Characteristics of abrupt changes of snow cover and seasonal freeze-thaw layer in the Tibetan Plateau and their impacts on summer precipitation in China

In this paper, a variation series of snow cover and seasonal freeze-thaw layer from 1965 to 2004 on the Tibetan Plateau has been established by using the observation data from meteorological stations. The sliding T-test, M-K test and B-G algorithm are used to verify abrupt changes of snow cover and seasonal freeze-thaw layer in the Tibetan plateau. The results show that the snow cover has not undergone an abrupt change, but the seasonal freeze-thaw layer obviously witnessed a rapid degradation in 1987, with the frozen soil depth being reduced by about 15 cm. It is also found that when there ~s less snow in the plateau region, precipitation in South China and Southwest China increases. But when the frozen soil is deep, precipitation in most of China apparently decreases. Both snow cover and seasonal freeze-thaw layer on the plateau can be used to predict the summer precipitation in China. However, if the impacts of snow cover and seasonal freeze-thaw layer are used at the same time, the predictability of summer precipitation can be significantly improved. The significant correlation zone of snow is located in middle reaches of the Yangtze River covering the Hexi Corridor and northeastern Inner Mongolia, and the seasonal freeze-thaw layer exists in Mt. Nanling, northern Shannxi and northwestern part of North China. The significant correlation zone of simultaneous impacts of snow cover and seasonal freeze-thaw layer is larger than that of either snow cover or seasonal freeze-thaw layer. There are three significant correlation zones extending from north to south： the north zone spreads from Mr. Daxinganling to the Hexi Corridor, crossing northern Mt. Taihang and northern Shannxi; the central zone covers middle and lower reaches of the Yangtze River; and the south zone extends from Mt. Wuyi to Yunnan and Guizhou Plateau through Mt. Nanling.

Nitrous oxide emissions following seasonal freeze-thaw events from arable soils in Northeast China

Seasonal soil freeze-thaw events may enhance soil nitrogen transformation and thus stimulate nitrous oxide （N2O） emissions in cold regions. However, the mechanisms of soil N2O emission during the freeze-thaw cycling in the field remain unclear. We evaluated N2O emissions and soil biotic and abiotic factors in maize and paddy fields over 20 months in Northeast China, and the structural equation model （SEM） was used to determine which factors affected N2O production during non-growing season. Our results verified that the seasonal freeze-thaw cycles mitigated the available soil nitrogen and carbon limitation during spring thawing period, but simultaneously increased the gaseous N2O-N losses at the annual time scale under field condition. The N2O-N cumulative losses during the non-growing season amounted to 0.71 and 0.55 kg N ha 1 for the paddy and maize fields, respectively, and contributed to 66 and 18% of the annual total. The highest emission rates （199.2- 257.4 μg m-2 h-1） were observed during soil thawing for both fields, but we did not observe an emission peak during soil freezing in early winter. Although the pulses of N2O emission in spring were short-lived （18 d）, it resulted in approximately 80% of the non-growing season N2O-N loss. The N2O burst during the spring thawing was triggered by the combined impact of high soil moisture, flush available nitrogen and carbon, and rapid recovery of microbial biomass. SEM analysis indicated that the soil moisture, available substrates including NH4＋ and dissolved organic carbon （DOC）, and microbial biomass nitrogen （MBN） explained 32, 36, 16 and 51% of the N2O flux variation, respectively, during the non-growing season. Our results suggested that N2O emission during the spring thawing make a vital contribution of the annual nitrogen budget, and the vast seasonally frozen and snow-covered croplands will have high potential to exert a positive feedback on climate change considering the sensitive response of nitrogen biogeochemical cycling to the freeze-thaw disturbance.

Water migration in subgrade soil under seasonal freeze-thaw cycles in an alpine meadow on the Qinghai-Tibet Plateau

Highway frost heave and thawing settlement caused by water migration towards the freezing front and ice lens development is widespread in the alpine meadow area of the southeast QinghaiTibet Plateau(QTP). A laboratory experiment on a highway reconstruction and expansion project in the QTP was carried out in this work to analyze the effects of fine particle content, initial water content, and the number of freeze-thaw cycles(FTCs) on frost depth, temperature gradient(Grad T), total water intake, and water intake flux. Based on the results of the laboratory experiment, a modified model of migration potential related to fine particle content, freeze-thaw history, and freezing time was established. The results show that, with the increase of fine particle content, the frost depth of soil decreases, the curve of total water intake over time is transformed from an Sshape to an arch, and the curve of water intake flux over time is transformed from a peak shape to descending shape. The variation trend of migration potential with freezing time and the freeze-thaw history is the same as that of water intake flux with freezing time and freeze-thaw history. The variation trend of soil intake flux can be used as a reference to determine the variation trend of soil migration potential. This study provides a reference for the design and construction of highway subgrade in the alpine meadow area of the QTP.

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 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.

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.

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.

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 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.

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.

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.

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.

