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Freshwater ecosystems, including rivers and floodplain wetlands, face severe stress from unsustainable water resources development, with climate change exerting further pressure. This study compares the relative effects of river regulation and projected climate change on river flows to the semi-arid Lowbidgee Floodplain (3250 km 2 ), the largest wetland ecosystem on the heavily regulated Murrumbidgee River, Australia's second longest river, within the Murray-Darling Basin. We modelled annual natural streamflow in the lower Murrumbidgee River before major dam constructions and water diversions (1890-1927), linking river flows to runoff from the upper Murrumbidgee catchment. Extending this analysis to the full rainfall-runoff dataset (1890-2018), we compared modelled natural flows to observed river flows affected by dams and water withdrawals. Additionally, we modelled climate change impacts on river discharge and overbank flows, which reduced inundation of riparian habitats. Current river regulation has reduced median annual streamflow by 43% from 2565 × 10 6  m³ to 1490 × 10 6  m³ during 1958-2018, relative to modelled natural flows, with a more pronounced 55% reduction in the last three decades (1988-2018). The return period of major overbank flows, essential for river-floodplain habitat connectivity, more than doubled from once every 2.0 years to once every 4.4 years (1916-2018). Mean climate change projections indicated an additional 7-10% decrease in median annual streamflow by 2047-2075, relative to 1977-2005. The annual duration of major floods declined from an average of 11.3 days under natural flow conditions to 4.5 days under the current regulated river flow regime, with a further reduction to 1.6-1.8 days (83-85% decrease) projected by 2047-2075, due to climate change. We recommend prioritising mitigation of river regulation effects, as these pose the most immediate threats to riverine ecosystems, including their native biodiversity, in the Murrumbidgee River catchment. Our 'natural flow' model offers critical insights for shaping environmental policy and managing environmental flows to mimic natural flow regimes, supporting the conservation and restoration of freshwater ecosystems, like the Lowbidgee Floodplain wetlands. Our approach is transferable to other large river systems globally, using available or modelled streamflow data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Over two centuries of economic growth have put undeniable pressure on the ecological systems that underpin human well-being. While it is agreed that these pressures are increasing, views divide on how they may be alleviated. Some suggest technological advances will automatically keep us from transgressing key environmental thresholds; others that policy reform can reconcile economic and ecological goals; while a third school argues that only a fundamental shift in societal values can keep human demands within the Earth's ecological limits. Here we use novel integrated analysis of the energy-water-food nexus, rural land use (including biodiversity), material flows and climate change to explore whether mounting ecological pressures in Australia can be reversed, while the population grows and living standards improve. We show that, in the right circumstances, economic and environmental outcomes can be decoupled. Although economic growth is strong across all scenarios, environmental performance varies widely: pressures are projected to more than double, stabilize or fall markedly by 2050. However, we find no evidence that decoupling will occur automatically. Nor do we find that a shift in societal values is required. Rather, extensions of current policies that mobilize technology and incentivize reduced pressure account for the majority of differences in environmental performance. Our results show that Australia can make great progress towards sustainable prosperity, if it chooses to do so.

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 is a written version of the Munro Oration delivered at the Hydrology and Water Resources Symposium in Sydney on 13 November 2023. The presentation reflects on climate change and water resources, and on decades of research and practice in hydroclimate and hydrological modelling. The presentation highlights Australian contribution to global research and synthesis, challenges posed by Australia's unique hydrology, key hydroclimate trends and future projections for Australia, and knowledge, opportunities and challenges in water resources adaptation to hydroclimate variability and climate change. [ABSTRACT FROM AUTHOR]

