Assessing the Impact of Man&amp;ndash;Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins

Sayah, Mario J. Al; Nedjaï, Rachid; Kaffas, Konstantinos; Abdallah, Chadi; Khouri, Michel

doi:10.20944/preprints201910.0206.v1

Cited by 4 publications

(1 citation statement)

References 27 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sayah, Al, et al presented a study of the impact of ponds in limnologically rich basins on soil erosion and sediment transport. By exposing the various levels of soil loss and giving an understanding of the examination of erosion-prone areas and sediment yield zones of various levels, this assignment answered recommendations of the European framework for the Thematic Strategy on Soil Protection (Al Sayah et al, 2019).…”

Section: Introductionmentioning

confidence: 97%

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

et al. 2022

View full text Add to dashboard Cite

With ever advancing computer technology in machine learning, sediment load prediction inside the reservoirs has been computed using various artificially intelligent techniques. The sediment load in the catchment region of Gobindsagar reservoir of India is forecasted in this study utilizing the data collected for years 1971–2003 using several models of intelligent algorithms. Firstly, multi-layered perceptron artificial neural network (MLP-ANN), basic recurrent neural network (RNN), and other RNN based models including long-short term memory (LSTM), and gated recurrent unit (GRU) are implemented to validate and predict the sediment load inside the reservoir. The proposed machine learning models are validated for Gobindsagar reservoir using three influencing factors on yearly basis [rainfall (Ra), water inflow (Iw), and the storage capacity (Cr)]. The results demonstrate that the suggested MLP-ANN, RNN, LSTM, and GRU models produce better results with maximum errors reduced from 24.6% to 8.05%, 7.52%, 1.77%, and 0.05% respectively. For future prediction of the sediment load for next 22 years, the influencing factors were first predicted for next 22 years using ETS forecasting model with the help of data collected for 33 years. Additionally, it was noted that each prediction’s error was lower than that of the reference model. Furthermore, it was concluded that the GRU model predicts better results than the reference model and its alternatives. Secondly, by comparing the prediction precision of all the machine learning models established in this study, it can be evidently shown that the LSTM and GRU models were superior to the MLP-ANN and RNN models. It is also observed that among all, the GRU took the best precision due to the highest R of 0.9654 and VAF of 91.7689%, and the lowest MAE of 0.7777, RMSE of 1.1522 and MAPE of 0.3786%. The superiority of GRU can also be ensured from Taylor’s diagram. Lastly, Garson’s algorithm and Olden’s algorithm for MLP-ANN, as well as the perturbation method for RNN, LSTM, and GRU models, are used to test the sensitivity analysis of each influencing factor in sediment load forecasting. The sediment load was discovered to be most sensitive to the annual rainfall.

show abstract

Section: Introductionmentioning

confidence: 97%

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

Modelling 2050 Water Retention Scenarios for Irrigated and Non-Irrigated Crops for Adaptation to Climate Change Using the SWAT Model: The Case of the Bystra Catchment, Poland

