Lake evaporation in arid zones: Leveraging Landsat 8's water temperature retrieval and key meteorological drivers.

This study assessed the accuracy of various methods for estimating lake evaporation in arid, high-wind environments, leveraging water temperature data from Landsat 8. The evaluation involved four estimation techniques: the FAO 56 radiation-based equation, the Schendel temperature-based equation, the Brockamp & Wenner mass transfer-based equation, and the VUV regression-based equation. The study focused on the Chah Nimeh Reservoirs (CNRs) in the arid region of Iran due to its distinctive wind patterns and dry climate. Our analysis revealed that the Split-window algorithm was the most precise for satellite-based water surface temperature measurement, with an R2 value of 0.86 and an RMSE of 1.61 °C. Among evaporation estimation methods, the FAO 56 stood out, demonstrating an R2 value of 0.76 and an RMSE of 4.36 mm/day in comparison to pan evaporation measurements. A subsequent sensitivity analysis using an artificial neural network (ANN) identified net radiation as the predominant factor influencing lake evaporation, especially during both wind and no-wind conditions. This research underscores the importance of incorporating net radiation, water surface temperature, and wind speed parameters in evaporation evaluations, providing pivotal insights for effective water management in arid, windy regions.

Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran.

A machine learning-based approach is applied to simulate and forecast forest fires in the Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and high confidence (HC) fire events is constructed from MODIS-MOD14A2. Nine climate variables from NASA's FLDAS are used as input variables, and 12 dates and 915 study points are considered. Three machine learning ensemble multi-label classifiers, gradient boosting (GBC), random forest (RFC), and extremely randomized tree (ETC), are used for forest fire simulation for the period 2000 to 2021, and ETC is found to be the most accurate classifier. Future fire projection for the near-future period of 2030 to 2050 is carried out with the ETC model, using CMIP6 EC-Earth3-SSP245 General Circulation Model (GCM) data. It is projected that MC forest fire occurrences will decrease, while HC forest fire occurrences will increase, and that the summer months, especially September, will be the most affected by fire.

Quantification of the meteorological and hydrological droughts links over various regions of Iran using gridded datasets.

Drought is a gradual phenomenon that occurs slowly and directly impacts human life and agricultural products. Due to its significant damage, comprehensive studies must be conducted on drought events. This research employs precipitation and temperature from a satellite-based gridded dataset (i.e., NASA-POWER) and runoff from an observation-based gridded dataset (i.e., GRUN) to calculate hydrological and meteorological gical droughts in Iran during 1981-2014 based on the Standardised Precipitation-Evapotranspiration Index (SPEI) and Hydrological Drought Index (SSI) indices, respectively. In addition, the relationship between the meteorological and hydrological droughts is assessed over various regions of Iran. Afterward, this study employed the Long Short-Term Memory (LSTM) method to predict the hydrological drought based on the meteorological drought over the northwest region of Iran. Results show that hydrological droughts are less dependent on precipitation in the northern regions and the coastal strip of the Caspian Sea. These regions also have a poor correlation between meteorological and hydrological droughts. The correlation between hydrological and meteorological drought in this region is 0.44, the lowest value among the studied regions. Also, on the margins of the Persian Gulf and southwestern Iran, meteorological droughts affect hydrological droughts for 4 months. Besides, except the central plateau, most regions experienced meteorological and hydrological droughts in the spring. The correlation between droughts in the center of the Iranian plateau, which has a hot climate, is less than 0.2. The correlation between these two droughts in the spring is stronger than in other seasons (CC = 0.6). Also, this season is more prone to drought than other seasons. In general, hydrological droughts occurred one to two months after the meteorological drought in most regions of Iran. LSTM model for northwest Iran showed that the predicted values had a high correlation with the observed values, and their RMSE was less than 1 in this region. CC, RMSE, NSE, and R-square of the LSTM model are 0.7, 0.55, 0.44, and 0.6, respectively. Overall, these results can be used to manage water resources and allocate water downstream to deal with hydrological droughts.

