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Simple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability. Plain Language Summary: We have assessed the performance of three simple models for estimating floodwater depth by evaluating their accuracy against different Digital Elevation Model inputs, characteristics of study area, and magnitude of flood events. Our results emphasize that selection of an appropriate model depends on the circumstances. This study provides recommendations for future studies that enhance remote sensing‐based flood maps with water depth, making use of the growing number of publicly available remote sensing water extent and digital elevation data sets globally. All the methods have been coded in Python and are freely available at https://github.com/csiro-hydroinformatics/water-depth-estimation. Key Points: We assessed three simple models for estimating floodwater depth by comparing their results with those of hydrodynamic modelsThe model performance was evaluated against reach geomorphology, magnitude of flood events, and different Digital Elevation Model inputsThe results emphasize that a model should be chosen for the intended application. Guidelines provided for possible applications [ABSTRACT FROM AUTHOR]

Within the Australian southern Murray‐Darling Basin (sMDB) water markets have become a key institutional response to water scarcity and environmental problems. These markets facilitate trading of water among many users and across multiple connected regions. Increasingly, these markets also support intertemporal transfers via the carryover of user water allocations within public water storages. While trading can improve the spatial allocation of water, carryover can improve the temporal allocation by ensuring storage reserves reflect prevailing user preferences. This paper presents an economic model of water markets and irrigated agriculture in the sMDB. A reduced‐form statistical model of water demand is estimated using historical irrigation and water market data. This statistical model is placed within an economic partial‐equilibrium framework accounting for both interregional trade and interyear carryover. The model is solved numerically for a dynamic market equilibrium given assumptions over future stochastic water supplies. Out‐of‐sample tests show the model can realistically simulate water market prices and irrigation activity across the basin. The model is applied to estimate the economic benefits of interregional water trading and interyear carryover. Results show around a third of the economic benefits of water markets in the region can be attributed to interyear carryover. Plain Language Summary: The southern Murray‐Darling Basin (sMDB) is an important river system in Australia. Water from this river system is used both for irrigation farming and to support the environment. Water markets have been introduced allowing water users (farmers) in different parts of the river system to trade water among each other. These markets also allow farmers to store any unused water in dams for use in future years. This study presents a model of water markets in the sMDB. This model is developed using historical data on water supply, farm water use, and water market prices. The model can be used to simulate outcomes in the region's water market, given assumptions on the supply of water. Testing shows the model can produce results that are consistent with historical data. The model estimates that water markets generate benefits for water users of around $AUD172 m per year: around 14% of the value of water in the region. The ability to hold unused water in dams accounts for around one‐third of these benefits. Key Points: An economic model of water markets in the Australian southern Murray‐Darling Basin is developedThis model accounts for both interregional water trading and interyear water storage (carryover)Water markets are shown to generate significant economic benefits with around a third from interyear carryover [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]

Attributing nitrogen export to specific land use within heterogeneous catchments remains a challenge due to the spatio‐temporal variability in conditions influencing the mobilization and fate of nitrogen species. This study demonstrates that the measurement of dual stable isotopes of nitrate, taken along with routine tributary measurement of nitrogen in nitrate (NO3−‐N) and ammonium (NH4+‐N), aids in apportioning sources of the overall nitrogen load during wet periods. An inverse modeling technique was developed to estimate the land use‐specific export rates of NO3−‐N and NH4+‐N from the Caboolture River Catchment in Queensland, Australia. Measurements of nitrogen in streamflow at 51 locations during six sampling campaigns (May 2012 to April 2013) were made along with catchment geospatial data that was used to disaggregate sub‐catchments into six land use fractions. A hydrological model was applied to compute the runoff from each fraction and water routing through the stream network. This data was used within a nitrogen mixing model with inclusion δ15NNO3 and δ18ONO3. The land uses specific export rate was computed inversely as the posterior of a Bayesian interference applied to the model. During higher rainfall periods when export rates were highest, the main land use exporting nitrogen was wetland (110 g/ha/day NO3−‐N, 27 g/ha/day NH4+‐N) resulted from mineralization and nitrification of organic N, followed by urban (16 g/ha/day NO3−‐N, 2.3 g/ha/day NH4+‐N). The advantage of using the dual isotopes in conjunction with the nitrogen concentration data was demonstrated by reduced uncertainty in the computed rates during the higher rainfall periods, relative to calculations without δ15NNO3 and δ18ONO3. Plain Language Summary: Attributing nitrogen export to specific land use types within heterogeneous catchments is difficult. This paper demonstrates the use of an inverse modeling technique, dissolved inorganic nitrogen concentration data, a dual stable isotope measurement data, and geospatial data to estimate the land use‐specific export rates of dissolved inorganic nitrogen from six land use types in the Caboolture River Catchment in Queensland, Australia. The results show that during higher rainfall periods when export rates were highest, the main source of nitrogen was found to be wetland (110 g/ha/day NO3−‐N and 27 g/ha/day NH4+‐N), followed by urban (16 g/ha/day NO3‐N and 2.3 g/ha/day NH4+‐N). Key Points: An inverse modeling technique was developed to estimate the land use‐specific export rate of nitrogen using isotopes signaturesThe model was embedded within a Bayesian framework to assess the predictive uncertainty with and without inclusion of dual isotopes of nitrateDuring wet periods, when nitrogen export rates were highest, the predictive uncertainty was reduced when the model included dual isotopes of nitrate [ABSTRACT FROM AUTHOR]

Forested catchments are critical to water supply in major cities. Many of these catchments face the threat of postwildfire erosion, which can contaminate reservoir water. The aim of this paper is to determine the probability and duration of disruptions to treatability due to runoff‐generated debris flows in the first year after a wildfire, before substantial vegetation recovery takes place. We combine models of reservoir hydrodynamics, postfire erosion, and stochastic rainfall to determine probability and magnitude of sediment concentration at the reservoir water offtake. Central to the paper is our technique for linking model components into a risk framework that gives probabilities to the number of days that the turbidity threshold for treatment is exceeded. The model is applied to the Upper Yarra reservoir, which is the linchpin of the water supply system for Melbourne in SE Australia. However, the framework is applicable to other unfiltered water supply systems where suspended sediment is a risk to treatability. Results show that postwildfire erosion poses a substantial threat, with a relatively high probability (annual exceedance probability = 0.1–0.3) of water being untreatable for >1 year following a high‐severity wildfire. Important factors that influence the risk include postwildfire runoff potential, reservoir temperature, and the amount of clay‐sized grains in eroding headwaters. Assumptions about spatial‐temporal rainfall attributes, reservoir hydrodynamics, and the catchment erosion potential are all important sources of error in our estimate of risk. Our approach to risk quantification will help support planning, risk management, and strategic investment to mitigate impacts. Key Points: Consequence of wildfire for an unfiltered water supply system is modeled based on erosion, stochastic rainfall, and reservoir hydrodynamicsThere is a 10–30% chance that water is untreatable for >1 year due to high suspended sediment concentration after a high‐severity wildfireAssumptions about spatiotemporal rainfall patterns, reservoir hydrodynamics, and erosion are all important sources of error in risk [ABSTRACT FROM AUTHOR]

Groundwater response to climate variations is often pivotal to managing groundwater sustainably. However, this relationship is rarely explicitly examined because of the complexity of surface to subsurface processes and the diverse impacts of multiple drivers, such as groundwater pumping and land use changes. In this paper, we address this challenge by proposing methods to quantify the sensitivity of groundwater level and recharge to temporal climate variability across Australia. Using the HydroSight groundwater hydrograph toolbox we first identify 1,143 out of a total of 4,350 bores as climate‐driven, where historically, head was primarily driven by climate variations. Streamflow elasticity measures are then adapted to groundwater to quantify the long‐term head and recharge sensitivity. We find that the national median sensitivity of head and recharge to precipitation change are 42 and 0.43 mm mm−1, respectively (interquartiles: 20–77 and 0.30–0.55 mm mm−1); both of which are ∼8 times that of potential evapotranspiration. Nationally, the results are spatially correlated, suggestive of large‐scale effects. The responses of head and recharge appear to be primarily related to climate type and hydrogeology. The more arid the climate, the higher the head sensitivity but the lower the recharge sensitivity. Porous media generally show higher head sensitivity than fractured media due to smaller aquifer specific yield, and again contrarily for that of recharge. These findings contribute to understanding the long‐term impact of climate change on groundwater and thus provide valuable insights for sustainable groundwater management. Plain Language Summary: In this study, we assess the response of groundwater to meteorological variations by using long‐term groundwater level records across Australia. We first identify the sites where the groundwater level has been primarily impacted by climate variations alone. The changes in groundwater level and replenishment rate (also called "recharge") at the natural sites are simulated under a range of precipitation and evapotranspiration shifts and their relationships are statistically quantified. Results show that the national median groundwater level changes by 42 mm and the recharge changes by 0.43 mm per 1 mm change in precipitation. The response of groundwater level and recharge are found to be primarily governed by the inherent properties of the sites, such as climate type and hydrogeology. This study provides valuable insights into climate change impacts on groundwater availability and contributes to developing adaptive water management strategies. Key Points: Climate‐dominated groundwater bores throughout Australia are statistically identified (n = 1,143, 26%)Median head and recharge sensitivity to precipitation are 42 and 0.43 mm mm−1Head and recharge sensitivity are primarily controlled by climate type and hydrogeology [ABSTRACT FROM AUTHOR]

