Impacts of Human Activities and Season on Species Diversity and Abundance of Butterflies in Mpanga Kipengere Game Reserve and Surrounding Farmlands, Tanzania

Maintaining natural habitats is crucial for the preservation of insects and other species that indicate environmental changes. However, the Mpanga/Kipengere Game Reserve and its surrounding farmlands are facing disturbance due to human activities, which is putting many wildlife species, particularly larger mammals, at risk. To determine the impact of human activities on butterfly species diversity and abundance in the reserve and its surrounding areas, we conducted a study from November 2021 to October 2023. We collected butterfly data using transect walks and baited traps in two habitat types. Our study yielded 2799 butterfly Individuals ranging in 124 species divided into five families habitat, season, and anthropogenic factors are significant environmental variables influencing species diversity and abundance of butterflies. Therefore, it’s important to protect habitat and dry-season water for the conservation of invertebrates such as butterflies. Our study findings provide essential information for ecological monitoring and future assessment of the Mpanga/Kipengere Game Reserve ecosystem health.

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area,which is also of great significance for understanding coastal ecological environment changes and even global changes.Based on field measurements,combined with Linear Regression(LR)model and Inverse Distance Weighing(IDW)method,this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay.The shoreline faces a high erosion chance under the action of natural factors,while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities.The implication of change rule for ecological protection and recovery is also discussed.Measures should be taken to protect the coastal ecological environment.The models used in this paper show a high correlation coefficient between observed and modeling data,which means that this method can be used to predict the changing trend of shoreline and tidal flat.The research results of present study can provide scientific supports for future coastal protection and management.

Evaluating the Impacts of Human Activities on Diversity, Abundance, and Distribution of Large Mammals in Nimule National Park, South Sudan

Globally, human activities have a significant impact on the diversity, abundance, and distribution of large mammals in Protected Areas (PAs). These disturbances increase human pressure on biodiversity and species habitats, highlighting the need for conservation. This study aimed to assess the abundance and distribution of large mammals in different habitat types within Nimule National Park (NNP) and understand the impacts of human activities on them. Data on the abundance and distribution of large mammals and their respective habitat types were collected through line transect surveys. Human activity signs were observed and recorded along the transect lines. To estimate the impacts of human activities on the diversity, abundance, and distribution of large mammal species, as well as to identify any significant differences between them and their habitat types, the study utilized the Kruskal Wallis test, Polynomial multiple regressions, and diversity indices. The findings from the Shannon-Weiner and Simpson indices indicated that large mammal species were more diverse inside the park (H’ = 1.136;D = 0.570) compared to the buffer zone (H’ = 0.413;D = 0.171), with 85% (443 out of 510 samples) recorded within Nimule National Park. The species abundance showed a semi-balanced status (0.58). The diversity results among different habitat types revealed that large mammals were more diverse and highly distributed in both open woodlands (244) and dense woodlands (192), while riverine vegetation had the lowest diversity (8). Statistical tests demonstrated a highly significant difference at a 99% confidence interval (p-value = 0.01) between habitat types and identified species of large mammals. Additionally, the results highlighted the high abundance of Uganda kob (274), baboons (141), and warthog (57) across most habitat types, accounting for at least 75% of their distribution. The most prevalent human activities observed were cattle footprints (27%) and cattle dung (14%). Human footprints and tree cutting combined accounted for 9%, indicating the practice of livestock grazing, poaching, encroachment, and fuelwood collection by local communities. However, these activities did not appear to significantly impact the diversity, abundance, and distribution of large mammals in Nimule National Park. Therefore, it is crucial to foster shared responsibilities and engage relevant stakeholders in the management and conservation of large wildlife species. Regular community awareness programs should be implemented to cultivate a sense of ownership. Moreover, it is recommended that a comprehensive survey be conducted on the population status of all mammal species in Nimule National Park, including its surrounding Buffer Zone. Monitoring the impact of human activities on their behavior and habitats using satellite images should also be carried out at least every five to ten years.

TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

Centennial Analysis of Human Activity Intensity and Associated Historical Events in Heilongjiang River Sino-Russo Watershed

Human activities in a transborder watershed are complex under the influence of domestic policies,international relations,and global events.Understanding the forces driving human activity change is important for the development of transborder watershed.In this study,we used global historical land cover data,the hemeroby index model,and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River(Amur River in Russia)watershed(HLRW).The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century(1900-2016).On the Chinese side,the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction.On the Russian side,human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline.Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events.The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity.Meanwhile,policy shifts were often confined by the natural features of the watershed.These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population,eco-nomic,social,and environmental activities.

Influence of Human Activity Intensity on Habitat Quality in Hainan Tropical Rainforest National Park,China

Due to long-term human activity interference,the Hainan Tropical Rainforest National Park(HTRNP)of China has experienced ecological problems such as habitat fragmentation and biodiversity loss,and with the expanding scope and intensity of human activity impact,the regional ecological security is facing serious challenges.A scientific assessment of the interrelationship between human activity intensity and habitat quality in the HTRNP is a prerequisite for achieving effective management of ecological disturbances caused by human activities and can also provide scientific strategies for the sustainable development of the region.Based on the land use change data in 2000,2010,and 2020,the spatial and temporal variations and the relationship between habitat quality(HQ)and human activity intensity(HAI)in the HTRNP were explored using the integrated valuation of ecosystem services and trade-offs(InVEST)model.System dynamics and land use simulation models were also combined to conduct multi-scenario simulations of their relationships.The results showed that during 2000–2020,the habitat quality of the HTRNP improved,the intensity of human activities decreased each year,and there was a negative correlation between the two.Second,the system dynamic model could be well coupled with the land use simulation model by combining socio-economic and natural factors.The simulation scenarios of the coupling model showed that the harmonious development(HD)scenario is effective in curbing the increasing trend of human activity intensity and decreasing trend of habitat quality,with a weaker trade-off between the two compared with the baseline development(BD)and investment priority oriented(IPO)scenarios.To maintain the authenticity and integrity of the HTRNP,effective measures such as ecological corridor construction,ecological restoration,and the implementation of ecological compensation policies need to be strengthened.

Impacts of human activities on morphological evolution in the Modaomen Estuary, China

The morphology of the Modaomen Estuary(ME)has undergone drastic changes in recent decades,and quantifying the contribution of human activities and natural processes is crucial for estuary management.Using Landsat images,chart data,and hydrological and meteorological data,this study analyzed the evolution of the shoreline and subaqueous topography of the ME and attempted to quantify the extent of the contributions of human activities.The results show that local human activities dominated morphological evolution in some periods.From 1973 to 2003,the shoreline advanced rapidly seaward,resulting in approximately half of the water area being converted into land.Human activity is critical to this process,with the direct contribution of local land reclamation projects reaching more than 85%.After 2003,the shoreline remained relatively stable,probably due to a decrease in land reclamation projects.Regarding the evolution of subaqueous topography,the shoals in the estuary were heavily silted and gradually disappeared during 1983–2003,and the waterways narrowed and deepened.The average siltation rate decreased from 15.43 mm/a to-1.02 mm/a,indicating that the ME changed from sedimentation to slight erosion.By detecting variations of sediment load,we found that upstream human activities reduced river sediment,while downstream human activities significantly increased sediment input to the ME,leaving little change in the actual sediment input to the ME for a relatively long period.In addition,based on the empirical relationship between the sediment input and siltation rate,local human activities influenced the shift in the siltation state more than upstream and downstream human activities did.These findings suggest that more attention should be paid to local human activities to improve the estuarine management in the ME.

