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Lakes and reservoirs are critical for sustaining water resources, serving as key sources of water supply. Monitoring changes in lake properties offers valuable insight into water resource management, agricultural demand, watershed analysis, and environmental monitoring. This study introduces an innovative approach that utilizes the Google Earth Engine platform, artificial intelligence (AI), and genetic algorithms to analyze water surface areas and estimate lake volume without the need for bathymetric data. The method was applied to Lake Okeechobee in Florida, Using Landsat-8 imagery and the normalized difference water index to calculate the lake’s surface Area. Ai techniques, including image segmentation and thresholding, were employed to refine the images. These processed images were then analyzed using a genetic algorithm to estimate the lake’s volume. The estimated volume was compared with calculations derived from the lake’s bathymetry, achieving a root mean square error of 273 million cubic meters (Mm3), a mean absolute percentage error of 28.85%, and a percent bias of 21.8%. These findings suggest that AI techniques can be highly useful for estimating lake volumes, and further exploration of their application is recommended in future studies.
Soil temperature is a critical factor influencing plant growth, crop yield, and ecological processes. This study evaluates feature selection techniques to improve soil temperature forecasting. We applied these techniques to 39 weather stations across Florida, using meteorological data spanning 2000 to 2022, with 13 input variables, including evapotranspiration and minimum temperature. Three models, namely Multi-Layer Perceptron (MLP) Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Time series (N-BEATS), are used. Moreover, three optimization algorithms are applied to improve the MLP model’s accuracy: Adam, RangerAdaBelief, and AdaBelief. When integrated with the innovative SS_MLP_AdaBelief model, the standout method, Stability Selection demonstrated significant predictive accuracy, underscoring the importance of evapotranspiration and minimum temperature as key variables. The model achieved an RMSE of 0.328, an NSE of 0.873, and a CC of 0.95 at the Alachua station, demonstrating strong predictive performance. Similar trends were observed across multiple locations, indicating the model’s consistency and reliability in soil temperature forecasting. Despite the N-Beats model’s limitations, our comparative analysis, visualized through Taylor diagrams, emphasizes the necessity for precise feature selection and the synergistic application of variables and models. This research not only advances the field of soil temperature prediction but also offers valuable insights for future applications, highlighting the potential of methodical feature selection and model integration in overcoming the challenges of traditional deep learning approaches. Future research should explore hybrid deep learning architectures, larger datasets, and real-time predictive applications. This study advances soil temperature forecasting by demonstrating the synergistic impact of feature selection and optimization techniques, contributing to precision agriculture, climate change adaptation, and environmental sustainability.
Global warming presents an urgent environmental challenge, marked by disrupted climate patterns, increased flooding and droughts, reduced biodiversity, and accelerated species extinction rates. Our study offers a detailed analysis and estimation of hot summer days (HD) patterns and examines their association with Summer Daily Maximum Temperature (SDMT). Employing a estimation model grounded in the normal distribution of temperature records, the exceedance probability of HD occurrences was estimated. The study also applies the K-means clustering algorithm to categorize meteorological stations, enabling a deeper understanding of regional variances and warming trends. To show the applicability of the proposed methodology, 28 meteorological stations in the State of Florida, USA, were selected for the period from 1959 to 2022. The results revealed a significant increase of approximately 0.12 °C in Florida's average Maximum temperature over the past decades, coupled with an average rise of 2.5 HD per decade. Geographical analysis identifies the north and some central as the most affected regions with the highest rise in SDMT, while the parts of central and western show the most substantial increase in HD during summer. The data conclusively indicates that as average SDMTs increase, the frequency of HD escalates dramatically. Projections up to the year 2050 suggest a continued rise in HD across Florida, classified into three severity categories: severe, moderate, and mild. These findings underscore the critical implications of global warming on the frequency of hot days in Florida, necessitating urgent and effective climate change mitigation strategies.
