Application of genetic programming in presenting novel equations for longitudinal dispersion coefficient in natural streams considering rivers geometry - Implementation in assimilation capacity simulation.

Precise estimation of the longitudinal dispersion coefficient (LDC) is crucial for the accurate simulation of water quality management tools such as assimilation capacity. Previous research analyzed the LDC of natural streams in two general categories: ignoring or considering the river sinuosity (σ). Genetic programming (GP) is used in this study to investigate both mentioned categories by applying two experimental datasets from 56 to 24 different rivers worldwide. The first proposed LDC equation of this research (without σ) improves the amounts of statistical measures R2 (Determination Coefficient), OI (Overall Index), NSE (Nash-Sutcliffe Efficiency), WI (Willmott's Index of Agreement), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error) by 3.75%, 4.71%, 7.81%, 0.85%, 13.72%, and 0.68%, respectively, compared to the best values of these indicators in the previous investigations. Regarding the second category, relative and absolute sensitivity analyses are conducted, which reveal that σ is the most influential parameter in the accurate prediction of the LDC among all hydraulics and geometric parameters of the river. This part of the investigation presents four unique LDC equations that closely match the experimental results. Significant improvement of the most accurate presented LDC for statistical indices R2, OI, NSE, WI, RMSE, MAE, and accuracy percentage are obtained equal to 3.27%, 2.41%, 3.16%, 0.81%, 35.1%, 24.47%, 3.8%, respectively, in comparison with the best previous relations. Also, a new indicator for measuring the efficiency of mathematical equations called Mean Normalized Statistical Index (MNSI) is introduced and applied in different parts of this research. Finally, the assimilation capacity of the Kashafrud River is determined based on the analytical method of pollution propagation for three types of water demands utilizing the accurately presented LDC in 1993-2020. The average amount of river assimilation capacity using accurate LDC is simulated at 91.93 tons/day, much lower than the currently reported pollution entrance, which equals 540 tons/day.

Virtual field trips in hydrological field laboratories: The potential of virtual reality for conveying hydrological engineering content.

With the start of the COVID-19 pandemic and the resulting contact restrictions, conducting field trips to hydrological research basins became close to impossible. Hydrological field knowledge is an essential part of hydrological education and research. In order to impart this knowledge to students of hydrological engineering subjects in times or situations where on-site exploration is not possible, the VR4Hydro tool was developed. VR4Hydro is a virtual reality platform built from 360° panoramas that allows users to interactively explore the Gersprenz River basin in Germany. The following study seeks to investigate the applicability of performing virtual field trips in the context of hydrological education by evaluating user experience. Sixteen students of the subject engineering hydrology were asked to document their experiences with VR4Hydro using a qualitative approach by answering a series of multiple-choice questions as well as long-answer text questions. The analysis and discussion of the results showed that virtual excursions generally met with great interest among users. The majority rated the virtual tour as a valuable addition to traditional teaching methods. All students found the tool particularly appealing in cases where external circumstances did not allow for a real excursion. The findings of this study show that the application of virtual field trips (VFT) in hydrological engineering can be a valuable supplement to real field trips to improve the interest and learning outcome of students.

Evaluation of Land Use, Land Management and Soil Conservation Strategies to Reduce Non-Point Source Pollution Loads in the Three Gorges Region, China.

The construction of the Three Gorges Dam in China and the subsequent impoundment of the Yangtze River have induced a major land use change in the Three Gorges Reservoir Region, which fosters increased inputs of sediment and nutrients from diffuse sources into the water bodies. Several government programs have been implemented to mitigate high sediment and nutrient loads to the reservoir. However, institutional weaknesses and a focus on economic development have so far widely counteracted the effectiveness of these programs. In this study, the eco-hydrological model soil and water assessment tool is used to assess the effects of changes in fertilizer amounts and the conditions of bench terraces in the Xiangxi catchment in the Three Gorges Reservoir Region on diffuse matter releases. With this, the study aims at identifying efficient management measures, which should have priority. The results show that a reduction of fertilizer amounts cannot reduce phosphorus loads considerably without inhibiting crop productivity. The condition of terraces in the catchment has a strong impact on soil erosion and phosphorus releases from agricultural areas. Hence, if economically feasible, programmes focusing on the construction and maintenance of terraces in the region should be implemented. Additionally, intercropping on corn fields as well as more efficient fertilization schemes for agricultural land were identified as potential instruments to reduce diffuse matter loads further. While the study was carried out in the Three Gorges Region, its findings may also beneficial for the reduction of water pollution in other mountainous areas with strong agricultural use.

Effects of sampling strategy in rivers on load estimation for Nitrate-Nitrogen and total Phosphorus in a lowland agricultural area.

The transport of nutrients into water bodies is one of the main causes of water eutrophication. It is therefore important to estimate the loads of nutrients. Discharge and nutrient concentrations are the fundamental elements to estimate the loads of nutrients, the latter can be affected by sampling strategies. As conducting sampling campaign and laboratory analysis are both expensive, it is necessary to find the best effective sampling strategy. The aim of this paper is to show how autocorrelation and standard statistical methods can be used to test the effects of different sampling strategies on the nutrient load estimation and to find the optimal sampling strategy. The data set in this study is from the 50 km² Kielstau catchment, a UNESCO demo site for ecohydrology in Northern Germany and consists of 14 years daily values of climate, hydrology, and water quality from 2006 to 2019. We calculated the autocorrelation (AC) of discharge (Q), precipitation, Nitrate-Nitrogen (NO3-N) and total Phosphorus (Ptot). Then we tested the effects of sampling intervals from 7 to 56 days (1-8 weeks) on the nutrient loads. Our results showed a high AC of Q and NO3-N for a long period, but the AC of Ptot and precipitation decreased very fast. An increase of the sampling interval (less frequent) increased the error of estimating the concentrations and loads. Consequently, we recommend that (1) the optimal sampling strategy for nutrient load estimation in an agriculture-dominant catchment should be continuously monitoring discharge combined with periodic grabbed samples; (2) the sampling frequency for NO3-N is suggested to be monthly (every 28 days) and for Ptot weekly (every 7 days). The information will help those tasked with catchment monitoring to design appropriate sampling strategy to ensure adequate data for nutrients load estimation in lowland rivers.