Bureau Of Meteorology Research Articles

Using Machine Learning Approaches to Enhance Heatwave Measurement for Vulnerability Assessment and Timely Management of Heat-related Health Services

Climate change is one of the most critical challenges facing Australia and the global community today. Data from the Australian Bureau of Meteorology (BoM) indicates that Australia has been experiencing rising temperatures, particularly since the late 20th century. The frequency, duration, and intensity of heatwaves are projected to continue increasing . Since national records began in 1910, Australia has warmed by an average of 1.47°C (±0.24°C), with the highest official temperature recorded at 50.7 degrees Celsius in Onslow, Western Australia (WA), on January 13, 2022. Furthermore, a recent unprecedented high temperature of +41.6°C was recorded during winter on August 26, 2024, in Yampi Sound, WA. Among all natural disasters in Australia, heatwave (HW) represents a leading silent killer and pose a significant public health threat. However, innovative methods for assessing vulnerability for HW-related health services remain limited.

Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed.

Increased ambient temperature and hyponatraemia presentations: A ten-year retrospective study at an Australian hospital.

To determine the relationship between ambient air temperature and the incidence of hyponatraemia in a heat-prone region. We conducted a retrospective study that correlated serum sodium concentrations documented at The Austin Hospital in Melbourne over ten years from January 2014 to December 2023, with publicly available temperature data from the Australian Bureau of Meteorology. Serum sodium concentrations and incidence of hyponatraemia admissions when correlated to temperature, and following heatwave events, defined as temperature above 30°C over five consecutive days. Over this period, 45718 low serum sodium results were identified from 26557 unique patients. Serum sodium concentrations in January (Australian Summer) were 0.55mmol/L lower (95% CI 0.36 to 0.77, p < 0.001) compared to September (Australian early Spring). Women had lower sodium concentrations compared to men (-0.21 mmol/L, 95% CI -0.29 to -0.12, p<0.001), as did patients older than 80 years when compared to those younger than 65 years (-0.39 mmol, 95% CI -0.50 to -0.29, p<0.001). Hospital admissions with hyponatraemia were more frequent during summer months. Profound hyponatraemia admissions (sodium ≤125 mmol/L) were more frequent following a heatwave, compared to without (7.6% vs 6.5%, p=0.04). Our study demonstrates that serum sodium concentrations are lower and profound hyponatraemia-related hospital admissions higher when ambient temperatures are warmer. This suggests that hyponatraemia is a climate associated health issue. Local public health advice for water consumption during heatwaves should consider this risk, and prompt action to limit climate change is required to mitigate this risk.

Monitoring and predicting marine heatwaves in Vanuatu

Abstract Marine Heatwaves (MHWs) have disastrous impacts on ecosystems and communities in the south-west Pacific but there is limited research investigating their onset and evolution in this region. In collaboration with local fisheries and marine sectors, this study applied a MHW framework to define and categorize MHW events in the waters around Vanuatu. A range of events amongst the most intense and longest were investigated, as well as an anecdotally notable event from February 2016. This event was neither in the top five longest, nor the most intense of events, highlighting how impacts in marine ecosystems are non-linear and have cascading interactions with a region's exposure and vulnerability. In analysing these events, we also explore how sub-seasonal to seasonal (S2S) prediction can be used to forecast MHW events. Hindcasts from the Bureau of Meteorology's operational coupled ocean-atmosphere model were used to create weekly and monthly forecasts for each MHW event in the period from 1980 to 2018. We found that chance above 90th percentile hindcasts had promising accuracy with average hit rates highest for lead 0 weekly and monthly hindcast but conclude that hindcast accuracy is not always indicative of real time forecast accuracy as real time forecasts use a larger set of model ensembles. We also investigated an event outside the hindcast study period (May 2022) due to its notably 'off the charts' impacts. This event was the longest and most intense event on record, surpassing the previously longest and most intense event by 144 days and 0.56°C. As climate change intensifies, such extreme events will become more frequent and will likely compound with other extremes, making the use and uptake of monitoring and prediction services critical to the long-term resilience of marine-reliant communities and sectors.&amp;#xD;

The Jive Verification System and Its Transformative Impact on Weather Forecasting Operations

Abstract Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and “Evidence Targeted Automation” dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library, which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.