Hydrology relates to many complex challenges due to climate variability, limited resources, and especially, increased demands on sustainable management of water and soil. Conventional approaches often cannot respond to the integrated complexity and continuous change inherent in the water system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources. In soil and land modeling, AI techniques could further enhance accuracy in soil texture analysis, moisture estimation, and erosion prediction for better land management. Advanced AI models could also be used as a tool to forecast streamflow and groundwater levels, therefore providing valuable lead times for flood preparedness and water resource planning in transboundary basins. In water quality, AI-driven methods improve contamination risk assessment, enable the detection of anomalies, and track pollutants to assist in water treatment processes and regulatory practices. AI techniques combined with remote sensing open new perspectives on monitoring water resources at a spatial scale, from flood forecasting to groundwater storage variations. This paper’s synthesis emphasizes AI’s immense potential in hydrology; it also covers the latest advances and future prospects of the field to ensure sustainable water and soil management.
Predicting the surface area and shape of lakes is critical for ecological, hydrological, and climatic studies. Accurate predictions enhance the understanding of lake dynamics, facilitate water resource management, and support environmental change assessments. Traditional methods, while foundational, often lack the efficiency, accuracy, and scalability required to handle complex lake systems, necessitating modern, technology-driven approaches. This study introduces a novel methodology for predicting changes in lake surface area and shape, including shoreline dynamics. The approach employs advanced remote sensing techniques and mathematical modeling, integrating Mathematica simulations with Net-Encoder and Deconvolution models. The framework achieved a high accuracy rate of 93% in water pixel extraction. Results indicate significant reductions in surface area, with Lake Eucumbene shrinking by 4.3% and the Salton Sea by 14.54%. The most notable shoreline changes occurred in the southwestern and northern regions of Lake Eucumbene and the southwestern region of the Salton Sea. This research highlights the effectiveness of a remote sensing-based approach for monitoring lake surface dynamics, offering a low-cost, high-accuracy tool for environmental monitoring and climate change impact assessment. Compared to existing methods, the proposed approach provides equivalent accuracy while delivering enhanced operational simplicity and flexibility.
In this research, the impact of climate change on annual maximum daily precipitation (AMP) during the period 2024-2050 and the evaluation of flood risk in Ilam province have been investigated using the outputs of CMIP6 models. After identifying the top-performing CMIP6 models, the annual maximum daily precipitation for return periods of 2, 10, 100, and 1000 years was determined based on fitting 65 probability distributions, considering the 1992-2018 observational period and future periods (SSP1-2.6 and SSP5-8.5). The study also integrates hazard, vulnerability, and exposure components to assess flood risk at various return periods (2, 10, 100, and 1000 years). Vulnerability and exposure assessment involved the selection of indicators such as hamlet density (HD), land use (LU), population density (PD), land cover (LC), SAVI vegetation index, digital elevation model (DEM), slope, soil erodibility (SE), drainage density (DD), and distance from drainage (DFD). The AHP-Entropy weight method was employed to determine the relative importance of each component. The results indicated that changes in annual maximum daily precipitation and flood risk under the SSP1 scenario did not differ significantly from the observational period, exhibiting similar trends and patterns. However, conditions under the SSP5 scenario differed, showing significant fluctuations in annual maximum daily precipitation, particularly for the 1000-year return period, resulting in increased high-risk areas. For instance, in the SSP5-8.5 scenario, the moderate-risk area for the 1000-year return period expanded from 7% to over 13%, and a new high-risk classification arose, covering 5% of the province’s area, which is unprecedented in the other scenarios.