The paper compares future streamflow projections for 133 catchments in the Murray–Darling Basin simulated by a hydrological model with future rainfall inputs generated from different methods informed by climate change signals from different global climate models and dynamically downscaled datasets. The results show a large range in future projections of hydrological metrics, mainly because of the uncertainty in rainfall projections within and across the different climate projection datasets. Dynamical downscaling provides simulations at higher spatial resolutions, but projections from different datasets can be very different. The large number of approaches help provide a robust understanding of future hydroclimate conditions, but they can also be confusing. For water resources management, it may be prudent to communicate just a couple of future scenarios for impact assessments with stakeholders and policymakers, particularly when practically all of the projections indicate a drier future in the Basin. The median projection for 2046–2075 relative to 1981–2010 for a high global warming scenario is a 20% decline in streamflow across the Basin. More detailed assessments of the impact and adaptation options could then use all of the available datasets to represent the full modelled range of plausible futures. [ABSTRACT FROM AUTHOR]

Accurately estimating actual evapotranspiration (ET) across global land surface is one of the key challenges in terrestrial hydrological cycles and energy flux balance studies. Gridded ET products have the potential for application in ungauged basins, but their uncertainties are possibly large and it remains unclear which one is best for a given basin. The water balance (WB) method provides a direct estimate of basin scale ET, but it cannot be used in ungauged basins where streamflow data are unavailable. Here, we first assess the performance of ET from 10 global ET products against WB ET estimates in 43 large river basins. The paper then uses three indirect evaluation methods [Three Cornered Hat (TCH), Arithmetic Average (AA), and Bayesian Three Cornered Hat] to identify the optimal ET products without the need of prior information, and to generate fusion products combining the ET from multiple products. Using the evaluation results derived from the WB method as the reference, the results show that the three methods have great success in identifying poorer products, suggesting that they are useful in filtering poor ET products in applications. However, the ability of such methods in identifying better ET products degrades slightly. The AA fusion product, which combines ET outputs from multiple products, is marginally better than the best single ET product in many of the 43 basins. Because of its simplicity, it could be used to reduce the uncertainty in ET estimates from multiple products for ungauged basins and regions. Key Points: A comprehensive study on reducing the uncertainty in evapotranspiration (ET) estimates from 10 global ET products for application in ungauged river basinsThe indirect evaluation methods without using prior information, particularly Arithmetic Average (AA) and Bayesian Three Cornered Hat can successfully identify the poor ET productsThe AA fusion product, which combines ET outputs from multiple products, is generally marginally better than the best single ET product [ABSTRACT FROM AUTHOR]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

Abstract: Accurate quantification of terrestrial evapotranspiration and ecosystem productivity is of significant merit to better understand and predict the response of ecosystem energy, water, and carbon budgets under climate change. Existing diagnostic models have different focus on either water or carbon flux estimates with various model complexity and uncertainties induced by distinct representation of the coupling between water and carbon processes. Here, we propose a diagnostic model to estimate evapotranspiration and gross primary production that is based on biophysical mechanism yet simple for practical use. This is done by coupling the carbon and water fluxes via canopy conductance used in the Penman–Monteith–Leuning equation (named as PML_V2 model). The PML_V2 model takes Moderate Resolution Imaging Spectrometer leaf area index and meteorological variables as inputs. The model was tested against evapotranspiration and gross primary production observations at 9 eddy‐covariance sites in Australia, which are spread across wide climate conditions and ecosystems. Results indicate that the simulated evapotranspiration and gross primary production by the PML_V2 model are in good agreement with the measurements at 8‐day timescale, indicated by the cross site Nash–Sutcliffe efficiency being 0.70 and 0.66, R2 being 0.80 and 0.75, and root mean square error being 0.96 mm d−1 and 1.14 μmol m−2 s−1 for evapotranspiration and gross primary production, respectively. As the PML_V2 model only requires readily available climate and Moderate Resolution Imaging Spectrometer vegetation dynamics data and has few parameters, it can potentially be applied to estimate evapotranspiration and carbon assimilation simultaneously at long‐term and large spatial scales. [ABSTRACT FROM AUTHOR]

