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Water is a limited and highly valuable resource. In many parts of the world, water agencies allocate water according to agreed entitlement systems. The allocations are largely based on water already available in storages and rivers. Water agencies may also issue seasonal water allocation outlooks by anticipating future inflows to the storages and rivers. These outlooks are meant to assist water entitlement holders to plan for their crop planting, irrigation, and participation in water markets. Currently, these outlooks are generally based on historical inflow observations (climatology) and are often determined for a small selection of possible climatic scenarios (e.g., extreme dry, dry, average, and wet). These outlooks have large uncertainties, which require users to manage high risks themselves, leading to inefficient water use. In this study, we investigate the use of ensemble seasonal inflow forecasts to improve the production of seasonal water allocation outlooks through a case study of the Goulburn system in central Victoria, Australia. This is a complex system with active water trade both within the region and outside with the larger connected southern Murray-Darling Basin. In this case study, we integrate Australian Bureau of Meteorology's seasonal streamflow forecasts with Goulburn-Murray Water's water allocation to produce fully probabilistic water allocation outlooks. We evaluate the outlooks for three irrigation seasons from 2017 to 2020. We compare these outlooks with those produced from using inflows based on climatology only, an approach akin to the current practice of Goulburn-Murray Water. Using seasonal streamflow forecasts resulted in outlooks up to 60% (average 20%) closer to actual determinations, with uncertainty reduced by up to 65% (average 19%) Improvements were most obvious for short lead times and later in the irrigation season. This is a clear demonstration of how integration of streamflow forecasts can improve end-user products, which can lead to more efficient water use and water market participation. This paper demonstrates how skillful forecasts of streamflow can be used to produce meaningful products for end users. Specifically, improvements are made to outlooks of water that will be made available to irrigators in dry years. The new outlooks are up to 60% closer, with up to 65% less uncertainty. These improved outlooks allow entitlement holders to more confidently plan for the irrigation season, leading in turn to more efficient water use. The method presented here should provide benefits for any water management systems where skillful streamflow forecasts are available. Beyond its use for high-reliability water share outlooks, it could easily be adapted for other water manager applications such as outlooks of low-reliability water shares in very dry years, or risk of spill (insufficient storage capacity) in wet years. [ABSTRACT FROM AUTHOR]

Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend‐aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30‐year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30‐year trends is to robustly represent long‐term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20‐year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day‐of‐year climatology, raw week‐1 forecasts reproduce temperature trends of the 20‐year observations in many regions while raw week‐4 forecasts do not exhibit the 20‐year observed trends. After trend‐aware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week‐1 forecasts show apparent trends consistent with the 20‐year observations, as the calibration transfers forecast skill and embeds the 20‐year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week‐4 forecasts exhibit the 30‐year observed trends, as the calibration reverts the forecasts to the 30‐year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend‐aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend‐aware method can be applied to deliver high‐quality subseasonal forecasts and support decision‐making in a changing climate. [ABSTRACT FROM AUTHOR]

Accurate and reliable seasonal climate forecasts are frequently sought by climate‐sensitive sectors to support decision‐making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP‐t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium‐Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP‐t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP‐t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration. [ABSTRACT FROM AUTHOR]

Calibration and downscaling of ensemble GCM forecasts is becoming increasingly important for hydrological and agricultural modelling in support of the management and protection of valuable natural resources. Moreover, skilful and reliable daily forecast sequences are required to drive decision support models that operate on a daily time step. While downscaling of daily GCM outputs has been developed extensively in climate impacts studies, much less attention has been paid to the downscaling of ensemble GCM forecasts, which has the confronting aspect of low and diminishing skill with increasing lead time. Evidence is building that simple bias‐correction methods that do not model correlation between forecasts and observations, nor attempt to correct spatial, temporal and inter‐variable correlations, produce downscaled forecasts that perform poorly in applications models. Thus downscaling GCM forecasts requires inclusion of a full calibration component to reduce bias, improve reliability, capture skill where it is available and remove negative skill. In this study, we propose a new methodology for coupling a full GCM forecast calibration with empirical methods to (a) instil correct temporal, spatial and inter‐variable correlations in ensembles and (b) perform a multivariate downscaling of ensembles to daily sequences at multiple sites. Through a case study application, the proposed methodology is shown to produce skilful monthly–seasonal forecasts of rainfall, temperature and solar radiation at regional spatial scales. It also produces realistic and coherent multivariate daily sequences at multiple sub‐grid locations. The new methodology can be applied to more effectively integrate climate forecasts into hydrological and crop models and to support proactive decision‐making in agriculture and natural resources management. [ABSTRACT FROM AUTHOR]

