Unveiling the Spatial Distribution and Temporal Trends of Total Phosphorus in the Yangtze River: Towards a Predictive Time-Series Modeling for Environmental Management DOI Creative Commons
Tianqi Ma,

Xing Chen,

Fazhi Xie

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 25, 2024

Abstract The accurate prediction of total phosphorus in water quality is crucial for monitoring ecosystem stability and eutrophication status. However, the distribution natural environmental data such as (TP) often undergoes complex changes over time. Stable reliable predictive outcomes not only necessitate a degree periodicity within data, but also require that TP models exhibit strong adaptability to random fluctuations drifts data. Therefore, adapting accommodate presents challenge. This study provides detailed description spatiotemporal variations Yangtze River from 2019 2023. Utilizing cleaning mining techniques, time series were analyzed generate dataset, with particular emphasis on investigating fluctuations. By comparing various forecasting models, MTS-Mixers was ultimately selected experimental baseline model, different modes employed prediction. results demonstrate model maintains relatively high accuracy 20 steps. research findings offer comprehensive River, provide effective methods tools management. They serve scientific basis protection improvement Basin, facilitating formulation implementation relevant policies advancing sustainable development environment. Furthermore, confirms applicability machine learning hydrological forecasting, which can be utilized addressing changes. Future directions include ensuring critical exploring time-domain sub-band reconstruction better understand frequency characteristics revealing hidden information features.

Язык: Английский

Human Activity's Impact on Urban Vegetation in China During the COVID-19 Lockdown: An Atypical Anthropogenic Disturbance DOI Creative Commons
Yujie Li, Shaodong Huang,

Panfei Fang

и другие.

iScience, Год журнала: 2025, Номер 28(4), С. 112195 - 112195

Опубликована: Март 12, 2025

The COVID-19 lockdown led to reduced industrial and transportation emissions in Chinese cities, improving air quality affecting large-scale vegetation. This study examines changes net primary productivity (NPP) across 283 prefecture-level cities China (PCC) during the lockdown, focusing on aerosol optical depth (AOD), nighttime light (NTL), temperature, precipitation. Results from spring 2020 show that 53.5% of experienced increased NPP, with greater gains high traffic activity due AOD. Structural equation modeling revealed urban characteristics, particularly levels, influenced NPP primarily through AOD, human shifts playing a larger role than climate factors. In substantial changes, effects were especially pronounced. These findings highlight complex interactions among environmental vegetation responses, offering insights for ecological management planning face future disruptions.

Язык: Английский

Процитировано

0
Aggravated forest fragmentation undermines productivity stability and amplifies climate impact DOI Creative Commons
Jia Wang, Shaodong Huang, Rui Li

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

Abstract African tropical forests have undergone extensive fragmentation, with an increasing proportion of previously intact now influenced by edge effects. It has become a pressing necessity to develop comprehensible index assess forest fragmentation and its interplay climate factors influencing ecosystem productivity (FEP). Using high-resolution cover maps, we developed Forest Fragmentation Gradient Index (FFGI), novel metric derived from two-dimensional framework incorporating landscape configuration edge-to-interior gradient distance. Results reveal that 2000 2023, 76.03% exhibited increased particularly in Central Africa the Congo region. Statistical analysis FEP under different levels shows low are more conducive accumulation, indicated kNDVI values 0.617 ± 0.118 0.669 0.102 2023. With increase static interaction temperature variation wind speed explain gradually increased. Over past 20 years, addition, corresponding degree effects variations radiation coupling on all show trend. Furthermore, as dynamic FFGI (ΔFFGI) intensified, stability progressively declined. Thus, curbing further moderately restoring afforestation imperative for sustaining mitigating change impacts.