Natural wetlands efficiency assessment in removing sugarcane fields' drainage contaminants: a case study in Khuzestan, Southwest Province of Iran.

The present study investigated the efficiency of a real-scale natural wetland (Naseri Wetland) in the qualitative treatment of agricultural drainage of Khuzestan sugarcane for 1 year (2019-2020). This study divides the wetland length into three equal parts in W1, W2, and W3 stations. The efficiency of the wetland in removing contaminants such as Cr, Cd, BOD5, TDS, TN, and TP is evaluated by field sampling, laboratory analysis, and t-test. Results indicate that the highest mean difference in Cr, Cd, BOD, TDS, TN, and TP are observed between W0 and W3. At W3, the farthest station from the entry point, the highest removal efficiency is obtained for each factor. Removal percentage of Cd, Cr, and TP in all seasons is equal to 100% up to station 3 (W3), and BOD5 and TN are 75% and 65%, respectively. Also, the results show a gradual rise in TDS along the wetland's length due to high evaporation and transpiration in the area. Naseri Wetland reduces the Cr, Cd, BOD, TN, and TP compared to the initial level. This decrease is more significant at W2 and W3, and it is worth mentioning that W3 has the most considerable reduction. With increasing distance from the entry point, the effect of timing of 1.10, 1.26, 1.30, and 1.60 on removing heavy metals and nutrients is high. The highest efficiency is observed for each retention time at W3.

Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran.

Drought directly impacts the human economy and society, so a proper understanding of its spatiotemporal characteristics in different time scales and return periods can be effective in its evaluation and risk warning. In this research, the spatiotemporal variation of drought characteristics in 70 investigated stations in Iran during 1981-2020 was examined, evaluated, and compared. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) have been used on time scales of 1, 3, 6, 9, 12, and 24 months to calculate the meteorological drought. Drought characteristics have been calculated through the run theory method, and the correlation between these characteristics has been checked. Statistical distribution functions have been used to calculate drought characteristics for the 10-, 20-, 50-, and 100-year return periods. Results show that the duration, severity, and peak of the drought in rainy areas increase as the return period increases. The drought features obtained from the SPI and SPEI show that the average value of severity obtained based on the SPI (43.5) is higher than that of the SPEI (40.9) while the average values of the peak are 3.9 and 2.6 for SPI and SPEI, respectively. Extreme drought was identified in 1990 in all regions of Iran. The highest severity in the current study is from 1999 to 2003. At the end of this period, Iran faced wet years. These results are evident on all time scales. The results obtained in this study can identify drought-prone regions and the beneficial use of water resources in the region.

Developing a generic relation for predicting sediment pick-up rate using symbolic soft computing techniques.

Sediment pick-up rate has been investigated using experimental and numerical approaches. However, the use of soft computing methods for its prediction has received less attention so far. In this study, genetic programming (GP), grammatical evolution (GE), and gradient boosting machine (GBM) algorithms are employed to develop a relation in dimensionless form for predicting sediment pick-up rate in open channel flow based on two experimental datasets. Dimensionless Froude number, particle diameter, and depth-averaged turbulent kinetic energy are input variables for prediction. Prediction performance is evaluated with performance indices (root mean square error, mean absolute error, and coefficient of correlation), visual comparisons (scatter, dot, and Bland-Altman plots), and uncertainty indicators (Tsallis and Renyi entropies). Three mathematical expressions for sediment pick-up rate prediction are obtained, with GE producing the most accurate results.

A robust stochastic approach in correcting the TRMM precipitation product and simulating flood features.