• We develop a new calibration strategy to enhance ET o forecasting. • This strategy improves the representation of ET o seasonality. • This strategy adds skills to calibrated ET o forecasts. • We validate the effectiveness and robustness of the strategy. Forecasting of short-term reference crop evapotranspiration (ET o) provides valuable information for hydrological, agricultural, and ecological applications. ET o forecasts can be derived from weather forecasts of Numerical Weather Prediction (NWP) models, but such raw forecasts need to be calibrated to correct errors and improve reliability. This study calibrates the short-term ET o forecasts constructed with weather forecasts from the Australian Bureau of Meteorology's Australian Community Climate and Earth-System Simulator G2 version (ACCESS-G2) model, using the recently developed Seasonally Coherent Calibration (SCC) model. The monthly parameterization of the SCC model will not be able to capture the increasing or decreasing trends in ET o at the submonthly scale, posing a challenge for effective forecast calibration. To address this challenge, we developed a new calibration strategy based on ET o anomalies and climatological mean. We thoroughly evaluated this strategy at both the continental and weather station scales. Results indicate that calibrating ET o anomalies improves the correlation coefficient between calibrated forecasts and observations by up to 10%, and increases forecast skill scores by up to 200%, with more significant improvements found at longer lead times (9-day lead time vs. 1-day lead time). Improvements in forecast quality in calibrations across two spatial scales based on different types of observations (gridded observations vs. weather station observations) validate the effectiveness and robustness of the developed strategy. We anticipate that this strategy will be applicable to other calibration models to enhance NWP-based ET o forecasting. [ABSTRACT FROM AUTHOR]

• A regionally optimized approach to power transformation for data normalization is proposed for post-processing NWP precipitation forecasts between gauges. • The proposed approach is compared with other transformation approaches in terms of their impacts on the performance of calibrated forecasts. • The regionally optimized approach is recommended because of its strong performance and its advantage for regional applications. Short-term precipitation forecasts are mainly derived from numerical weather prediction (NWP) models. Raw NWP forecasts typically require post-processing to improve their accuracy and reliability through statistical calibration. For post-processing precipitation forecasts, several well-known calibration models employ power transformation to remove the positive skewness of precipitation. The most common practice is to use a pre-fixed transformation parameter value for both observation and forecast variables. Another approach is to allow the parameter values to differ for the two variables and locally optimize them at a spatial point to ensure the best performance of a calibration model. However, when calibrating forecasts across many grid points in a region, there is a considerable advantage in keeping the transformation parameter values constant, to enable spatial consistency in the calibration model so that model information transfers from gauged to ungauged locations can be easily achieved. For this reason, a third approach is proposed here to regionally optimize the parameter values for the two variables. A question then arises: how do the three different approaches affect forecast calibration performance? This question is answered in this study by evaluating the calibration performance of precipitation forecasts across 20 locations in Australia. The pre-fixed one parameter value approach is found to lead to poor performance. The regionally optimized two parameter value approach is almost as good as the locally optimized two parameter value approach, but has the additional advantage of spatial consistency for regional applications. [ABSTRACT FROM AUTHOR]