Hypoxic blackwater events occur worldwide, affecting inland and coastal waters. These events have been exacerbated by man‐made floodplain drainage, leading to large‐scale fish kills and ecological degradation. This paper presents a new method to identify estuarine catchment areas that are most likely to generate hypoxic conditions. The method uses established risk factors, including vegetation type, inundation extent and duration, ground‐truthed in eastern Australia. A catchment is at higher risk of hypoxic blackwater generation if (a) it is located where floodwaters are high and/or drainage is impeded, (b) the site topography includes an extensive, low‐lying floodplain; and/or (c) the land‐use and environmental characteristics have a high deoxygenation potential. Consequent impacts to an estuary are determined by the floodplain connectivity with the estuary, and the discharge characteristics of the catchment drainage system. Where multiple, proximate catchments have similar drainage conditions, compounding plumes of hypoxic blackwater can overwhelm the assimilation capacity of the estuary. Climate change may significantly increase the volume and frequency of blackwater events in estuarine environments as a result of reduced drainage due to sea level rise, higher temperatures, and more intense and sporadic rainfall events. It is recommended that management measures be introduced to mitigate the effects of climate change and avoid further widespread hypoxic blackwater events. Key Points: A methodology is presented to differentiate estuarine catchments by their potential to generate hypoxic blackwater eventsLocal topographic and hydrodynamic conditions strongly influence the potential volume of hypoxic blackwater generated over a catchmentClimate change may increase the frequency and severity and change the distribution of blackwater risk throughout an estuary [ABSTRACT FROM AUTHOR]

Institutional change is fundamental to regime transformation, and a necessary part of moving toward integrated water management. However, insight into the role of institutional change processes in such transitions is currently limited. A more nuanced understanding of institutional frameworks is necessary, both to advance understanding of institutional change in the context of transitions toward improved water management and to inform strategies for guiding such processes. To this end, we examine two contemporary cases of transformative change in Australia's urban water sector, exploring the evolution of institutional change in each city. This paper offers insights into regime transformation, providing guidance on types of institutional structures and the ways structure-change initiatives can be sequenced to support a transition. The results reveal the importance of regulation in embedding regime change and suggest that engagement with structural frameworks should begin early in transition processes to ensure the timely introduction of supporting regulation. Our findings also highlight the inextricable link between culture-based and structure-based change initiatives, and the importance of using a diverse range of institutional change mechanisms in a mutually reinforcing way to provide a strong foundation for change. These findings provide a foundation for further scholarly examination of institutional change mechanisms, while also serving to inform the strategic activities of transition-oriented organizations and actors. [ABSTRACT FROM AUTHOR]

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual "bucket" rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the "Millennium Drought" in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model "buckets" are already "emptying" on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate. Key Points: Environmental state variables such as groundwater display multiyear trends in response to sustained climate anomaliesWe show that five commonly used conceptual "bucket" rainfall runoff models are unable to replicate such trendsBecause of this, the models fail to extrapolate realistically to different climatic conditions, compromising runoff projections Plain Language Summary: It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13‐year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used "bucket" models. The models do not show the long, slow drying up—they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow. [ABSTRACT FROM AUTHOR]

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies. Key Points: We investigate which water use behaviors can be identified from nonintrusive, single‐point, smart meter dataWe identify and cluster primary water use behaviors of single‐family households from disaggregated end use dataWe reveal the main water end uses driving different behaviors, usage patterns, and regularity, to support customized demand management [ABSTRACT FROM AUTHOR]

Estimates of hydrological risk are crucial to enable adequate planning and preparation for extreme events. However, the accurate estimation of hydrological risk is hampered by relatively short instrumental records in many parts of the world. Information derived from climate-sensitive paleoclimate proxies provide an opportunity to resolve hydroclimatic variability, but many regions, such as Australia's Murray-Darling Basin (MDB), currently lack the suitable in situ proxies necessary to do this. Here new MDB rainfall reconstructions are presented based on a novel method using paleoclimate rainfall proxies in the Australasian region spanning from 749 B.C.E. to 1980 C.E. Our results emphasize the need to develop additional reconstructions and, with the companion paper, demonstrate how this information can be used to benefit water resource management. This study shows that prior to the twentieth century, both dry and wet epochs have persisted for longer periods than observed in the instrumental record--with the probability of both dry and wet periods exceeding a decade at least 10 times more likely prior to 1883 than suggested by the instrumental records. Some reconstructed rainfalls exceeded the instrumental range (i.e., drier dry epochs and wetter wet spells) despite a systematic underestimation of extremes due to a combination of proxy quality and model bias. Importantly, the results demonstrate that the instrumental record does not cover the full range of hydroclimatic variability possible in the MDB. Therefore, hydroclimatic risk assessments based on the instrumental record likely underestimate, or at least misinterpret, the frequency, duration, and magnitude of wet and dry epochs. [ABSTRACT FROM AUTHOR]

From ~1997 to 2009 the Murray-Darling Basin (MDB), Australia's largest water catchment and reputed "food bowl," experienced a severe drought termed the "Millennium Drought" or "Big Dry" followed by devastating floods in the austral summers of 2010/2011, 2011/2012, and 2012/2013. The magnitude and severity of these extreme events highlight the limitations associated with assessing hydroclimatic risk based on relatively short instrumental records (~100 years). An option for extending hydroclimatic records is through the use of paleoclimate records. However, there are few in situ proxies of rainfall or streamflow suitable for assessing hydroclimatic risk in Australia and none are available in the MDB. In this paper, available paleoclimate records are reviewed and those of suitable quality for hydroclimatic risk assessments are used to develop preinstrumental information for the MDB. Three different paleoclimate reconstruction techniques are assessed using two instrumental rainfall networks: (1) corresponding to rainfall at locations where rainfall-sensitive Australian paleoclimate archives currently exist and (2) corresponding to rainfall at locations identified as being optimal for explaining MDB rainfall variability. It is shown that the optimized rainfall network results in a more accurate model of MDB rainfall compared to reconstructions based on rainfall at locations where paleoclimate rainfall proxies currently exist. This highlights the importance of first identifying key locations where existing and as yet unrealized paleoclimate records will be most useful in characterizing variability. These results give crucial insight as to where future investment and research into developing paleoclimate proxies for Australia could be most beneficial, with respect to better understanding instrumental, preinstrumental and potential future variability in the MDB. [ABSTRACT FROM AUTHOR]

The cosmic-ray probe (CRP) provides continuous estimates of soil moisture over an area of ∼30 ha by counting fast neutrons produced from cosmic rays which are predominantly moderated by water molecules in the soil. This paper describes the setup, measurement correction procedures, and field calibration of CRPs at nine locations across Australia with contrasting soil type, climate, and land cover. These probes form the inaugural Australian CRP network, which is known as CosmOz. CRP measurements require neutron count rates to be corrected for effects of atmospheric pressure, water vapor pressure changes, and variations in incoming neutron intensity. We assess the magnitude and importance of these corrections and present standardized approaches for network-wide analysis. In particular, we present a new approach to correct for incoming neutron intensity variations and test its performance against existing procedures used in other studies. Our field calibration results indicate that a generalized calibration function for relating neutron counts to soil moisture is suitable for all soil types, with the possible exception of very sandy soils with low water content. Using multiple calibration data sets, we demonstrate that the generalized calibration function only applies after accounting for persistent sources of hydrogen in the soil profile. Finally, we demonstrate that by following standardized correction procedures and scaling neutron counting rates of all CRPs to a single reference location, differences in calibrations between sites are related to site biomass. This observation provides a means for estimating biomass at a given location or for deriving coefficients for the calibration function in the absence of field calibration data. [ABSTRACT FROM AUTHOR]

The consensus in the scientific community is that the intensity of extreme precipitation will increase in a warmer climate. However, as there is limited observational evidence to this effect, there is a growing body of research which focuses on directly investigating the relationship between temperature and precipitation. This is currently performed by binning precipitation data in temperature bins and then investigating the trend in the precipitation percentiles in each bin with temperature. In this paper, we highlight limitations in the binning approach and present quantile regression as an alternative to the above process. Quantile regression allows estimation of this scaling directly and, unlike binning, is unbiased with sample size. Moreover, quantile regression presents a natural framework for investigation into other factors (covariates) that may be affecting the nature of the scaling relationship. Results using subdaily rainfall data for Australia show the efficacy of the proposed quantile regression method, as well as the presence of season indicators as significant covariates that affect the scaling relationship of precipitation with temperature. A general increase in the scaling coefficient in winter versus summer is observed. [ABSTRACT FROM AUTHOR]

In regulated river systems, such as the River Murray in Australia, the efficient use of water to preserve and restore biota in the river, wetlands, and floodplains is of concern for water managers. Available management options include the timing of river flow releases and operation of wetland flow control structures. However, the optimal scheduling of these environmental flow management alternatives is a difficult task, since there are generally multiple wetlands and floodplains with a range of species, as well as a large number of management options that need to be considered. Consequently, this problem is a multiobjective optimization problem aimed at maximizing ecological benefit while minimizing water allocations within the infrastructure constraints of the system under consideration. This paper presents a multiobjective optimization framework, which is based on a multiobjective ant colony optimization approach, for developing optimal trade-offs between water allocation and ecological benefit. The framework is applied to a reach of the River Murray in South Australia. Two studies are formulated to assess the impact of (i) upstream system flow constraints and (ii) additional regulators on this trade-off. The results indicate that unless the system flow constraints are relaxed, there is limited additional ecological benefit as allocation increases. Furthermore the use of regulators can increase ecological benefits while using less water. The results illustrate the utility of the framework since the impact of flow control infrastructure on the trade-offs between water allocation and ecological benefit can be investigated, thereby providing valuable insight to managers. [ABSTRACT FROM AUTHOR]