Observed Associations between Fire Danger and Climate Modes and Their Representation in ACCESS-S2

Abstract The increasing frequency and severity of wildfires in Australia, driven by climate change, pose a significant threat to ecosystems, lives, and property. This study examines the impact of climate drivers, specifically El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Southern Annular Mode (SAM), Madden–Julian oscillation (MJO), and two modes of persistent high pressure in the Australia–Pacific region, on extreme fire danger. By analyzing observed and simulated fire danger in relation to these climate drivers, we aim to enhance our understanding of climate–fire mechanisms and contribute to Australia’s bushfire preparedness. Our findings indicate that all assessed drivers influence extreme fire danger, with key influences related to the drivers’ established relationships with rainfall and temperature. El Niño, positive IOD, and negative SAM events generally increase extreme fire danger across most of Australia and in most seasons. The two modes of Australian blocking exhibit similar effects, varying spatially. Specific phases of the MJO have significant seasonal relationships with fire danger. In some instances, increased fire danger is not directly linked to temperature or precipitation changes but rather driven by remote teleconnections, airflow, or pressure anomalies. Evaluating the accuracy of weather and climate forecasting systems in representing these relationships is crucial for the effective prediction and mitigation of fire hazards. The leading Australian climate simulation model effectively reproduces observed relationships but reveals biases in capturing certain aspects of climate variability. Advancements in this field would enhance fire weather forecasts, including those by the Australian Bureau of Meteorology with lead times of up to 4 months. Significance Statement This assessment of climate–fire relationships and modeling accuracy is pivotal in understanding the factors behind severe fire events and improving future predictions and mitigation. Prior research has already established links between elevated fire weather conditions and these climate drivers, highlighting their potential for predicting severe fire events. This study aims to deepen our comprehension of the factors driving severe fire events and facilitate improved bushfire risk management in Australia.

Impact of ENSO on Cloud Distribution and Rainfall Variability in Tangerang Regency

A type of climate variation in the Pacific Ocean known as El Niño Southern Oscillation (ENSO) is defined by an increase in sea surface temperature (SST) in the Central and Eastern equatorial areas, which affects the amount of rainfall that falls and increases. In Indonesia, the rainy season typically persists from October to March, whereas the dry season persists from April to September. Research on the influence of ENSO on rainfall has been carried out in several areas, but has not been carried out in the Tangerang area. This study aims to ascertain how the ENSO phenomena affect rainfall fluctuations and cloud distribution in the Regency of Tangerang. The secondary data, which includes information on rainfall, cloud cover, wind direction, and speed, was acquired from BMKG Meteorology Budiarto. In addition, data from the Australian Bureau of Meteorology (BOM) website's Southern Oscillation Index (SOI) was used. Surface weather analysis and Pearson correlation analysis are the data analysis techniques used. The results presented that based on surface weather study, ENSO events had a consequence on cloud distribution in Tangerang Regency. The clouds lead when the ENSO phenomenon occurs, namely Cumulus and Nimbostratus clouds with a 4-8 octa cloud cover. Meanwhile, the correlation test results show that ENSO influences seasonal and annual rainfall variations in the district of Tangerang. The largest correlation between SOI and annual rainfall occurred in 2015 with a correlation value of r-0.84 (very strong). Meanwhile, the greatest correlation between SOI and seasonal rainfall occurred in July-September with a correlation value of r=0.67 (strong).

Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone

AbstractThe Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability.

The limitation of machine learning methods for water supply and demand forecasting: A case study for Greater Melbourne, Australia