• Quantile-quantile rainfall mapping methods are extended to improve streamflow simulations. • Extensions correct rainfall bias at multiple time scales and model rainfall autocorrelation. • Methods reduce bias in rainfall sequencing metrics for downscaled GCM simulations. • Methods reduce bias in streamflow simulations forced by downscaled GCM simulations. Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated. All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall. [ABSTRACT FROM AUTHOR]

Future changes in floods and water availability across China under representative concentration pathway 2.6 (RCP2.6) and RCP8.5 are studied by analyzing discharge simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) with the consideration of uncertainties among global climate models (GCMs) and hydrologic models. Floods and water availability derived from ISI-MIP simulations are compared against observations. The uncertainties among models are quantified by model agreement. Only model agreement >50% is considered to generate reliable projections of floods and water availability and their relationships with climate change. The results show five major points. First, ISI-MIP simulations have acceptable ability in modeling floods and water availability. The spatial patterns of changes in floods and water availability highly depend on the outputs of GCMs. Uncertainties from GCMs/hydrologic models predominate the uncertainties in the wet/dry areas in eastern/northwestern China. Second, the magnitudes of floods throughout China increase during 2070-99 under RCP8.5 relative to those with the same return periods during 1971-2000. The increase rates of larger floods are higher than those of the smaller ones. Third, water availability decreases/increases in southern/northern China under RCP8.5, but changes negligibly under RCP2.6. Fourth, more severe floods in the future are driven by more intense precipitation extremes over China. The negligible change in mean precipitation and the increase in actual evapotranspiration reduce the water availability in southern China. Fifth, model agreements are higher in simulated floods than water availability because increasing precipitation extremes are more consistent among different GCM outputs compared to mean precipitation. [ABSTRACT FROM AUTHOR]

Southeastern Australia experienced the worst drought of the instrumental record from 1997 to 2009, which was broken by Australia's wettest 2 year period on record (2010/2011). This drought was primarily a cool season (April to October) phenomenon. In contrast, the breaking of the drought was a warm season (November to March) phenomenon. This reduction in winter rainfall along with an absence of very wet months led to a greater reduction in streamflow across the region than would be anticipated based on the 12% reduction in mean annual rainfall alone. The results presented in this article have linked the extent, duration, and severity of this drought to the ongoing observed expansion of the Southern Hemisphere Hadley cell at a rate of 0.5°/decade. This expansion has intensified the subtropical ridge over southern Australia, pushing cool season midlatitude storm tracks further south, leading to a reduction in winter rainfall and runoff across the region. [ABSTRACT FROM AUTHOR]

Abstract: Previous climate risk assessments provide important methodological insights into how to derive tractable research questions and the appropriate use of data under uncertainty, as well as identifying steps that benefit from stakeholder involvement. Here we propose the use of a framework for the systematic and objective exploration of climate risk assessments. The matrix facilitates a breakdown of information about aim and context, main results, methodological choices, stakeholder involvement, sources and characteristics of uncertainties and overall weaknesses. We then apply the matrix to three risk assessments in the water sector to explore some methodological strengths and weaknesses of approaches strongly linked to climate model outputs (top-down) versus those that originate from local knowledge of climate exposures (bottom-up), and demonstrate that closer integration with social and physical sciences is more likely to yield robust climate risk assessments. [Copyright &y& Elsevier]

This study examines the representation of one of the key drivers of southeastern Australian rainfall, the sub-tropical ridge (STR) in mean sea level pressure, in the World Climate Research Programme's Coupled Model Intercomparison Project phase 3 multi-model dataset. In particular, the mean state and variability of the ridge's intensity and position is diagnosed and compared to observations for the 20th Century. The mean annual cycle of the STR intensity and position are found to be moderately well represented compared to two observational datasets. The models project that STR intensity will increase over the 21st Century whilst its mean position will be further south. Given the historical relationship between the STR and rainfall, this suggests that southeastern Australia will most likely be drier in the future. Most models fail to reproduce the spatial pattern of correlation between the STR intensity and precipitation but some models show a correlation (significant in some cases) between high rainfall and a weaker STR intensity. However, these correlations are much weaker than the observations. Given that the annual cycle of the STR has significant implications for rainfall in southeastern Australia, this is of some concern and leads to questions about which processes are driving rainfall in the models. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