Trend in seasonal amplitude of northern net ecosystem production:Simulated results from IAP DGVM in CAS-ESM2

北方陆地净生态系统生产力(NEP)具有明显的季节变化特征,这是大气CO_(2)季节变化的关键驱动.研究这些碳循环过程并理解潜在的驱动因素是气候研究的一个关键问题.本文利用第二代中国科学院地球系统模式(CAS-ESM2)中的全球植被动态模型(IAPDGVM),研究了1990-2014年北方NEP(40°-90°N)的季节振幅及其变化趋势.在初始化试验的基础上,本文开展了一个控制试验来评估模拟的北方NEP季节幅度的变化趋势,同时开展了三个敏感性试验来研究气候和大气CO_(2)的贡献.结果表明:1990-2014年,模拟的北方NEP季节振幅显著增加,趋势为9.69万吨碳/月/年,这主要是由于最大NEP增加所致.当分别排除CO_(2)施肥效应和气候效应时,上述增加趋势大大减弱.这些显著的减少表明大气CO_(2)和气候变化对北方NEP的季节性振幅有重要影响.尽管模式存在不确定性,但这些结果有利于进一步提升IAPDGVM对陆地碳循环的精确模拟,也为CAS-ESM研究碳-气候相互作用的应用提供了重要参考.

Seasonal constraint of dynamic water temperature on riverine dissolved inorganic nitrogen transport in land surface modeling

水体温度变化对河流可溶性无机氮(DIN)输送有着强烈控制作用.然而,在全球尺度上河流DIN输送量对水温度变化的响应尚不清楚.因此,本文基于陆面过程模式,耦合河流水温估算和DIN传输方案,设定有,无动态水温情景,对比研究陆面模拟中水温变化对河流DIN通量变化的影响.结果表明:在考虑水温动态变化后,在30°N和30°S之间, DIN通量年振幅减小5%–25%.在中国东部地区,水温动态变化使河流DIN通量在夏季减少1%–3%,在冬季增加1%–5%,对DIN通量具有明显的季节性约束作用,表明动态水温的表达在河流DIN输送模拟中的重要性.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

Effects of autumn plowing on the movement and correlation of moisture,heat and nitrate nitrogen in seasonal freeze-thaw soil

A two-year experiment was established in northern Xinjiang to investigate the effects of autumn plowing methods on nitrate nitrogen accumulation,spring-sown soil conditions and cotton emergence rate,and to explore the response relationship between soil water,heat and nitrate nitrogen.The experiment included five autumn plowing treatments,namely,plough tillage(FG),no-tillage(MG),ridge and furrow alternation(LG),plough tillage with straw mulch(FJ)and plough tillage with activated charcoal mulch(FH).The results showed that both FH and FJ treatments were beneficial to promote the nitrate-nitrogen accumulation in topsoil,while FG,MG and LG treatments aggravated the nitrate nitrogen leaching in topsoil.During the freezing period,FH and FJ treatments were beneficial to reduce soil heat loss and facilitate the coordinated upward migration of soil water and nitrate nitrogen.In the thawing period,FH and FJ treatments favored suppressing the synergistic downward transport of soil water and nitrate-nitrogen and motivated the synergistic upward migration of heat and nitrate nitrogen in deep soil.Binary regression analysis suggested that the interaction between water,heat and nitrate nitrogen under FH and FJ treatments showed a highly significant correlation.FH and FJ treatments showed obvious advantages in regulating soil conditions and optimizing soil water,heat and nitrate nitrogen co-transport mechanism.During the spring sowing period,the FH and FJ treatments increased the average soil temperature by 0.99℃and 1.29℃,and the average soil moisture content by 6.01%and 8.70%,and the average soil nitrate content by 10.20 mg/kg and 10.47 mg/kg,in the 0-25 cm soil layer,respectively.FH and FJ treatments significantly grew the emergence rate of cotton,which can be used as the main autumn tillage strategies in arid areas of northern Xinjiang.

Seasonal Prediction of the Record-Breaking Northward Shift of the Western Pacific Subtropical High in July 2021

The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.

Seasonal Prediction of Extreme High-Temperature Days in Southwestern China Based on the Physical Precursors

Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.

Stable seasonal migration patterns in giant pandas

A critical function of animal movement is to maximize access to essential resources in temporally fluctuating and spatially heterogeneous environments.Seasonally mediated resource fluctuations may influence animal movements,enabling them to track changing resource distributions,resulting in annual migration patterns.The conservation-dependent giant panda(Ailuropoda melanoleuca) displays seasonal movement patterns;however,the key factor driving these seasonal migration patterns remains poorly understood.Here,we used GPS tracking collars to monitor the movements of six giant pandas over a 12-year period across different elevations,and performed statistical analysis of seasonal migration directions,routes,habitat revisitation,home range overlap,first arrival events,and stability.Our results revealed a compelling pattern of seasonal migrations that facilitated the ability of the pandas to forage at the appropriate time and place to maximize nutritional intake.Our results indicated that pandas utilize spatial memory to locate reliable food resources,as evidenced by their annual return to the same or similar winter and summer home ranges and the consistently maintained percentage of home range overlap.These novel insights into giant panda foraging and movement ecology not only enhance our understanding of its ability to adapt to nutritionally poor dietary resources but also provide important information for the development of resource utilization-based protection and management strategies.