ABSTRACT Many cities around the world have faced water scarcity due to climate change, population growth, and urbanization. Accurate water supply and demand forecasting is critical for sustainable urban water management. Machine learning (ML) models provide new possibilities for forecasting compared with traditional models in handling non-linearity. This study aims to address the efficacy of ML models, long short-term memory (LSTM), stacked LSTM, bidirectional LSTM (Bi-LSTM), and multilayer perceptron (MLP), for forecasting water supply and demand in Greater Melbourne, Australia. The ML modelling utilized daily water supply and demand, and climatic variables (rainfall and maximum temperature) recorded by Melbourne Water and the Bureau of Meteorology from 1990 to 2019. The stacked LSTM performs better than other models in forecasting with R2, RMSE, and MAPE values of 0.908, 335.74 ML, and 23.5% for water supply and 0.791, 94.88 ML, and 5.3% for water demand, respectively. The inclusion of climatic variables enhanced the accuracy of forecasting by improving R2 and reducing RMSE and MAPE. The results indicate effectiveness of ML models, particularly LSTM-based architectures, in forecasting water supply and demand. However, these models have limitations, particularly in forecasting extreme values, emphasizing the need to improve ML models for more reliable and accurate water management.

Impacts of Climate Change on Rainfall ‘Seasonality Index’ and Its Potential Implications on Water Savings and Reliability through Household Rainwater Tanks

This study depicts potential climate change impacts on annual rainwater savings from household rainwater harvesting using two different climate projection models; ACCESS 1.0 and CSIRO-Mk3.6. This paper also investigates potential changes in the relationships of ‘water saving efficiency’ and reliability with rainfall ‘seasonality index’ under the mentioned climate change scenarios. The annual water savings were calculated for three weather conditions: dry, average, and wet. Historical daily rainfall amounts provided by the Australian Bureau of Meteorology were used for three locations within the city of Brisbane (Australia). For the same locations, projected future daily rainfall amounts were collected from an online data portal facilitated by the Australian government. Potential annual water savings, water saving efficiency, and reliability values for the selected locations were calculated through a widely used tool, eTank, developed on water balance methodology at a daily scale. It was found that for the coastal location, Manly, the future water savings are not likely to change significantly. However, for the inland location, Sunnybank, the future water savings are expected to decrease under all the weather conditions through both the considered climate projections. For the far inner location, Oxley, the water savings are likely to decrease in the dry year, whereas in wet year, they are likely to increase. Also, it was found that the overall average relationship of SI–water saving efficiency is steeper for ACCESS 1.0 projected data compared to that produced through CSIRO-Mk3.6 data, and that significant differences exist among individual relationships for each location. The overall reliabilities calculated through the model projected data show lower values compared to the reliabilities calculated using historical data.

Typhoon Trajectory Prediction by Three CNN+ Deep-Learning Approaches

The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction.

Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology

Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology

Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes.

ACCESS-S2 seasonal forecasts of rainfall and the SAM–rainfall relationship during the grain growing season in south-west Western Australia

South-west Western Australia (SWWA) is home to a world class grains industry that is significantly affected by periods of drought. Previous research has shown a link between the Southern Annular Mode (SAM) and rainfall in SWWA, especially during winter months. Hence, the predictability of the SAM and its relationship to SWWA rainfall can potentially improve forecasts of SWWA drought, which would provide valuable information for farmers. In this paper, focusing on the 0-month lead time forecast, we assess the bias and skill of ACCESS-S2, the Australian Bureau of Meteorology’s current operational sub-seasonal to seasonal forecasting system, in simulating seasonal rainfall for SWWA during the growing season (May–October). We then analyse the relationship between the SAM and SWWA precipitation and how well this is captured in ACCESS-S2 as well as how well ACCESS-S2 forecasts the monthly SAM index. Finally, ACCESS-S2 rainfall forecasts and the simulation of SAM are assessed for a case study of extreme drought in 2010. Our results show that forecasts tend to have greater skill in the earlier part of the season (May–July). ACCESS-S2 captures the significant inverse SAM–rainfall relationship but underestimates its strength. The model also shows overall skill in forecasting the monthly SAM index and simulating the MSLP and 850-hPa wind anomaly patterns associated with positive and negative SAM phases. However, for the 2010 drought case study, ACCESS-S2 does not indicate strong likelihoods of the upcoming dry conditions, particularly for later in the growing season, despite predicting a positive (although weaker than observed) SAM index. Although ACCESS-S2 is shown to skillfully depict the SAM–SWWA rainfall relationship and generally forecast the SAM index well, the seasonal rainfall forecasts still show limited skill. Hence it is likely that model errors unrelated to the SAM are contributing to limited skill in seasonal rainfall forecasts for SWWA, as well as the generally low seasonal-timescale predictability for the region.

