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1.
Base flows are important for tropical regions with pronounced dry seasons, which are facing increasing water demands. Base flow generation, however, is one of the most challenging hydrological processes to characterize in the tropics. In many years during the May–December wet season in the Panama Canal Watershed (PCW), base flows in rivers abruptly increase. This increase persists until the start of the December–April dry season. Understanding this unusual base flow jump (BFJ) behaviour is critical to improve water provisioning in the seasonal tropics, especially during droughts and extended dry seasons. This study developed an integrated approach combining piecewise regression on cumulative average base flow and sensitivity analysis to calculate the timing and magnitude of BFJ. Rainfall, forest cover, mean land surface slope, catchment area, and estimated subsurface storage were tested as predictors for the occurrence and magnitude of the BFJs in seven subcatchments of the PCW. Sensitivity analysis on correlated predictors allowed ranking of predictor contributions due to isolated and cross-correlation effects. Correlations between observed BFJs and BFJs predicted by watershed and rainfall-related predictors were 0.92 and 0.65 for BFJ timing and magnitude, respectively. Forest cover was the second most significant predictor after cumulative rainfall for jump magnitude, owing to larger subsurface storage and groundwater recharge in forests than pastures. Catchments in the mountainous eastern PCW always generated larger jumps due to their higher rainfall and greater forest cover than the western PCW catchments. The cross-correlations between predictors contributed to more than 50% of the jump variances. The results demonstrate the importance of rainfall gradient and catchment characteristics in affecting the sudden and sustained BFJs, which can help inform land management decisions intended to enhance water supplies in the tropics. This study underscores the need for more research to further understand the hydrological processes involved in the BFJ phenomenon, including better BFJ models and field characterizations, to help improve tropical ecosystem services under a changing environment.  相似文献   
2.
强暴雨淹没耕地形成灾害的同时,对耕地作物的生长也产生着极大的影响,而暴雨灾害对耕地作物生长的影响是一个渐变过程,需要由时空动态的观测进行监测。多源卫星遥感观测技术具有捕捉地面瞬间状态和刻画过程的优势。论文利用Terra/MODIS、Landsat和Sentinel卫星观测数据,挖掘多源卫星遥感观测数据,提出了一种利用NDVI变化的特征值进行灾情动态信息提取方法;并以2016年发生暴雨灾害的巢湖地区为实验区进行了方法的应用和讨论。结果表明,基于MODIS多时相NDVI变化结果提取的信息能够获得受灾害影响开始时期和持续时长等丰富的时空动态信息,根据这些信息可以统计得出大范围区域中受灾害影响的面积。另外,结合利用30 m和10 m的Landsat和Sentinel观测数据提取的水淹区,可为在暴雨致灾范围方面提供准确的参考信息。多源遥感作为评估灾情信息的依据之一,其获取的灾情动态信息能够为灾后耕地的恢复情况以及国家灾后损失评估和救助决策提供科学的数据依据。  相似文献   
3.
Several studies on tropical cyclone genesis potential index (GPI) mainly using atmospheric parameters (relative/absolute vorticity, relative humidity, vertical wind shear, potential instability, vertical velocity etc.) have been reported earlier. Though the ocean plays a vital role in the genesis and intensification of cyclones, no ocean parameter has been included in most of the studies. In this study, we have made an attempt to develop a new GPI for Bay of Bengal during peak post-monsoon (October-November) season including upper ocean heat content (UOHC) using the data for the period 1995–2015. It is found that the new GPI is better correlated with the total number of depressions, cyclones and severe cyclones (TNDC) compared with the existing GPI which was developed for the north Indian Ocean and presently used by India Meteorological Department (IMD), New Delhi. The correlation has significantly enhanced (r=0.86:significant at >99% level) by using the first differences [year(0) –year(?1)] of the time series data. Since, the new GPI which considers atmosphere and ocean (UOHC) parameters, it appears to be more suitable for Bay of Bengal during the peak post-monsoon season.  相似文献   
4.