• Statistical analysis is used to quantify nonstationary rainfall-runoff relationship. • Millennium Drought and pre and post drought data in south-east Australia are used. • Rainy days and wet-spell length can predict streamflow in varied hydroclimates. • Key rainfall characteristics in different periods help explain nonstationarity. • Results explain climate change and variability impacts on regional streamflow. This study demonstrates the benefits of using a simple statistical analysis to investigate the attributions of climate variables to the nonstationary rainfall-streamflow relationships. Rainfall and streamflow data from 65 catchments in southeast Australia are used to quantify the rainfall-streamflow relationship prior to, during, and after, the 1997–2009 Millennium Drought. The results showed that: (1) the annual streamflow is strongly correlated to the annual rainfall, however the relationship is different in the different hydroclimate periods; (2) the number of rainfall days and the mean length of wet-spell (MeWS) are the two most "stable variables" that can best predict annual streamflow in the different hydroclimate periods, followed by the maximum and 99th percentile of 90-day accumulated total rainfall (D90Max and D90P99); (3) the combination of MeWS with several other rainfall characteristics can explain the nonstationary rainfall-streamflow relationship through the different hydroclimate conditions in the eastern part of the study region; and (4) the relative differences in the key rainfall characteristics can help explain the nonstationary rainfall-streamflow relationship. The results here can help identify the key rainfall and climate characteristics to be considered in assessing the impacts of climate variability and change on streamflow, and potentially for developing hydrological models that can be better extrapolated to predict streamflow under a changing climate. [ABSTRACT FROM AUTHOR]

The trend to a hotter and drier climate, with more extended droughts, has been observed in recent decades in southern Australia and is projected to continue under climate change. This paper reviews studies on the projected impacts of climate change on groundwater and associated environmental assets in southern Australia, and describes groundwater planning frameworks and management responses. High-risk areas are spatially patchy due to highly saline groundwater or low-transmissivity aquifers. The proportional reduction in rainfall is amplified in the groundwater recharge and some groundwater discharge fluxes. This leads to issues of deteriorating groundwater-dependent ecosystems, streamflow depletion, reduced submarine discharge, groundwater inundation and intrusion in coastal regions and reduced groundwater supply for extraction. Recent water reforms in Australia support the mitigation of these impacts, but groundwater adaptation is still at its infancy. Risk management is being incorporated in regional water and groundwater management plans to support a shift to a more sustainable level of use and more climate-resilient water resources in affected areas. The emerging strategies of groundwater trade and managed aquifer recharge are described, as is the need for a national water-focused climate change planning process. [ABSTRACT FROM AUTHOR]

