Spatial Differentiation and Convergence Trend of High-quality Development Level of China’s Tourism Economy

This paper aims to interpret the connotation of high-quality development of tourism economy(HQTE)from the perspective of the new development concepts of innovation,coordination,green,openness and sharing,and then to evaluate the spatial differenti-ation of China’s HQTE based on provincial panel data from 2009 to 2018.Specifically,we employ the spatial convergence model to ex-plore the absolute and conditionalβconvergence trends of HQTE in the whole country and the eastern,central and western regions of China.Our empirical results reveal that:1)within the decade,from 2009 to 2018,regions of China with the highest HQTE index is its eastern region followed by the central region and then the western region,but the fastest growing one is the western region of China fol-lowed by the central region and then the eastern region.2)Whether or not the spatial effect is included,there are absolute and condition-alβconvergence in HQTE in the whole country and aforementioned three regions.3)The degree of government attention as well as the level of economic development and location accessibility are the positive driving factors for the convergence of HQTE in the whole country and the three regions.The degree of marketization and human capital have not passed the significance test either in the whole country or in the three regions.The above conclusions could deepen the understanding of the regional imbalance and spatial conver-gence characteristics of HQTE,clarify the primary development objects,and accomplish the goal of China’s HQTE.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Why lowland riparian trees are difficult to use for streamflow reconstruction

Existing streamflow reconstructions based on tree-ring analysis mostly rely on species from upland,mainly montane areas,while lowland species(generally plain)areas are rarely used.This limits the understanding of streamflow change history in the lowlands,which is an important basis for water resource management.This study focused on Populus euphratica stands located along the main stream,eastern and western tributaries in the lower reaches of the Heihe River basin(HRb),in arid northwestern China.We investigated how streamflow regulation interferes with ripar-ian trees in lowlands when they used for streamflow recon-struction.Tree-ring width chronologies were developed and analyzed in conjunction with meteorological and hydrologic observation data.The results show streamflow regulation leads in sharp fluctuations in the streamflow allocation between the eastern tributaries and western tributaries.This resulted in instability of the correlation between streamflow at the two tributaries and at the Zhengyixia hydrologic station,with corresponding fluctuations in radial growth of poplar trees on the banks of the two tributaries and at the station.Streamflow regulation altered the natural patterns of seasonal streamflow below the station,changing the time window of poplar response.This study provides useful insight into tree-ring width based streamflow reconstruction in the lowlands.

Emerging glacier forelands alter carbon dynamics on the Tibetan Plateau

The critical challenge of ongoing climate warming is resulting in glacier melting globally,a process accompanied by the formation of substantial glacier forelands.This phenomenon emerges as a pivotal area of study,especially in the Tibetan Plateau(TP),known as the Third Pole and the Asian Water Tower.In particular,the rapid retreat of temperate glaciers in the southeastern TP has led to the formation of expansive glacier forelands.These forelands are not merely evidence of climate shifts but are also key areas for transformative carbon dynamics.Moreover,the newly exposed land surface actively adjusts the balance of dissolved organic carbon,especially in meltwater,and influences the release of greenhouse gases from a range of sources including glacial lakes,subglacial sediments,and supraglacial/proglacial rivers.These processes play a crucial role in the dynamics of atmospheric carbon dioxide.Drawing from our intensive and detailed observations over several years,this perspective not only emphasizes the importance of the underexplored impact of glacier forelands on carbon cycles but also opens a window into understanding potential future trajectories in a warming world.

Adaptive Integrated Coastal Zone Planning:History,Challenges,Advances,and Perspectives

Coastal zones are dynamic,rich environments,now densely populated,and increasingly challenged by human and climatechange pressures.Effective long-term integrated coastal zone planning is needed to ensure sustainable environmental protection and economic development.In this study,we reviewed the history of coastal zone planning since its birth in the 1950s based on the literature retrieved from the Web of Science(Core Collection)from 2000–2023,then summarized the tools and spatial allocation methods commonly used in the planning process,and finally proposed potential solutions to the challenges faced.The results show that after decades of development,coastal zone planning has changed from a decentralized activity to a targeted and integrated one,with an increasing emphasis on the ecosystem approach and the use of multiple planning tools.Spatial analysis techniques and environmental modelling software have become increasingly popular.Linear programming and overlay analysis are common approaches when performing spatial optimization,but land-sea interactions and planning in the marine parts still lack in-depth analysis and practical experience.We are also aware that the challenges posed by the integration of administrative hierarchies,scoping and conservation objectives,stakeholder participation,consideration of social dimensions,and climate change are pervasive throughout the planning process.There is an urgent need to develop more flexible and accurate spatial modelling tools,as well as more efficient participatory methods,and to focus on the holistic nature of the land-sea system to create more resilient and sustainable coastal zones.

