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.

On the performance of streamflow gauging using CCTV-integrated LSPIV in diverse hydro-environmental conditions.

Addressing the critical need for precise streamflow measurements in hydro-environmental research, this study evaluates large-scale particle image velocimetry (LSPIV) using cost-effective closed-circuit television (CCTV) cameras, providing a detailed sensitivity analysis in both laboratory and real-world canal settings. In laboratory conditions, a 45° camera angle notably enhanced performance, exhibiting a 12% decrease in MAE and a remarkable 40% reduction in RMSE compared to the performance of orthographic form. Tracer particles further enhanced LSPIV accuracy, decreasing both mean absolute error (MAE) and root mean square error (RMSE) by around 0.05 m/s. Optimal velocity coefficients for the lab ranged between 0.85 and 0.90. Nighttime measurements, using projection-based illumination, showed a minor 3% MAE variation and 0.02 RMSE difference versus daytime. In field experiments, a 45° upstream CCTV camera configuration notably improved LSPIV accuracy, achieving a 3% MAE and 0.055 m/s RMSE. For best results across different turbidity levels, we recommend a velocity coefficient range of 0.84 to 0.88. This study highlights the robustness and cost-efficiency of LSPIV as a transformative method for streamflow gauging, demonstrating its wide applicability in diverse hydro-environmental scenarios.

Human-induced pollution and toxicity of river sediment by potentially toxic elements (PTEs) and accumulation in a paddy soil-rice system: A comprehensive watershed-scale assessment.

This study aimed to assess pollution by potentially toxic elements (PTEs) in the Zarjoub and Goharroud river basins in northern Iran. Due to exposure to various types of pollution sources, these rivers are two of the most polluted rivers in Iran. They also play an important role in irrigation of paddy fields in the study area, increasing concerns about the contamination of rice grains by PTEs. Hence, we analyzed the concentrations of eight PTEs (i.e., As, Co, Cr, Cu, Mn, Ni, Pb, and Zn) at ten channel bed sediment sampling sites in each river, fifteen samples of paddy soils and fifteen co-located rice samples in the relevant watersheds. Results of the index-based assessment indicate moderate to heavy pollution and moderate toxicity for sediments in the Goharroud River, while both pollution and toxicity of the Zarjoub River sediment were characterized as moderate. Paddy soils in the watersheds were found to be moderate to heavily polluted by PTEs, but the values of the rice bioconcentration factor (RBCF) indicated intermediate absorption for Cu, Zn, and Mn, and weak and very weak absorption for Pb/Ni and As/Co/Cr, respectively. The concentration of Zn, Cu, Pb, and Cr was negatively correlated to the corresponding values of RBCF, highlighting the ability of rice grains to control bioaccumulation and regulate concentrations. Industrial/agricultural effluents, municipal wastewater, leachate of solid waste, traffic-related pollution, and weathering of parent materials were found to be responsible for pollution of the Zarjoub and Goharroud watersheds by PTEs. Mn, Cu, and Pb in rice grains might be responsible for non-carcinogenic diseases. Although weak absorption was observed for As and Cr in rice grains, the concentrations of these elements in rice grains indicate a high level of cancer risk if ingested. This study provides insights to control the pollution of sediment, paddy soils, and rice.

COVID-19 and urban rivers: Effects of lockdown period on surface water pollution and quality- A case study of the Zarjoub River, north of Iran.

Due to the spreading of the coronavirus (COVID-19) in Iran, restrictions and lockdown were announced to control the infection. In order to determine the effects of the lockdown period on the status of the water quality and pollution, the concentrations of Al, As, Ba, Cr, Cu, Mo, Ni, Pb, Se, and Zn, together with Na+, Mg2+, Ca2+ and electrical conductivity (EC), were measured in the Zarjoub River, north of Iran, in both pre-lockdown and post-lockdown periods. The results indicated that water pollution and associated human health risk reduced by an average of 30% and 39%, respectively, during the lockdown period. In addition, the multi-purpose water quality index also improved by an average of 34%. However, the water salinity and alkalinity increased during the lockdown period due to the increase of municipal wastewater and the use of disinfectants. The major sources of pollution were identified as weathering, municipal wastewater, industrial and agricultural effluents, solid waste, and vehicular pollution. PCA-MLR receptor model showed that the contribution of mixed sources of weathering and municipal wastewater in water pollution increased from 23 to 50% during the lockdown period. However, the contribution of mixed sources of industrial effluents and solid wastes reduced from 64 to 45%. Likewise, the contribution of traffic-related sources exhibited a reduction from 13% in the pre-lockdown period to 5% together with agricultural effluent in the post-lockdown period. Overall, although the lockdown period resulted in positive impacts on diminishing the level of water pollution caused by industrial and vehicular contaminants, the increase of municipal waste and wastewater is a negative consequence of the lockdown period.