Whether human-induced activities could change the gradient pattern of coastal land use along the sea-land direction:a case study in Manila Bay,Philippines

Increasing intense human activities have largely changed the coastal landscape and caused many environmental issues.However,whether human-induced activities could change the coastal land use gradient pattern,an important coastal zonal characteristic along the sea-land direction,remains unclear.Manila Bay was selected as the study area in this work.According to the distance of the land use and land cover(LULC)to the coastline,we clustered the typical coastal land use sequence patterns(CLUSPs)along the sea-land direction between 1988 and 2016 in Manila Bay and found the following.(1)Four typical CLUSPs,including the natural CLUSP dominated by forest land and grassland,the agricultural CLUSP dominated by dry farm and paddy field,the urbanised CLUSP dominated by construction land and the fishery CLUSP dominated by fishing farm,were mined in 1988.Three typical CLUSPs(a natural CLUSP,an intermediate CLUSP between the agricultural and urbanised CLUSPs,and a fishery CLUSP)were mined in 2016.(2)Affected by the dominant LULC,these typical CLUSPs showed a regular spatial pattern along the sea-land direction.For example,the typical natural CLUSP showed a landward pattern due to the long distance between the forest land and grassland and the coastline.(3)However,influenced by urban and aquaculture expansion,the land intensification of the CLUSP exhibited an obvious increase and caused the decrease of the CLUSP diversity from 1988 to 2016.The increase in the area of LULC coverage showed no obvious correlation with its distance from the coastline(DFC),but the net increase rate of LULC coverage had a significant negative correlation with the DFC.Therefore,human-induced activities have a large impact on the gradient pattern of coastal land use along the sea-land direction.

Assessing the impacts of natural conditions and human activities on terrestrial water storage in Loess Plateau,China

The gravity recovery and climate experiment(GRACE)has emerged as a crucial source of land water storage information in hydrological analysis and research.Numerous factors contribute to regional terrestrial water storage(TWS),resulting in a complex mechanism.In the Loess Plateau region,the continuous alteration of natural conditions and profound impact of human activities have posed a serious threat to the natural ecosystem,leading to an escalating trend of TWS reduction.Addressing the specific analysis of how natural conditions and human activities affect TWS represents a pressing issue.This study employed the residual analysis method to discern the contribution rates of natural conditions and human activities,elucidated the spatial and temporal changes associated with each factor,and ascertained their individual influence.The findings indicated that TWS on the Loess Plateau exhibited a downward trend of-4.89 mm·a^(-1)from 2003 to 2017.The combined effects of climate change and human activities accounted for alterations in water resource reserves across most areas of the Loess Plateau,with human activities predominantly driving these changes.Precipitation emerged as the primary natural factor influencing TWS variations,and NDVI demonstrated a positive feedback effect on TWS at approximately 30%.Substantial spatial disparities in TWS existed within the Loess Plateau,with human activities identified as the primary cause for the decreasing trend.Vegetation restoration plays a positive role in saving water resources in the Loess Plateau to some extent,and vegetation growth exceeding the regional load will lead to water shortage.

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.

Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,etc.Though considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so on.From the literature,it is observed that hard to deal with the temporal dimension in the action recognition process.Convolutional neural network(CNN)models could be used widely to solve this.With this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity recognition.The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose estimation.In the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human pose.Then,an optimal DCNN model is developed to classify the human activities label based on the extracted key points.For improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch count.The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

Antibacterial activities of selected medicinal plants in traditional treatment of human wounds in Ethiopia

Objective:To evaluate the activity of selected Ethiopian medicinal plants traditionally used for wound treatment against wound-causing bacteria.Methods:Samples of medicinal plants(Achyranthes aspera,Brucea antidysenteriea,Datura stramonium,Croton macrostachyus,Acokanthera xchimperi.,Phytolacca dodecandra,Milhttia ferruginea,and Solanum incanum)were extracted using absolute methanol and water and tested for their antimicrobial activities against clinical isolates and standard strains of wound-causing bacteria using agar well diffusion and micro titer plate methods.Results:Most of the plant extracts had antibacterial activities,among which Acokanthera schimperi and Brucea antidysenteriea inhibited growth of 100%and 35%of the test organisms,respectively.Methanolic extracts had higher activities compared with their corresponding aqueous extracts.The most susceptible organism to the extracts was Streptococcus pyogens while the most resistant were Escherichia coli and Proteus vulgaris.Conclusions:This finding justifies the use of the plants in wound healing and their potential activity against woundcausing bacteria.Their toxicity level and antimicrobial activity with different extraction solvents should further be studied to use them as sources and templates for the synthesis of drugs to control wound and other disease-causing bacteria.

