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• A framework to evaluate stochastic rainfall models with a quantitative scale. • 'Good' modelled rainfall does not guarantee 'good' modelled streamflow. • Streamflow-based evaluation is more holistic than rainfall-based evaluation. • Component-wise evaluation can misrepresent ultimate model performance. Stochastic rainfall models are important tools for evaluating hydrological risks such as flooding and drought because of their ability to randomly generate alternative plausible climatic timeseries. The stochastic generation of climatic timeseries is not an end in itself, since they are typically applied to a catchment to determine the performance of water-related infrastructure systems, such as reservoirs or flood-control measures. This methodology typically involves a train of models to determine the end-of-system impact, yet the evaluation of stochastically generated rainfall timeseries is usually a stand-alone procedure focused on metrics directly related to the stochastic generator. This paper demonstrates discrepancies in this approach by evaluating two, daily-timestep, stochastic rainfall models in terms of rainfall metrics and their subsequently generated flow metrics after rainfall-runoff transformation. The two models are a Markov-based model and a latent-variable model, where each model is calibrated and evaluated showing 'overall good' performance. Stochastically generated timeseries, alongside observed rainfall timeseries are inputted to a calibrated catchment model (GR4J) to derive daily flow timeseries. Whereas the rainfall metrics typically showed 'good' performance, streamflow-based metrics are not necessarily 'good'. The procedure is repeated for 277 stations from Australia and 106 stations from the United States of America. Depending on the strictness of the flow-based comparison and region analysed, using the Markov-based model 12–26% of sites were classified as 'poor' performing, and 1%-9% of sites were classified as 'poor' using the latent-variable model. The results demonstrate that catchment-based performance of flow metrics is more holistic since it magnifies features of the rainfall not otherwise visible to rainfall-based evaluation. [ABSTRACT FROM AUTHOR]

Highlights • Simple lumped surrogate models can emulate the behaviour of high-fidelity models. • States and simulation time is reduced by 3 and 6 orders of magnitude, respectively. • The surrogate models are robust to type of training data and sensitive to model scale. • The surrogate models can be fitted to both warning, RTC, and planning applications. • Uncertainty of surrogate models are within acceptable ranges. Abstract Distributed physically based models (DPMs) have become the standard tool for urban drainage modelling. However, high computational demands limit these in applications where fast or multiple simulations are needed. This paper presents simple fit-for-purpose cheaper-to-run surrogate models (SMs) for pipe network simulations which are validated against a DPM. The SMs are set up by lumping the DPM network into compartments in which the volume of water is governed by mass balances. Outgoing discharges to downstream compartment(s) and surcharging are computed from unambiguous volume-discharge curves. The SMs are applied on a 45 km2 catchment, Elster Creek in Melbourne, Australia. The number of simulated states and simulation times are reduced by approximately 3 and 6 orders of magnitude, respectively. Different SM complexities are examined. The simplest SM using steady state training data performed well with NSE of 0.98 for volume in the most upstream compartment. When emulating the aggregated surcharge from that compartment, the SM captured all surcharge events correctly. NSE improved from 0.35 to 0.84 when subdividing the compartment into 17 subcompartments. Uncertainty of SM parameters was examined using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. Two different sampling methods were applied. Limits of acceptability for real-time control, warning and planning, resulted in many accepted models upstream and few to none in downstream backwater-prone areas. All applications showed SM uncertainty bands within expected uncertainty bands for DPM, supporting the use of a simpler conceptual model in fit-for-purpose modelling in urban water systems when computational demands of DPMs are prohibitive. [ABSTRACT FROM AUTHOR]

Summary Design rainfall statistics are the primary inputs used to assess flood risk across river catchments. These statistics normally take the form of Intensity–Duration–Frequency (IDF) curves that are derived from extreme value probability distributions fitted to observed daily, and sub-daily, rainfall data. The design rainfall relationships are often required for catchments where there are limited rainfall records, particularly catchments in remote areas with high topographic relief and hence some form of interpolation is required to provide estimates in these areas. This paper assesses the topographic dependence of rainfall extremes by using elevation-dependent thin plate smoothing splines to interpolate the mean annual maximum rainfall, for periods from one to seven days, across Australia. The analyses confirm the important impact of topography in explaining the spatial patterns of these extreme rainfall statistics. Continent-wide residual and cross validation statistics are used to demonstrate the 100-fold impact of elevation in relation to horizontal coordinates in explaining the spatial patterns, consistent with previous rainfall scaling studies and observational evidence. The impact of the complexity of the fitted spline surfaces, as defined by the number of knots, and the impact of applying variance stabilising transformations to the data, were also assessed. It was found that a relatively large number of 3570 knots, suitably chosen from 8619 gauge locations, was required to minimise the summary error statistics. Square root and log data transformations were found to deliver marginally superior continent-wide cross validation statistics, in comparison to applying no data transformation, but detailed assessments of residuals in complex high rainfall regions with high topographic relief showed that no data transformation gave superior performance in these regions. These results are consistent with the understanding that in areas with modest topographic relief, as for most of the Australian continent, extreme rainfall is closely aligned with elevation, but in areas with high topographic relief the impacts of topography on rainfall extremes are more complex. The interpolated extreme rainfall statistics, using no data transformation, have been used by the Australian Bureau of Meteorology to produce new IDF data for the Australian continent. The comprehensive methods presented for the evaluation of gridded design rainfall statistics will be useful for similar studies, in particular the importance of balancing the need for a continentally-optimum solution that maintains sufficient definition at the local scale. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wildfires are becoming more severe and frequent and their effects increasingly expose water resources to stress. • Climate, wildfire, streamflow records (1975 to 2018), topography, and landcover data from 92 catchments were combined to quantify the contribution of wildfire to streamflow variability across Australian temperate forests. • On average wildfire regimes relatively results in 8.8% increases in streamflow across Australian temperate forests. • Wildfire effects on regional streamflow varied spatially depending on hydroclimate factors, particularly precipitation. As wildfires become more frequent and severe, there are concerns regarding their impacts on water yield from forested catchments. While there are many studies in Australia about the effects of individual wildfires on streamflow at fine scales (<1 km2) in specific geographic settings, the effects of wildfire regimes on streamflow at broad spatial scales across temperate forests in Australia are not well understood. In this paper, we combined climate, wildfire, streamflow records (1975 to 2018), topographic, and landcover data from 92 catchments (74 – 4740 km2) in the Australian temperate zone to quantify the contribution of wildfire regimes over time to streamflow variability in different hydroclimatic settings (humid, dry sub-humid, and semi-arid) and geographic regions (Southeast Australia (SEA), Southwest Western Australia (SWWA), and Tasmania (TAS)). Wildfire regimes were represented by two metrics: the burnt area to drainage area (BDA) ratio for wildfire events in each year and a spatially averaged metric of Time Since Fire (TSF), which is a spatial average of time since the last wildfire in the catchment. By comparing prefire and postfire runoff ratios our study found that on average there was a short-term increase in runoff ratio (∼3% in year 1 and ∼6% in year 2 post-fire) after wildfires with BDA > 25%. No influence of fire was found in the long-term (15–20 years after the wildfire) runoff ratio. We found that wildfire regime, measured by TSF, explained ∼8.8% of the variation in annual streamflow across the Australian temperate zone, and that with decreasing TSF (i.e., increased wildfire impact), the average streamflow increased. Streamflow variation explained by wildfire regimes varied with hydroclimate. The explained variance of streamflow by wildfire regimes in semi-arid catchments (23%) and dry sub-humid catchments (13%) were higher than humid catchments (5%). Our results provide a broad-scale understanding of how wildfire regimes influence streamflow variability at broad temporal and spatial scales, and provided important context and baseline information for determining the implications of changes in climate and fire regimes for regional water availability. [ABSTRACT FROM AUTHOR]