This paper is the second of two in the current issue that presents a framework for simulating continuous (uninterrupted) rainfall sequences at both gaged and ungaged locations. The ultimate objective of the papers is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. In this paper we complete the regionalized algorithm by adopting a rationale for generating daily sequences at any location by sampling daily rainfall records from "nearby" gages with statistically similar rainfall sequences.The approach consists of two distinct steps: first the identification of a set of locations with daily rainfall sequences that are statistically similar to the location of interest, and second the development of an algorithm to sample daily rainfall from those locations. In the first step, the similarity between all bivariate combinations of 2708 daily rainfall records across Australia were considered, and a logistic regression model was formulated to predict the similarity between stations as a function of a number of physiographic covariates. Based on the model results, a number of nearby locations with adequate daily rainfall records are identified for any ungaged location of interest (the "target" location), and then used as the basis for stochastically generating the daily rainfall sequences. The continuous simulation algorithm was tested at five locations where long historical daily rainfall records are available for comparison, and found to perform well in representing the distributional and dependence attributes of the observed daily record. These daily sequences were then used to disaggregate to a subdaily time step using the rainfall state-based disaggregation approach described in the first paper, and found to provide a good representation of the continuous rainfall sequences at the location of interest. [ABSTRACT FROM AUTHOR]

This paper is the first of two in the current issue that presents a framework for generating continuous (uninterrupted) rainfall sequences at both gaged and ungaged point locations. The ultimate objective is to present a methodology for stochastically generating continuous subdaily rainfall sequences at any location such that the statistics at a range of aggregation scales are preserved. This first paper presents a regionalized nonparametric daily disaggregation model in which, conditional on a daily rainfall amount and previous- and next-day wetness states at the location of interest, subdaily fragments are resampled using continuous records at nearby locations. The second paper then focuses on a regionalized daily rainfall generation model.To enable the substitution of subdaily rainfall at nearby locations for subdaily rainfall at the location of interest, it is necessary to identify locations with "similar" daily to subdaily scaling characteristics. We use a two-sample, two-dimensional Kolmogorov-Smirnov (K-S) test to identify whether the daily to subdaily scaling relationships are statistically similar between all possible station pairs sampled from 232 gages located throughout Australia. This step is followed by a logistic regression to determine the influence of the covariates of latitude, longitude, elevation, and distance to the coast on the probability that the scaling at any two locations will be similar. The model is tested at five locations, where recorded subdaily data was available for comparison, and results indicate good model performance, particularly in preserving the probability distribution of extremes and the antecedent rainfall prior to the storm event. [ABSTRACT FROM AUTHOR]

Well-validated rainfall-runoff models are able to capture the relationships between rainfall and streamflow and to reliably estimate initial catchment states. While future streamflows are mainly dependent on initial catchment states and future rainfall, use of the rainfall-runoff models together with estimated future rainfall can produce skilful forecasts of future streamflows. This is the basis for the ensemble streamflow prediction system, but this approach has not been explored in Australia. In this paper, two conceptual rainfall-runoff models, together with rainfall ensembles or analogues based on historical rainfall and the Southern Oscillation index (SOI), were used to forecast streamflows at monthly and 3-monthly scales at two catchments in east Australia. The results showed that both models forecast monthly streamflow well when forecasts for all months were evaluated together, but their performance varied significantly from month to month. Best forecasting skills were obtained (both monthly and 3 monthly) when the models were coupled with ensemble forcings on the basis of long-term historical rainfall. SOI-based resampling of forcings from historical data led to improved forecasting skills only in the period from September to December at the catchment in Queensland. For 3 month streamflow forecasts, best skills were in the period from April to June at the catchment in Queensland and in the period from October to January for the catchment in New South Wales, both of which were the periods after the rainy season. The forecasting skills are indicatively comparable to the statistical forecasting skills using a Bayesian joint probability approach. The potential approaches for improved hydrologic modeling through conditional parameterization and for improved forecasting skills through advanced model updating and bias corrections are also discussed. [ABSTRACT FROM AUTHOR]

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment. [ABSTRACT FROM AUTHOR]

The application of formal Bayesian inferential approaches in hydrologic modeling is often criticized for requiring explicit assumptions to be made about the distribution of the errors via the likelihood function. These assumptions can be adequate in some situations, but often little attention is paid to the selection of an appropriate likelihood function. This paper investigates the application of Bayesian methods in modeling ephemeral catchments. We consider two modeling case studies, including a synthetically generated data set and a real data set of an arid Australian catchment. The case studies investigate some typical forms of the likelihood function which have been applied widely by previous researchers, as well as introducing new implementations aimed at better addressing likelihood function assumptions in dry catchments. The results of the case studies indicate the importance of explicitly accounting for model residuals that are highly positively skewed due to the presence of many zeros (zero inflation) arising from the dry spells experienced by the catchment. Specifically, the form of the likelihood function was found to significantly impact the calibrated parameter posterior distributions, which in turn have the potential to greatly affect the uncertainty estimates. In each application, the likelihood function that explicitly accounted for the nonconstant variance of the errors and the zero inflation of the errors resulted in (1) equivalent or better fits to the observed discharge in both timing and volume, (2) superior estimation of the uncertainty as measured by the reliability and sharpness metrics, and (3) more linear quantile-quantile plots indicating the errors were more closely matched to the assumptions of this form of the likelihood function. [ABSTRACT FROM AUTHOR]

Improved data collection techniques as well as increasing computing power are opening up new opportunities for the development of sophisticated models that can accurately reproduce hydrodynamic and biochemical conditions of water bodies. While increasing model complexity is considered a virtue for scientific purposes, it is a definite disadvantage for management (engineering) purposes, as it limits the model applicability to what-if analysis over a few, a priori defined interventions. In the recent past, this has become a significant limitation, particularly considering recent advances in water quality rehabilitation technologies (e.g., mixers or oxygenators) for which many design parameters have to be decided. In this paper, a novel approach toward integrating science-oriented and engineering-oriented models and improving water quality planning is presented. It is based on the use of a few appropriately designed simulations of a complex process-based model to iteratively identify the multidimensional function (response surface) that maps the rehabilitation interventions into the objective function. On the basis of the response surface (RS), a greater number of interventions can be quickly evaluated and the corresponding Pareto front can be approximated. Interesting points on the front are then selected and the corresponding interventions are simulated using the original process-based model, thus obtaining new decision-objective samples to refine the RS approximation. The approach is demonstrated in Googong Reservoir (Australia), which is periodically affected by high concentrations of manganese and cyanobacteria. Results indicate that significant improvements could be observed by simply changing the location of the two mixers installed in 2007. Furthermore, it also suggests the best location for an additional pair of mixers. [ABSTRACT FROM AUTHOR]

The management of groundwater to generate net community benefits is challenging because of the complexity of impacts that can be involved, the varying interests of different stakeholder groups, time lags between changes in extraction rates and aquifer levels, and the level of technical and scientific uncertainty. In an economic framework, decisions about conserving groundwater reserves by limiting extraction rates should be made by comparing the benefits of conservation activities with the associated costs. However, limited information about benefits and costs makes it difficult to apply a benefit-cost framework to issues of groundwater management. This can be addressed to some extent by sourcing nonmarket values from other studies in a benefit transfer process. In this paper, benefit transfer techniques are applied to an evaluation case study about limiting extractions from the Great Artesian Basin in Australia. The results demonstrate some of the issues with the benefit transfer approach and confirm that the publicly funded bore capping program in Australia has been delivering net benefits to the community. [ABSTRACT FROM AUTHOR]

To accurately project future water availability under a drying climate, it is important to understand how precipitation is partitioned into other terrestrial water balance components, such as fluxes (evaporation, transpiration, runoff) and changes in storage (soil moisture, groundwater). Many studies have reported unexpected large runoff reductions during drought, particularly for multi‐year events, and some studies report a persistent change in partitioning even after the meteorological drought has ended. This study focused on understanding how actual evapotranspiration (AET) and change in subsurface storage (ΔS) respond to climate variability and change, examining Australia's Millennium Drought (MD, 1997–2009). The study initially conducted a catchment‐scale water balance analysis to investigate interactions between ΔS and AET. Then the water balance analysis was extended to regional scale to investigate ΔS using interpolated rainfall and discharge with remotely sensed AET. Lastly, we evaluated conceptual rainfall‐runoff model performance of two commonly used models against these water balance estimates. The evaluation of water‐balance‐derived ΔS against Gravity Recovery and Climate Experiment (GRACE) estimates shows a significant multiyear storage decline; however, with different rates. In contrast, AET rates (annualized) remained approximately constant before and during the MD, contrasting with some reports of evapotranspiration enhancement elsewhere. Overall, given AET remained approximately constant, drought‐induced precipitation reductions were partitioned into ΔS and streamflow. The employed conceptual rainfall‐runoff models failed to realistically represent AET during the MD, suggesting the need for improved conceptualization of processes. This study provides useful implications for explaining future hydrological changes if similar AET behavior is observed under a drying climate. Key Points: For a multiyear drought, we investigated the impact of precipitation reductions on water balance fluxes and change in storageActual evapotranspiration was approximately unchanged even under prolonged droughts in a water‐limited regionBecause of this, precipitation reduction during the multiyear drought was partitioned into reductions in streamflow and subsurface storage [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]

Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments. Key Points: Direct calibration of hydrological models solely against remotely sensed ET (RS‐ET) data is used to predict runoff in ungauged regionsThe RS‐ET runoff‐free calibration approach is tested on 222 catchments across AustraliaThe RS‐ET runoff‐free calibration can estimate mean annual runoff and monthly runoff series in wet catchments reasonably well [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) provides a robust connection between hydrological cycles and surface energy balance. Accurate and near‐daily ET estimation has utility in water resources, agricultural management applications, crop yields and drought monitoring. This study describes the implementation of an ET modeling system based on a Priestley‐Taylor version of the Two‐Source (soil and vegetation) Energy Balance Model (TSEB‐PT) within Google Earth Engine environment. TSEB‐PT performance was compared with the simpler single‐source HSEB (Hybrid Surface Energy Balance) ET model to assess relative advantages and disadvantages for operational application. Results were evaluated across multiple biomes and climatic zones across the US, Europe, and Australia in comparison with eddy covariance data from 30 flux tower sites. Both models produced similar results when considering all biomes at daily, weekly, and monthly timescales. Daily ET metrics for all sites combined yielded comparable results for both models, with a slightly lower root‐mean‐square error for TSEB‐PT (HSEB) of 1.2 (1.3) mm/d and a higher correlation (r) of 0.83 (0.80), but a larger mean percent bias error (MPBE = −9%) than HSEB (MPBE = 1%). TSEB‐PT performance was lowest for sites in warm summer humid continental and hot semi‐arid climates and in evergreen broadleaf forest cover, while HSEB showed lowest performance in tropical savanna hot semi‐arid climates and in savanna covers. Model performance was improved for both cropland and non‐cropland sites when TSEB‐PT and HSEB ET estimates were combined through simple averaging due to cancellation of opposing errors, showing a promise as potential tools for water resource management on a global scale. Key Points: A Novel 30 m actual evapotranspiration mapping based on the dual (Priestley‐Taylor version of the Two‐Source Energy Balance model, TSEB‐PT) and single (hybrid single‐source energy balance model, HSEB) source energy balance models is presentedModels leverage globally available state‐of‐the‐art thermal, surface reflectance, reanalysis, and land cover data in Google Earth EngineModels are comparable in performance, and HSEB is more computationally efficient than TSEB but is slightly less accurate in croplands [ABSTRACT FROM AUTHOR]

Accurate flood inundation modeling using a complex high‐resolution hydrodynamic (high‐fidelity) model can be very computationally demanding. To address this issue, efficient approximation methods (surrogate models) have been developed. Despite recent developments, there remain significant challenges in using surrogate methods for modeling the dynamical behavior of flood inundation in an efficient manner. Most methods focus on estimating the maximum flood extent due to the high spatial‐temporal dimensionality of the data. This study presents a hybrid surrogate model, consisting of a low‐resolution hydrodynamic (low‐fidelity) and a Sparse Gaussian Process (Sparse GP) model, to capture the dynamic evolution of the flood extent. The low‐fidelity model is computationally efficient but has reduced accuracy compared to a high‐fidelity model. To account for the reduced accuracy, a Sparse GP model is used to correct the low‐fidelity modeling results. To address the challenges posed by the high dimensionality of the data from the low‐ and high‐fidelity models, Empirical Orthogonal Functions analysis is applied to reduce the spatial‐temporal data into a few key features. This enables training of the Sparse GP model to predict high‐fidelity flood data from low‐fidelity flood data, so that the hybrid surrogate model can accurately simulate the dynamic flood extent without using a high‐fidelity model. The hybrid surrogate model is validated on the flat and complex Chowilla floodplain in Australia. The hybrid model was found to improve the results significantly compared to just using the low‐fidelity model and incurred only 39% of the computational cost of a high‐fidelity model. Plain Language Summary: Floods are the most common type of natural disaster and therefore it is important to predict when and where flooding occurs. This is normally done using a complex computer model that divides the area of interest into small subareas and then calculates how the water moves between each subarea. However, to predict flooding accurately over large areas, it is necessary to use millions of small subareas and it takes a long time to calculate the movement of flood water between subareas. To mitigate this issue, this study proposes an alternative approach based on a simpler computer model. This simpler model uses larger subareas to predict flooding, which makes the model less accurate but much faster. To compensate for the reduced accuracy, the results are corrected using an advanced computer method that is calibrated to predict the relationship between the predictions made using the complex and simpler models. The new approach is used to predict flooding on a large, flat floodplain in Australia. The predictions show a significant improvement compared to just using the simpler computer model. Furthermore, the calculations only take about 39% of the time taken by a complex model with the small subareas, but the accuracy is similar. Key Points: A new hybrid surrogate model for predicting the dynamic evolution of flood inundation extent is proposedThe hybrid model significantly improves the accuracy of flood inundation extent predictions compared to a low‐fidelity modelThe computational cost is substantially reduced compared to a high‐fidelity model [ABSTRACT FROM AUTHOR]

The intensity, duration, and frequency (IDF) of low streamflow events are often considered stationary in time. Given droughts are likely to become more frequent and extreme, here those IDFs are analyzed for their non‐stationarity in relation to three climate drivers, namely, the El Niño‐Southern Oscillation, the Indian Ocean Dipole, and the Southern Annular Mode. For this, low flow events were identified for 161 unregulated catchments in Victoria, Australia. The relationship between the climate drivers and the IDFs were developed using covariate‐based non‐stationary statistical models. We found that several of these catchments exhibit strong non‐stationary influences from the climate modes. Such catchments have a higher probability of experiencing more intense, prolonged, and/or frequent low flows when a dry phase of these climate drivers occurs. We found that catchments having strong evidence of non‐stationary frequency (n = 77) were more compared to those having non‐stationary intensity (n = 19) or duration (n = 34). Further, we derived projected indices of these climate drivers from CMIP5 models for the RCP8.5 scenario. These indicate an intensification in the dry phase of the climate drivers. Using information of the projected indices in the IDF models, we found that future dry phases of these climate drivers are likely to further increase the probability of experiencing higher magnitudes of low flow IDFs in catchments non‐stationary in these characteristics in relation to one or more of these climate drivers. Frequency shows greater response to future dry phases of these drivers than intensity or duration of low flows. Plain Language Summary: The interaction of the ocean and atmosphere can lead to conditions that can control how dry or wet a season might be for a region. These conditions are called climate modes or climate drivers. Drought periods in many regions of the world are known to be impacted by these climate modes. The activity of these climate modes can make an ongoing drought more severe compared to a situation when these modes are not active. Drought spells often entail abnormally low flows in streams. In this study, we show that low flows in southeast Australia have a significant link with the activity of the climate drivers. In fact, when a drought coincides with a dry phase of these climate modes, low flow events can become more intense, frequent and/or can last longer. Unabated climate change has the potential to make these climate modes more intense in future. Our study shows that if this happens, low flow events can become more extreme. Thus future dry phases of these climate drivers may likely cause shortage of freshwater availability, disruption of ecosystems vitally dependent on flows, uncertainty in dam operations, all of which increases the risk associated with future droughts for such regions. Key Points: Low flow characteristics of catchments in southeast Australia exhibit significant non‐stationary influences from large‐scale climate modesDry phase of these climate drivers is expected to intensify under climate change, leading to reduced water availability when they occurFrequency of low flows shows greater response to non‐stationary influences from the climate modes than intensity or duration [ABSTRACT FROM AUTHOR]

Long‐term hydrological partitioning of catchments can be well described by the Budyko framework with a parameter (e.g., Fu's equations with parameter ω). The Budyko framework considers aridity index as the dominant control on hydrological partitioning, while the parameter represents integrated influences of catchment properties. Our understanding regarding the controls of catchment properties on the parameter is still limited. In this study, two machine learning methods, that is, boosted regression tree (BRT) and CUBIST, were used to model ω. Interpretable machine learning methods were adopted for better physical understanding including feature importance, accumulated local effects (ALE), and local interpretable model‐agnostic explanations. Among the 15 properties of 443 Australian catchments, analysis of feature importance showed that root zone storage capacity (SR), vapor pressure, vegetation coverage (M), precipitation depth, climate seasonality and asynchrony index (SAI), and water use efficiency (WUE) were the six primary control factors on ω. ALE showed that ω varied nonlinearly with all factors, and varied non‐monotonically with M, SAI, and WUE. LIME showed that the importance of the six dominant factors on ω varied between regions. CUBIST was further used to build regionally varying relationships between ω and the primary factors. Continental scale ω and evapotranspiration were further mapped across Australia based on the most robust BRT‐trained parameterization scheme with a resolution of 0.05°. Instead of using the machine learning method as a black box, we employed interpretability approaches to identify the controls. Our findings not only improved the capability of the Budyko method for hydrological partitioning across Australia, but also demonstrated that the controls of catchment properties on hydrological partitioning vary in different regions. Key Points: Machine learning and further interpretation methods were jointly used for better understanding of controls on hydrological partitioningSix climatic and vegetation variables were identified as dominant controls but had region‐specific relationships with Budyko parameterParameterization of the Budyko parameter using machine learning outperforms traditional methods for mapping surface evaporation [ABSTRACT FROM AUTHOR]

