Degree of shade tolerance shapes seasonality of chlorophyll, nitrogen and phosphorus levels of trees and herbs in a temperate deciduous forest

Forest productivity is closely linked to seasonal variations and vertical differentiation in leaf traits.However,leaf structural and chemical traits variation among co-existing species,and plant functional types within the canopy are poorly quantified.In this study,the seasonality of leaf chlorophyll,nitrogen(N),and phosphorus(P)were quantified vertically along the canopy of four major tree species and two types of herbs in a temperate deciduous forest.The role of shade tolerance in shaping the seasonal variation and vertical differentiation was examined.During the entire season,chlorophyll content showed a distinct asymmetric unimodal pattern for all species,with greater chlorophyll levels in autumn than in spring,and the timing of peak chlorophyll per leaf area gradually decreased as shade tolerance increased.Chlorophyll a:b ratios gradually decreased with increasing shade tolerance.Leaf N and P contents sharply declined during leaf expansion,remained steady in the mature stage and decreased again during leaf senescence.Over the seasons,the lower canopy layer had significantly higher chlorophyll per leaf mass but not chlorophyll per leaf area than the upper canopy layer regardless of degree of shade tolerance.However,N and P per leaf area of intermediate shade-tolerant and fully shade-tolerant tree species were significantly higher in the upper canopy than in the lower.Seasonal variations in N:P ratios suggest changes in N or P limitation.These findings indicate that shade tolerance is a key feature shaping inter-specific differences in leaf chlorophyll,N,and P contents as well as their seasonality in temperate deciduous forests,which have significant implications for modeling leaf photosynthesis and ecosystem production.

Seasonal changes in Hermit Thrush (Catharus guttatus) song syntax

In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.

Seasonal Variation of the Sea Surface Temperature Growth Rate of ENSO

El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature(SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Ni?o-3.4 region(5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection(surface heat flux) is positive(negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Seasonal variation of mesoscale eddy intensity in the global ocean

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

Seasonal Variation of Hearing Sensitivity in the Red-eared Slider(Trachemys scripta elegans)

Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.

Seedling dynamics differ between canopy species and understory species in a tropical seasonal rainforest,SW China

We used 11 years of census data from 450 seedling quadrats established in a 20-ha forest dynamics plot to study seedling dynamics in tree species of a tropical seasonal rainforest in Xishuangbanna,southwestern China.We found that overall seedling recruitment rate and relative growth rate were higher in the rainy season than in the dry season.Both the recruitment rate of seedlings from canopy tree species(two species)and the relative growth rate of seedlings from understory species(nine species)were higher in the rainy season than in the dry season.However,in the rainy season,the recruitment rate of seedlings was higher for canopy tree species than for understory tree species.In addition,relative growth rate of seedlings was higher in the canopy species than in understory seedlings in the dry season.We also observed that,in both rainy and dry seasons,mortality rate of seedlings was higher for canopy species than for understory species.Overall,canopy tree species appear to have evolved a flexible strategy to adapt to the seasonal changes of a monsoon climate.In contrast,understory tree species seem to have adopted a conservative strategy.Specifically,these species mainly release seedlings in the rainy season and maintain relatively stable populations with a lower mortality rate and recruitment rate in both dry and rainy seasons.Our study suggests that canopy and understory seedling populations growing in forest understory may respond to future climate change scenarios with distinct regeneration strategies.

Seasonal variation of melatonin secretion across various segments of the gastrointestinal tract in rats

Objective:To investigate whether melatonin(MT)secretion in different parts of the gastrointestinal tract(GIT)exhibits seasonal variations and its correlation with immune regulation.Methods: Sixty Sprague-Dawley rats were divided into control and model groups,and the pineal gland was removed in the model group.Stomach,jejunum,ileum,and colon tissues were obtained during the spring equinox,summer solstice,beginning of autumn,autumn equinox,and winter solstice.The levels of MT,MT receptors(MR),arylalkylamine N-acetyltransferase(AANAT),hydroxyindole-O-methyltransferase(HIOMT),interleukin-2(IL-2),and interleukin-10(IL-10)in the GIT were measured using enzyme-linked immunosorbent assay.Results: Except for the stomach,the jejunum,ileum,and the colon showed seasonal tendencies in MT secretion.In the control group,MT secretion in the jejunum and ileum was the highest in the long summer,and colonic MT secretion was the highest in winter.In the model group,MT levels in the colon were highest in the summer.The seasonal rhythms of the MR,AANAT,HIOMT,IL-2,and IL-10 in the colon were roughly similar to those of MT,and changed accordingly after pinealectomy.Conclusions: Gastrointestinal MT secretion is related to seasonal changes,and MT secretion in each intestinal segment is influenced by different seasons.The biological effects of MT in the gut are inextricably linked to the mediation of MR,and a hormone-receptor linkage exists between MT and MR.The effect of seasonal changes on the gastrointestinal immune system may be mediated through the regulation of seasonal secretion of MT.