The impact of rainfall on beef cattle growth across diverse climate zones

Genetics, animal husbandry, and the feedbase all impact cattle growth. Australia’s cattle feedbase covers 40% of the continent and encompasses diverse climates and landscapes, making stocking rate decisions challenging. Of the factors contributing to climate change, rainfall is a primary determinant of feedbase growth and, with this, cattle growth. Understanding the interplay between rainfall and cattle growth across the diverse Australian landscape is thus critical to aid farmer decision-making. However, revealing such interactions between landscape and rainfall for cattle growth for such decision-making has until now been infeasible due to a lack of sufficient temporal and spatial cattle growth data. The Optiweigh (OW) system has been deployed across Australia’s extensive beef production systems as a voluntary weighing unit, opportunistically monitoring cattle liveweight (LW). This study determined the impact of rainfall on the temporal and spatial variability of beef cattle growth across three of Australia’s agro-climatic zones (grassland, subtropical, and temperate), aiming to describe the diverse feedbase through patterns of LW variability. A total of 1.3 million cattle LW observations were collected from 82 026 cattle over 2 years (2020–2022). Rainfall data from the Australian Bureau of Meteorology were also collated for the closest meteorological station to each OW unit. Cattle LW average daily gain (ADG) was clustered by season and zone. A series of linear mixed models were used to examine ADG for each zone-season combination, with random effects for individual animals and farms, and fixed effects for climate zone and current and lagged rainfall. The overall mean ADG for the dataset was 0.68 kg/day, with greater growth variability between farms within a zone (SD: 0.349 ± 0.021 kg/day, estimate ± SE) than between cattle within a farm (SD: 0.229 ± 0.004 kg/day). This ADG variability can be partly attributed to the timing and amount of rainfall, with agro-climatic zones showing unique interactions between rainfall and ADG. Seasonal lagged rainfall effects were present in the grassland and temperate zones, while rainfall in the temperate zone had a year-round effect on cattle growth. Furthermore, season-wise lagged rainfall had mixed effects on ADG, whereas rainfall occurring in a season reduced growth in the same season across zones (P < 0.001). These findings provide valuable initial insights into the variability of ADG across the landscape over time and markedly improve our understanding of the interplay between climate and Australia’s diverse feedbase, contributing to improved management strategies.

Enhancing severe weather predictions with the I-ConvGRU model: An iterative approach for radar echo time series through ConvGRU and RainNet integration

ABSTRACT Precipitation nowcasting plays a crucial role in disaster prevention and mitigation. Existing forecasting models often underutilize output data, leading to suboptimal forecasting performance. To tackle this issue, we introduce the I-ConvGRU model, a novel radar echo timing prediction model that synergizes the temporal dynamics optimization of ConvGRU with the spatial feature enhancement capabilities of RainNet. The model forecasts future scenarios by processing 10 sequential time-series images as input while employing skip connections to boost its spatial feature representation further. Evaluation of the radar echo data set from the Hong Kong Hydrological and Meteorological Bureau spanning from 2009 to 2015 demonstrates the I-ConvGRU model's superiority, with reductions of 17(3.8%) and 49(3.2%) in MSE and MAE metrics, respectively, compared with the TrajGRU model; meanwhile, the I-ConvGRU model had 52(5.8%) and 144(3.8%) lower values on the B-MSE and B-MAE metrics, respectively, than the slightly better performing TrajGRU model. Notably, it significantly improves the prediction of severe precipitation events, with the CSI and HSS metrics increasing by 0.0251(9.6%) and 0.0277(6.8%). These results affirm the model's enhanced effectiveness in radar echo forecasting, particularly in predicting heavy rainfall events.

TrajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning

：Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Existing models, such as ConvLSTM, TrajGRU, and PredRNN, exhibit limitations in capturing fine-grained appearances due to insufficient memory units or addressing positional misalignment issues, thereby compromising the accuracy of model predictions. In this study, we propose trajPredRNN+, an innovative approach that integrates the trajectory segmentation model and the PredRNN deep learning model to address both limitations in nowcasting precipitation using weather radar echo images. By incorporating attention mechanisms, the model demonstrates an enhanced focus on short-term and imminent heavy rainfall events. To ensure improved stability during training, a residual network is introduced. Lastly, a more rational and effective training loss function is proposed, encompassing weight mechanism, SSIM index, and GAN loss. To validate the proposed model, we conducted a comparative experiment and an ablation experiment using the radar echo map dataset obtained from the Shenzhen Meteorological Bureau. The results of these experiments demonstrate that our model has achieved significant improvements across multiple key performance indicators.

Bringing the Value Chain for High-Impact Weather Warnings into the Classroom

Abstract University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on value chain approaches to evaluate the end-to-end warning chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.

Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia

Sea level rise (SLR) attributed to the melting of ice caps and thermal expansion of seawater is of great global significance to vast populations of people residing along the world’s coastlines. The extent of SLR’s impact on physical coastal areas is determined by multiple factors such as geographical location, coastal structure, wetland vegetation and related oceanic changes. For coastal communities at risk of inundation and coastal erosion due to SLR, the modelling and projection of future sea levels can provide the information necessary to prepare and adapt to gradual sea level rise over several years. In the following study, a new model for predicting future sea levels is presented, which focusses on two tide gauge locations (Darwin and Milner Bay) in the Northern Territory (NT), Australia. Historical data from the Australian Bureau of Meteorology (BOM) from 1990 to 2022 are used for data training and prediction using artificial intelligence models and computation of mean sea level (MSL) linear projection. The study employs a new double data decomposition approach using Multivariate Variational Mode Decomposition (MVMD) and Successive Variational Mode Decomposition (SVMD) with dimensionality reduction techniques of Principal Component Analysis (PCA) for data modelling using four artificial intelligence models (Support Vector Regression (SVR), Adaptive Boosting Regressor (AdaBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU). It proposes a deep learning hybrid CNN-BiGRU model for sea level prediction, which is benchmarked by SVR, AdaBoost, and MLP. MVMD-SVMD-CNN-BiGRU hybrid models achieved the highest performance values of 0.9979 (d), 0.996 (NS), 0.9409 (L); and 0.998 (d), 0.9959 (NS), 0.9413 (L) for Milner Bay and Darwin, respectively. It also attained the lowest error values of 0.1016 (RMSE), 0.0782 (MABE), 2.3699 (RRMSE), and 2.4123 (MAPE) for Darwin and 0.0248 (RMSE), 0.0189 (MABE), 1.9901 (RRMSE), and 1.7486 (MAPE) for Milner Bay. The mean sea level (MSL) trend analysis showed a rise of 6.1 ± 1.1 mm and 5.6 ± 1.5 mm for Darwin and Milner Bay, respectively, from 1990 to 2022.

Assessing irrigation water demand and pumping operations for rice farming in the Bengawan Solo River, Indonesia

Owing to population growth, the rice demand in Indonesia has been increasing, which has led to an increase in rice consumption. One way to boost rice production is to enhance pump irrigation in rainfed fields. The aim of this study is to evaluate irrigation water usage and water pumping practices in the Bengawan Solo River, focusing on enhancing rice production. Data were sourced from governmental entities, which include the Indonesian Bureau of Meteorology, Climatology, and Geophysics and the Ministry of Public Works and Housing. Water requirement was calculated using the FAO Penman–Monteith equation. The study highlights that throughout the three distinct growing seasons (GS), the water requirements for irrigating rainfed rice fields vary, with the most substantial demand observed during the first growing season (GS I), followed by the third growing season (GS III), and the second growing season (GS II). In dry years, a consistent pattern of low water balances occurs, which persists below 500 mm across all months. Compared with the other two scenarios, the dry year shows higher variability in rainfall, as evidenced by its higher coefficient of variation of 0.620 compared with 0.347 and 0.416 for the wet and normal years, respectively. The electricity cost rate peaks in GS I, trailed by GS II and GS III, with rates of IDR 2,400, 1,180, and 1,028 per kilowatt-hour, respectively. The findings play a pivotal role in shaping regional planning decisions regarding the utilization and necessity of river water resources and the development of cropping calendars.