基于WaSSI模型的珠江流域水-碳耦合模拟

流域水循环和碳循环通过植物光合作用与蒸散发(ET)过程紧密相联,两者在流域尺度上的耦合模拟研究是实现水、碳资源综合管理的重要基础。本研究通过对WaSSI(供水压力指数)生态水文模型进行适当改进,建立并验证了珠江流域水-碳耦合模拟模型(WaSSI-PRB),将珠江流域划分为1715个子流域,选定1980—2004年为率定期、2005—2016年为验证期,并对径流、蒸散发和总初级生产力(GPP)等3个关键水-碳通量的模拟结果进行验证。结果表明: 西江、北江、东江的水文控制站,径流模拟纳什效率系数(NSE)和决定系数(R²)在率定期均达0.80以上,在验证期均达0.75以上;相较于MODIS、PML、SSEBop、VPM等大尺度遥感数据产品,WaSSI-PRB模型能更好地模拟ET和GPP的时空分布特征。总体而言,WaSSI-PRB模型在珠江流域具有较好的适用性,可作为量化分析流域水-碳平衡及其对变化环境响应关系的一种有效工具。     

LUCC及气候变化对澜沧江流域径流的影响

运用SWAT模型,通过设置不同情景,定量分析了澜沧江流域土地利用与土地覆被变化(Land Use and Land Cover Change,LUCC)和气候变化对径流的影响,并结合RCP4.5、RCP8.5两种排放情景对流域未来径流的变化进行预估。结果表明:SWAT模型在澜沧江流域径流模拟中具有很好的适用性,率定期和验证期的模型参数R~2分别达到0.80、0.74,Ens分别达到0.80、0.73;从土地利用变化方面考虑,流域内的农业用地转化为林地或草地,均会导致径流量的减少,而林地转化为草地则会引起径流量的增加,农业用地、林地、草地三者对径流增加贡献顺序为农业用地草地林地,从气候变化方面考虑,流域内的径流量与降雨量成正比,与气温成反比;2006—2015年间澜沧江流域气候变化引起的月均径流减少幅度强于LUCC引起的月均径流增加幅度,径流变化由气候变化主导;在RCP4.5和RCP8.5两种排放情景下,2021—2050年间澜沧江流域的径流均呈增加趋势,这与1971—2015年间流域实测径流的变化趋势相反。     

Spatial–temporal distribution of rainfall erosivity,erosivity density and correlation with El Niño–Southern Oscillation in the Huaihe River Basin,China

Rainfall erosivity (RE) is the ability of rainfall to cause soil or regolith erosion. Understanding the spatial distribution and temporal trends of RE is critical for assessing soil erosion risk and improving upon soil conservation planning. The aim of this paper is to study the temporal and spatial changes in RE in the Huaihe River Basin, China. This will be based on daily precipitation data from 67 meteorological stations in the Huaihe River Basin for the period 1971 to 2016. The assessment of the resulting RE values involved inverse distance-weighted (IDW) interpolation, Sen's slope estimation, and the Mann-Kendall nonparametric test. In addition, the possible influence of the El Niño – Southern Oscillation (ENSO) on RE in the Huaihe River Basin will be examined by the use of Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC), where the relationship between RE and the multivariate ENSO index (MEI) are analyzed. The results showed that the spatial distribution of RE is characterized by it increasing from west to east and north to south. In most areas, the annual RE has increased slightly, while this upward trend in the southeastern part of the study area was more significant. The rainfall erosivity of rainstorms (RE-SM) in the Huaihe River Basin played a leading role in the annual RE, and 97% of the stations displayed the order of importance of the different categories of rainfall as rainfall erosivity of rainstorms (RE-SM) > rainfall erosivity of heavy rain (RE-HR) > rainfall erosivity of moderate rain (RE-MR). Over monthly time scales, the RE was the highest in July, while it was lowest in December, with the monthly differences being apparent. The RE in the Huaihe River Basin were relatively large during non-El Niño/La Niña periods and relatively small during El Niño/La Niña periods. Finally, correlations between RE and the MEI in various parts of the basin showed different characteristics over time and space, with both displaying similarities and differences.     

