Impact of dam construction on precipitation: a regional perspective

Zhu, Xiaolei; Zhao, Xu; Liu, Zhixin; Liu, Mingzhe; Yin, Zhouping; Yin, Liangyu; Zheng, Wenfeng

doi:10.1071/mf22135

Cited by 91 publications

(28 citation statements)

References 21 publications

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“…Sustainable management of water and ecosystem requires attention to the components of water production and consumption (Li et al 2022a;Zhu et al 2022;Qi et al 2023). Different studies have been conducted to develop the sustainable strategies of agricultural water management.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Integrated modeling of food–water–energy nexus for maximizing water productivity

2023

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One of the needs of a sustainable decision-making system in agriculture is to determine the role of energy in the food production cycle. Wind energy turbines can be built in agricultural fields for groundwater exploitation and reduce the cost of energy supply for the pumping system. This study was conducted to evaluate the effect of wind energy and economics on sustainable planning of agricultural water resources. A multiobjective framework was developed based on the nondominated sorting principle and water cycle optimizer. Maximization of benefit per cost ratio for the total cropping pattern and minimization of energy consumption for the growing season were addressed as the objectives of the nonlinear problem. The prediction of biomass production was made by simulating a hybrid structure between the soil moisture balance in the root zone area and the development of the canopy cover of each crop. The results showed that the objectives of the problem have been met by irrigation planning using climatic constraints and drought stresses. About 35% of the total water requirement of plants with a higher harvest index (watermelon, melon, etc.) is in the maturing stage of the shade cover.

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Section: Introductionmentioning

confidence: 99%

Retracted: Integrated modeling of food–water–energy nexus for maximizing water productivity

2023

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“…In spite of this, it is still unclear how climate change will affect floods, especially when it comes to the impacts of seasonality (Wang, Zhang, et al, 2021;Wang, Fang, et al, 2021). Although it is true that rainfall is the most significant climatic factor causing flooding in some areas, there is quite a bit of controversy regarding that (Dou et al, 2019;Zhu et al, 2022). Even though rainfall is considered a major cause of flooding in some parts of the country and the precipitation factor triggers floods, lithology, elevation, slope, and distance from a river are the most influential factors for floods to occur (Ali et al, 2020;Gao et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

et al. 2023

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Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute—weights of evidence (forest‐PA‐WOE), best first decision tree—WOE, alternating decision tree—WOE, and logistic regression—WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest‐PA‐WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data‐scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

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“…In the context of Saudi cities, unprecedented urban expansion with a rapid increase in impervious surfaces could impact the LST and lead to deterioration of the urban thermal environment [ 25 , 26 , 27 ]. The application of remote sensing indices offers significant opportunities to understand the relationship between biophysical indices and LST [ 28 , 29 ]. In previous literature, a number of remote sensing technologies were used to estimate the impact of the biophysical environment on LST through such indices as the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), normalized difference water index (NDWI), and modified normalized difference water index (MNDWI) [ 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

Addas

2023

IJERPH

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The deteriorations of the thermal environment due to extreme land surface temperature (LST) has become one of the most serious environmental problems in urban areas. The spatial distribution of urban biophysical composition (UBC) has a significant impact on the LST. Therefore, it is essential to understand the relationship between LST and biophysical physical composition (BPC) to mitigate the effects of UHIs. In this study, an attempt was made to understand the relationship between LST and BPC in a hot desert coastal megacity (Jeddah megacity) in Saudi Arabia. Principal component analysis (PCA) was used to understand the factors affecting LST based on remote sensing indices. Correlation and regression analyses were carried out to understand the relationship between LST and BPC and the impact of BPC on LST. The results showed that, in Jeddah city from 2000 to 2021, there was a substantial increase in the built-up area, which increased from 3085 to 5557.98 hectares. Impervious surfaces had a significant impact on the LST, and green infrastructure (GI) was negatively correlated with LST. Based on the PCA results, we found that the GI was a significant factor affecting the LST in Jeddah megacity. The findings of this study, though not contributing to further understanding of the impact of BPC on LST, will provide planners and policy makers with a foundation for developing very effective strategies to improve the eco-environmental quality of Jeddah megacity.

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Impact of dam construction on precipitation: a regional perspective

Cited by 91 publications

References 21 publications

Retracted: Integrated modeling of food–water–energy nexus for maximizing water productivity

Retracted: Integrated modeling of food–water–energy nexus for maximizing water productivity

Flood hazard potential evaluation using decision tree state‐of‐the‐art models

Understanding the Relationship between Urban Biophysical Composition and Land Surface Temperature in a Hot Desert Megacity (Saudi Arabia)

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