The evaluation of biological productivity by triple isotope composition of oxygen trapped in ice-core bubbles and dissolved in ocean:a review

The ^(17)O anomaly of oxygen(Δ^(17)O,calculated from δ^(17)O and δ^(18)O)trapped in ice-core bubbles and dissolved in ocean has been respectively used to evaluate the past biosphere productivity at a global scale and gross oxygen production(GOP)in the mixed layer(ML)of ocean.Compared to traditional methods in GOP estimation,triple oxygen isotope(TOI)method provides estimates that ignore incubation bottle effects and calculates GOP on larger spatial and temporal scales.Calculated from TOI of O_(2) trapped in ice-core bubbles,the averaged global biological productivities in past glacial periods were about 0.83-0.94 of the present,and the longest time record reached 400 ka BP(thousand years before the present).TOI-derived GOP estimation has also been widely applied in open oceans and coastal oceans,with emphasis on the ML.Although the TOI method has been widely used in aquatic ecosystems,TOI-based GOP is assumed to be constant at a steady state,and the influence of physical transports below the ML is neglected.The TOI method applied to evaluate past total biospheric productivity is limited by rare samples as well as uncertainties related to O_(2) consumption mechanisms and terrestrial biosphere’s hydrological processes.Future studies should take into account the physical transports below the ML and apply the TOI method in deep ocean.In addition,study on the complex land biosphere mechanisms by triple isotope composition of O_(2) trapped in ice-core bubbles needs to be strengthened.

Quantification of Effects of Natural Geographical Factors and Land-scape Patterns on Non-point Source Pollution in Watershed Based on Geodetector:Burhatong River Basin,Northeast China as An Example

Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed;however,to slow down non-point source pollution,it is necessary to distinguish their effects.But the non-point source pollution process is interactional as a result of multiple factors,and the collinearity between multiple independent variables limits our ability of reason diagnosis.Thus,taking the Burhatong River Basin,Northeast China as an example,the methods of hydrological simulation,geographic detectors,and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed.The Soil&Water Assessment Tool(SWAT)has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed.The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index(LWLI)are the main indicators influencing the rivers total nitrogen and total phosphorus.The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen(TN)and total phosphorus(TP).Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons.TN and TP loads are affected mainly by the change in landscape pattern,especially in the wet season.Although the ecological restoration program has improved forest coverage,the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation.The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.

Spatial Distribution and Changes of Heavy Metals of Agricultural Lands in Typical Pregrading Coast in Dongtai City,Jiangsu Province,China

According to the historical changes of coastal lines, seven soil sampling districts, from land to sea, were arranged in Dongtai City, Jiangsu Province to sample soils from surface and profile. Concentrations of seven major heavy metals (HMs), granularity, pH, organic matters and C/N of the soil samples were analyzed. Results show that concentrations of heavy metals in agricultural land present a certain spatial variance, decreasing from land to sea. Pollution assessment indicates that the agricultural soils were not polluted by HMs, but the potential pollution of Cu and Hg needs to be alerted. Different HMs accumulate in the surface and sub-surface of the soil profiles, and concentrations of Hg and Pb decrease significantly with the increment of soil depth. Concentrations of HMs exhibit a significantly negative correlation to pH, but have no significant relation with organic matters in soil. Principle component analysis show that the concentrations of HMs relate to the land use history. Concentrations of Hg, Ni and Cr in soil are closely related with land use history, and concentrations of Pb, Cu and Cr are affected by land use history as well as other factors. However, there is no significant relation between concentration of As and land use history.

Spatial Pattern and Benefit Allocation in Regional Collaborative Innovation of the Yangtze River Delta, China