This study extends the single parameter precipitation elasticity of streamflow index into a two parameter climate elasticity index, as a function of both precipitation and temperature, in order to assess climatic effects on annual streamflow. Application of the proposed index to two basins indicates that the single parameter precipitation elasticity index may give a general relationship between precipitation and streamflow in some cases, but that it cannot reflect the complicated non-linear relationship among streamflow, precipitation and temperature. For example, for the Spokane River basin the climate elasticity of streamflow index varies from 2.4 to 0.2, for a precipitation increase of 20%, as temperature varies from 1°C lower to 1.8°C higher than the long term mean. Thus a 20% precipitation increase may result in a streamflow increase of 48% if the temperature is 1°C lower but only a 4% increase if the temperature is 1.8°C higher than the long-term mean. The proposed method can be applied to other basins to assess potential climate change effects on annual streamflow. The results of the two case studies can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Hydrological model sensitivity to climate change can be defined as the response of a particular hydrological model to a known quantum of climate change. This paper estimates the hydrological sensitivity, measured as the percentage change in mean annual runoff, of two lumped parameter rainfall-runoff models, SIMHYD and AWBM and an empirical model, Zhang01, to changes in rainfall and potential evaporation. These changes are estimated for 22 Australian catchments covering a range of climates, from cool temperate to tropical and moist to arid. The results show that the models display different sensitivities to both rainfall and potential evaporation changes. The SIMHYD, AWBM and Zhang01 models show mean sensitivities of 2.4%, 2.5% and 2.1% change in mean annual flow for every 1% change in mean annual rainfall, respectively. All rainfall sensitivities have a lower limit of 1.8% and show upper limits of 4.1%, 3.4% and 2.5%, respectively. The results for potential evaporation change are −0.5%, −0.8% and −1.0% for every 1% increase in mean annual potential evaporation, respectively, with changes rainfall being approximately 3–5 times more sensitive than changes in potential evaporation for each 1% change in climate. Despite these differences, the results show similar correlations for several catchment characteristics. The most significant relationship is between percent change in annual rainfall and potential evaporation to the catchment runoff coefficient. The sensitivity of both A and B factors decreases with an increasing runoff coefficient, as does the uncertainty in this relationship. The results suggest that a first-order relationship can be used to give a rough estimate of changes in runoff using estimates of change in rainfall and potential evaporation representing small to modest changes in climate. Further work will develop these methods further, by investigating other regions and changes on the subannual scale. [Copyright &y& Elsevier]

Presents the likely impacts of climate change on runoff, evapotranspiration and soil moisture in agricultural regions of Australia. Comparison of the water fluxes simulated by a hydrologic model using present climate data and greenhouse-enhanced climate scenarios predicted by general circulation models; Results indicating that changes in rainfall are amplified in runoff; Estimates of annual change in runoff Australia and Tasmania by the year 2030; Results indicating the potential for climate change to bring about runoff modifications that may require a significant planning response.

Climate change is threatening water security in water-scarce regions across the world, challenging water management policy in terms of how best to adapt. Transformative new approaches have been proposed, but management policies remain largely the same in many instances, and there are claims that good current management practice is well adapted. This paper takes the case of the Murray–Darling Basin, Australia, where management policies are highly sophisticated and have been through a recent transformation in order to critically review how well adapted the basin's management is to climate change. This paper synthesizes published data, recent literature, and water plans in order to evaluate the outcomes of water management policy. It identifies several limitations and inequities that could emerge in the context of climate change and, through synthesis of the broader climate adaptation literature, proposes solutions that can be implemented when basin management is formally reviewed in 2026. [ABSTRACT FROM AUTHOR]

This paper investigates the prediction of different streamflow characteristics in ungauged catchments and under climate change, with three rainfall-runoff models calibrated against three different objective criteria, using a large data set from 780 catchments across Australia. The results indicate that medium and high flows are relatively easier to predict, suggesting that using a single unique set of parameter values from model calibration against an objective criterion like the Nash–Sutcliffe efficiency is generally adequate and desirable to provide a consistent simulation and interpretation of daily streamflow series and the different medium and high flow characteristics. However, the low flow characteristics are considerably more difficult to predict and will require careful modelling consideration to specifically target the low flow characteristic of interest. The modelling results also show that different rainfall-runoff models and different calibration approaches can give significantly different predictions of climate change impact on streamflow characteristics, particularly for characteristics beyond the long-term averages. Predicting the hydrological impact from climate change, therefore, requires careful modelling consideration and calibration against appropriate objective criteria that specifically target the streamflow characteristic that is being assessed. [ABSTRACT FROM AUTHOR]