Streamflow in Australia's northern rivers has been steadily increasing since the 1970s, most likely due to increased intensity in the Indo‐Australian monsoon. However, because of limited data availability, it is hard to assess this recent trend and therefore contextualize potential future climatic changes. In this study, we used a network of 63 precipitation‐sensitive tree‐ring chronologies from the Indo‐Australian and Asian monsoon regions to reconstruct streamflow in the Daly catchment in the Northern Territory of Australia from 1413 to 2005 CE. We used a novel wavelet‐based method to transform the variance structure of the tree‐ring chronologies to better match the hydroclimate prior to reconstruction with a hierarchical Bayesian regression model. Our streamflow reconstruction accounts for 72%–78% of the variance in the instrumental period and closely matches both historical flood events and independent proxy records, increasing confidence in its validity. We find that while streamflow has been increasing since the 1800s, the most recent 40‐year period is unprecedented in the last ∼600 years. Comparison to an independent coral‐based streamflow record shows regional coherency in this trend. Extreme high flows were found to be linked to La Niña events, but we found no significant relationship between streamflow and El Niño events, or streamflow and other regional climatic drivers. More work is therefore needed to understand the drivers of the recent streamflow increase, but, regardless of the cause, water managers should be aware of the paleoclimatic context before making decisions on water allocations. Plain Language Summary: Large‐scale agricultural development has been proposed for the Daly catchment in the Northern Territory of Australia. Since the start of record keeping in the Daly catchment in the 1970s, streamflow has been steadily increasing, most likely due to increases in Australian monsoon rainfall. However, because of the limited amount of data, it is hard to assess whether this recent increase in streamflow is unusual, part of a longer trend, or a natural cycle in monsoon rainfall. In this study, we used rainfall‐sensitive tree growth and a statistical model to reconstruct Daly River streamflow over the past 592 years. Our streamflow reconstruction closely matches known past flood events, increasing confidence in its validity. We find that streamflow has increased since the 1800s but that the most recent 40‐year period is unprecedented in the last 600 years. More work is needed to understand the drivers of this increased streamflow but regardless of the cause, water managers should be aware of the Daly River's history before making decisions on allocating water to agriculture or other large users. Key Points: 592‐year annual streamflow reconstruction of the Daly River Catchment, Australia, derived using a tree‐ring networkWavelet‐based techniques to transform tree ring spectral characteristics improve the reconstruction abilityHigh streamflow in the twentieth Century is regionally coherent and unprecedented in the preceding five centuries [ABSTRACT FROM AUTHOR]

Climate change is projected to impact multiple components of the flow regime. However, changes in some ecologically important aspects of flow seasonality and variability are not well‐represented by global climate models. We used a stress testing method and global sensitivity analysis to investigate whether interactions between five different, but plausible, change "dimensions" (hydroclimatic variables or relationships) led to worse ecological outcomes than individual changes. The five dimensions include changes in long‐term average rainfall and temperature, low‐frequency variability of rainfall, seasonality of rainfall, and the rainfall‐runoff relationship. Our case study involved regulated and unregulated sections of the Goulburn River, Australia. We found that four different modeled ecological outcomes (condition of small bodied fish, large bodied fish, in‐channel vegetation, and floodplain vegetation) are most sensitive to changes in long‐term average rainfall. Sensitivity to changes in rainfall seasonality depends on river characteristics and appears to be heavily dampened by regulation and actively managed environmental water. Changes to the rainfall‐runoff relationship (which may be triggered by long‐term drying) were found to greatly influence ecological outcomes, but remain poorly understood. However, when considering the worst outcomes that are likely to present severe threats to ecological survival, all five dimensions were significant. These worst outcomes only manifest under certain combinations of changes with interactive effects. These joint interactions have implications for climate risk assessments that do not consider multiple dimensions of change, particularly those aimed at evaluating and mitigating severe threats or extinction probability. Plain Language Summary: Different kinds of climate changes, such as lower rainfall overall, increased temperatures, or changes to the time of year when the most rain falls can all threaten aquatic plants and animals. By manually changing the inputs to our water resource models, we can answer questions like "what happens if rainfall decreases by 20% and temperatures go up by two degrees?" We can do this many times, and build a picture of how rivers respond to a range of changes. We used this concept to understand how river ecology responds to up to five different changes at once and asked whether the outcomes are worse when these changes are combined versus when they occur separately. Ecological outcomes were most sensitive to changes in overall rainfall. The second biggest impact was changes in how much rainfall makes it into the river, which sometimes happens during long droughts. The other changes were less significant overall. Importantly, the most severe threats which could even lead to extinction only come about under combinations of the five changes. This means that in planning to avoid the worst outcomes under climate change, we might need to look at a wider range of combined changes. Key Points: We assessed sensitivity of four modeled freshwater ecosystem outcomes to five different hydroclimatic changes using a stress testVariation in ecological outcomes were most sensitive to changes in long‐term annual rainfall and rainfall‐runoff relationshipsHowever, in differentiating the worst ecological outcomes, a larger number of hydroclimatic changes were significant [ABSTRACT FROM AUTHOR]

Modeling network‐scale sediment (dis)connectivity and its response to anthropic pressures provides a baseline understanding of river processes and sediment dynamics that can be used to forecast future hydro‐morphological changes in river basins. However, this requires a solid understanding of how a system is currently operating, and how it operated in the past. We present the basin‐scale, dynamic sediment connectivity model D‐CASCADE, which combines concepts of network modeling with empirical sediment transport formulas to quantify spatiotemporal sediment (dis)connectivity in river networks. D‐CASCADE accounts for multiple factors affecting sediment transport, such as spatiotemporal variations in hydrological regime, different sediment grain sizes, sediment entrainment and deposition. Add‐ons are included in D‐CASCADE to model local changes in river geomorphology driven by sediment‐induced variations in features such as channel width. We apply D‐CASCADE to the well‐documented Bega River catchment, NSW, Australia, where significant geomorphic changes to rivers have occurred post European colonization (after 1850s), including widespread channel erosion and sediment mobilization. The Bega catchment provides a useful case study to test D‐CASCADE, as original source data on the historical sediment budget are available. By introducing historic drivers of change in the correct chronological sequence, the D‐CASCADE model successfully reproduced the timing and magnitude of major phases of sediment transport and associated channel adjustments over the last two centuries. With this confidence, we then ran the model to test how well it performs at estimating future trajectories of basin‐scale sediment transport and sediment budgets at the river reach scale. Key Points: We present the new dynamic D‐CASCADE model for network‐scale river sediment (dis)connectivity modelingD‐CASCADE successfully reproduced major historic geomorphological changes observed in the Bega river network after European settlementD‐CASCADE is used to test future trajectories of basin‐scale sediment transport and reach‐scale sediment budget in the Bega river system [ABSTRACT FROM AUTHOR]

Links between climate variability modes, rainfall, and streamflow are important for understanding the trajectories of change and dynamics in water availability. In this study, we examined the influence of the El Nino Southern Oscillation, Indian Ocean Dipole, Southern Annular Mode, and Interdecadal Pacific Oscillation modes on interannual variations in rainfall and streamflow in four hydroclimate regions. We also explored the link between climate variability modes and extreme rainfall and streamflow years. Climate mode indices, rainfall, and streamflow data from 1975 to 2018 were analyzed for 92 predominately forested catchments located across temperate Australia. Climate modes had divergent influences on streamflow and rainfall between and within regions. Across temperate Australia, a higher proportion of interannual variation in rainfall was explained by climate modes than for streamflow, indicating factors other than atmosphere‐ocean phenomena are important in determining interannual streamflow variability. Extremes in rainfall and streamflow across regions were related to the co‐occurrence of climate modes, with a stronger relationship between teleconnections and low rainfall/streamflow years than high rainfall/streamflow years. The study provides new insights into the regional drivers of hydrological extremes and consolidates our understanding of the role of teleconnections on water availability in the temperate zone of Australia. Key Points: We investigate the concurrent response of annual rainfall and streamflow to multiple climate variability modes and their interactionsClimate modes have divergent influences on streamflow and rainfall between and within regionsIn comparison with extreme high rainfall/streamflow, a higher number of extreme low rainfall/streamflow events are related to climate modes [ABSTRACT FROM AUTHOR]