As an important step enhancing regional innovation, researches on collaborative innovation have attracted much more attention recently. One significant reason is that cities can get excessive benefits while they take collaborative innovation activities. Based on the theories of innovation geography, this paper takes the collaborative innovation of the Yangtze River Delta(YRD) Urban Agglomeration as a case study and measures the collaborative innovation capacity from innovation actors and innovation cities by adopting the catastrophe progression model. Then on this basis, the study depicts the spatial pattern and the benefit allocation of collaborative innovation by using the coupling collaborative degree model and benefit allocation model of collaborative innovation. The results show that:1) The collaborative innovation capacity of cities in the Yangtze River Delta has strengthened largely, while the capacity still is not high enough. Cities with high collaborative innovation capacity are concentrated in Shanghai, the southern part of Jiangsu, and Hangzhou Bay, yet the cooperation of the universities-industries-research institutes need to improve. 2) The spatial pattern of collaborative innovation of the Yangtze River Delta presents several innovation circles, which are in Suzhou-Wuxi-Changzhou Metropolitan Circle, Nanjing Metropolitan Circle, Hangzhou Metropolitan Circle, Ningbo Metropolitan Circle, and Hefei Metropolitan Circle. Shanghai plays the role of the central city of collaborative innovation, while Suzhou, Nanjing, Hangzhou, Ningbo, and Hefei act as sub-central cities. 3) The benefit each city allocated from collaborative innovation activities has increased. However, the allocations of the benefit show that cities with higher innovation capacity have significant advantages in most cases, which lead to serious disparities in space.

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents’safety,property,and lives,particularly at offshore locations with shallow water levels.Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans.In addition to experienced predictions and numerical models,artificial intelligence(AI)techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations.Convolutional neural network(CNN)and long short-term memory(LSTM)are two of the most important models among AI techniques.However,they have been scarcely utilised for surge level(SL)forecasting,and combinations of the two models are even rarer.This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information.The architectures of the CNN,LSTM,and two sequential techniques of combining the models(LSTM–CNN and CNN–LSTM)were constructed via a trial-and-error approach and knowledge obtained from previous studies.As a case study,11 a of hourly observed SL and wind data of the Xiuying Station,Hainan Province,China,were organised as inputs for training to verify the feasibility and superiority of the proposed models.The results show that CNN and LSTM had evident advantages over support vector regression(SVR)and multilayer perceptron(MLP),and the combined models outperformed the individual models(CNN and LSTM),mostly by 4%–6%.However,on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges,the accuracy was found to improve by over 10%at all forecasting steps.

A Modified Self-adaptive Method for Mapping Annual 30-m Land Use/Land Cover Using Google Earth Engine:A Case Study of Yangtze River Delta

Annual Land Use/Land Cover(LULC)change information at medium spatial resolution(i.e.,at 30 m)is used in applications ranging from land management to achieving sustainable development goals related to food security.However,obtaining annual LULC information over large areas and long periods is challenging due to limitations on computational capabilities,training data,and workflow design.Using the Google Earth Engine(GEE),which provides a catalog of multi-source data and a cloud-based environment,we developed a novel methodology to generate a high accuracy 30-m LULC cover map collection of the Yangtze River Delta by integrating free and public LULC products with Landsat imagery.Our major contribution is a hybrid approach that includes three major components:1)a high-quality training dataset derived from multi-source LULC products,filtered by k-means clustering analysis;2)a yearly 39-band stack feature space,utilizing all available Landsat data and DEM data;and 3)a self-adaptive Random Forest(RF)method,introduced for LULC classification.Experimental results show that our proposed workflow achieves an average classification accuracy of 86.33%in the entire Delta.The results demonstrate the great potential of integrating multi-source LULC products for producing LULC maps of increased reliability.In addition,as the proposed workflow is based on open source data and the GEE cloud platform,it can be used anywhere by anyone in the world.

Retrieving volume FeO and TiO_2 abundances of lunar regolith with CE-2 CELMS data using BPNN method

The volume FeO and TiO_2 abundances(FTAs) of lunar regolith can be more important for understanding the geological evolution of the Moon compared to the optical and gamma-ray results. In this paper, the volume FTAs are retrieved with microwave sounder(CELMS) data from the Chang'E-2 satellite using the back propagation neural network(BPNN) method. Firstly, a three-layered BPNN network with five-dimensional input is constructed by taking nonlinearity into account. Then, the brightness temperature(TB) and surface slope are set as the inputs and the volume FTAs are set as the outputs of the BPNN network.Thereafter, the BPNN network is trained with the corresponding parameters collected from Apollo, Luna,and Surveyor missions. Finally, the volume FTAs are retrieved with the trained BPNN network using the four-channel TBderived from the CELMS data and the surface slope estimated from Lunar Orbiter Laser Altimeter(LOLA) data. The rationality of the retrieved FTAs is verified by comparing with the Clementine UV-VIS results and Lunar Prospector(LP) GRS results. The retrieved volume FTAs enable us to re-evaluate the geological features of the lunar surface. Several important results are as follows. Firstly, very-low-Ti(<1.5 wt.%) basalts are the most spatially abundant, and the surfaces with TiO_2> 5 wt.% constitute less than 10% of the maria. Also, two linear relationships occur between the FeO abundance(FA) and the TiO_2 abundance before and after the threshold, 16 wt.% for FA. Secondly, a new perspective on mare volcanism is derived with the volume FTAs in several important mare basins, although this conclusion should be verified with more sources of data. Thirdly, FTAs in the lunar regolith change with depth to the uppermost surface,and the change is complex over the lunar surface. Finally, the distribution of volume FTAs hints that the highlands crust is probably homogeneous, at least in terms of the microwave thermophysical parameters.

