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

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

Generating plausible future climate time series is needed for bottom-up climate impact modeling, as well as downscaling climate model output for hydrological applications. A novel method for generating multisite daily stochastic climate series is developed based on 1) linear regression between climate teleconnection time series (e.g., IPO/SOI) and annual rainfall, 2) clustered method of fragments for subannual disaggregation, and 3) a regression-based approach to daily potential evapotranspiration (PET) for hydrological modeling. We demonstrate that bias (i.e., oversampling) occurs with the standard method of fragments disaggregation in the multisite context and show that selection of an analog year from clustered rainfall amounts provides better sampling properties than the standard method of fragments. Using hydrological data for southeastern Australia, we model runoff from observed and simulated rainfall and PET using the GR4J (Génie Rural à 4 paramètres Journalier) model. Simulated annual and daily rainfall and runoff characteristics from the new method are similar to existing methods, with improvements demonstrated in wet–wet transition probabilities and spatial (between-site) correlations. Significance Statement: In this paper we develop a novel method for generating multisite daily stochastic climate series regressing climate teleconnections (e.g., ENSO, IPO) to annual site rainfall, and disaggregation using a clustered version of the method of fragments. The modular nature of the method also allows for the possibility of the generation of replicates from GCM output of climate teleconnections, as well as perturbed climate futures for scenario-neutral modeling. Results demonstrate that rainfall and runoff metrics of interest for water resource modeling are reproduced well using the model. [ABSTRACT FROM AUTHOR]

The Millennium drought which occurred around 1997–2009 throughout southeastern Australia has led to recorded low groundwater levels causing considerable economical losses. Improving the drought resilience of at-risk groundwater systems has been recognized as a priority for sustainable water resources management in the region. This study introduces the standardized groundwater discharge index (SGDI) based on groundwater discharge to river to assess groundwater drought performance at multi-timescales for catchments in the southeastern Murray-Darling Basin. The response time of groundwater drought to precipitation drought is found to be above 12 months according to the relationships between SGDI and the standardized precipitation index. The performance of groundwater drought, indicated by resilience and resistance, overall shows that catchments with higher drought resilience are often accompanied by lower drought resistance and vulnerability. Groundwater drought is found to be less resilient but more resistant than precipitation drought due to the buffer capacity of the groundwater system. The determinants of groundwater drought performance at different timescales are identified by a machine learning approach. The relationships between groundwater drought performance metrics and their determinants are found to be highly nonlinear and distinctly different among the timescales. Climate factors and catchment physical properties can explain up to 60% of the spatial variance in drought resilience and resistance across the region at the 12-month scale. Our findings provide crucial insight into groundwater drought management and open potential pathways for improving groundwater drought resilience and resistance to mitigate the drought impacts potentially driven by a changing climate. [ABSTRACT FROM AUTHOR]

The trend and variability in rainfall characteristics that influence annual streamflow of southeast Australia is assessed using two methods, non‐parametric Kendall test and linear slope, and two gridded daily rainfall datasets (SILO and AWAP) and 1,196 station rainfall data for 1971–2021 period. The rainfall anomaly before, during and after the Millennium Drought (1997–2009) is compared to quantify the variability. The results show: (a) a declining rainfall trend is detected as the recent dry decades were preceded by wet decades. The declining trend largely occurs from autumn through to spring with the largest trend in August to October followed by April and May; (b) the trend is more significant for number of rainfall days and for multi‐day rainfall totals (and more so for longer accumulations) than for annual rainfall; (c) the very high extreme rainfall shows a small increasing trend in summer and mixed signal in the other seasons; (d) the streamflow in the post drought period is lower than the long‐term mean, despite the mean annual rainfall being slightly higher. This can be partly explained by the rainfall‐runoff relationship of catchments not having fully recovered from the prolonged drought and partly by changes in rainfall characteristics, like the wet spell length and multi‐day rainfall, influencing annual streamflow; (e) The general conclusions for analyses with different sources of daily rainfall data are the same. However, some differences do exist, with SILO gridded rainfall and station data having larger areas with statistically significant trend than the AWAP gridded rainfall, as well as larger declining trends in the high elevation areas. The general conclusions of this study are drawn from an Australia case study but could have implications for other regions to investigate the attributions of rainfall characteristics other than annual rainfall to the non‐stationary rainfall‐streamflow relationship. [ABSTRACT FROM AUTHOR]