• Remote sensing provides a way to monitor irrigation water use at the catchment scale. • Accurate irrigation simulations are observed over a range of catchment sizes and crop types. • Better results are seen in wetter years (water allocations above 40%). • Long term negative drought indices (SPIs), indicating dry years, shows a decrease in accuracy. Irrigated agriculture has been identified as using approximately 72% of water globally. Australia, like many places in the world, is subject to water sharing plans that cross government boarders and are subject to a mixture of management policies. There is a pressing need to develop a method to monitor irrigation water use to aid in water resource assessments and monitoring. This paper aims to test a method previously developed which monitors irrigation water use using remotely sensed observations over the catchment scale, without the need for in-situ observations, ground data or in-depth knowledge of crops and their planting dates. Using conservative assumptions about agricultural land management practice, irrigation is calculated as I r r = A E T - P. The method uses three vegetation indices derived from Landsat images to calculate crop coefficients (K c) based on multiple published relationships. These are combined through the FAO56 methodology using gridded rainfall and two reference evapotranspiration ( E T o) products to find actual evapotranspiration as A E T = E T o × K c , providing six E T o - K c combinations. Results indicate this study method can effectively assess irrigation water use over a range of catchment sizes from ~6000 to ~600,000 ha, although issues arise when regions have a designated low allocation volume for that season (less than40%). Comparisons with the Standardised Precipitation Index (SPI) and Evaporative Stress Index (ESI) show that the proposed method is robust to the rapid onset and short-term droughts, However, its performance was poor during the long term droughts with low water allocation years. The study results during these years has been predominately attributed to water stress in certain crops being undetected, agricultural managers skipping annual crop commodities as well as stock and domestic water use making up larger portions of total water use. This is a limitation of this approach, although when only comparing results in years with greater than 40% allocations, the results improved significantly showing it can monitor water use effectively. When adequate water is available, this approach is able to accurately predict irrigation water use for the sites examined. [ABSTRACT FROM AUTHOR]

• Restoration of water quality in watersheds is a major challenge in many parts of the world. • In south-western Australia stream salinity has been reversed in a 502 km2 watershed. • This was achieved through government planning and regulation and private sector investment in reforestation. Restoration of water quality in deforested watersheds is a major environmental and economic challenge in many parts of the world. In south-western Australia water quality issues manifest as salinisation, where reactivation of groundwater systems has occurred post-deforestation with the consequent discharge of salts stored in deep regolith into rivers. Prior to deforestation the stream salinity of the Denmark River (a forested watershed of 502 km2) was between 150 and 350 mg L−1TDS (Total Dissolved Solids) and was developed as a small water supply with potential for a much larger development. By the 1970s, 20% of deep rooted vegetation in the watershed was removed resulting in annual flow-weighted stream salinity of 1500 mg L−1TDS making the river unsuitable as a water supply. Two main policy approaches were used to restore this watershed: (1) the control of further deforestation on private land through regulation; and (2) a program to encourage private reforestation with eucalypt pulp-wood plantations. By 2010, 14.5% of the watershed was reforested leaving only 5.5% still deforested, with a strong relationship between streamflow and stream salinity and the amount of reforestation. River salinity had fallen to 500 mg L−1 TDS by 2017. Although streamflow had fallen from a mean 28.6 GL yr−1 in 1985–1990 to 13.6 GL yr−1 in 2012–2017 this was with water that was potable. The challenge into the future is to ensure the lower stream salinity is maintained through maintenance of forest cover. Importantly, this paper demonstrates that stream salinity can be reversed following deforestation if an appropriate scale of reforestation is deployed. [ABSTRACT FROM AUTHOR]