2023

View full text Add to dashboard Cite

The paper presents the estimated changes in the soil water content, the total runoff, the sediment yield and the actual evapotranspiration for the small Bystra catchment in the east of Poland. The findings are based on the results of three simulations covering the years of 2041–2050. The simulations were based on a calibrated and validated SWAT model (2010–2017). The first variant covers just the climate change and the existing structure of soil cultivation for the three regional climate models supported by the EC-EARTH global climate model in the emission scenarios RCP4.5 and RCP8.5. Variants two and three are based on the first variant in terms of the changing climate. The second variant, however, involves placing a pond in each farm in the catchment, while the third variant involves designing huge reservoirs as a result of land consolidation. Variants two and three occur in five adaptation scenarios each. The first adaptation scenario (V2.1 and V3.1) involves only increasing the number of ponds on the farm or increasing the number of reservoirs for non-irrigated arable land crops, i.e., WWHT (winter cereals), BARL (spring cereals), CANP (rapeseed) and CRDY (other crops). The second adaptation scenario (V2.2 and V3.2) involves growing vegetables without irrigation (instead of cereals). The third adaptation scenario (V2.3 and V3.3) involves growing vegetables with irrigation (instead of cereals). The fourth adaptation scenario (V2.4 and V3.4) involves partial cultivation of vegetables and cereals. The fifth adaptation scenario (V2.5 and V3.5) involves partial cultivation of orchards and cereals. The adaptation scenarios of the irrigation of vegetables from deep water-bearing layers (second variant) or reservoirs (third variant) contribute to the increase in water content in the soil, especially in summer, in comparison with the adaptation scenarios for vegetable cultivation without irrigation. What is more, the actual evapotranspiration was higher in the adaptation scenarios involving irrigation than in scenarios without irrigation. It is known that the changes in water content in soil and the intensification of water erosion are gravely affected by modifications in crops and soil cultivation. A change from cereal cultivation to irrigated vegetable cultivation or orchards increased the water content in the soil in most climatic projections. However, the increase in the number of ponds in the second variant had little impact on the soil water content, actual evapotranspiration and overall runoff, while the erosion loss decreased. With the lower precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 4.5, the soil water content decreases by 1–14% for most variants. Total runoff for most variants will also be lower by 4–35%. The percentage change in sediment yield will fluctuate between −86% and 116%. On the other hand, the actual evapotranspiration for most variants will be higher. With higher precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 8.5, the soil water content changes slightly from −7% to +3%. Total runoff for most variants will also be higher by as much as 43%. Sediment yield for most scenarios may increase by 226%. The actual evapotranspiration for most variants will also be higher. Irrigation variants tend to increase soil available water while increasing evapotranspiration and total outflow in the catchment as compared to non-irrigated LULC. The largest increase in the soil water content is observed in most irrigation variants for RCP 4.5 (annual average 316–319 mm) (V2.3-V2.5, V3.2, and V3.3) and RCP 8.5 (annual average 326–327 mm) (V2.3-V2.5 and V3.3) as compared to V1 (BaU) (315 mm–RCP 4.5 and 324 mm–RCP 8.5) for the years 2041–2050. On the other hand, the lowest increase in soil water content is observed in the V3.5 variant, with an annual average of 292 mm for RCP 4.5 and an annual average of 311 mm for RCP 8.5. Thus, for future climate change scenarios, irrigation with water reservoirs (ponds and storage reservoirs) should be considered. The study proves the rationale behind building ponds in small catchments in order to increase water resources in a landscape and also to counteract adverse effects of climate changes, i.e., sediment outflow and surface water erosion.

show abstract

Impact of Fish Ponds on Stream Hydrology and Temperature Regime in the Context of Freshwater Pearl Mussel Conservation

Hoess

Generali

Kühn

et al. 2022

Water

View full text Add to dashboard Cite

Conservation of endangered, cold-stenothermic species, such as the freshwater pearl mussel (FPM) and its salmonid host fish, are particularly challenging in headwater streams as their last refuge areas. Understanding the impact of anthropogenic catchment features such as fish ponds on the hydrology and the temperature regime of such streams is, therefore, important. In this study, runoff in a FPM catchment with more than 150 small ponds was simulated using SWAT and compared to a scenario without ponds. Additionally, water temperature was monitored hourly along three steams over 2.5 years, at sites upstream and downstream of the inflow of pond outlet channels. Temperature metrics were related to land use within a 180 m corridor along the streams. Peak flows were reduced by 1.5% with ponds, while low flows were increased by 4.5%. In summer, temperature in pond effluents was higher than in the receiving stream, depending on the proximity of the inflow points. Discharge from close-by ponds increased summer stream temperature directly downstream of the inflow by up to 5.5 °C. These increased temperatures were partly compensated by groundwater contribution in forested areas. In contrast, stream temperature significantly further increased along stretches flowing through open land, persisting independently of pond inflows. We suggest incorporating this knowledge on pond- and land use-dependent effects on stream temperature regimes into the conservation management of FPM and other cold-stenothermic species, as well as into climate change mitigation strategies targeting an increased resilience against temperature extremes.

show abstract

Assessing the Impact of Man–Made Ponds on Soil Erosion and Sediment Transport in Limnological Basins

Cited by 4 publications

References 27 publications

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

Sediment load forecasting of Gobindsagar reservoir using machine learning techniques

Modelling 2050 Water Retention Scenarios for Irrigated and Non-Irrigated Crops for Adaptation to Climate Change Using the SWAT Model: The Case of the Bystra Catchment, Poland

Impact of Fish Ponds on Stream Hydrology and Temperature Regime in the Context of Freshwater Pearl Mussel Conservation

Contact Info

Product

Resources

About