Logical management and decision-making on water resources require reliable weather variables, where precipitation is considered the main weather variable. Accurate estimation of precipitation is the most important topic in hydrological studies. Due to the lack of a dense network and low temporal and spatial resolution levels at ground-level rain gauges, especially in developing countries, remote sensing methods have been used widely. In recent years, a combination of satellite-ground data on precipitation has led to a more accurate insight into precipitation and improved hydrological model performance. In this study, the Kosar Dam Basin in the Khuzestan province of Iran is selected as the research zone. The TRMM satellite data is used on 50 events to analyze the satellite precipitation data. Copula theory is then employed to check the uncertainties of precipitation estimation, and new precipitations are generated through original data and bias errors. A comparison of the results of the improved TRMM, which was bias-corrected by Gaussian copula, and ground-based rainfall demonstrated the efficacy of this method, with nearly 104% and 51% improvement in the CC and RMSE performance indicators, respectively. The HEC-HMS model was used to simulate flood features based on copula-corrected precipitation over different quartiles (10%, 30%, 50%, 70%, and 90%) and rainfall duration (3, 6, 9, and 24 h). The obtained R-factor values show that the associated uncertainty decreases with rainfall duration, down to 46 and 20% for discharge peak and volume, respectively. In general, the copula approach is a robust approach to improve the accuracy of the TRMM precipitation product for simulating hydrological processes.

Satellite-based monitoring of meteorological drought over different regions of Iran: application of the CHIRPS precipitation product.

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI < - 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products.

Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran.

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by -4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model.

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model is compared with support vector machine (SVM) and artificial neural network (ANN) models, formerly shown to be robust AI algorithms. The Fanno Creek in Oregon (USA) is selected as a case study and daily values of water temperature, specific conductance, streamflow discharge, pH, and DO concentration are used as input variables to predict DO concentration for three different lead times ("t + 1," "t + 3," and "t + 7"). Based on Pearson's correlation coefficient, several input variable combinations are formed and used for prediction. The model prediction performance is evaluated using various indices such as correlation coefficient, Nash-Sutcliffe efficiency, root mean square error, and mean absolute error. The results identify the DRNN model ([Formula: see text]) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.

Estimating the transient storage parameters for pollution modeling in small streams: a comparison of newly developed hybrid optimization algorithms.

The transient storage model (TSM) is a common approach to assess solute transport and pollution modeling in rivers. Several formulas have been developed to estimate TSM parameters. This study develops a new hybrid optimization algorithm consisting of the dragonfly algorithm and simulated annealing (DA-SA) algorithms. This robust method provides accurate formulas for estimating TSM parameters (e.g., kf, T, [Formula: see text]). A dataset gathered by previous scholars from several rivers in the USA was used to assess the proposed formulas based on several error metrics ([Formula: see text] and [Formula: see text]) and visual indicators. According to the results, DA-SA-based formulas adequately estimated the [Formula: see text] ([Formula: see text], [Formula: see text]), [Formula: see text] ([Formula: see text] [Formula: see text]), and [Formula: see text] ([Formula: see text] [Formula: see text]) parameters. Moreover, the DA-SA-1 showed higher accuracy by improving the RMSE and MAE by 98% compared to the DA and DA-SA-1 as alternatives. The formulas developed in this study significantly outperformed the results of previously proposed models by enhancing the NSE up to 70%. The hybrid DA-SA algorithm method proved highly reliable models to estimate the TSM parameters in the water pollution routing problem, which is vital for reactive solute uptake in advective and transient storage zones of stream ecosystems.

Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh.

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

New formulations for prediction of velocity at limit of deposition in storm sewers based on a stochastic technique.

Sedimentation in storm sewers strongly depends on velocity at limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition. A number of performance indices are calculated in training and testing phases to compare the developed models with the conventional regression-based equations available in the literature. Based on the obtained performance indices and some graphical techniques, the research findings confirm that a significant enhancement in prediction performance is achieved through the proposed GBTP compared with the previously developed formulas in the literature. To make a quantified comparison between the established and literature models, an index, called improvement index (IM), is computed. This index is a resultant of all the selected indices, and this indicator demonstrates that GBTP is capable of providing the most performance improvement in both training (IMtrain = 9.2%) and testing (IMtrain = 11.3%) phases, comparing with a well-known formula in this context.