The potential cumulative impact of coal mining and coal seam gas extraction on water resources and water-dependent assets from proposed developments in eastern Australia have been recently assessed through a Bioregional Assessment Programme. This study investigates the sensitivity of the Bioregional Assessment results to climate change and hydroclimate variability, using the Gloucester sub-region as an example. The results indicate that the impact of climate change on streamflow under medium and high future projections can be greater than the impact from coal mining development, particularly where the proposed development is small. The differences in the modelled impact of coal resource development relative to the baseline under different plausible climate futures are relatively small for the Gloucester sub-region but can be significant in regions with large proposed development. The sequencing of hydroclimate time series, particularly when the mine footprint is large, significantly influences the modelled maximum coal resource development impact. The maximum impact on volumetric and high flow variables will be higher if rainfall is high in the period when the mine footprint is largest, and vice-versa for low flow variables. The results suggest that detailed analysis of coal resource development impact should take into account climate change and hydroclimate variability. [ABSTRACT FROM AUTHOR]

• A framework developed for investigating cumulative impact of damming on water. • Tibetan damming having serious downstream impact but limited to local scale. • Runoff increase under warmer climate most likely offsetting damming impact. • This framework can be applicable for various anthropogenic impact analysis. Damming is one of the major methods that human being secure reliable freshwater. It can change flow magnitude and regime along a river, particularly for Transboundary Rivers shared by multiple jurisdictions. This study develops a coupled framework to assess both the current and future cumulative impacts of damming on downstream water availability for data-sparse regions like the Tibetan Plateau. The framework is applied to the Brahmaputra River Basin (BRB), where dam developments on the Tibetan Plateau are highly concerned. The results show that, despite being part of the 'Asian water tower', the Yarlung Tsangpo River (YTR) contributes just 15–16% to Brahmaputra streamflow, for 48% of its contributing area. We found that the maximum impact due to five recently constructed dams in YTR suggests about 8–11% reduction of mean annual streamflow at outlet of Brahmaputra River. The impacts of damming would be largely offset by runoff increases under the climate scenarios projected by 28 CMIP5 model outputs from RCP4.5 median-level emission scenarios and 29 CMIP5 model outputs from RCP8.5 high-level emission scenarios for the period of 2049 to 2075, compared to the historical period of 1979 to 2005. Our results suggest that it is necessary to use our framework or a similar one to comprehensively evaluate the maximum possible damming impact across the Transboundary Rivers. [ABSTRACT FROM AUTHOR]

• On-farm water storages used in rural areas impact catchment water balances. • Modelling on-farm water storages improves hydrological model calibration. • On-farm water storage reduces catchment runoff by 13% in the Murray-Darling Basin. • Climate change impacts on streamflow are amplified by on-farm storages. Storage of water in farm dams is important to support irrigation, stock requirements and domestic uses when reticulated water is unavailable. Farm dams that fill by intercepting landscape runoff change the total volume and seasonality of catchment streamflow, potentially impacting water policy outcomes. While numerous studies have quantified how climate change and farm dams independently change streamflow characteristics, few studies have investigated their interactions. This study investigates the interactions between farm dams and climate change in the Murray-Darling Basin of southern and eastern Australia. We use hydrological modelling that explicitly represent farm dams and remotely sensed data describing historical farm dam development to assess the impacts of farm dams for 112 catchments on catchment runoff under current and future climates. Our analysis compares current and future runoff estimates to those derived without explicitly incorporating farm dam impacts to better understand the importance of farm dams in water resources assessments. We find that modelling farm dams in these catchments improves the performance of the hydrological model relative to a traditional rainfall-runoff model ignoring the effects of farm dams. The current level of farm dam development in the Murray-Darling Basin is estimated to reduce annual catchment runoff by 13% (in the range of 8%–19%) under the historical climate across the 112 catchments investigated here. We find that explicitly modelling farm dams increases the sensitivity of simulated streamflow to climate by approximately 10%. This is primarily because farm dams introduce additional catchment storage and water extractions that do not directly follow processes represented in traditional rainfall-runoff models. Our analysis offers important information for water policymakers in Australia and globally. Firstly, explicit modelling of farm dams is critical to understanding the future availability of water resources. Secondly, explicit modelling of farm dams allows their impacts on catchment runoff to be explored under current and future climates, and for policies to manage the impacts of dams to be investigated. [ABSTRACT FROM AUTHOR]