The trend and variability in rainfall characteristics that influence annual streamflow of southeast Australia is assessed using two methods, non‐parametric Kendall test and linear slope, and two gridded daily rainfall datasets (SILO and AWAP) and 1,196 station rainfall data for 1971–2021 period. The rainfall anomaly before, during and after the Millennium Drought (1997–2009) is compared to quantify the variability. The results show: (a) a declining rainfall trend is detected as the recent dry decades were preceded by wet decades. The declining trend largely occurs from autumn through to spring with the largest trend in August to October followed by April and May; (b) the trend is more significant for number of rainfall days and for multi‐day rainfall totals (and more so for longer accumulations) than for annual rainfall; (c) the very high extreme rainfall shows a small increasing trend in summer and mixed signal in the other seasons; (d) the streamflow in the post drought period is lower than the long‐term mean, despite the mean annual rainfall being slightly higher. This can be partly explained by the rainfall‐runoff relationship of catchments not having fully recovered from the prolonged drought and partly by changes in rainfall characteristics, like the wet spell length and multi‐day rainfall, influencing annual streamflow; (e) The general conclusions for analyses with different sources of daily rainfall data are the same. However, some differences do exist, with SILO gridded rainfall and station data having larger areas with statistically significant trend than the AWAP gridded rainfall, as well as larger declining trends in the high elevation areas. The general conclusions of this study are drawn from an Australia case study but could have implications for other regions to investigate the attributions of rainfall characteristics other than annual rainfall to the non‐stationary rainfall‐streamflow relationship. [ABSTRACT FROM AUTHOR]

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization. [ABSTRACT FROM AUTHOR]

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

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

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Key Points: Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data [ABSTRACT FROM AUTHOR]

Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile–quantile mapping (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the state of Victoria, Australia (237 629 km 2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km × 10 km grid point across Victoria, the GR4J rainfall–runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection. [ABSTRACT FROM AUTHOR]

Dynamical downscaling of future projections of global climate model outputs can provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand in regional water resource planning processes. By explicitly modelling climate processes within and across global climate model grid cells for a region, dynamical downscaling can provide higher-resolution hydroclimate projections and independent (from historical time series), physically plausible future rainfall time series for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias-correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile–quantile mapping to rainfall dynamically downscaled by the NSW and ACT Regional Climate Modelling (NARCliM) Project in the state of Victoria, Australia, and examine the effect of this on (i) biases both before and after bias correction in different rainfall metrics, (ii) change signals in metrics in comparison to the bias and (iii) the effect of bias correction on wet–wet and dry–dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile–quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. Representing quantile–quantile mapping in this way demonstrates that any quantile mapping bias-correction method is unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias-correct dynamical downscaling rainfall outputs. [ABSTRACT FROM AUTHOR]

The precipitation elasticity of runoff, defined as the percentage change in mean annual runoff for a given percentage change in mean annual precipitation, has been widely used as a simple rule of thumb for estimating runoff sensitivity to changes in precipitation. Most applications assume the same runoff sensitivity for both an increase and a decrease in precipitation. This study examines this assumption using long historical annual precipitation and streamflow data from 353 watersheds across the United States. The results show that the assumption is acceptable in humid and subhumid regions (aridity index less than 1.0). However, in arid and semiarid regions (aridity index greater than 1.0), the sensitivity of runoff to precipitation is greater for a decrease in precipitation than for an increase in precipitation. Therefore, the use of precipitation elasticity of runoff estimated using the entire data set in semiarid and arid regions will result in an overestimation of runoff increase from precipitation increase and an underestimation of runoff decrease from a decrease in precipitation. These findings suggest that multiple precipitation elasticities of runoff may be needed to estimate nonlinear responses of streamflow to precipitation change in a changing climate, especially for persistent increase and decrease in precipitation. Key Points: Precipitation elasticity of runoff is generally assumed to be the same for an increase as well as a decrease in precipitationResults show that runoff change in semiarid and arid watersheds is more sensitive to precipitation decreases than precipitation increasesIt is useful to characterize different precipitation elasticity for reducing uncertainties in regional projection of runoff [ABSTRACT FROM AUTHOR]

