Regional Forecasts Research Articles

Spatial and temporal analysis of decomposition models in China

This study presents a comprehensive evaluation of 15 decomposition models (12 empirical and 3 atmospheric transmittance models) for estimating diffuse horizontal irradiance at 17 radiation sites in China, using hourly radiation data from 2011 to 2020. Our results show distinct patterns in model performance across different geographical regions, seasons, and sky conditions. The Liu model demonstrates the best overall performance with an RMSE of 78.20 W/m2, while model accuracy shows significant geographical variation, performing best in South and Southeast China (RMSE<70 W/m2) and worst in the Qinghai-Tibet Plateau and Northwest China (RMSE>90 W/m2). Seasonal analysis reveals better performance in winter than in summer, with RMSE differences approaching 40 W/m2, mainly due to the higher proportion of solar elevation angles exceeding 30° in summer. Under different sky conditions (classified by clearness index: 0–0.35 for overcast, 0.35–0.65 for partly cloudy, 0.65–1 for clear skies), most models follow an RMSE pattern of partly cloudy > clear sky > overcast. However, the Reindl2, Boland, DIRINT, and DIRINDEX models deviate from this trend due to their formula structure and sensitivity to atmospheric parameters. To reduce these regional disparities, we propose a new region-specific model selection strategy: the DIRINDEX model for eastern regions, DIRINT for central areas, and Karatasou for western regions. This combined approach reduces the overall RMSE to 73.17 W/m2. This research deepens our understanding of the application of decomposition models in China's complex geographical and climatic conditions, offering valuable references for solar radiation modeling and renewable energy forecasting in diverse climatic regions.

Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast.

Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of -0.3 μg/m3 vs. -1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of -7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Changes in urban residential and agricultural sources induce a decrease in PM10 levels in cold clean area: A thirteen-year monitoring at the Longfengshan WMO/GAW regional background station in Northeast China

Atmospheric background monitoring provides insights into the long-term changes in atmospheric composition resulting from human activities on both global and regional scales. It is important to understand the local characteristics of key air pollutants to evaluate and manage air quality control policies effectively. This study analyzed 13 years of continuous data of PM10 (particulate matter with aerodynamic diameter ≤10 μm) concentration from the only atmospheric background station in Northeastern China, the Longfengshan (LFS) World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) regional station. The results revealed a significant decrease of 58.02% in PM10 concentrations in the LFS from 2007 to 2019, which was primarily influenced by transport from Changchun and Harbin. Over periods exceeding eight months, the correlation between PM10 levels at the LFS atmospheric background station and those in Changchun and Harbin became more pronounced. The observed decline in PM10 concentrations at the LFS atmospheric background station was largely attributed to reduced emissions from urban residential and agricultural sources. Emissions of carbon monoxide and ammonia from these sources, along with temperature and relative humidity (RH), significantly affected the PM10 concentrations. Using machine learning (ML) methods and PM10 data from the LFS atmospheric background station, this study effectively predicted PM10 concentrations in neighboring cities, demonstrating that the LFS atmospheric background station can effectively assess the trend of PM10 changes in Northeast China. This study quantified the influence of urban areas on atmospheric background stations, highlighting the effectiveness of existing environmental policies, and offering a reference for similar studies and forecasts in other regions.

Regional, local and current forecast of the impact hazard of coal seam sites based on seismic monitoring

Relevance. Insufficient validity of the quantitative criteria of the regional, local and current forecast of the impact hazard of the coal seams being developed, the absence of algorithms linking the use of geophysical and direct geomechanical methods in forecasting. Aim. To develop a comprehensive method of geodynamic forecasting, including determining the extent of an impact-prone area identified by the results of a regional forecast based on the registration data of the GITS seismic monitoring system, for conducting local and current forecasts of impact hazard by regulatory methods. Object. An array of rocks of the excavation column 4-1-5-7 of the Osinnikovskaya mine during mining operations in the zone of a group of tectonic disturbances and the intersection of advanced workings. Methods. Regional geomechanical forecast by the geophysical method of seismic activity registration (GITS system), local (current) forecast by natural electromagnetic radiation methods (Angel-M equipment) and the output of drilling fines. Results. The paper introduces the results of complex studies in the excavation column 4 1-5-7 of the Osinnikovskaya mine to establish a correlation between seismic activity and the parameters of the stress-strain state of the array. The authors have introduced the concept of integral indicators of the considered parameters IF and Ikσ. They take into account the area of the zone for the i-th range according to the complex parameter of seismic activity F and the coefficient of vertical stresses in the roof of the formation Kσ. It is established that the informative value of the integral indicators is almost twice as high as the informative value of the initial parameters. Experimental nomograms и are presented to determine the category "DANGEROUS/NON-DANGEROUS" for the regional forecast of impact hazard according to the GITS seismic monitoring system. The authors developed a general algorithm for predicting the impact hazard during clean-up operations. This algorithm uses an integrated approach that includes a regional forecast for recording seismic activity within the mine field and local forecasting methods, both geophysical and direct, to clarify the boundaries of seismic energy release zones and confirm the category of impact hazard. Thу paper introduces the example of determining the width of the zone of increased seismic energy release in the mine closest to the seismically active zone. The dynamics of the occurrence, development and decrease of the seismic energy release zone in the considered areas in the mine workings is shown. The authors give the examples of the implementation of a local forecast of impact hazard in previously established potentially dangerous areas by methods of recording natural electromagnetic radiation of rocks and by the output of drilling fines.

Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye

This study examines the ability of different methods such as machine learning, ensemble models, and metaheuristic algorithms to predict streamflow. For this purpose, five different methods were used: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Boosting, Particle Swarm Optimization (PSO), and BSPSO hybridized with Band Similarity (BS), a relatively new method. Additionally, the impact of seasonality and trend components obtained through Seasonal-Trend decomposition using LOESS (locally weighted regression and scatterplot smoothing) (STL) data decomposition technique on prediction success was investigated. Models were developed in three basins with three different climate characteristics: continental, temperate, and arid. The results showed higher prediction success in input structures including seasonality and trend components. While higher prediction successes were achieved at Karasu in the continental climate class and Körkün in the temperate climate class, model performances were lower at Küçük Muhsine in the arid climate class. While the most successful modeling for Küçük Muhsine (NSE = 0.696) and Karasu stations (NSE = 0.811) was obtained with the BSPSO method, the SVM method produced the best results for Körkün station (NSE = 0.818). Moreover, BSPSO models outperformed the prediction successes obtained by using PSO alone for each scenario at all three stations. The percentages of the BSPSO method improving the prediction success according to the NSE metric ranged from 3.53% to 17.40% at Küçük Muhsine, 0.49%–3.72% at Karasu, and 1.24%–7.24% at Körkün. The competitive results achieved by the BSPSO approach compared to ANN and SVM in flow prediction constitute the innovative aspect of this study.

An Internal Marking Method for Adult Spodoptera frugiperda Smith Using an Artificial Diet Containing Calco Oil Red N-1700.

As a migratory invasive pest, Spodoptera frugiperda (fall armyworm, FAW) has recently posed a serious threat to food security in newly invaded areas (especially in Africa and Asia). Understanding its migration (or dispersal) patterns in newly invaded areas is crucial for regional forecasting and management efforts. By screening an appropriate marking technique to conduct mark-release-recapture (MRR) experiments, the migration patterns of the FAW can be effectively studied. In this study, we added different concentrations of Calco Oil Red N-1700 (an oil-soluble marker) to a self-made artificial diet and assessed the rearing and marking efficacy. The results indicated that a concentration of 0.2% of Calco Oil Red N-1700 in the diet was optimal for marking adult FAWs. The biological indicators (e.g., developmental duration, reproductive parameters, and flight ability) of FAWs fed this diet were basically consistent with those of FAWs fed a normal diet, with a larval stage of 15.46 days, a pupal stage of 9.81 days, a pupal mass of 278.18 mg, an adult longevity of 15.41 days, and an egg deposition count of 1503.51. Meanwhile, the flight distance, duration, and velocity were 24.91 km, 7.16 h, and 3.40 km/h, respectively (12 h tethered-flight tests), without difference with the control. Females and males exhibited distinctive marking colors (red or pink) that persisted for at least 5 and 9 days, respectively. This study developed an economically effective internal marking method for the adult FAW, laying the foundation for conducting MRR experiments. This will help clarify the migration behavior and routes of the FAW, providing a scientific basis for formulating effective pest management strategies.

The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation

Abstract. Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed.

Winter Storm Severity Index in Alaska: Understanding the Usefulness for Impact-Based Winter Weather Severity Forecast Information

Abstract There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users’ needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the winter storm severity index (WSSI) [operational for the contiguous United States (CONUS)] to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska-specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions, and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are the important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, and ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable. Significance Statement Impact-based support services can assist decision-makers in prioritizing preparedness and mitigation actions related to winter storm events. The winter storm severity index adapted for specific considerations in Alaska (such as including wind and visibility components) can extend winter weather impact-based forecasting’s utility. Additionally, lessons learned from the process of adapting a national product to specific regional needs may inform best practices for gathering stakeholder input and feedback.