Monitoring the impact of climate change and human activities on grassland vegetation dynamics in the northeastern Qinghai-Tibet Plateau of China during 2000–2015

Climate change and human activities can influence vegetation net primary productivity(NPP), a key component of natural ecosystems. The Qinghai-Tibet Plateau of China, in spite of its significant natural and cultural values, is one of the most susceptible regions to climate change and human disturbances in the world. To assess the impact of climate change and human activities on vegetation dynamics in the grassland ecosystems of the northeastern Qinghai-Tibet Plateau, we applied a time-series trend analysis to normalized difference vegetation index(NDVI) datasets from 2000 to 2015 and compared these spatiotemporal variations with trends in climatic variables over the same time period. The constrained ordination approach(redundancy analysis) was used to determine which climatic variables or human-related factors mostly influenced the variation of NDVI. Furthermore, in order to determine whether current conservation measures and programs are effective in ecological protection and reconstruction, we divided the northeastern Qinghai-Tibet Plateau into two parts: the Three-River Headwater conservation area(TRH zone) in the south and the non-conservation area(NTRH zone) in the north. The results indicated an overall(73.32%) increasing trend of vegetation NPP in grasslands throughout the study area. During the period 2000–2015, NDVI in the TRH and NTRH zones increased at the rates of 0.0015/a and 0.0020/a, respectively. Specifically, precipitation accounted for 9.2% of the total variation in NDVI, while temperature accounted for 13.4%. In addition, variation in vegetation NPP of grasslands responded not only to long-and short-term changes in climate, as conceptualized in non-equilibrium theory, but also to the impact of human activities and their associated perturbations. The redundancy analysis successfully separated the relative contributions of climate change and human activities, of which village population and agricultural gross domestic product were the two most important contributors to the NDVI changes, explaining 17.8% and 17.1% of the total variation of NDVI(with the total contribution >30.0%), respectively. The total contribution percentages of climate change and human activities to the NDVI variation were 27.5% and 34.9%, respectively, in the northeastern Qinghai-Tibet Plateau. Finally, our study shows that the grassland restoration in the study area was enhanced by protection measures and programs in the TRH zone, which explained 7.6% of the total variation in NDVI.

Desertification dynamic and the relative roles of climate change and human activities in desertification in the Heihe River Basin based on NPP

Relative roles of climate change and human activities in desertification are the hotspot of research on desertification dynamic and its driving mechanism.To overcome the shortcomings of existing studies,this paper selected net primary productivity （NPP） as an indicator to analyze desertification dynamic and its impact factors.In addition,the change trends of actual NPP,potential NPP and HNPP （human appropriation of NPP,the difference between potential NPP and actual NPP） were used to analyze the desertification dynamic and calculate the relative roles of climate change,human activities and a combination of the two factors in desertification.In this study,the Moderate Resolution Imaging Spectroradiometer （MODIS）-Normalised Difference Vegetation Index （NDVI） and meteorological data were utilized to drive the Carnegie-Ames-Stanford Approach （CASA） model to calculate the actual NPP from 2001 to 2010 in the Heihe River Basin.Potential NPP was estimated using the Thornthwaite Memorial model.Results showed that 61％ of the whole basin area underwent land degradation,of which 90.5％ was caused by human activities,8.6％ by climate change,and 0.9％ by a combination of the two factors.On the contrary,1.5％ of desertification reversion area was caused by human activities and 90.7％ by climate change,the rest 7.8％ by a combination of the two factors.Moreover,it was demonstrated that 95.9％ of the total actual NPP decrease was induced by human activities,while 69.3％ of the total actual NPP increase was caused by climate change.The results revealed that climate change dominated desertification reversion,while human activities dominated desertification expansion.Moreover,the relative roles of both climate change and human activities in desertification possessed great spatial heterogeneity.Additionally,ecological protection policies should be enhanced in the Heihe River Basin to prevent desertification expansion under the condition of climate change.

