Effects of urbanization on the water cycle in the Shiyang River basin: based on a stable isotope method

Li, Rui; Zhu, Guofeng; Lu, Siyu; Sang, Liyuan; Meng, Gaojia; Chen, Longhu; Jiao, Yinying; Wang, Qinqin

doi:10.5194/hess-27-4437-2023

Cited by 38 publications

(2 citation statements)

References 68 publications

(67 reference statements)

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“…As they mainly happen on a large scale, ground truth data collection is very time-and labor-consuming, thus costly, and sometimes even impossible to perform. Multi-spectral satellite imagery and remote sensing techniques can be used as important tools in studying these landscape changes [28], as well as the effects on vital factors, including water sources [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Mansourmoghaddam,

Rousta,

Ghafarian Malamiri

et al. 2024

Remote Sensing

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The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

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Section: Introductionmentioning

confidence: 99%

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Mansourmoghaddam,

Rousta,

Ghafarian Malamiri

et al. 2024

Remote Sensing

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“…Vegetation is known to be a widely used biome in ecological assessments due to such characteristics as immobility, or relatively rapid growth. The increase in vegetation cover in an area reduces atmospheric carbon dioxide [12], mitigates lead concentrations, regulates soil moisture, safeguards water resources [13,14], curtails soil erosion, and diminishes flood risks, thereby contributing to ecological sustainability [15,16]. While in some regions droughts are the main problem, in others flooding takes place [17].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

Rousta,

Shakiba,

Olafsson

et al. 2024

Atmosphere

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The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period.

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Modelling, quantification and estimation of the soil water erosion using the Revised Universal Soil Loss Equation with Sediment Delivery Ratio and the analytic hierarchy process models

Fatima,

Rachid,

Khalil

et al. 2024

Earth Surf Processes Landf

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This research used the Revised Universal Soil Loss Equation (RUSLE) with Sediment Delivery Ratio (SDR) model. The analytic hierarchy process (AHP) method, while also incorporating the use of a geographic information system (GIS) and remote sensing (RS) to predict the annual soil loss rate and spatialise the processes of water erosion at the scale of the Loukkos Watershed, Morocco. The RUSLE model and AHP parameters were estimated using RS data, and the erosion vulnerability zones were determined using GIS. We used five parameters, including precipitation erosivity, soil erodibility, slope length and steepness, vegetation cover, and soil erosion control practices in the RUSLE. For the AHP technique, we used seven geo‐environmental factors, including annual average precipitation, drainage density, lineament density, slope, soil texture, land use/land cover and landform maps. The results of RUSLE indicated that the average annual soil loss varied from 0 to 2388.27 . The total estimated annual potential soil loss was approximately 40 790 220.11 , and a sediment yield estimated by RUSLE‐SDR was 8 647 526.66 , equivalent to 6.65 Mm3. This value is very close to the measured value of 6.81 Mm3, for a difference of 0.16 Mm3. Furthermore, the results of the AHP indicate that the soil erosion potential index varies from 0 to 0.205315 . Overall, nearly 13.7% of the area suffered from severe soil erosion exceeding 50 . Approximately 80% of the Loukkos Watershed area experienced only slight erosion, while the remaining 6% incurred moderate erosion. Integrating GIS and RS into the RUSLE model and AHP helped us robustly estimate the extent and degree of erosion risk. Territorial decision‐makers should adopt our results to develop soil conservation strategies, water management plans and other necessary soil and water conservation measures for this region.

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Effects of urbanization on the water cycle in the Shiyang River basin: based on a stable isotope method

Cited by 38 publications

References 68 publications

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

Modelling, quantification and estimation of the soil water erosion using the Revised Universal Soil Loss Equation with Sediment Delivery Ratio and the analytic hierarchy process models

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