Язык: Английский

Процитировано

0
Time Series Analysis for the Adaptive Prediction of Total Phosphorus in the Yangtze River: A Machine Learning Approach DOI Open Access
Tianqi Ma,

Xing Chen

Water, Год журнала: 2025, Номер 17(4), С. 603 - 603

Опубликована: Фев. 19, 2025

Accurate prediction of total phosphorus (TP) in water quality is critical for monitoring ecosystem stability and eutrophication status. However, the distribution natural environmental data such as tends to undergo complex changes over time. Stable reliable results not only require a certain degree periodicity but also that TP model be highly adaptable random fluctuations distributional drifts data. Therefore, it challenge adapt models drift In this study, spatial temporal variations Yangtze River from 2019 2023 were described detail. Using mining techniques, time series analyzed generate forecast dataset focusing on fluctuations. By comparing various models, MTS-Mixers was finally selected experimental baseline different modes used prediction. The show after parameter adjustment, can achieve high accuracy (MAE: 0.145; MSE: 0.277), which guarantee at 20 steps. These research comprehensively reliably predicted provided effective methods tools management. They provide scientific basis protection improvement Basin help formulation implementation relevant policies promote sustainable development environment. addition, study confirms applicability machine learning hydrological responding changes.

Язык: Английский

Процитировано

0
Drought threat to terrestrial gross primary production exacerbated by wildfires DOI Creative Commons
Xuezheng Zong, Xiaorui Tian, Xiaodong Liu

и другие.

Communications Earth & Environment, Год журнала: 2024, Номер 5(1)

Опубликована: Апрель 29, 2024

Abstract Frequent droughts have aggravated the occurrence of wildfires and led to substantial losses in terrestrial ecosystems. However, our understanding compound drought-wildfire events, including hotspots, spatiotemporal patterns, trends, their impacts on global vegetation growth, remains unclear. Utilizing satellite data water storage, burned areas, gross primary production (GPP) from 2002 2020, we identified a positive correlation between mapped patterns events. Approximately 38.6% vegetated areas across globe witnessed rise probability events ( < 0.016 events/10a). This increasing trend is spatially asymmetric, greater amplification observed Northern hemisphere due frequent droughts. Furthermore, GPP reductions induced by are more than twice as high that caused isolated These findings identify hotspots for offer quantitative evidence ecosystems, aiding assessment event risks implementation future climate actions.

Язык: Английский

Процитировано

3
Patterns and drivers of terrace abandonment in China: Monitoring based on multi-source remote sensing data DOI
Dan Lu, Kangchuan Su, Zhanpeng Wang

и другие.

Land Use Policy, Год журнала: 2024, Номер 148, С. 107388 - 107388

Опубликована: Окт. 21, 2024

Язык: Английский

Процитировано

2
MCD18 V6.2: A New Version of MODIS Downward Shortwave Radiation and Photosynthetically Active Radiation Products DOI
Ruohan Li, Dongdong Wang, Sadashiva Devadiga

и другие.

IEEE Geoscience and Remote Sensing Letters, Год журнала: 2024, Номер 22, С. 1 - 5

Опубликована: Дек. 9, 2024

Язык: Английский

Процитировано

0
Unveiling the Spatial Distribution and Temporal Trends of Total Phosphorus in the Yangtze River: Towards a Predictive Time-Series Modeling for Environmental Management DOI Creative Commons
Tianqi Ma,

Xing Chen,

Fazhi Xie

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 25, 2024

Abstract The accurate prediction of total phosphorus in water quality is crucial for monitoring ecosystem stability and eutrophication status. However, the distribution natural environmental data such as (TP) often undergoes complex changes over time. Stable reliable predictive outcomes not only necessitate a degree periodicity within data, but also require that TP models exhibit strong adaptability to random fluctuations drifts data. Therefore, adapting accommodate presents challenge. This study provides detailed description spatiotemporal variations Yangtze River from 2019 2023. Utilizing cleaning mining techniques, time series were analyzed generate dataset, with particular emphasis on investigating fluctuations. By comparing various forecasting models, MTS-Mixers was ultimately selected experimental baseline model, different modes employed prediction. results demonstrate model maintains relatively high accuracy 20 steps. research findings offer comprehensive River, provide effective methods tools management. They serve scientific basis protection improvement Basin, facilitating formulation implementation relevant policies advancing sustainable development environment. Furthermore, confirms applicability machine learning hydrological forecasting, which can be utilized addressing changes. Future directions include ensuring critical exploring time-domain sub-band reconstruction better understand frequency characteristics revealing hidden information features.

Язык: Английский

Процитировано

0