This research utilized the outputs from three models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), specifically CanESM5, GFDL-ESM4, and IPSL-CM6A-LR. These models were used under the SSP1-2.6 and SSP5-8.5 scenarios, along with the SPI and SPEI, to assess the impacts of climate change on drought in Iran. The results indicated that the average annual precipitation will increase under some scenarios and decrease under others in the near future (2022–2050). In the distant future (2051–2100), the average annual precipitation will increase in all states by 8–115 mm. The average minimum and maximum temperature will increase by up to 4.85 ℃ and 4.9 ℃, respectively in all states except for G2S1. The results suggest that severe droughts are anticipated across Iran, with Cluster 5 expected to experience the longest and most severe drought, lasting 6 years with a severity index of 85 according to the SPI index. Climate change is projected to amplify drought severity, particularly in central and eastern Iran. The SPEI analysis confirms that drought conditions will worsen in the future, with southeastern Iran projected to face the most severe drought lasting 20 years. Climate change is expected to extend drought durations and increase severity, posing significant challenges to water management in Iran.
Soil temperature is a key meteorological parameter that plays an important role in determining rates of physical, chemical and biological reactions in the soil. Ground temperature can vary substantially under different land cover types and climatic conditions. Proper prediction of soil temperature is thus essential for the accurate simulation of land surface processes. In this study, two intelligent neural models—artificial neural networks (ANNs) and Sperm Swarm Optimization (SSO) were used for estimating of soil temperatures at four depths (5, 10, 20, 50 cm) using seven-year meteorological data acquired from Archbold Biological Station in South Florida. The results of this study in subtropical grazinglands of Florida showed that the integrated artificial neural network and SSO models (MLP-SSO) were more accurate tools than the original structure of artificial neural network methods for soil temperature forecasting. In conclusion, this study recommends the hybrid MLP-SSO model as a suitable tool for soil temperature prediction at different soil depths.
The present study evaluates the capability of a novel optimization method in modeling daily crop reference evapotranspiration (ETo), a critical issue in water resource management. A hybrid predictive model based on the artificial neural network (ANN) algorithm that is embedded within the COOT method (COOT bird natural life model-artificial neural network (COOT-ANN)) is developed and evaluated for its suitability for the prediction of daily ETo at seven meteorological stations in different states of Australia. Accordingly, a daily statistical period of 12 years (01-01-2010 to 31-12-2021) for climatic data of maximum temperature, minimum temperature, and ETo were collected. The results are evaluated using six performance criteria metrics: correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), RMSE-observation standard deviation ratio (RSR), Scatter Index (SI), and mean absolute error (MAE) along with the Taylor diagrams. The performance of the COOT-ANN model was compared with those of the conventional ANN model. The results showed that the COOT-ANN hybrid model outperforms the ANN model at all seven stations by 0.803%, 4.127%, 3.359%, 4.072%, 4.148%, and 3.665% based on the average values of the R, RMSE, NSE, RSR, SI, and MAE criteria, respectively. So, this study provides an innovative method for prediction in agricultural and water resource studies.
This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019–2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series.
Determining optimal exploitation from aquifers is always a major challenge, especially for aquifers facing a drop in their groundwater level. In aquifers with artificial recharge, more complex algorithms are required to determine the optimal exploitation amount. Therefore, in this study, the optimal amount of harvest from the exploitation wells has been determined using a combined simulation–optimization model considering the artificial recharge in Yasouj aquifer in Iran. The model is based on a combination of MODFLOW code and gene expression programming (GEP) simulator tool to simulate the aquifer and particle swarm optimization (PSO) to maximize the total exploitation from the aquifer. The simulation results showed that the artificial recharge was ineffective in maximum exploitation from the aquifer. As a result, considering several constraints, including the maximum pumping rate from the aquifer and the permissible drop in the groundwater level, the maximum exploitation from the aquifer was defined as the objective function. The optimization results showed that the optimal exploitation rate is equal to 8.84 million cubic meters (MCM) per year, and only 74% of the water from artificial recharge can be used based on this amount. Additionally, the most appropriate locations to exploit this amount of water are the northwest and east of the aquifer. According to the findings, it is suggested to ban exploitation from the central and southern parts of the aquifer due to the low groundwater level. The results of the sensitivity analysis show that the reduction in the maximum exploitation rate along with a 50% drop in the groundwater level play an effective role in decreasing the optimal exploitation amount.