Terrace identification is the basis for understanding quantity, quality, and land covers of terraces and their effects on agriculture production and various surface processes, for example, hydrological and ecological processes. However, there are some drawbacks and limitations in the automatic extraction of terraces, such as difficulty of outlining the overall boundary for individual terrace and the limitation of applying methods and parameters. In this study, we used machine learning methods on the basis of Geographic Object‐Based Image Analysis using K‐nearest neighbours and spectral angle mapper algorithms to extract terraces in the hilly and gully regions on the Loess Plateau, China. This study relied on medium‐resolution image Landsat‐8 combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model to extract the overall boundary of terraces and relies on high‐resolution images GaoFen‐1 to extract the terraced edges of terraces. It used the methods of principal component analysis and Laplacian convolution filter to enhance and extract terraced edges inside of the boundary of terraces. Our estimates are with overall accuracy of 62.2% in K‐nearest neighbours and 74.8% in spectral angle mapper methods, indicating the advantages of the proposed method despite the use of much lower resolution data than previous studies that used both high‐resolution terrain and remote sensing imageries data. This study highlights the importance of using appropriate methods plus reasonable spatial resolution of remote sensing data for outlining the overall boundary of terraces in the hilly and gully regions. [ABSTRACT FROM AUTHOR]

Realistic projections of changes to daily rainfall frequency and magnitude, at catchment scales, are required to assess the potential impacts of climate change on regional water supply. We show that quantile-quantile matched (QQM) bias-corrected daily rainfall from dynamically downscaled WRF simulations of current climate produce biased hydrological simulations, in a case study for the State of Victoria, Australia (237 629 km2). While the QQM bias correction can remove bias in daily rainfall distributions at each 10 km2 grid point across Victoria, the GR4J rainfall-runoff model underestimates runoff when driven with QQM bias-corrected daily rainfall. We compare simulated runoff differences using bias-corrected and empirically scaled rainfall for several key water supply catchments across Victoria and discuss the implications for confidence in the magnitude of projected changes for mid-century. Our results highlight the imperative for methods that can correct for temporal and spatial biases in dynamically downscaled daily rainfall if they are to be suitable for hydrological projection. [ABSTRACT FROM AUTHOR]

Dynamical downscaling of future projections of global climate model outputs can potentially provide useful information about plausible and possible changes to water resource availability, for which there is increasing demand for regional water resource planning processes. By explicitly modelling climate processes within and across global climate model gridcells for a region, dynamical downscaling can provide higher resolution hydroclimate projections, as well as independent (from historical timeseries) and physically plausible future rainfall timeseries for hydrological modelling applications. However, since rainfall is not typically constrained to observations by these methods, there is often a need for bias correction before use in hydrological modelling. Many bias correction methods (such as scaling, empirical and distributional mapping) have been proposed in the literature, but methods that treat daily amounts only (and not sequencing) can result in residual biases in certain rainfall characteristics, which flow through to biases and problems with subsequently modelled runoff. We apply quantile-quantile mapping to rainfall dynamically downscaled by NARCliM in the State of Victoria, Australia and examine the effect of this on: (i) biases both before and after bias correction in different rainfall metrics; (ii) change signals in metrics in comparison to the bias; and (iii) the effect of bias correction on wet-wet and dry-dry transition probabilities. After bias correction, persistence of wet states is under-correlated (i.e. more random than observations), and this results in a significant bias (underestimation) of runoff using hydrological models calibrated on historical data. A novel representation of quantile-quantile mapping is developed based on lag-one transition probabilities of dry and wet states, and we use this to explain residual biases in transition probabilities. This demonstrates that any quantile mapping bias correction methods are unable to correct the underestimation of autocorrelation of rainfall sequencing, which suggests that new methods are needed to properly bias correct dynamical downscaling rainfall outputs. [ABSTRACT FROM AUTHOR]

