ (in genebanks or botanical gardens) and in situ (in protected areas), and examined changes in taxa richness of RWR in predicted areas under future climate scenarios.The RWR were primarily distributed in tropical Asia to tropical Australia, tropical Africa, and South and Central America. We identified 4 out of 22 taxa as high priority (HP) for further conservation action and 18 taxa as medium priority (MP).Hotspots requiring further collecting for ex situ conservation are concentrated in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas. Meanwhile, habitat protection should be enhanced in Southeast and South Asia, Northern Australia, and West Africa. Under future climate change, suitable habitats near the equator are expected to shift toward higher latitudes and some in lower‐latitude basins may become unsuitable due to excessive heat, resulting in decreased taxa richness in these areas. Additionally, suitable habitats in high mountain areas may shift to higher altitudes, potentially augmenting taxa richness in the highlands. Our findings provide vital insights to guide future rescue conservation efforts for RWR. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

A reliable understanding of linkages between meteorological, hydrological and agricultural droughts (MD, HD, and AD respectively) is crucial to building resilience and planning for future climate changes. Despite Australia being prone to severe droughts, lagtimes of propagation (and recovery), from meteorological to hydrological and agricultural droughts across its large hydroclimatic regions, are poorly understood. Therefore, we investigate the characteristics of drought propagation and recovery time lags for droughts of four timescales and a combination of drought onset and cessation criteria in 407 unregulated catchments within six major precipitation zones across the country. We find that the propagation and recovery lags depend on climatic conditions, drought criteria and timescales. The median of catchment average propagation times from MD to HD across Australia varied from 0.8 to 1.7 months for 1‐month timescales, increasing to 2.2–5.0 months for 12‐month timescales. The corresponding recovery lagtimes were 1.3–3.7 and 1.7–7.0 months respectively. Similarly, the median of catchment average propagation times from MD to AD ranged from 0.8 to 1.9 months for 1‐month timescales, increasing to 0.6–5.0 months for 12‐month. The corresponding recovery lagtimes were 0.7–2.8 and 0.3–8.7 months respectively. For droughts of smaller timescales, propagation and recovery lags are linearly correlated with recovery lagtimes consistently greater than propagation times. However, as the timescale increases, these relationships weaken suggesting effects of other catchment attributes (e.g., groundwater contributions) on lag relationships. Plain Language Summary: The primary focus of the research is to investigate the time delay between the occurrence of the lack of precipitation (meteorological droughts) and its subsequent impact on river flow (hydrological drought) and soil moisture (agricultural drought). Understanding these delays is crucial for drought planning and management. This study uses observed precipitation, river flow, and satellite‐based soil moisture data spanning over 40 years across six major precipitation zones within Australia. The delays between drought types (meteorological, hydrological, and agricultural) vary depending on the specific location within Australia. The criteria used to define the onset and end of droughts, as well as the drought duration, also influence the observed delays. The average delay from meteorological to hydrological or agricultural droughts increases with longer timescales. For example, hydrological droughts measured on 1‐month timescale have shorter delays (0.8–1.7 months), while 12‐month droughts have longer delays (2.2–5 months). Similarly, shorter droughts have shorter recovery times, while longer droughts have longer recovery times. For shorter timescale droughts, there is a clear relationship between the delays with end delays consistently greater than the start delays. However, as droughts become larger and more prolonged, the relationship weakens, suggesting the influence of other catchment attributes. Key Points: Drought propagation and recovery behaviors for a large sample of catchments under varied climatic zones within Australia are investigatedPropagation and recovery lag relationships are well‐defined for shorter droughts but are increasingly indefinable for longer droughtsPropagation (TP) and recovery lags (TR) depend on drought severity with shorter TP to milder droughts and longer TR to milder droughts [ABSTRACT FROM AUTHOR]

Climate change involves increases in mean temperature and changes in temperature variability at multiple temporal scales but research rarely considers these temporal scales. The climate variability hypothesis (CVH) provides a conceptual framework for exploring the potential effects of annual scale thermal variability across climatic zones. The CVH predicts ectotherms in temperate regions tolerate a wider range of temperatures than those in tropical regions in response to greater annual variability in temperate regions. However, various other aspects of thermal regimes (e.g. diel variability), organisms' size and taxonomic identity are also hypothesised to influence thermal tolerance. Indeed, high temperatures in the tropics have been proposed as constraining organisms' ability to tolerate a wide range of temperatures, implying that high annual maximum temperatures would be associated with tolerating a narrow range of temperatures. We measured thermal regimes and critical thermal limits (CTmax and CTmin) of freshwater insects in the orders Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera (caddisflies) along elevation gradients in streams in temperate and tropical regions of eastern Australia and tested the CVH by determining which variables were most correlated with thermal breadth (Tbr = CTmax − CTmin). Consistent with the CVH, Tbr tended to increase with increasing annual temperature range. Tbr also increased with body size and Tbr was generally wider in Plecoptera than in Ephemeroptera or Trichoptera. We also find some support for a related hypothesis, the climate extreme hypothesis (CEH), particularly for predicting upper thermal limits. We found no evidence that higher annual maximum temperature constrained individuals' abilities to tolerate a wide range of temperatures. The support for the CVH we document suggests that temperate organisms may be able to tolerate wider ranges of temperatures than tropical organisms. There is an urgent need to investigate other aspects of thermal regimes, such as diel temperature cycling and minimum temperature. [ABSTRACT FROM AUTHOR]