Storage and subsequent release of water is a key function of catchments that moderates the impact of meteorological and climate extremes. Despite the fact that many key hydrological processes depend upon storage, there are relatively few studies that focus on storage itself. Storage is difficult to quantify due to catchment heterogeneity and the paucity of data on key catchment characteristics that largely determine storage, such as soil, hydrogeology, and topography. We adopt a multi‐method approach to estimate the dynamic and extended dynamic storages using hydrometric data in 69 catchments in the Murray‐Darling Basin in south‐eastern Australia. We test relationships between the derived catchment storages and hydrological and physical characteristics that potentially control storage. The study catchments tended to have small dynamic storages relative to the extended dynamic storage; proportionally the dynamic storages were all less than 10% of the extended dynamic storage. While storage estimates produced by the different methods and study catchments varied, the order in which they ranked was consistent. Correlations between catchment characteristics and estimates of storage were inconsistent; however, the results indicated that greater storage is strongly associated with steeper catchments and smoother hydrographs. This study highlights limitations in the current methodology to derive storage and the quality of widely applied hydrometric data. We reinforce the need to collect data that can validate storage estimates and call for new approaches that can broadly estimate storage at the catchment scale. Key Points: Storage is poorly understood and understudied in hydrologyWe adopt a multi‐method and multi‐catchment approach to estimate storage in south‐eastern AustraliaThe results highlight that the methods available to broadly derive storage at the catchment scale are inadequate [ABSTRACT FROM AUTHOR]

Rivers, wetlands, and floodplains are in need of management as they have been altered from natural conditions and are at risk of vanishing because of river development. One method to mitigate these impacts involves the scheduling of environmental flow management alternatives (EFMA); however, this is a complex task as there are generally a large number of ecological assets (e.g., wetlands) that need to be considered, each with species with competing flow requirements. Hence, this problem evolves into an optimization problem to maximize an ecological benefit within constraints imposed by human needs and the physical layout of the system. This paper presents a novel optimization framework which uses ant colony optimization to enable optimal scheduling of EFMAs, given constraints on the environmental water that is available. This optimization algorithm is selected because, unlike other currently popular algorithms, it is able to account for all aspects of the problem. The approach is validated by comparing it to a heuristic approach, and its utility is demonstrated using a case study based on the Murray River in South Australia to investigate (1) the trade-off between plant recruitment (i.e., promoting germination) and maintenance (i.e., maintaining habitat) flow requirements, (2) the trade-off between flora and fauna flow requirements, and (3) a hydrograph inversion case. The results demonstrate the usefulness and flexibility of the proposed framework as it is able to determine EFMA schedules that provide optimal or near-optimal trade-offs between the competing needs of species under a range of operating conditions and valuable insight for managers. [ABSTRACT FROM AUTHOR]

This paper investigates the actual extrapolation capacity of three hydrological models in differing climate conditions. We propose a general testing framework, in which we perform series of split-sample tests, testing all possible combinations of calibration-validation periods using a 10 year sliding window. This methodology, which we have called the generalized split-sample test (GSST), provides insights into the model's transposability over time under various climatic conditions. The three conceptual rainfall-runoff models yielded similar results over a set of 216 catchments in southeast Australia. First, we assessed the model's efficiency in validation using a criterion combining the root-mean-square error and bias. A relation was found between this efficiency and the changes in mean rainfall (P) but not with changes in mean potential évapotranspiration (PE) or air temperature (T). Second, we focused on average runoff volumes and found that simulation biases are greatly affected by changes in P. Calibration over a wetter (drier) climate than the validation climate leads to an overestimation (underestimation) of the mean simulated runoff. We observed different magnitudes of these models deficiencies depending on the catchment considered. Results indicate that the transfer of model parameters in time may introduce a significant level of errors in simulations, meaning increased uncertainty in the various practical applications of these models (flow simulation, forecasting, design, reservoir management, climate change impact assessments, etc.). Testing model robustness with respect to this issue should help better quantify these uncertainties. [ABSTRACT FROM AUTHOR]

Climate change impact assessments of water resources systems require simulations of precipitation and evaporation that exhibit distributional and persistence attributes similar to the historical record. Specifically, there is a need to ensure general circulation model (GCM) simulations of rainfall for the current climate exhibit low-frequency variability that is consistent with observed data. Inability to represent low-frequency variability in precipitation and flow leads to biased estimates of the security offered by water resources systems in a warmer climate. This paper presents a method to postprocess GCM precipitation simulations by imparting correct distributional and persistence attributes, resulting in sequences that are representative of observed records across a range of time scales. The proposed approach is named nesting bias correction (NBC), the rationale being to correct distributional and persistence bias from fine to progressively longer time scales. In the results presented here, distributional attributes have been represented by order 1 and 2 moments with persistence represented by lag 1 autocorrelation coefficients at monthly and annual time scales. The NBC method was applied to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mk3.5 and MIROC 3.2 hires rainfall simulations for Australia. It was found that the nesting method worked well to correct means, standard deviations, and lag 1 autocorrelations when the biases in the raw GCM outputs were not too large. While the bias correction improves the representation of distributional and persistence attributes at the time scales considered, there is room for representation of longer-term persistence by extending to time scales longer than a year. [ABSTRACT FROM AUTHOR]

In most urban cities across Australia, water restrictions remain the dominant policy mechanism to restrict urban water consumption. The extensive adoption of water restrictions as a means to limit demand, over several years, means that Australian urban water prices have consistently not reflected the opportunity cost of water. Given the generally strong political support for water restrictions and the likelihood that they will persist for some time, there is value in understanding households' attitudes in this context. More specifically, identifying the welfare gains associated with avoiding urban water restrictions entirely would be a nontrivial contribution to our knowledge and offer insights into the benefits of alternative policy responses. This paper describes the results from a contingent valuation study that investigates consumers' willingness to pay to avoid urban water restrictions. Importantly, the research also investigates the influence of cognitive and exogenous dimensions on the utility gain associated with avoiding water restrictions. The results provide insights into the impact of the current policy mechanism on economic welfare. [ABSTRACT FROM AUTHOR]

Chemical base flow separation is a widely applied technique in which contributions of groundwater and surface runoff to streamflow are estimated based on the chemical composition of stream water and the two end-members. This method relies on the assumption that the groundwater end-member can be accurately defined and remains constant. We simulate solute transport within the aquifer during and after single and multiple river flow events, to show that (1) water adjacent to the river will have a concentration intermediate between that of the river and that of regional groundwater and (2) the concentration of groundwater discharge will approach that of regional groundwater after a flow event but may take many months or years before it reaches it. In applying chemical base flow separation, if the concentration in the river prior to a flow event is used to represent the pre-event or groundwater end-member, then the groundwater contribution to streamflow will be overestimated. Alternatively, if the concentration of regional groundwater a sufficient distance from the river is used, then the pre-event contribution to streamflow will be underestimated. Changes in concentration of groundwater discharge following changes in river stage predicted by a simple model of stream-aquifer flows show remarkable similarity to changes in river chemistry measured over a 9 month period in the Cockburn River, southeast Australia. If the regional groundwater value was used as the groundwater end-member, chemical base flow separation techniques would attribute 8% of streamflow to groundwater, as opposed to 25% if the maximum stream flow value was used. [ABSTRACT FROM AUTHOR]

Statistical trend analyses of observed precipitation (P) time series are key to validate theoretical arguments and climate projections suggesting that extreme P will increase in a warmer climate. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered. Here, we investigate these two aspects focusing on extreme P frequencies derived from 100‐year daily records of 1,087 worldwide gauges of the Global Historical Climatology Network. For this aim, we perform Monte Carlo experiments based on count time series generated with the Poisson integer autoregressive model and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (a) Empirical autocorrelations are consistent with those of uncorrelated and stationary or nonstationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; incorporating the impact of serial correlation in trend tests on extreme P frequency has then limited impacts on tests' performance. (b) Accounting for field significance improves interpretation of test results by limiting type‐I errors, but it also decreases test power; results of local tests could complement field significance outcomes and help identify weak trend signals where several trends of coherent sign are detected. (c) Based on these findings, evident patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Eurasia, and central Australia (southwestern America, southern Europe, and southern Australia). The methodological insights of this work support trend analyses of any hydroclimatic variable. Key Points: Monte Carlo simulations based on the integer autoregressive model allow assessing trends on observed extreme precipitation frequencyAccounting for serial correlation in observed extreme precipitation frequency has limited impact on statistical trend analysesAccounting for field significance limits type‐I error of statistical trend tests, but reduces power when the trend signal is low [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]

Constructed shallow waterbodies are often designed and built to limit harmful algal blooms in urban regions. Efforts to reduce algal bloom occurrence in these waterbodies have largely focused on waterbody design, catchment criteria and onsite engineering options. However, many constructed shallow waterbodies that comply with design guidelines still experience harmful algal blooms. Identifying the knowledge gaps in current guidelines and examining their recommended design criteria can improve their effectiveness to reduce algal outbreaks. Here, we reviewed 66 global guidelines and identified common design criteria. The use of a 'one size fits all' empirical approach and dated literature are common issues associated with the design criteria recommended. Further, only approximately one third of the guidelines that were analyzed directly mentioned harmful algal bloom‐related design criteria. To test the validity of these design values in a real‐world setting, the suitability of design factors in limiting harmful algal blooms was assessed by analyzing 222 shallow waterbodies monitored over a 9 year period in southeastern Australia. The site analysis indicated that macrophyte area to surface area ratio, shoreline development index, and fetch are the three most influential single design factors associated with harmful algal bloom reduction. The analyses highlighted the ineffectiveness of the existing design criteria globally, with blooms occurring even though some waterbodies were designed in accordance with recommended parameters. The analysis suggested that understanding interactions between multiple design factors may be a useful approach, for example, when considering the macrophyte area to surface area ratio in combination with the shoreline development index. Plain Language Summary: Constructed shallow waterbodies, including wetlands, lakes and ponds used for stormwater management, are vulnerable to harmful algal blooms (HABs). Appropriate engineering design is important to reduce the risk of HAB occurrence in these waterbodies. A review of current design criteria in these guidelines was conducted to understand the recommended controls for HABs and whether sufficient design advice is available. The effectiveness of these criteria was then tested by statistically analyzing over 200 waterbodies in southeastern Australia over 9 years. Comparing the guideline review and the data analysis indicated that three lake factors, including macrophyte area to surface area ratio, shoreline development index and fetch, could reduce HAB risk. It also showed that the current design criteria may not be adequate to minimize HABs. This study provides a practical approach for investigating waterbody design guidelines and is important for water managers, government staff and industry consultants involved in planning activities related to harmful algal blooms. Key Points: Sixty six shallow waterbody design guidelines from around the world related to harmful algal blooms were reviewedTo assess the effectiveness of current guidelines on harmful algal bloom control, over 200 constructed shallow waterbodies were analyzedSuggested improvements to waterbody design guidelines are provided [ABSTRACT FROM AUTHOR]

