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Lake surface-water temperature (LSWT) regulates physical and biochemical processes in lakes. Therefore, understanding the LSWT dynamics is important, especially in Arctic zone since the region is experiencing a warming rate that is greater than the Earth's average. However, regular measurements of LSWT in the remote Arctic lakes always face difficulties or cannot be done by satellites accurately due to the cloud cover and their limited spatiotemporal resolution. Here, we used a historically rich data (1960–2023) to develop four machine learning-based algorithms for the daily LSWT modeling in Lake Inari, situated in Arctic zone, using the air-temperature data. Our results showed that both air-temperature (0.030 °C/yr) and LSWT (0.023m °C/yr) were warming with a rate faster than those in the globe. The long-short-term memory model, with the coefficients of determination varied from 0.96 to 0.98, outperformed other algorithms in modeling of the daily LSWT dynamics in Lake Inari, followed by both support vector regression and neural network tools, and random forest model. As the air-temperature data are widely accessible through synoptic stations and remote sensing techniques, our suggested models can be simply adopted for other Arctic lakes, where the local water-temperature data are often lacking or contain large windows of missing data due to harsh atmospheric conditions and equipment failure. [ABSTRACT FROM AUTHOR]

Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran—a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former's contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i.e., ~40 mm/yr) is more than the reported average annual runoff in Iran (i.e., ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge. Such a decline in groundwater recharge could further exacerbate the already dire aquifer depletion situation in Iran, with devastating consequences for the country's natural environment and socio-economic development. Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran. The authors here show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge. [ABSTRACT FROM AUTHOR]

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will reduce under the sole influence of SSP2-4.5 and SSP5-8.5 climate scenarios. An increase in sediment load and inflow was observed under the combined effects of climate and LULC changes, this is mainly due to the projected increased deforestation and urbanization across the AWW. The findings suggest that the densely vegetated regions, mainly located in the zones with steep slope, significantly prevents large sediment load and high streamflow input to the AWW. Under the combined effects of the climate and LULC changes, by 2100, the projected total sediment input to the wetland will reach 22.66, 20.83, and 19.93 million tons under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The results highlight that without any robust environmental interventions, the large sediment inputs will significantly degrade the Anzali wetland ecosystem and partly-fill the wetland basin, resulting in resigning the wetland from the Montreux record list and the Ramsar Convention on Wetlands of International Importance. [ABSTRACT FROM AUTHOR]

This study aims to analyze flood resilience (FR) in Karaj City, Iran, using a new fuzzy method which combines several qualitative and quantitative indices. The qualitative part was estimated by a questionnaire consisting of 42 questions distributed into five indices (social-cultural, economic, infrastructural-physical, organizational-institutional, and hydraulic). A fuzzy method was used for analyzing the results. To quantify the hydraulic index, a 25-year flood was simulated in the Storm Water Management Model and the flooding volume at every grid was estimated. The idea was that the flooding amount could be representative of structural FR of drainage network that cannot be evaluated through a questionnaire well. To calculate the FR of different districts, the obtained FR indices were fuzzified then aggregated. Considering that clustering can assist managers and decision makers for more effective flood risk management, a fuzzy equivalence matrix concept was used for clustering FR in the city. Friedman test showed the significance of differences between FR of every two districts. Based on the results, northwestern and southeastern districts had the highest and the lowest resilience, respectively. Although the impact of infrastructure-physical index on the FR was similar in most of the districts, the contribution of social-cultural, organizational-institutional, and hydraulic indices was significantly different. Also, districts with low scores in the infrastructure-physical, organizational-institutional, and hydraulic indices need more attention for flood risk management. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster that causes much damage to the communities. Recently, water demand has been increasing sharply due to the population growth and the development process. By approaching the amount of water demand to the natural supplies, any decrease in the water supply may lead to a considerable negative socio-economic consequence. In this condition, the sense of drought prevails over the physical drought. Therefore, usual drought indices can not be used for characterizing and monitoring the drought in a basin. In this paper, multivariate standardized drought feeling index (MSDFI) is introduced which represents two dimensions of water management: (1) water supply in terms of precipitation and (2) water demand in terms of population. The MSDFI is calculated and its variation over time is compared to the standardized precipitation index (SPI). According to the results, MSDFI values in the early years were usually higher than SPI values and vice versa in the last years. This situation is highly correlated with the population trend in the basin. Thereafter, intensity of drought index (IDI) was defined as the difference between MSDFI and SPI to show the role of water demand in the drought feeling. Results show that IDI has an increasing trend in the populated areas, generally downstream of the basin, where population growth is high. In contrast, in the sparsely populated areas generally upstream of the basin where population growth is low and even negative due to migration, the IDI does not show any significant sense of drought. [ABSTRACT FROM AUTHOR]

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems. [ABSTRACT FROM AUTHOR]