Globally, irrigation accounts for more than two thirds of freshwater demand. Despite their importance, the geographic distribution and water use of many important irrigated areas remains uncertain. This paper advances a methodology to map summer cropping irrigated areas experiencing extreme climate and different management practices in the Murray–Darling Basin (Australia). A Random Forest classification model was implemented to austral hemisphere summer irrigated areas for the water-years 2004/05 to 2010/11. The methodology used training samples from Landsat TM/ETM + reflectance data and monthly time-series of remotely-sensed observations from the MODerate resolution Imaging Spectroradiometer (MODIS). The covariates included in the classification model characterised the monthly dynamics and rates of change of: (i) the vegetation phenology via the recurrent and persistent absorbed fractions of photosynthetically active radiation (fPAR rec and fPAR per , respectively); (ii) water use via remotely-sensed estimates of actual evapotranspiration ( ET a ), precipitation ( P ) and the difference between ET a and P . Observed agreement – in terms of the kappa coefficient – for correctly classified pixels in the training sample was 96%. Independent comparisons of yearly irrigated area estimates showed linear relationships with Pearson's correlation coefficients ( r ) generally greater than 0.7 for: (i) reported areas; (ii) areas with available metered water withdrawals; and (iii) estimates of agricultural yields. Sequential covariate optimisation suggested that the most important predictors to identify of irrigation areas included the emergence–senescence period (as determined by the fPAR rec and corresponding rates of change) and the ET a surplus over P during this period. The latter can be important when identifying supplementary irrigation due to periodically unreliable water supply in areas with otherwise high precipitation that are in-phase with summer crop growth. [ABSTRACT FROM AUTHOR]

Summary: Six methods of downscaling GCM simulations to multi-site daily precipitation were applied to a set of 30 rain gauges located within South-Eastern Australia. The methods were tested at reproducing a range of statistics important within hydrological studies including inter-annual variability and spatial coherency using both NCEP/NCAR reanalysis and GCM predictors, thus testing the validity of GCM downscaled predictions. The methods evaluated, all having found application in Australia previously, are: (1) the dynamical downscaling Conformal-Cubic Atmospheric Model (CCAM) of ; the historical data based (2) Scaling method applied by and (3) Analogue method of ; and three stochastic methods, (4) the GLIMCLIM (Generalised Linear Model for daily Climate time series) software package (), (5) the Non-homogeneous Hidden Markov Model (NHMM) of Charles et al. (1999), and (6) the modified Markov model–kernel probability density estimation (MMM–KDE) downscaling technique of . The results showed that the simple Scaling approach provided relatively robust results for a range of statistics when GCM forcing data was used, and was therefore recommended for regional water availability and planning studies (subject to certain limitations as mentioned in conclusion section). The stochastic methods better capture changes to a fuller range of rainfall statistics and are recommended for detailed catchment modelling studies. In particular, the stochastic methods show better results for daily extreme rainfall (e.g. flooding/low flow) as the simulations are not based purely on temporal/spatial rainfall patterns observed in the past, and a hybrid GLIMCLIM occurrence-KDE amounts model is recommended based on performance for individual statistics. For GCM downscaled simulations, biases in annual mean and standard deviation of ±5% and −30% were observed typically, and no single model performed well over all timescales/statistics, suggesting that the user beware of model limitations when applying downscaling methods for various purposes. A brief demonstration of predictor biases is presented, highlighting that biases observed in GCM predictors can cause poorer performance during GCM validation, and that investigation of these biases should inform choice of GCMs, GCM predictors, and the downscaling methods that use them. [Copyright &y& Elsevier]