Unveiling the Global Economic Impacts from Demographic Change in East Asia: An Estimation and Web Explores

This paper explores the economic consequences of demographic changes on development in 208 countries through 2100. We employ panel fixed effects models to estimate long-run equilibrium relationships between capital stock, labor force, and economic growth. Cointegration analysis confirms the existence of these long-run relationships. We affirm that there is a steady, long-run relationship between GDP, capital stock, and population. We also present our findings in an interactive web application, allowing users to explore how population shifts might impact economies worldwide. Highlighting key trends, we offer an illustrative case study of East Asia, while emphasizing the tool's universal applicability. Thus, this work does not seek to engage in specific academic discourse, but rather endeavors to contribute a valuable resource for informed public dialogue concerning the global economic implications of anticipated demographic transformations. The results show that the "Comparative Global Weights Forecasts" section showcases economic clout, illustrating Japan's decline and Indonesia's rise in global GDP rankings. Lastly, the "Comparative Regional Forecasts" section contrasts regional dynamics, such as East Asia &amp; the Pacific versus South Asia, highlighting population peaks, economic growth disparities, and shifts in global GDP shares.

Weather Forecasting

My Project is on implementation of a weather forecasting using python and machine learning. real-time weather data from locations across the world. Through Python scripting, the project generates accurate forecasts for diverse regions, providing essential meteorological parameters such as temperature, wind speed, humidity, and weather conditions. Weather forecasting plays a crucial role in various aspects of human life, from planning outdoor activities to making strategic decisions in sectors like agriculture, transportation, and emergency management. In this project, we explore the application of Python programming language in weather forecasting, employing both open-source libraries and APIs to access and analyze weather data. Through this project, we aim to demonstrate the versatility of Python in harnessing weather data for forecasting purposes, showcasing its capabilities in data retrieval, analysis, visualization, and predictive modelling. By empowering users with accessible tools and methodologies, we envision a future where weather forecasting becomes more accurate, reliable, and actionable, contributing to informed decision-making and enhanced societal resilience in the face of changing weather patterns. Weather forecasting is the scientific process of predicting atmospheric conditions at a specific location and time. It involves the collection and analysis of data from various sources, such as satellites, weather stations, and radar systems. This data is then input into numerical weather prediction models, which use mathematical equations to simulate the behavior of the atmosphere. Advances in technology, such as high-performance computing and machine learning, have significantly improved the accuracy and reliability of weather forecasts. These predictions are crucial for a wide range of applications, including agriculture, disaster management, transportation, and daily planning, helping to mitigate the impacts of severe weather events and enhance societal preparedness and safety. Despite significant progress, challenges remain due to the inherently chaotic nature of the atmosphere, which limits the precision of long-term forecasts

Distribution of invasive scud, Apocorophium lacustre (Vanhoffen, 1911) in the Illinois Waterway, USA: Do habitat and water quality variables influence spatial distribution and relative abundance?

Apocorophium lacustre – a species of benthic amphipod native to American and European estuaries along the North Atlantic Ocean – has rapidly expanded outside of its native range and is now established in the Illinois, Upper Mississippi, and Ohio river systems, USA. A. lacustre is considered high risk for colonization and disruption of the Laurentian Great Lakes’ benthic communities. To further our understanding of factors influencing A. lacustre distribution and its threat to the Great Lakes, zoobenthic and habitat data were collected from colonization samplers (i.e., rock bags) deployed at 370 sites along the Illinois Waterway. A. lacustre was found in the lower six pools of the Illinois Waterway and was the most abundant amphipod collected in those pools. Our results parallel other studies in that A. lacustre was not observed upstream of Dresden Island Pool, but A. lacustre was found ~11 km farther upstream of any previous records. Generalized linear mixed effects modeling indicated that parameters pertaining to food availability, water quality, and impoundment influenced A. lacustre abundance. Model averaging identified five statistically significant variables: A. lacustre abundance was negatively associated with turbidity, fluorescent dissolved organic matter, and vegetation density and positively associated with temperature and downstream distance (i.e., closer to the next downstream dam). Our findings of what factors influence A. lacustre abundance should be of broad interest to risk assessment and invasion forecasting in other regions where A. lacustre have been or may be introduced.