Wet‐surface evaporation equations related to the Penman equation can be represented graphically on vapor pressure (e) versus temperature (T) graphs (Qualls & Crago, 2020, https://doi.org/10.1029/2019wr026766). Here, actual regional evaporation is represented graphically on (e, T) graphs using the Complementary Relationship (CR) between actual and apparent potential evaporation. The CR proposed by the authors can be represented in a simple and intuitive geometric form, in which lines representing the regional latent heat flux, LE, and the wet surface (Priestley & Taylor, 1972, https://doi.org/10.1175/1520-0493(1972)100<0081:otaosh>2.3.co;2) evaporation rate, LEPT, intersect at e = 0. This approach allows a graphical estimate of LE (or the corresponding mathematical formulation), provided available energy, wind speed, air temperature and humidity, and roughness lengths for momentum and sensible heat are available. The wet surface temperature is needed, and a calculation method for it is provided. The formulation works well using monthly data from seven sites in Australia, even when the same value of the Priestley & Taylor parameter α is used for all sites. Overall, compared to eddy covariance measurements, root mean square difference averaged 19 W m−2; this compares favorably with the CR formulation proposed by Brutsaert (2015, https://doi.org/10.1002/2015wr017720). Plain Language Summary: The Complementary Relationship (CR) between actual regional evaporation and apparent potential evaporation has received considerable support in recent years. Here, a representation of one version of the CR is developed and expressed on a single temperature/vapor pressure graph. Features such as available energy, wet surface temperature, actual surface temperature, potential temperature, and apparent potential temperature are all clearly represented graphically. The method is compared with that proposed by Brutsaert (2015, https://doi.org/10.1002/2015wr017720). Both methods performed well, but the graphical method has lower root mean squared errors. Key Points: Latent heat flux or evaporation can be depicted on graphs of vapor pressure (e) versus temperature (T)The Complementary Relationship (CR) of evaporation can be expressed graphically using an e versus T graphThe CR performs well with data from seven sites in Australia [ABSTRACT FROM AUTHOR]

Understanding daytime (ETD) and nighttime (ETN) evapotranspiration is critical for accurately evaluating terrestrial water and carbon cycles. However, unlike ETD, the factors influencing ETN remain poorly understood. Here, long‐term ETD and ETN data from five FLUXNET sites along a climate gradient in Northern Australia were analyzed to compare their responses to environmental drivers at different temporal scales. We found that (a) across the sites, mean annual ETN/ETD (ETN¯/ETD¯) ranged between 5.1% and 11.7%, which was mainly determined by ETD¯ variations. Particularly, vegetation and meteorological variables mostly controlled ETD¯, while ETN¯ was largely related to air temperature and net radiation (Rn) due to lower nighttime atmospheric water demands; (b) At individual sites, ETD and ETN exhibited higher correlations with meteorological and vegetation variables at monthly timescales than at annual timescales. Monthly ETD and ETN were also strongly coupled, especially under drier climatic conditions. At daily timescales, leaf area index and soil water content (SWC) controlled ETD with SWC being more important at drier sites; whereas, SWC was the dominant factor controlling ETN. At half‐hourly timescales, the boosted regression tree method quantitively showed that ETD and ETN were controlled by Rn and SWC, respectively. Overall, the results showed that ETN was less responsive to environmental variables, illustrating that ETD and ETN responded differently to diverse climate regimes and ecosystems at varying temporal scales. These findings provide a critical evaluation for contrasting ETD and ETN interactions in constantly changing environments, which has important implications for ecosystem water balance and land surface processes modeling. Key Points: Variations in mean annual ETN/ETD ratio (5.1%–11.7%) across the sites were mostly determined by the spatial variability in ETDETD and ETN responded differently to climatic drivers from half‐hourly to annual scales, which also depended on ecosystems typesAt site levels, leading controls were Rn for half‐hourly ETD, leaf area index (LAI), and soil water content (SWC) for daily to annual ETD, and SWC for half‐hourly to annual ETN [ABSTRACT FROM AUTHOR]

Measuring the physical complexity of habitats or ecological resources is often achieved using system‐specific methods that make comparisons across ecosystems difficult. One measure that is applicable across multiple ecosystems and scales is the fractal dimension, which has the benefit of generality as well as potential scale independence. This study evaluated the use of box‐counting and entropy fractal dimensions for characterizing the complexity of emergent rock distributions in six streams across Scotland and Australia. Emergent rocks (ER) are important hydraulic features and ecological resources, including as oviposition sites for aquatic insects and cover for fish. We completed fractal analysis on counts of ER in 5‐m segments along longitudinal stretches of the six streams. All six streams exhibited fractal behavior (self‐similarity), suggesting that fractals can be used to measure the complexity of longitudinal ER distributions in a way that is scale independent. Entropy was a superior measure due to its ability to differentiate among the six streams whereas box‐counting could not. Together, field results and numerical simulations showed that fractal dimensions of emergent rock distributions were related to stream geomorphology. Well‐developed bedforms, like alternating pools and riffles had better organized emergent rocks because large bed materials were more likely to be emergent in topographic highs. Streams with coarser bed materials had more chaotic arrangements of emergent rocks because this increased the general abundance of emergent rocks, making differentiation between topographic highs and lows less distinctive. Fractal dimensions, therefore, can measure the complexity of river systems in a way that is relevant to geomorphological and ecological processes. Plain Language Summary: Fractal dimensions are used to characterize the complexity of a wide range of patterns in nature, from single objects (e.g., branched twigs) to whole environments (rainforests), and learn where consistent patterns may occur. We measured the complexity of rock patterns (specifically rocks that emerge above the water's surface) in six rivers from Scotland and Australia using fractal dimensions. These rocks provide important habitat for plants, insects, and fish in rivers, and so are important to overall stream condition and functioning. Less complex, more highly structured rock patterns (lower fractal dimensions) occurred in streams with smaller rocks, which had areas where emergent rocks were concentrated (riffles) and many long pools without emergent rocks. These results suggest that fractal dimensions may be a promising measure of complexity that can help us understand relationships between physical characteristics of streams and their ecology. Fractal dimensions also allow comparison of rock patterns in rivers with other habitats, such as shrubs in grasslands for example. This may allow future research to explain patterns that are consistent across these different ecosystems and so advance general ecological theory. Key Points: Longitudinal patterns of emergent fluvial rocks in six streams in Scotland and Australia exhibited fractal behavior (self‐similarity)Fractal dimensions were related to development of bedform topography and the density and size of available bed materials in the streamsFractal dimensions are a promising measure of physical complexity that enable comparisons across ecosystems, scales and disciplines [ABSTRACT FROM AUTHOR]

Flood inundation forecasts from hydrodynamic models can help with flood preparedness, but uncertainty in the inputs and parameters can lead to erroneous flood inundation estimates. However, Synthetic aperture radar (SAR)‐based flood extent information can be used to constrain such model forecasts through data assimilation thus making them more accurate. Since high‐resolution SAR satellites can only provide partial coverage for medium to large catchments, it is expedient to evaluate the combination of observation footprint, timing, and frequency which can lead to maximum forecast improvements. Consequently, multiple spatiotemporal SAR‐based flood extent assimilation scenarios have been simulated here to identify the optimum observation design for improved flood inundation forecasts. A mutual information‐based particle filter was implemented in a synthetic setup for the 2011 flood event in the Clarence Catchment, Australia, to combine SAR‐based flood extents with the hydraulic model LISFLOOD‐FP. The open loop ensemble was forced using uncertain inflows and the impact of assimilating flood extents in morphologically homogenous river reaches was evaluated for different first visit and revisit scenarios. Results revealed that the optimum temporal acquisition strategy strongly depends on reach morphology and flood wave arrival timing. Further, it was found that a single image at the right time could improve the 8‐days forecast by ∼95% when assimilated at reaches with large flat floodplains but limited tidal influence, while in reaches with narrow valleys over 10 images were needed to achieve the same outcome. Experiments such as the one presented here can therefore inform targeted observation strategies to ensure cost effective flood monitoring and maximize the forecast accuracy resulting from flood extent assimilation. Plain Language Summary: Satellite observations of flood inundation have the potential to increase the reliability and accuracy of flood forecasts, thereby contributing to improved flood resilience of vulnerable populations. However, new generation high‐resolution satellites can only observe small portions of large river systems river during a flood. Since, the model‐data integration methods used to combine flood forecasting models with satellite data are sensitive to the observation coverage, timing, and frequency best case scenarios can be constructed to obtain maximum improvements in accuracy. This study investigated the possibility of designing targeted observation strategies that can lead to more accurate flood forecasts after the model‐data integration. Synthetic experiments were used to simulate multiple different image acquisition scenarios and assess the impacts on flood forecast accuracy. Results indicate that the location and timing of the images is more important than the revisit interval. Findings from this study can therefore be used to inform future satellite acquisitions, to ensure more cost effective flood monitoring from space leading to more reliable flood inundation forecasts. Key Points: The potential of targeted observations for optimal flood inundation forecast improvements from flood extent assimilation is demonstratedImpact of assimilating images in morphologically uniform reaches at different times, on channel, and floodplain water depth quantifiedAssimilating a single ideal flood extent could lead to persistent improvements comparable to assimilating multiple sub‐optimal images [ABSTRACT FROM AUTHOR]