Impact assessment of climate change and human activities on annual highest water level of Taihu Lake

The annual highest water level of Taihu Lake （Zm） is very significant for flood management in the Taihu Basin. This paper first describes the inter-annual and intra-annual traits of Zm from 1956 to 2000. Then, using the Mann-Kenall （MK） and Spearman （SP） nonparametric tests, the long-term change trends of area precipitation and pan evaporation in the Taihu Basin are determined. Meanwhile, using the Morlet wavelet transformation, the fluctuation patterns and change points of precipitation and pan evaporation are analyzed. Also, human activities in the Taihu Basin are described, including land use change and hydraulic project construction. Finally, the relationship between Zm, the water level of Taihu Lake 30 days prior to the day of Zm （Z0）, and the 30-day total precipitation and pan evaporation prior to the day of Zm （P and E0, respectively） is described based on multi-linear regression equations. The relative influence of climate change and human activities on the change of Zm is quantitatively ascertained. The results demonstrate that： （1） Zm was distinctly higher during the 1980-2000 period than during the 1956-1979 period, and the 30 days prior to the day of Zm are the key phase influencing Zm every year; （2） P increased significantly at a confidence level of 95% during the 1956-2000 period, while the reverse was true for E0; （3） The relationship between Zm, P and E0 distinctly changed after 1980; （4） Climate change and human activities together caused frequent occurrences of high Zm after 1980; （5） Climate change caused a substantially greater Zm difference between the 1956-1979 and 1980-2000 periods than human activities. Climate change, as represented by P and E0, was the dominant factor raising Zm, with a relative influence ratio of 83.6%, while human activities had a smaller influence ratio of 16.4%.

Hydrological response to climate change and human activities:A case study of Taihu Basin,China

Climate change and human activities have changed a number of characteristics of river flow in the Taihu Basin.Based on long-term time series of hydrological data from 1986 to 2015,we analyzed variability in precipitation,water stage,water diversion from the Yangtze River,and net inflow into Taihu Lake with the Mann-Kendall test.The non-stationary relationship between precipitation and water stage was first analyzed for the Taihu Basin and the Wuchengxiyu(WCXY)sub-region.The optimized regional and urban regulation schemes were explored to tackle high water stage problems through the hydrodynamic model.The results showed the following:(1)The highest,lowest,and average Taihu Lake water stages of all months had increasing trends.The total net inflow into Taihu Lake from the Huxi(HX)sub-region and the Wangting Sluice increased significantly.(2)The Taihu Lake water stage decreased much more slowly after 2002;it was steadier and higher after 2002.After the construction of Wuxi urban flood control projects,the average water stage of the inner city was 0.16e0.40 m lower than that of suburbs in the flood season,leading to the transfer of flooding in inner cities to suburbs and increasing inflow from HX into Taihu Lake.(3)The regional optimized schemes were more satisfactory in not increasing the inner city flood control burden,thereby decreasing the average water stage by 0.04e0.13 m,and the highest water stage by 0.04e0.09 m for Taihu Lake and the sub-region in the flood season.Future flood control research should set the basin as the basic unit.Decreasing diversion and drainage lines along the Yangtze River can take an active role in flood control.

Recent Progress in Studies on the Influences of Human Activity on Regional Climate over China

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments.This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature(SAT),precipitation,droughts,and surface wind speed,based on studies published since 2018.The reviewed evidence indicates that human activities,including greenhouse gas and anthropogenic aerosol emissions,land use and cover change,urbanization,and anthropogenic heat release,have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China.The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region.Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events,the anthropogenic precipitation signal over China is relatively obscure.Moreover,human activities have also contributed to a decline in surface wind speed,weakening of monsoon precipitation,and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades.This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation.Additionally,a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research.