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

Accurate prediction of runoff signatures is important for numerous hydrological and water resources applications. However, there are lack of comprehensive evaluations of various approaches for predicting hydrological signatures. This study, for the first time, introduces regression tree ensemble approach and compares it with other three widely used approaches (multiple linear regression, multiple log‐transformed linear regression, and hydrological modeling) for assessing prediction accuracy of 13 runoff characteristics or signatures, using a large data set from 605 catchments across Australia. The climate, in particular, mean annual precipitation and aridity index, has the most significant influence on the runoff signatures. Physical catchment attributes including forest ratio, slope, and soil water holding capacity also have significant influence (p < 0.05) on the runoff signatures. All four approaches can predict the long‐term average and high flow signatures accurately. The regression approaches can also well predict majority of the other runoff signatures, with the Nash‐Sutcliffe Efficiency larger than 0.60. The regression tree ensemble outperforms the two linear regressions in predicting signatures of flow dynamics. The hydrological models, calibrated to one specific objective criterion, cannot predict many of the runoff signatures, particularly those reflecting low flows and flow dynamics. This is because in most hydrological model applications, the simulations allow satisfactory predictions of long‐term average and high flow signatures. In applications where a specific runoff signature is needed, regression relationships that directly relate that runoff signature to catchment attributes give the best predictions. Here the regression tree ensemble is overall best and offers significant potential, being able to predict most of the runoff signatures very well. Key Points: Four approaches are compared for predicting 13 runoff signatures for 605 catchments across AustraliaThe regression tree ensemble approach is overall best; the linear regressions are intermediate and only marginally different from each otherHydrological modeling can predict reasonably well the average and high flow signatures, but in general not the low flow and flow dynamic signatures [ABSTRACT FROM AUTHOR]

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

Quantifying the responses of the coupled carbon and water cycles to current global warming and rising atmospheric CO2 concentration is crucial for predicting and adapting to climate changes. Here we show that terrestrial carbon uptake (i.e. gross primary production) increased significantly from 1982 to 2011 using a combination of ground-based and remotely sensed land and atmospheric observations. Importantly, we find that the terrestrial carbon uptake increase is not accompanied by a proportional increase in water use (i.e. evapotranspiration) but is largely (about 90%) driven by increased carbon uptake per unit of water use, i.e. water use efficiency. The increased water use efficiency is positively related to rising CO2 concentration and increased canopy leaf area index, and negatively influenced by increased vapour pressure deficits. Our findings suggest that rising atmospheric CO2 concentration has caused a shift in terrestrial water economics of carbon uptake. [ABSTRACT FROM AUTHOR]

It is widely recognized that vegetation changes can significantly affect the local water availability. Methods have been developed to predict the effects of vegetation change on water yield or total streamflow. However, it is still a challenge to predict changes in base flow following vegetation change due to limited understanding of catchment storage-discharge dynamics. In this study, the power law relationship for describing catchment storage-discharge dynamics is reformulated to quantify the changes in storage-discharge relationship resulting from vegetation changes using streamflow data from six paired-catchment experiments, of which two are deforestation catchments and four are afforestation catchments. Streamflow observations from the paired-catchment experiments clearly demonstrate that vegetation changes have led to significant changes in catchment storage-discharge relationships, accounting for about 83-128% of the changes in groundwater discharge in the treated catchments. Deforestation has led to increases in groundwater discharge (or base flow) but afforestation has resulted in decreases in groundwater discharge. Further analysis shows that the contribution of changes in groundwater discharge to the total changes in streamflow varies greatly among experimental catchments ranging from 12% to 80% with a mean of 38 ± 22% ( μ ± σ). This study proposed a new method to quantify the effects of vegetation changes on groundwater discharge from catchment storage and will improve our predictability about the impacts of vegetation changes on catchment water yields. [ABSTRACT FROM AUTHOR]