Litter production and leaf nutrient concentration and remobilization in response to climate seasonality in the central Amazon

Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seasonality remain largely unknown for the central Amazon.This study meas-ured litterfall production,leaf nutrient remobilization,and leaf area index on a forest plateau in the central Amazon.Litterfall was measured at monthly intervals during 2014,while nitrogen,phosphorus,potassium,calcium and mag-nesium concentrations of leaf litter and canopy leaves were measured in the dry and rainy seasons,and remobilization rates determined.Leaf area index was also recorded in the dry and rainy seasons.Monthly litterfall varied from 33.2(in the rainy season)to 87.6 g m^(-2) in the dry season,while leaf area index increased slightly in the rainy season.Climatic seasonality had no effect on concentrations of nitrogen,calcium,and magnesium,whereas phosphorous and potassium responded to rainfall seasonality oppositely.While phosphorous increased,potassium decreased during the dry season.Over seasons,nitrogen,potassium,and phosphorous decreased in leaf litter;calcium increased in leaf litter,while magnesium remained unaffected with leaf aging.Regardless,the five nutrients had similar remobilization rates over the year.The absence of climate seasonality on nutrient remobilization suggests that the current length of the dry season does not alter nutrient remobilization rates but this may change as dry periods become more prolonged in the future due to climate change.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.

Modeling the Seasonal Temperature from Hourly Data: Seasonal Divisions

In this work, we present a general theoretical study leading to analytical expression of the seasonal temperature at the near surface that is expected to evaluate any area seasonal temperature of the world using the least square method to fit the hourly data to the theoretical curve of the temperature. It is shown that the temperature is globally the result of two contributions: the contribution of the revolution movement of the terrestrial globe on its elliptical orbit around the sun, the contribution of the spin-orbit coupling for the rotation movement of the terrestrial globe around its polar axis and its revolution movement. The orbital behavior of the temperature is used to find the seasonal divisions of the climate for the local area considered. The whole expression of the temperature is very useful for the meteorological needs. The contribution of the human activities and natural instabilities are the results of discrepancies which increase errors (standard deviations).

Effect of Seasonal Variations on the Behavior of Flexible Pavements in Burkina Faso: Towards Alternating and Periodic Loading of Multi-Axle Heavy Goods Vehicles for Road Durability

Bituminous materials are heat-sensitive, and their mechanical properties vary with temperature. This variation in properties is not without consequences on the performance of flexible road structures under the repeated passage of multi-axles. This study determines the influence of seasonal variations on the rate of permanent deformation, the rut depth of flexible pavements and the effect of alternating loading of heavy goods vehicles following the temperature variations on the durability of roads. Thus, an ambient and pavement surface temperature measurement was carried out in 2022. The temperature profile at different layers of the modelled pavement, the evaluation of deformation rates and rutting depth were determined using several models. The results show that the permanent deformation and rutting rates are higher at the level of the bituminous concrete layer than at the level of the asphalt gravel layer because the stresses decrease from the surface to the depth of the pavement. On the other hand, the variations in these rates, permanent deformations and ruts between the hot and so-called cold periods are more pronounced in the bitumen gravel than in bituminous concrete, showing that gravel bitumen is more sensitive to temperature variations than bituminous concrete despite its higher rigidity. Of these results, we suggested a periodic and alternating loading of the different types of heavy goods vehicles. These loads consist of fully applying the WAEMU standards with a tolerance of 15% during periods of high and low temperatures. This regulation has increased 2 to 3 times in the durability of roadways depending on the type of heavy goods vehicle.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

The Impact of Seasonal Variation on Clinical Pregnancy and Live Birth Rates in Assisted Reproductive Technology: A Retrospective Cohort Study in Hainan

Objective:To investigate the influence of season on live birth and clinical pregnancy rates,as well as assisted reproductive technology(ART)outcomes,in the Hainan region.Methods:Patients were categorized into four groups based on the dates of artificial insemination and transplantation:spring,summer,autumn,or winter.The main outcome measures were clinical pregnancy rates and live birth rates.Secondary outcomes included body mass index(BMI),oocyte number,two pronuclei(2PN)cleavage rate,total gonadotropin(Gn)dosage and days,age,2PN fertilization rate,sperm concentration,sperm PR rate,anti-Müllerian hormone(AMH),and endometrial thickness.Outpatient semen quality indicators included sperm PR rate,total sperm count,sperm concentration,and total sperm motility.Results:This retrospective cohort study analyzed 2,016 artificial insemination cycles and 1,783 ovarian retrieval cycles from January 2017 to October 2022,and assessed the semen quality of 6,651 outpatients from May 2017 to October 2022.In artificial insemination cycles,sperm PR rate and clinical pregnancy rate were highest in winter,with a statistically significant difference between groups(P<0.05).Clinical pregnancy rate was influenced by both age and sperm PR rate(P<0.05).In ovarian retrieval cycles,the winter group had significantly higher clinical pregnancy,2PN fertilization,and 2PN cleavage rates than the other groups.The autumn group had higher live birth rates,though not significantly different.Additionally,winter months showed higher total sperm concentration and total sperm number compared to other seasons.Conclusion:Seasonality affected clinical pregnancy and live birth rates in artificial insemination cycles but not in ovarian retrieval cycles in the Hainan region.These findings suggest that while there is no need to choose a specific season for ovarian retrieval cycles,artificial insemination in winter may be preferable for patients.