Accurate flood inundation forecasts have the potential to minimize socioeconomic losses, but uncertainties in inflows propagated from the precipitation forecasts result in large prediction errors. Recent studies suggest that by assimilating independent flood observations, inherent uncertainty in hydraulic flood inundation modeling can be mitigated. Satellite observations from Synthetic Aperture Radar (SAR) sensors, with demonstrated flood monitoring capability, can thus be used to reduce flood forecast uncertainties through assimilation. However, researchers have struggled to develop an appropriate cost function to determine the innovation to be applied at each assimilation time step. Thus, a novel likelihood function based on mutual information (MI) is proposed here, for use with a particle filter‐based (PF) flood extent assimilation framework. Using identical twin experiments, synthetic SAR‐based probabilistic flood extents were assimilated into the hydraulic model LISFLOOD‐FP using the proposed PF‐MI algorithm. The 2011 flood event in the Clarence Catchment, Australia was used for this study. The impact of assimilating flood extents was evaluated in terms of subsequent flood extent evolution, floodplain water depths, flow velocities and channel water levels (WLs). Water depth and flow velocity simulations improved by ∼60% over the open loop on an average and persisted for up to 7 days, following the sequential assimilation of two post‐peak flood extent observations. Flood extents and channel WLs also showed mean improvements of ∼10% and ∼80% in accuracy, respectively, indicating that the proposed MI likelihood function can improve flood extent assimilation. Plain Language Summary: Accurate forecasts of flood inundation can minimize socioeconomic losses from the frequent and often disastrous flood events occurring world‐wide. However, numerical models used to generate flood predictions are strongly dependent on the quality of inputs such as inflows and topography, which typically fail to meet the required accuracy standards. Recent studies suggest that integrating independent flood observations into these numerical models can mitigate some of the errors and increase the reliability of the resulting flood forecasts. Remotely sensed radar data, which has all‐weather/all‐day imaging capabilities, can thus accurately observe flooded areas. These flood extent observations can then be used to improve flood forecasts through model‐data integration, but studies have struggled to develop an effective approach to combine these with numerical models, as the area under water only varies slightly with time. Consequently, this study proposed a novel model‐data integration method, sensitive to slight variations in the flooded area, and verified its performance through synthetic experiments. At each time step shared information between the model predicted and the observed flooded area is quantified and used to combine their information content. This led to persistent improvements in predictions of flood extent, depth, and velocity, demonstrating the potential of the proposed integration method. Key Points: A novel mutual information‐based metric is proposed as the likelihood function for particle filter‐based flood extent assimilationDistributed impacts of the assimilation on simulated flood depth and flow velocities are illustrated for different lead timesImprovements in simulated water levels of ∼80% over the open loop are shown, persistent for up to one week after the assimilation [ABSTRACT FROM AUTHOR]

It is now standard practice for water supply agencies to use stochastic models to generate synthetic hydroclimate sequences that preserve the key statistics contained in the observed/instrumental hydroclimate data (usually rainfall, potential evapotranspiration, and streamflow) for use as inputs when determining water security. However, this approach is only adequate for determining current and future water supply risks if nonstationarity in past and future climate is accounted for. In this study, we develop an approach for stochastically generating future seasonal (monthly to annual) hydroclimatic conditions at multiple sites for water supply security assessment that capitalizes on an Australia‐wide relationship between annual average daily maximum temperature and annual rain (and flow). This approach is practical as it (i) avoids the extra time and additional uncertainties introduced by downscaling and bias correction of climate model produced rainfall information and (ii) takes advantage of the fact that climate model projections for temperature change are more realistic than climate model projections for rainfall. The approach is applied to a case study for catchments supplying Sydney, Australia's largest city. Two future scenarios were evaluated, 1 and 2°C warming over the next 50 years. Large reductions in streamflow were simulated, particularly for the 2°C scenario, resulting in major impacts on water security in the absence of any intervention. This is consistent with recent warming since 1990 being associated with more than a 40% decrease in average annual streamflow when compared with the average annual streamflow over the ∼110 years historical record. Key Points: Generate future hydroclimatic conditions for water security assessment using the relationship between temperature and rain (and stream flow)Recent warming (2000–2018) in Sydney, Australia associated with a 43% decrease in average annual streamflow when compared with the post‐1910 averageFuture warming is associated with even larger reductions in average annual streamflow resulting in major impacts on water security in the absence of any intervention [ABSTRACT FROM AUTHOR]

Potential (wet surface) evaporation is the basis for many methods to estimate actual evaporation. Penman's (1948, https://doi.org/10.1098/rspa.1948.0037) combination of energy budget with mass and energy transfer equations can be depicted on temperature‐vapor pressure graphs. Key to this depiction is two straight lines on the graph representing constant enthalpy of the air at measurement height and at the surface skin, with the gap between the lines representing a combination of aerodynamic resistance variables and available energy. The equations on which Penman based his formula are easily solved numerically (without need for Penman's famous assumption) for T0w, the temperature the surface would have if it was saturated, keeping all other variables constant. Wet surface evaporation is proportional to the vapor pressure difference between the measurement height and the surface skin. Equilibrium evaporation, based on the slope of the vapor pressure curve at T0w, is also easily represented in the graph. The difference between the correct wet surface evaporation rate and Penman's approximation is immediately visible on the graphs. The different wet surface evaporation rates are compared using data from a tropical savanna in Australia. Implications for the classic two‐component interpretation of Penman's equation are discussed. Key Points: Penman's (1948) combination method for wet surface evaporation does not require his famous linearization assumption in order to be usefulWet surface evaporation equations can be depicted easily on graphs of vapor pressure versus temperaturePenman's equation consistently underestimates the correct wet surface evaporation rate; graphs explain why this must be the case [ABSTRACT FROM AUTHOR]

We aimed to quantify the delayed effects of flows on aquatic ecosystems using 33 years of monitoring data on macroinvertebrates and water quality and 51 years of hydrological data, spanning 2,300 km of the Murray River, Australia. By incorporating the delayed effects of hydrological indices, along with physicochemical variables, into generalized additive models to form distributed lag nonlinear models for macroinvertebrate richness and abundance, we found that the effects of floods on macroinvertebrates could last up to 32 years, depending on their magnitude. The models indicated that a large flood can cause an initial depression in macroinvertebrate abundance and richness, followed by a sustained increase persisting for over 25 years before returning to preflood levels. Twelve hydrological indices representing the magnitude of flow events were evaluated, with the q90/med (the 6‐monthly flow exceeded 10% of time, divided by the long‐term median) performing (slightly) better than other indices. Competing hypotheses for the mechanisms underlying the apparent long‐term effect of floods were considered, with the most plausible explanation being the flood‐mediated influx of allochthonous organic matter, especially coarse and large woody debris, that might drive the persistent change in the aquatic community. It is generally believed that macroinvertebrate communities recover quickly after floods, but our findings suggest that we may need to reconceptualize the effects that floods can have on aquatic macroinvertebrates and the communities they support, especially in the context of flow restoration and climate change. Plain Language Summary: Research is being done around the world to understand how much water is required to sustain river ecosystems, and therefore, how much can be extracted for consumptive uses such as irrigated agriculture. We used long‐term monitoring data (1980–2012) on aquatic macroinvertebrates—which includes insects, crustaceans, mussels, snails, and worms that spend all or part of their life in water—collected along 2,300 km of the Murray River, Australia, to investigate the relationship between river flow and the health of aquatic ecosystems. These data indicate that small to medium flows might have an immediate effect through improvements in water quality, but large floods may have sizeable delayed impacts on macroinvertebrates lasting more than three decades. The number of species and overall abundance of macroinvertebrates initially decreased, due to washout, but then increased for a period of about 25 years after the historic flood of 1993, persisting throughout the Millennium drought (1996–2010). The positive legacy of floods might be due to an influx of woody debris during the event, which can provide habitat and food for macroinvertebrates until decomposition of the woody material is complete. Key Points: Long‐term data on macroinvertebrates, water quality, and hydrology in the Murray River enabled quantifying the flow‐ecology relationshipFlood impacts on macroinvertebrate abundance and richness may persist for decades, while small to medium flows have indiscernible effectsA flood‐induced influx of organic matter might underlie the apparent long‐term effect of floods [ABSTRACT FROM AUTHOR]