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

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

Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model [CSIRO Atmosphere Biosphere Land Exchange (CABLE)] and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall-runoff models [modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD)]. The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall-runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall-runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters. [ABSTRACT FROM AUTHOR]

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

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

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

Reliable predictions of water availability and streamflow characteristics, and the impact of climate and land use change on water availability, are central to water resources planning and management. This paper assesses the application of the widely used macroscale hydrologic model, the three-layer Variable Infiltration Capacity model (VIC-3L), to estimate daily streamflow in 191 unregulated catchments across southeast Australia and evaluates the regionalization of model parameters to predict streamflow in ungauged catchments. The parameter values in the VIC-3L model are estimated using three methods: default values, optimized values based on model calibration, and regionalized values based on spatial proximity method. The modeled streamflows from VIC-3L are assessed against the observed streamflows from the catchments. The authors discuss the model performance based on different parameter estimation methods and the effects of rainfall regimes on streamflow prediction. Also the implication of using a priori estimates of parameter values versus optimizing parameter values against observed streamflow to predict the impact of climate and land use change on streamflow is discussed. The VIC-3L model can simulate the streamflow in the catchments across southeast Australia reasonably well, with comparable results to those reported for the same region using conceptual rainfall-runoff models. The model performed better in summer-dominant rainfall catchments and wet catchments than in other catchments. The regionalization based on spatial proximity method performed reasonably well, which demonstrated the potential of VIC-3L model to predict streamflow in ungauged catchments in Australia. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Satellite and gridded meteorological data can be used to estimate evaporation ( E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in 'wet' regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments do not match trends in evaporation estimated using three alternative methods: 1) , a model-tree ensemble approach that uses statistical relationships between E measured across the global network of flux stations, meteorological drivers, and remotely sensed fraction of absorbed photosynthetically active radiation; 2) , a Budyko-style hydrometeorological model; and 3) , the Penman-Monteith energy-balance equation coupled with a simple biophysical model for surface conductance. Key model inputs for the estimation of and are remotely sensed radiation and gridded meteorological fields and it is concluded that these data are, as yet, not sufficiently accurate to explain trends in E for wet regions. This provides a significant challenge for satellite-based energy-balance methods. Trends in for 87 'dry' catchments are strongly correlated to trends in precipitation ( R2 == 0.85). These trends were best captured by , which explicitly includes precipitation and available energy as model inputs. [ABSTRACT FROM AUTHOR]

This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall-runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall-runoff models. The variability in the climate change impact on runoff results for one rainfall-runoff model informed by 15 GCMs (an about 28%%-35%% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall-runoff models informed by 1 GCM (a less than 7%% difference between the minimum and maximum results). The difference between the rainfall-runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall-runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate-runoff relationship in a warmer and higher CO2 environment are modeled. [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]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents a study which investigates the lag relationship between the runoff throughout southeast Australia rainfall throughout Australia. The study analyzes the relationship between the two events, wherein results show that the rainfall data suggests that the relationship is superior during summer and spring in northeast Australia and during spring in southeast Australia. Other findings show that the runoff certainty in the southeast part is superior during summer and spring season.

The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell rainfall data. The daily scaling and daily translation methods generally give higher extreme and annual runoff than the constant scaling method because they take into account the increase in extreme daily rainfall (which generates significant runoff) simulated by the large majority of the GCMs. However, the difference between the mean annual runoff simulated with future daily rainfall series obtained using the constant versus daily scaling methods is generally less than 5%, which is relatively smaller than the range of runoff results from the different GCMs of 30%–40%. [ABSTRACT FROM AUTHOR]

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and ERS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall–runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments. [ABSTRACT FROM AUTHOR]