Desiccation of the Namak Lake (NL) can result in the release of fine-grained dust contaminated with heavy metals, while there is little information available on the propagation of metals in the bed sediments of this lake. In this study, contamination of metals in the surface sediments of the NL was analyzed and the pollution status of sediments was assessed using geo-accumulation index (Igeo), enrichment factor (EF), the consensus-based sediment quality guidelines (CBSQGs), and mean probable effect concentration quotient (mPECQ). Results indicated that metal concentrations at the southern part were higher than at the middle and northern parts of the lake. Possible reasons are (i) pollution loads mainly entered the lake through the rivers at the west and northwest, but accumulated at the southern parts, (ii) hard layer of salt covering the bottom of the NL at the northern part suppresses adsorption of metals to the sediment, and (iii) the muddy nature of sediments at the southern part makes it easier for metals to be absorbed. EF results showed that sediments at the southern part of the lake were moderately enriched with lead (Pb). The low Igeo values suggested no pollution with the metals, and CBSQG values showed that the sediments of the NL were not toxic, while the mPECQ index suggested a toxicity probability of less than 25%. Cluster analysis classified the metals into two clusters. In general, the results showed that metal pollution in the surface sediments of NL was generally low although the concentration of Pb at the southern part of the lake was worrisome. [ABSTRACT FROM AUTHOR]

The PODMT3DMS-Tool, which consists of proper orthogonal decomposition (POD) linked to the Modular Transport 3-Dimensional Multi Species (MT3DMS) code for nitrate simulation in groundwater, is introduced. POD, as a statistical technique, reduces a large amount of information produced by the MT3DMS model to provide the main components of the PODMT3DMS-Tool, i.e., space- and time-dependent terms of nitrate. The low-dimensional components represent time- and space-dependent factors in the aquifer response such as hydraulic, hydrogeological and water quality variables represented in the simulation using the MT3DMS model. The PODMT3DMS-Tool is thus a combined statistical and conceptual model with a simple structure and comparable accuracy to MT3DMS. Practical application of the PODMT3DMS-Tool to the Karaj Aquifer in Iran for a period of 6 years revealed agreement between nitrate concentrations simulated by the PODMT3DMS-Tool and MT3DMS, with a mean absolute error of less than 0.5 mg/L in most parts of the aquifer. Moreover, the PODMT3DMS-Tool needed only about 10% of the calculation time required by MT3DMS. The PODMT3DMS-Tool can be used to predict nitrate concentration in the Karaj Aquifer, while its simplicity also makes it potentially useful for other water resources problems. [ABSTRACT FROM AUTHOR]

Determining the main sources of pollution (MSP) in groundwater is crucial to improve water quality (WQ) status. Field studies were conducted in this research, where five sampling campaigns were carried out from 36 wells in the southern Tehran aquifer. In all samples, WQ parameters were measured and evaluated regarding the Iranian drinking water standard (IDWS). Finally, by using the principal component factor analysis (PCFA), the probable MSP in the aquifer were determined. The results showed that all ions, total hardness, and total dissolved solids were above the IDWS. To analyze the PCFA results, only the first four of twenty rotated principal factors (RPFs) that conserved a high percentage of the variance of the data (about 90%) were considered. The results of the first PRF revealed that the geological structure was the MSP in the aquifer. Furthermore, the second RPF was mainly affected by nutrients (nitrate and orthophosphate) and microbial parameters (fecal and total coliforms), indicating the importance of agricultural activities and sewage effluents as another MSP in the aquifer. Finally, the remarkable share of heavy metals and pH in formation of the third and fourth RPFs, respectively, reflected the role of industrial activities as a probable MSP of groundwater. [ABSTRACT FROM AUTHOR]

Letter to the Editor [ABSTRACT FROM AUTHOR]

Effective estimation of scour parameters downstream ski-jump buckets is very important for risk management plan. This paper presents a new method for prediction of the depth, length, and width of the scour hole downstream ski-jump buckets based on granular computing (GrC) technique. This method employs various independent hydraulic, morphologic, and geotechnical factors to predict dependent scour parameters. Evaluation of the results indicated that the dependent scour parameters are affected more by the discharge, falling height, and mean sediment size and less by the lip angle of the bucket. Analyses of the obtained results demonstrated the high accuracy of the GrC, as the predicted values were in good agreement with the observations. Furthermore, statistical equations were derived based on the multiple linear regressions (MLR) to model the relationship between the scour parameters. Despite our expectations, the results of MLR, as a simple model, were excellent as compared to GrC. MLR results were also superior to those of well-known empirical equations presented to date. The GrC gives the best performance for the prediction of scour parameters; however, MLR model is also suggested for any real cases because it can be more applicable by practical engineers than GrC as a black box model. [ABSTRACT FROM AUTHOR]

Due to the limitations of hardware sensors for online measurement of the water quality parameters such as 5-day biochemical oxygen demand (BOD), the recent research efforts have focused on the software sensors for the rapid prediction of such parameters. The main objective in this research is to develop a reduced-order support vector machine (ROSVM) model based on the proper orthogonal decomposition to solve the time-consuming problem of the BOD measurements. The performance of the newly developed methodology is tested on the Sefidrood River Basin, Iran. Subsequently, the predicted values of BOD, resulted from the selected developed ROSVM model, are compared with the results of support vector machine (SVM) model. According to the obtained results, selected ROSVM model seems to be more accurate, showing Person correlation coefficient ( R) and root mean square error (RMSE) equal to 0.97 and 6.94, respectively. Further, the investigations based on developed discrepancy ratio (DDR) statistic for selection of the optimum model between the best accurate ROSVM and SVM models are carried out. Results of DDR statistic indicated superior performance of the selected ROSVM model comparing to the SVM technique for online prediction of BOD in the Sefidrood River. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario. [ABSTRACT FROM AUTHOR]