• An enhanced model DSC-YOLOv8n is introduced for urban flood level detection. • DSC-YOLOv8n shows superior performance than counterparts in terms of accuracy. • DSC-YOLOv8n demonstrates its effectiveness in edge terminal practical applications. Recent years, frequent floods have posed enormous challenges to urban systems worldwide. Real-time accurate urban flood information has a significant impact on emergency decisions. With the growth of citizen sensor science, video image is becoming a novel data source and emerging great potential in urban flood management. Recent studies have focused on detecting the flooded states of reference objects, like vehicles present in the video images to estimate urban flood. However, current detection models primarily focus on the accuracy testing while ignoring the lightweight and edge terminal execution, which limits their widespread deployment and applications. YOLOv8n, mainly oriented to edge terminals, has proven feasible in vehicle flooded state detection, but its performance in complex scenarios, such as multi-scale mixed vehicles and fuzzy targets, is yet to be enhanced. To address these challenges faced by YOLOv8n, an advanced approach is contributed in this research. Initially, Distributed Shifted Convolution is introduced to compensate for YOLOv8n's shortfall in flooding vehicle detection, and thirty improved options are implemented and trained with the flooded vehicle dataset. Subsequently, the optimal option is obtained as a new model, called DSC-YOLOv8n. Comparison with YOLOv8n and classic YOLO family models, DSC-YOLOv8n presents superior performance with the mAP50 of 75.4 %, representing a 3.6 % increase over baseline YOLOv8n, mAP50:95 of 58.6 %, marking a 3.1 % improvement, and the FLOPS of 7.4 G, achieving a decrease of 9.8 %. And then, the advancement and versatility of DSC-YOLOv8n in urban flood level detection are evidenced by its behavior in complex scenarios prediction and real case applications. Totally, this study successfully demonstrates the proposed method to combine both accuracy and lightweight in urban flood level detection and further promotes video images in urban flood management. [ABSTRACT FROM AUTHOR]

• A staged error model for hourly streamflow forecasting for ephemeral rivers. • Parameter estimation with zero flow assumptions. • A pragmatic approach to ease computational burdens. Ephemeral rivers pose considerable challenges for hydrological forecasters. A staged error model is devised for hourly streamflow forecasting with emphasis on ephemeral rivers. Apart from key components such as bias-correction, autoregressive updating and mixed Gaussian residual distributions, the model treats both observed and simulated zero flows as censored data. This assumption requires additional novel parameter estimation and ensemble generation methods. When inferring error model parameters by maximum likelihood estimation (MLE), the likelihood is derived to account for simulated and observed streamflow taking zero or non-zero values using data censoring. MLE is trialed to be replaced by least-squares-after-transformation (LST) estimation wherever possible to ease computational burdens. When generating ensemble forecasts, a data augmentation is used approach to recover censored data. The staged error model is applied to 18 ephemeral and 11 perennial rivers in Australia and generate hourly streamflow forecasts up to 10 days. LST leads to similar or even marginally better forecast performance than MLE by checking with various verification metrics, including bias, continuous ranked probability score, the α -index and the average width of confidence intervals. Skillful and reliable ensemble forecasts can be generated in 25 out of 29 catchments even at extended lead times. However, propagation of uncertainty can sometimes be challenging in four highly ephemeral catchments, leading to unreliable forecasts at long (e.g. > 40 h) lead times. Prospects for addressing this issue in future is discussed. [ABSTRACT FROM AUTHOR]

• Sophisticated climate forecast downscaling yields more accurate crop forecasts. • Reliable ensemble forecasts of simulated sugarcane biomass generated with APSIM. • Biomass forecasts based on local dynamical forecasts outperform ENSO-based predictions. Seasonal climate forecasts can improve the accuracy of early-season estimates of crop yield and influence seasonal crop management decisions. Climate forecasting centres around the globe now routinely run global climate models (GCMs) to provide ensemble forecasts. However, raw GCM forecasts require post-processing to improve their reliability and to enable systematic integration with crop models. Post-processing to meet crop model input requirements is highly challenging and simple bias-correction methods can perform poorly in this regard. As a result of the difficulties, GCM forecasts are often sidelined in favour of other inputs such as climate analogues. In this study, we evaluate two variants of a recently-developed post-processing method designed to systematically and reliably calibrate and downscale GCM forecasts for use in crop models. In one variant, local GCM forecasts of rainfall, temperature and solar radiation are post-processed directly. The second variant is a novel adaption in which the predictive input is instead the GCM's forecast of a large-scale climate pattern, in this case related to the El Nino-Southern Oscillation. The post-processed climate forecasts, which are in the form of ensemble time series, are used to drive an APSIM-sugar model to generate long-lead forecasts of biomass in north-eastern Australia from 1982 to 2016. A rigorous probabilistic assessment of forecast attributes suggests that local GCM forecast calibration provides the most skilful forecasts overall although the ENSO-related forecasts give more skilful biomass forecasts at certain times, implying model combination could be worthwhile to maximise skill. The generated biomass forecasts are unbiased and reliable for short to long lead times, suggesting that the downscaling methods will be of value to trial in a range of crop forecast applications, and support the quantitative, meaningful use of GCM forecasts in agriculture. [ABSTRACT FROM AUTHOR]