Yabello牧场是埃塞俄比亚博拉纳的一个半干旱地区的牧场,目前面临着草场退化的严重挑战。植被覆盖的变化、过度放牧和季节性变化极大地影响了Yabello牧场的牧草组成和生物量。本文评估了植被覆盖、放牧和季节对Yabello牧场的牧草组成和生物量的影响。首先采用1 m×1 m的随机样方进行实验,根据植被覆盖类型和放牧变化选择样点,并对季节影响进行评估。使用SAS统计软件和MicrosoftExcel分析牧草组成、牧草高度和质量数据。本研究总共记录了26种草种,其中Chloris roxburghiana, Chrysopogon aucheri和Chrysopogon aucheri草种均表现出最高的平均单种覆盖高度和生物量产量。因此,建议将这些草种用于研究区域退化草地的恢复。研究结果还表明,植被覆盖类型、放牧和季节变化是决定牧草种类组成、牧草高度和生物量产量的关键因素。最后,研究结论还认为控制灌木丛植被并平衡放牧水平的可持续管理对于该地区的可持续牧草生产和生物多样性保护至关重要。  相似文献   
5.
Phytoplankton growth rates and mortality rates were experimentally examined at 21 stations during the 2017 spring intermonsoon(April to early May) in the northern and central South China Sea(SCS) using the dilution technique, with emphasis on a comparison between the northern and central SCS areas which had different environmental factors. There had been higher temperature but lower nutrients and chlorophyll a concentrations in the central SCS than those in the northern SCS. The mean rates of phytoplankton growth(μ_0) and microzooplankton grazing(m) were(0.88±0.33) d~(–1) and(0.55±0.22) d~(–1) in the central SCS, and both higher than those in the northern SCS with the values of μ_0((0.81±0.16) d~(–1)) and m((0.30±0.09) d~(–1)), respectively.Phytoplankton growth and microzooplankton grazing rates were significantly coupled in both areas. The microzooplankton grazing impact(m/μ_0) on phytoplankton was also higher in the central SCS(0.63±0.12) than that in the northern SCS(0.37±0.06). The microzooplankton abundance was significantly correlated with temperature in the surface. Temperature might more effectively promote the microzooplankton grazing rate than phytoplankton growth rate, which might contribute to higher m and m/μ_0 in the central SCS. Compared with temperature, nutrients mainly affected the growth rate of phytoplankton. In the nutrient enrichment treatment,the phytoplankton growth rate(μn) was higher than μ_0 in the central SCS, suggesting phytoplankton growth in the central SCS was nutrient limited. The ratio of μ_0/μn was significantly correlated with nutrients concentrations in the both areas, indicating the limitation of nutrients was related to the concentrations of background nutrients in the study stations.  相似文献   
6.
Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr–1.  相似文献   
7.
利用2015—2019年辽宁省发布的暴雨红色预警信号和1605个自动站的分钟级降水资料,统计暴雨红色预警信号和短时大暴雨年际变化和时空分布,分析暴雨红色预警信号的高分布区、易发时段。结果表明:2015—2017年辽宁省暴雨红色预警信号发布站数逐年递增,最大值出现在2017年,发布站数为147个;2015—2018年预警信号准确率提升,提前时间略减少,最低值为2018年,提前时间为19 min;2019年比2018年暴雨红色预警信号发布站数减少59个,提前时间增加29 min;暴雨红色预警信号的空间分布为东南部地区多、中部地区少;暴雨红色预警信号多在夜间发布;在辽宁省发布的50%以上的暴雨红色预警信号中,降水量达到预警发布标准的时间滞后于最大雨强出现时间90 min,最大雨强出现时间为暴雨红色预警信号发布的重要指标。为了达到防灾减灾的服务效果,发布暴雨红色预警信号时,应充分考虑最大雨强出现时间、发布时机、短时大暴雨高发区及地形的影响。  相似文献   
8.
