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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.
Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R2 = 0.74, %RMSE = 23.1%), followed by stand age (R2 = 0.62, %RMSE = 35.6%), and stem density (R2 = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.  相似文献   
3.
沈宇恒 《北京测绘》2020,(4):490-494
为了研究黄土山区地形对煤层群开采地表位移的影响,以榆林某矿为地质原型,利用数值软件模拟地表为近水平、正坡、负坡、凸面和凹面五种地形条件下煤层群开采。结果表明:黄土山区地形的变化,对煤层群开采顶板垂直应力影响不明显,对地表位移影响较大;单向坡开采,斜坡滑移引起的下沉量在坡顶与开采沉陷量形成“叠加”,在坡底形成“抵消”;在组合坡中,煤层群开采地表位移量相差很大,说明在变坡点附近地表位移较为复杂。  相似文献   
4.
基于甘肃省清水县汤峪河径流小区2015—2017年的观测数据,研究不同植被恢复模式条件下坡面次降雨入渗、产流产沙特征。结果表明:不同植被恢复模式条件下的土壤入渗量与降雨强度呈二次函数关系,存在入渗量达到最大值的临界降雨强度。入渗速率与降雨历时可以用幂函数关系表达,符合考斯恰可夫入渗模型。不同植被恢复模式条件下的产流率在0.003 3~0.003 6 mm·min-1之间,相对裸地的减流率为54%~58%。产流率与降雨强度之间呈二次函数关系(R2>0.88),产流率的主要影响因素是降雨强度。径流含沙量平均值乔灌混合区(3.13 g·L-1)>灌木林(2.95 g·L-1)>乔木林(2.79 g·L-1)>草地(2.58 g·L-1),径流含沙量与降雨强度呈线性递增函数关系。裸地的产沙量显著高于各植被小区(P<0.05),是各植被小区的43~57倍,各植被小区的减沙率在93%~94%之间,减沙效益高于其减流效益。各植被坡面土壤流失量与降雨侵蚀力呈线性递增函数关系;产流率与侵蚀产沙率之间呈极显著正相关关系(P<0.01),二者间可采用二次函数关系表达。本研究成果可为黄土高原丘陵沟壑区水土保持优化配置提供理论依据。  相似文献   
5.
基于大连长兴岛的一套完整的晚更新世黄土剖面(贺家圈剖面),应用端元分析模型,对所采集的21个沉积样品的粒度数据进行分析。结论认为:从贺家圈剖面沉积物粒级组分中可以分离出3个代表不同沉积动力以及改造作用的沉积端元组分。并结合地球化学元素的对比分析,初步认为:端元1可能代表了剖面原始的粒度沉积组分,主峰为近源沉积,次峰为远源沉积;端元2可能代表了沉积物在沉积之后受到的淋溶作用和残积作用;端元3可能代表的是沉积物沉积之后受到的化学风化作用。  相似文献   
6.
Simulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation.  相似文献   
7.
晋西北地区表层土壤粒度与地球化学元素组成   总被引:1,自引:1,他引:0  
对晋西北地区表层土壤的物质组成进行研究,可以明确其物质来源、沉积环境及化学风化特征。选择区内表层土壤(深度0、10、20 cm)及其附近的河流沉积物,进行粒度和化学元素组成测试。结果表明:(1)晋西北地区表层土壤粒度组成以黏土和粉沙为主,且有自地表向下粗颗粒组分含量增加的趋势。(2)常量化学元素组成以SiO2、Al2O3、CaO、Fe2O3为主,其余元素含量均较低。化学元素组成模式表明研究区内表层土壤物质与黄土高原腹地黄土有相同的物质源区,黄河河流沉积物对其贡献不大。(3)晋西北表层土壤的CIA值为52.70~57.89,平均54.06,说明它们的化学风化程度较低,处于早期的脱Na、Ca阶段。这是由于研究区地理位置比洛川更靠北,气温和降水量均低于洛川,降水量与兰州相差不大,但是年平均气温和年温差比兰州低。该研究明确了晋西北地区表层土壤的物质组成特征,为区域潜在沙漠化危害防治提供了数据支撑。  相似文献   
8.
鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%~72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。  相似文献   
9.
Riparian land use is a key driver of stream ecosystem processes but its effects on water quality are still a matter of debate when proposing measures to improve freshwater quality. The aim of this study was to examine the influence of riparian land use on stream habitat and water chemistry, and to assess in what extent stream habitat also affects water quality. To that end, we selected eight reaches in the Ave River basin (northwestern Portugal) and compared longitudinal variations in water chemistry and stream habitat between reaches with different land use (urban, agricultural and natural), and between reaches with natural riparian areas and different habitats. Stream habitat was assessed using the Fluvial Functional Index, the HABSCORE, and the Riparian Forest Quality Index. Longitudinal variations in water chemistry were determined measuring differences in concentrations of ammonium, nitrate, phosphate and oxygen, and conductivity, pH and temperature between the downstream and the upstream ends of each reach. Nitrate concentration tended to decrease along reaches with more natural riparian areas and to increase along reaches with more urban and agricultural land uses. Longitudinal variations in water chemistry also differed between reaches with natural riparian areas, suggesting that water quality also depends on stream habitat. Moreover, longitudinal variation in water chemistry was proven a simple, useful and low-cost approach to assess the influence of land cover and stream habitat on water quality. Overall results demonstrated that both riparian land use and stream habitat influence water quality and that riparian forests are essential to reduce nutrient export to downstream ecosystems.  相似文献   
10.
Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.  相似文献   
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