Seasonal Performance of Solar Power Plants in the Sahel Region: A Study in Senegal, West Africa

The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.

Seasonal dynamics of wintering waterbirds in two shallow lakes along Yangtze River in Anhui Province

The shallow lake wetlands in the middle and lower Yangtze River floodplain are important wintering and stopover habitats for migratory waterbirds on the East Asia-Australia Flyway.With increasing fishery practices in recent years,however,the wetlands have deteriorated significantly and now threaten wintering waterbirds.To gain insight into the influence of deteriorating wetlands on waterbirds,we conducted a survey of wintering waterbird species,population size,and distribution across 11 belt transects in Caizi Lake and Shengjin Lake,two shallow lakes along the Yangtze River in Anhui Province from November 2007-April 2008 and from November 2008-April 2009,respectively.The impacts of different fishery patterns on the distribution of waterbirds were also analyzed.A total of 43 waterbirds species belong to 7 orders of 12 families were counted during the surveys,of which 38 were found in Caizi Lake with a density of 8.2 ind./hm2,and 42 in Shengjin Lake with a density of 3.5 ind./hm2.Geese（Anser cygnoides）,bean geese（Anser fabalis）,tundra swan（Cygnus columbianus）,and dunlin（Calidris alpina） were the dominant species in the two shallow lakes.Species number and individual assemble reached maximum at the end of December and in early January of the following year,without coincidence of the largest flock for different ecological groups.Based on waterbird diversity across the 11 belt transects and the fishery patterns,habitats could be divided into three groups.Gruiformes,Anseriformes and Charadriiformes had relatively higher densities in the natural fishery zones and lower densities in the cage fishery zones;whereas,the density of Ardeidae showed little change across all lake zones.It is important to develop sustainable fishery patterns in shallow lakes along the middle and lower Yangtze River floodplain to better protect resources of wintering waterbirds.

Seasonal Dynamics of Terrestrial Net Primary Production in Response to Climate Changes in China

Study on seasonal responses of terrestrial net primary production (NPP) to climate changes is to help understand feedback between climate systems and terrestrial ecosystems and mechanisms of increased NPP in the northern middle and high latitudes. In this study, time series dataset of normalized difference vegetation index (NDVI) and corresponding ground-based information on vegetation, climate, soil, and solar radiation, together with an ecological process model, were used to explore the seasonal trends of terrestrial NPP and their geographical differences in China from 1982 to 1999. As the results,. seasonal total NPP in China showed a significant increase for all four seasons (spring, summer, autumn and winter) during the past 18 years. The spring NPP indicated the largest increase rate, while the summer NPP was with the largest increase in magnitude. The response of NPP to climate changes varied with different vegetation types. The increased NPP was primarily led by an advanced growing season for broadleaf evergreen forest, needle-leaf evergreen forest, and needle-leaf deciduous forest, whilst that was mainly due to enhanced vegetation activity (amplitude of growth cycle) during growing season for broadleaf deciduous forest, broadleaf and needle-leaf mixed forest, broadleaf trees with groundcover, perennial grasslands, broadleaf shrubs with grasslands, tundra, desert, and cultivation. The regions with the largest increase in spring NPP appeared mainly in eastern China, while the areas with the largest increase in summer NPP occurred in most parts of Northwestern China, Qinghai-Xizang Plateau, Mts. Xiaoxinganling-Changbaishan, Sanjiang Plain, Songliao Plain, Sichuan Basin, Leizhou Peninsula, part of the middle and lower Yangtze River, and southeastern mountainous areas of China. In autumn, the largest NPP increase appeared in Yunnan Plateau-Eastern Xizang and the areas around Hulun Lake. Such different ways of the NPP responses depended on regional climate attributes and their changes.

Seasonal Impact of Agricultural Non-point Source Pollution on Water Environment of Dianshan Lake Basin in Shanghai City

Taken the Dalian lake region as the study area,which represents the typical agriculture production mode and agricultural non-point source pollution （ANSP） in Dianshan lake area in Shanghai City,basis on the characteristics of regional ANSP and combing with the seasonal water quality monitoring of Dalian Lake and reaches of its main influents,the laws of seasonal impact on the water environment were investigated.The results showed that,the seasonal change of TN and COD concentration of regional water had no significant correlation with the local ANSP emissions,while the seasonal changes of TP was consistent with seasonal emissions of regional TP pollution,and it had a significant correlation with Chl.a in four seasons,indicating that regional TP pollutant was the constriction factor influenced the eutrophication degree of Dalian lake.Because more than 80% of TP emissions came from the drainage of intensive pounds in winter,summer and fall,TP pollutant control should be adopted as the control target of regional ANSP control.