There have been numerous regionalization studies on runoff prediction in ungauged catchments. This study evaluates the relative benefits of different methods using two conceptual daily rainfall-runoff models, Xinanjiang and SIMHYD, on 210 relatively unimpacted catchments in southeast Australia. The results show that runoff predictions in ungauged catchments can benefit from a smart selection of donor catchments whose optimized parameter values are used to model runoff in the target ungauged catchment, output averaging of results from multiple-donor catchments and incorporating leaf area index data into the rainfall-runoff models. The biggest benefit comes from an educated selection of donor catchments (compared to a random selection of donor catchments) and output averaging of results from multiple-donor catchments. The difference between the three commonly used approaches for selecting donor catchments is relatively small. The spatial proximity approach (where the geographically closest catchment is used as the donor catchment) performs slightly better than the physical similarity approach (where the catchment with the most similar attributes is used as the donor catchment), and the integrated similarity approach, which combines the spatial proximity and physical similarity approaches, performs only very marginally better than the spatial proximity approach. The incorporation of leaf area index data into the rainfall-runoff models shows marginal improvements to the modeling results, although a more appropriate integration of vegetation and other remotely sensed data may further improve the results. [ABSTRACT FROM AUTHOR]

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

The determination of the annual exceedence probability (AEP) of extreme water levels in complex estuarine systems is an important and challenging issue in flood management. Extreme estuarine levels are caused by the combined effects of river flows, local winds, and coastal ocean levels. This paper describes a stochastic, event-based approach for generating concurrent hourly mean sea level pressure (MSLP) and wind speed, which are needed in a larger project to derive stochastic hourly river flows, winds, and coastal ocean levels to drive a hydrodynamic model for estuarine flood level simulation. The minimum MSLP versus rainfall and maximum wind speed versus rainfall relationships for the extreme flood events at the daily time scale are first used to generate minimum daily MSLP and maximum daily wind speed using a nonparametric resampling method. The generated minimum daily MSLP and maximum daily wind speed are then used to scale concurrent hourly MSLP and wind speed sequences resampled from the historical record. This approach is novel because it is simple, generic, and computationally efficient for generating stochastic cross-correlated forcing time series for any estuarine hydrodynamic and flood risk study. The approach is tested using 50 years of forcing data from the Gippsland Lakes system in southeast Australia. The results indicate that the approach produces realistic stochastic concurrent hourly sequences of MSLP and wind speed that preserve and distinguish the MSLP-rainfall and wind speed-rainfall relationships for different synoptic weather conditions and times of the year. The approach also produces hourly and daily minimum MSLP and maximum wind speed with similar AEP characteristics as the historical data. [ABSTRACT FROM AUTHOR]

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

This article discusses numerical and graphic methods for subdividing daily streamflows during flood events in the Gippsland Lakes area of southeasternAustralia. The authors argue that underestimation of peak flood levels can result in possible design failure. They use the steepness index unit volume flood hydrographic approach to disaggregate daily flood flows into sub-daily flows. This utilizes the instantaneous flood peak and the daily flood hydrograph rising-limb steepness index. They conclude that the model must simulate the event flood volume and the daily flood hydrograph rising-limb steepness index.

A significant amount of pollutants in urban stormwater runoff is transported as sediment-bound contaminants. It is important to have a clear understanding of the amount of pollutants attached to the different sediment sizes so that treatment facilities can be designed to effectively target the removal of the most polluted sediment sizes. This paper presents results from a study carried out to determine nutrient loads associated with different particle size ranges for dry surface pollutants and stormwater samples collected from an urban road surface. The results indicate that although more that half of the surface pollutant is coarser than 300 μm (microns), less than 15% of the total phosphorus (TP) and total nitrogen (TN) are attached to particle sizes greater than 300 μm. The pollutant loads in the different particle size ranges from different stormwater samples show a larger variability for TN than for TP. The dissolved component for TN ranges between 20 and 50% compared to 20–30% for TP. Practically all the particulate TP and TN in stormwater samples are attached to sediments between 11 and 150 μm. This suggests that to effectively remove TP and TN, pollutant treatment facilities must be able to remove particulates down to 11 μm. [ABSTRACT FROM AUTHOR]