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.