Distribution of benthic macrofaunal communities in intertidal flat under hydrodynamic influence:a case study of Jiangsu coast,East China

To identify the distribution pattern of macrofaunal assemblages of the Dafeng intertidal flats in response to hydrodynamic and sediment dynamic processes in the northern Jiangsu coast,East China,macrofauna sampling and hydrodynamic observations were carried out simultaneously across the mud flat,mixed mud-sand flat,and silt-sand flat of the intertidal zone in June 2018.Results show that there was a clear zonal distribution pattern of the macrofaunal communities,as is controlled by local hydrological and sedimentary environments.Principal component analysis(PCA)revealed three types of intertidal area in terms of hydrological and surficial sediment parameters.Similarly,three distinct groups of the macrofaunal communities,i.e.,mud flat,mix mud-sand,and silt-sand groups,were recognized at similarity level of 24%based on the CLUSTER analysis in similarity profile(SIMPROF)test.Correlation analysis upon best variables stepwise search(BVSTEP)indicated the importance of the hydrodynamics(e.g.,water temperature and salinity,tidal duration,flow speed,suspended sediment concentration,and wave height)in the differentiation of macrofaunal communities with different taxonomic classes over the intertidal zone.Therefore,macrofaunal assemblages,similar to hydrology and surficial sediment,have a unique zonation pattern.Small-sized deposit feeders adapt better to low energy environments,thus dominated the upper part of the intertidal flat,whilst the heavy and large-sized filter feeders and deposit feeders were dominant over the middle and lower parts.The hydrodynamic and sediment processes cause biota-niche separation,which affected the biological processes across the intertidal flat.

Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018

In August 2018,a remarkable polynya was observed off the north coast of Greenland,a perennial ice zone where thick sea ice cover persists.In order to investigate the formation process of this polynya,satellite observations,a coupled iceocean model,ocean profiling data,and atmosphere reanalysis data were applied.We found that the thinnest sea ice cover in August since 1978(mean value of 1.1 m,compared to the average value of 2.8 m during 1978-2017) and the modest southerly wind caused by a positive North Atlantic Oscillation(mean value of 0.82,compared to the climatological value of-0.02) were responsible for the formation and maintenance of this polynya.The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind.Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

Arctic sea ice volume export through the Fram Strait: variation and its effect factors

Arctic sea ice export is important for the redistribution of freshwater and sea ice mass.Here,we use the sea ice thickness,sea ice velocity,and sea ice concentration(SIC)to estimate the exported sea ice volume through the Fram Strait from 2011 to 2018.We further analyse the contributions of the sea ice thickness,velocity and concentration to sea ice volume export.Then,the relationships between atmospheric circulation indices(Arctic Oscillation(AO),North Atlantic Oscillation(NAO),and Arctic Dipole(AD))and the sea ice volume export are discussed.Finally,we analyse the impact of wind-driven oceanic circulation indices(Ekman transport(ET))on the sea ice volume export.The sea ice volume export rapidly increases in winter and decreases in spring.The exported sea ice volume in winter is likely to exceed that in spring in the future.Among sea ice thickness,velocity and SIC,the greatest contribution to sea ice export comes from the ice velocity.The exported sea ice volume through the zonal gate of the Fram Strait(which contributes 97%to the total sea ice volume export of the Fram Strait)is much higher than that through the meridional gate(3%)because the sea ice flowing out of the zonal gate has the characteristics of a high thickness(mainly thicker than 1 m),a high velocity(mainly faster than 0.06 m/s)and a high concentration(mainly higher than 80%).The AD and ET explain 53.86%and 38.37%of the variation in sea ice volume export,respectively.

Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Examining the pathway and mechanism of comprehensive land consolidation through the lens of rural neo-endogenous development

Land consolidation(LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation(CLC) to support the rural revitalization initiative. However, there are ongoing challenges in understanding CLC's specific pathway and mechanism, particularly its role in stimulating rural endogenous development. This study aims to investigate the localization process of international experiences, examine the pathway of CLC, and scrutinize its mechanism in rural development from a novel perspective of neo-endogenous development. Field research and semi-structured interviews were conducted in Nanzhanglou village, renowned for its early adoption of CLC practices inspired by German experiences since 1988. Overall, key findings underscore the advantages of CLC in spatial restructuring, industrial development, and human capital enhancement in rural areas. Additionally, international experiences emerge as crucial exogenous forces, primarily by knowledge embedding, which catalyzes rural neo-endogenous development via the “resource-engagement-identity-endogenous” mechanism. Collectively, by introducing a neo-endogenous theoretical framework, this study offers valuable insights into the CLC implementation in China and beyond, and emphasizes the positive impact of knowledge embedding as an exogenous force in promoting rural neo-endogenous development to address existing research gaps. Recommendations for sustainable rural development involve enhancing rural planning practicality, governance capacity, and local leadership, while prioritizing agricultural modernization and increasing investments in education and vocational training to ensure that villagers benefit from industrial development.

Potassium isotopic signatures of modern offshore detrital sediments from different climatic regimes and the implications

Potassium isotopes are a novel tracer for continental weathering.Previous K isotope studies on chemical weathering generally targeted weathering profiles under a particular climate region,yet the effects of chemical weathering on K isotopes under different climatic backgrounds remain unclear.Moreover,little is known about the K isotope signatures of modern unconsolidated detrital sediments.Here,we report K isotopic data of surficial seafloor sediments from continental shelves along the east coast of China(ECC),as well as those around the tropical Hainan island in the northern South China Sea.The ECC sediments have a relatively narrow distribution ofδ^(41)K(with reference to NIST3141a)values,which range from(-0.40±0.01)‰to(-0.57±0.04)‰,with an average of(-0.51±0.09)‰.By contrast,δ^(41)K values of Hainan offshore sediments display a larger variation,ranging from(-0.28±0.07)‰to(-0.67±0.02)‰.Theδ^(41)K values of Hainan offshore sediments exhibit negative correlations with the chemical index of alteration(CIA),Al/K,Ti/K,and total iron(FeT),which underlines the control of chemical weathering on K isotopic signatures of detritus inputs into oceans.We also measured Mg isotope compositions for the same samples;interestingly,the variability inδ^(26)Mg of the samples is small(~0.24‰)for all ECC and Hainan offshore sediments,andδ^(26)Mg values do not show clear correlations with indexes of chemical weathering.Our study demonstrates the link between K isotopic variability of detrital sediments and climatic conditions including rainfall intensity,which indicates that K isotopes of the detrital component of marine sediments could be applied to study Earth’s climate in deep time.Theδ^(41)K values of the offshore detrital sediments are significantly less variable than those of pelagic marine sediments,highlighting the importance of distinguishing the effects of diagenesis and neoformation of clay minerals from continental weathering in attempts to study deep-time climate-weathering link by K isotopes in detrital sedimentary records.

Ensemble projections of climate and streamflow in a typical basin of semi-arid steppes in Mongolian Plateau of 2021-2100

The Kherlen River is the main water source for Hulun Lake,the largest lake in northern China.Due to reduced inflow from the Kherlen River,Hulun Lake experienced rapid shrinkage at the beginning of the 21st century,posing a serious threat to the ecological security of northern China.However,there is still a significant lack of projections regarding future climate change and its hydrological response in the Kherlen River basin.This study analyzed the projected climate and streamflow changes in the Kherlen River basin,a vital yet vulnerable international semi-arid steppes type basin.A combination of multi-model ensemble projection techniques,and the soil and water assessment tool(SWAT)model was employed to examine the spatio-temporal changes in precipitation,temperature,streamflow,and the associated uncertainties in the basin.The temperature(an increase of 1.84-6.42℃)and the precipitation(an increase of 15.0-46.0 mm)of Kherlen River basin are projected to increase by 2100,leading to a rise in streamflow(1.08-4.78 m^(3) s^(-1)).The upstream of the Kherlen River exhibits remarkable increasing trends in precipitation,which has a dominant influence on streamflow of Kherlen River.Noteworthy increases in streamflow are observed in April,August,September,and October compared to the reference period(1971-2000).These findings suggest a partial alleviation of water scarcity in the Kherlen River,but also an increased likelihood of hydrological extreme events.The projected temperature increase in the Kherlen River basin exhibits the smallest uncertainty,while more pronounced uncertainties are found in precipitation and streamflow.The spread among the results of CMIP6 models is greater than that of CMIP5 models,with lower signal-to-noise ratio(SNR)values for temperature,precipitation,and streamflow.

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions.Results:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation.Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,respectively.We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and grassland.Conclusions:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China.We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region.These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.