Modeling of karstic basins can provide a better understanding of the interactions between surface water and groundwater, a more accurate estimation of infiltrated water amount, and a more reliable water balance calculation. In this study, the hydrological simulation of a karstic basin in a semiarid region in Iran was performed in three different stages. In the first stage, the original SWAT model was used to simulate surface-water flow. Then, the SWAT-MODFLOW conjunctive model was implemented according to the groundwater characteristics of the study area. Finally, due to the karstic characteristics of the region and using the CrackFlow (CF) package, the SWAT-MODFLOW-CF conjunctive model was developed to improve the simulation results. The coefficient of determination (R2) and the Nash-Sutcliffe efficiency coefficient (NSE) as error evaluation criteria were calculated for the models, and their average values were 0.63 and 0.57 for SWAT, 0.68 and 0.61 for SWAT-MODFLOW, 0.73 and 0.7 for SWAT-MODFLOW-CF, respectively. Moreover, the mean absolute error (MAE) and root mean squared error (RMSE) of the calibration for groundwater simulation using the SWAT-MODFLOW model were 1.23 and 1.77 m, respectively. These values were 1.01 and 1.33 m after the calibration of the SWAT-MODFLOW-CF model. After modifying the CF code and keeping the seams and cracks open in both dry and wet conditions, the amount of infiltrated water increased and the aquifer water level rose. Therefore, the SWAT-MODFLOW-CF conjunctive model can be proposed for use in karstic areas containing a considerable amount of both surface water and groundwater resources.
Assessing the status of water resources is essential for long-term planning related to water and many other needs of a country. According to climate reports, climate change is on the rise in all parts of the world; however, this phenomenon will have more consequences in arid and semi-arid regions. The aim of this study is to evaluate the effects of climate change on groundwater, surface water, and their exchanges in Shazand plain in Iran, which has experienced a significant decline in streamflow and groundwater level in recent years. To address this issue, we propose the use of the integrated hydrological model MODFLOW-OWHM to simulate groundwater level, surface water routing, and their interactions; a climate model, NorESM, under scenario SSP2, for climate data prediction; and, finally, the HEC-HMS model to predict future river discharge. The results predict that, under future climate conditions, the river discharges at the hydrometric stations of the region may decrease by 58%, 63%, 75%, and 81%. The average groundwater level in 2060 may decrease significantly by 15.1 m compared to 2010. The results of this study reveal the likely destructive effects of climate change on water resources in this region and highlight the need for sustainable management methods to mitigate these future effects.
The detrimental impacts of agricultural subsurface tile flows and their associated pollutants on water quality is a major environmental issue in the Great Lakes region and many other places globally. A strong understanding of water quality indicators along with the contribution of tile-drained agriculture to water contamination is necessary to assess and reduce a significant source of non-point source pollution. In this study, DRAINMOD, a field-scale hydrology and water quality model, was applied to assess the impact of future climatic change on depth to water table, tile flow and associated nitrate loss from an 8.66 ha agricultural field near Londesborough, in Southwestern Ontario, Canada. The closest available climate data from a weather station approximately 10 km from the field site was used by the Ontario Ministry of Natural Resources and Forestry (MNRF) to generate future predictions of daily precipitation and maximum and minimum air temperatures required to create the weather files for DRAINMOD. Of the 28 models applied by MNRF, three models (CGCM3T47-Run5, GFDLCM2.0, and MIROC3.2hires) were selected based on the frequency of the models recommended for use in Ontario with SRA1B emission scenario. Results suggested that simulated tile flows and evapotranspiration (ET) in the 2071–2100 period are expected to increase by 7% and 14% compared to 1960–1990 period. Results also suggest that under future climates, significant increases in nitrate losses (about 50%) will occur along with the elevated tile flows. This work suggests that climate change will have a significant effect on field hydrology and water quality in tile-drained agricultural regions.
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