2021年10月3—6日,我国北方地区经历了历史罕见的持续性极端强降水过程,暴雨中心稳定维持在陕西中部、山西、京津冀、辽宁等地南部和山东北部,给上述地区造成了巨大的经济损失和严重的人员伤亡。基于台站观测降水、NCEP/NCAR和ERA5再分析资料诊断了本次降水过程的极端性。结果表明,本次暴雨过程无论是降水强度、持续时长还是经向水汽输送均表现出典型北方夏季暴雨和大气环流配置特征。上述五省二市区域平均的过程累计雨量强度远远超过秋季其他暴雨个例,即使在夏季也位列第二。本次过程的极端性与强降水中心稳定在上述地区密切相关。上述五省二市区域平均降水连续4日均超过15 mm,这在秋季历史上从未出现过。除过程的极端性强外,9月山西等地降水异常偏多对10月初秋涝也起到了叠加作用。本次秋涝对应的大气环流呈现出典型的北方夏季主雨季环流型,表现为西太平洋副热带高压(副高)偏西偏北,副高西侧的经向水汽输送异常强盛,同时10月4—6日北方地区发生一次强冷空气过程,冷暖气流交汇在上述地区。水汽收支计算表明,本次过程的经向水汽输送强度为秋季历史之最,甚至超过了盛夏时期北方大部分暴雨过程水汽输送强度。上述分析结果表明,即使在仲秋时节亦可产生有利于北方极端持续暴雨的环流形势和水汽输送,并导致秋涝发生。  相似文献   
9.
本文利用常规观测资料、逐小时区域自动站观测资料、NCEP1°× 1°逐6小时再分析资料等对安顺2019年6月5-11日和9月5-10日的持续性暴雨天气进行分析,结果表明:(1)6月5-11日天气过程主要是由于两高之间不断有短波槽东移造成的,9月5-10日天气过程主要是由于副高稳定少动,西南涡在副高外围稳定维持较造成的;(2)垂直螺旋度垂直积分越大越有利于产生强的短时强降雨,垂直螺旋度强中心发展高度越高越有利于短时强降雨持续不断的产生;(3)水汽垂直螺旋度在这2次持续性暴雨天气过程中对短时强降雨的发生和降雨强度有很好的指示,水汽垂直螺旋度在短时强降雨出现前6小时出现增大,且中心值越大短时强降雨强度越强,在短时强降雨发生期间迅速减小;(4)6月5-11日天气过程中,质量垂直螺旋度值增大-减小得越多,出现的短时强降雨强度越强。  相似文献   
10.
利用全国2287个气象观测站1961—2016年逐日降水资料,基于对暴雨区进行连续追踪的思路,采用暴雨相邻站点数和暴雨区中心距离确定了中国区域性暴雨过程的客观识别方法;根据区域性暴雨过程的平均强度、持续时间和平均范围构建了区域性暴雨过程的综合强度评估模型。利用该客观方法对1961—2016年中国的区域性暴雨过程进行识别,并分析其气候和气候变化特征。结果显示:我国区域性暴雨过程年均38.5次;区域性暴雨过程一年各月均可出现,但主要出现在4—9月,其中7、8月发生最为频繁,6月区域性暴雨过程持续时间长、范围广、综合强度强,这与长江中下游地区梅雨现象有关。一年中,区域性暴雨过程首次出现日期平均为3月6日,末次出现日期平均为11月14日;1961—2016年,我国年区域性暴雨过程首次出现日期呈明显提前、末次日期呈显著推后、暴雨期呈显著延长的变化趋势;年发生总频次呈微弱增多,较强区域性暴雨过程次数呈明显增加趋势;区域性暴雨过程的覆盖范围和综合强度均呈显著增大趋势。南方型区域暴雨过程变化趋势与全国的基本一致;北方型首次日期呈提前、末次日期呈推后趋势,发生频次有微弱减少趋势,覆盖范围、持续时间、综合强度均无明显变化趋势。  相似文献   
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