Operational workflow to simulate biophysical variables, based on the coupled WRF/SEBAL models

Three subsystems including an atmospheric regional forecast and analysis system, satellite data retrieval and processing, and a surface energy balance model have been employed in a 24/7 operational system to measure and simulate some primary agricultural variables, including evapotranspiration, biomass production, and other associated productivity metrics such as NDVI and Albedo. The final products are generated in a daily routine, covering Iran as the domain of simulation. The WRF atmospheric model, VIIRS, and SENTINEL2 satellite data, as well as the SEBAL model (implemented in pySEBAL) are the main components of the operational system, with products in two different spatial resolutions of 375 m and 100 m. The output products can be used by farmers and stakeholders to manage agricultural water requirements and monitor biophysical parameters.

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Prospects and status of forecasting monthly mean subregional rainfall during the Indian summer monsoon using the coupled Unified Model

AbstractWhile there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions.

Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing

Climate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. Rainfall forecasting is considered an important tool that could help decisionmakers for managing water resources. Syria is facing several drought events that may affect its water resources. Due to the lack of available rainfall data from land stations in Syria after the civil war started in 2010 till now, one of the aims of this study is to depend on rainfall data from satellite images after correcting these data using the available land stations data. This study also aims to assess rainfall trends in Syria due to climate change and forecasting rainfall. The rainfall data were collected from 71 land stations from 1991 to 2009. Satellite images from Climate Data Record (CDR) were downloaded from 1983 to 2020 and ArcMap was used to extract rainfall data at locations of land stations. The extracted rainfall data from satellite images were corrected by land stations data from 1991 to 2009. Then Statistical Package for the Social Sciences (SPSS) program with Auto-Regressive Integrated Moving Average (ARIMA) models are used to fit and forecasting rainfall in Syria. The rainfall trends at the studied stations were analyzed for 40 years (1983–2022) and a forecast of one year is conducted. The results showed decreasing trends at all the stations that lead to a decrease the quantity of water received every year which could affect the water resources in Syria and agriculture and food security will be also affected. The results are consistent with Intergovernmental Panel on Climate Change (IPCC) reports that concluded a decrease in rainfall at southern and eastern Mediterranean regions. The results of the current study could help in the management of water resources in Syria and forecasting using ARIMA is recommended for rainfall predictions.

Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions.

Forecasting Agricultural Crop Profitability and Yield in India

Abstract: "India's heavy reliance on agriculture underscores the importance of accurately estimating agricultural production, considering the interplay of organic, economic, and seasonal variables, particularly amid a growing population. Predicting crop yields is crucial for farmers' planning, covering storage and marketing strategies. However, this task is complex and requires foresight. Data mining techniques emerge as a potent tool, leveraging extensive datasets to extract invaluable insights. By employing methods like Random Forest, this research offers a swift yet comprehensive examination of crop yield forecasts for specific regions. Predictive analyses like these act as a crucial asset, enabling stakeholders to make well-informed decisions grounded in expected patterns and trends.”

Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream

In the context of climate change and human influence, timely and reliable information about water level variations is crucial for downstream flood control, navigation, and water resource management, particularly for the Lancang-Mekong River, the largest transboundary river in Southeast Asia. However, accurate real-time water level forecasts remain challenging, especially in data-limited regions. This study focused on the Chiang Saen Station, the uppermost Mekong River hydrological station, comparing three methods to predict 48-hour water levels: the Variable Infiltration Capacity (VIC) and Hydrology-Hydraulic (HH) physics-based models, the Gated Recurrent Unit (GRU) model, and hybrid models (VIC-GRU and HH-GRU). Evaluations were conducted at the 1-h, 3-h, 6-h, 12-h, 24-h, and 48-h forecast lead times. Assessments at various lead times showed that hybrid models incorporating physics-based mechanisms significantly enhanced predictions. VIC-GRU demonstrated superior performance, with KGE values surpassing 0.94, NSE values exceeding 0.95, MAE below 0.17 m, and PBIAS within ± 1 % across all lead times. Compared to GRU, PBIAS decreased by 84 %, while MAE dropped by 76 % to 81 % relative to VIC and HH, respectively. Notably, seasonal variations affected hybrid model performance, especially in the dry season. Optimal parameter selections enhanced model accuracy by 48 % to 49 %. These results underscore the potential of combining physics-based and deep learning models for more accurate, high-temporal-resolution real-time water level forecasts in data-scarce regions. A comprehensive study of streamflow characteristics enhances the advantages of this integration. This research contributes valuable insights to water level prediction in data-scarce regions and informs flood risk reduction and management in the Lancang-Mekong mainstream.