利用遥感技术实现作物模拟模型区域应用的研究进展

作物模拟模型从单点发展到区域应用时,模型中一些宏观资料的获取和参数的区域化方面出现困难,利用遥感技术将实现作物模拟模型的区域应用．文中综述了近年来遥感反演作物模型所需的地表生物物理参数的方法、利用遥感信息直接获取生物量的途径和遥感信息与作物模拟模型之间时空匹配问题等方面的研究概况,重点介绍了利用遥感技术实现作物模拟模型区域应用的3种解决方案(强迫型、调控型和验证型)及其研究进展,并讨论了目前存在的问题和今后研究的方向．     

A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery

Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (α < 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R² values for beta regression models of vegetation cover from RapidEye image information varied from 0.1525 to 0.7732 in White River and 0.2455 to 0.6085 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, normalized RMSE was lower for any-hit cover (indicating better model performance) or minimally higher than top-hit cover for all indicators in each study area. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator in the ecosystem of interest, and second on how well the indicator performs in modeling applications.     

Response of African humid tropical forests to recent rainfall anomalies

During the last decade, strong negative rainfall anomalies resulting from increased sea surface temperature in the tropical Atlantic have caused extensive droughts in rainforests of western Amazonia, exerting persistent effects on the forest canopy. In contrast, there have been no significant impacts on rainforests of West and Central Africa during the same period, despite large-scale droughts and rainfall anomalies during the same period. Using a combination of rainfall observations from meteorological stations from the Climate Research Unit (CRU; 1950–2009) and satellite observations of the Tropical Rainfall Measuring Mission (TRMM; 1998–2010), we show that West and Central Africa experienced strong negative water deficit (WD) anomalies over the last decade, particularly in 2005, 2006 and 2007. These anomalies were a continuation of an increasing drying trend in the region that started in the 1970s. We monitored the response of forests to extreme rainfall anomalies of the past decade by analysing the microwave scatterometer data from QuickSCAT (1999–2009) sensitive to variations in canopy water content and structure. Unlike in Amazonia, we found no significant impacts of extreme WD events on forests of Central Africa, suggesting potential adaptability of these forests to short-term severe droughts. Only forests near the savanna boundary in West Africa and in fragmented landscapes of the northern Congo Basin responded to extreme droughts with widespread canopy disturbance that lasted only during the period of WD. Time-series analyses of CRU and TRMM data show most regions in Central and West Africa experience seasonal or decadal extreme WDs (less than −600 mm). We hypothesize that the long-term historical extreme WDs with gradual drying trends in the 1970s have increased the adaptability of humid tropical forests in Africa to droughts.     

Validation of species–climate impact models under climate change

Increasing concern over the implications of climate change for biodiversity has led to the use of species–climate envelope models to project species extinction risk under climate‐change scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present‐day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding‐bird species over the past ～20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision‐planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species–climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.     

The impact of rainfall magnitude on the performance of digital soil mapping over low-relief areas using a land surface dynamic feedback method

Previous studies have demonstrated that the pattern of land surface dynamic feedbacks (LSDF) based on remote sensing images after a rainfall event can be used to derive environmental covariates to assist in predicting soil texture variation over low-relief areas. However, the impact of the rainfall magnitude on the performance of these covariates has not been thoroughly investigated. The objective of this study was to investigate this impact during ten observation periods following rainfall events of different magnitudes (0–40 mm). An individual predictive soil mapping method (iPSM) was used to predict soil texture over space based on the environmental covariates derived from land surface dynamic feedbacks. The prediction error showed strong negative correlation with rainfall magnitude (Pearson’s r between root-mean squared error of prediction and rainfall magnitude = −0.943 for percentage of sand and −0.883 for percentage of clay). When the rainfall reaches a certain magnitude, the prediction error becomes stable. The recommended rain magnitude (threshold) using LSDF method in this study area is larger than 20 mm for both sand and clay percentage. The predictive maps based on different observed periods with similar rainfall magnitudes show only slight differences. Rainfall magnitude can thus be said to have a significant impact on the prediction accuracy of soil texture mapping. Greater rainfall magnitude will improve the prediction accuracy when using the LSDF. And high wind speed, high evaporation and low relative humidity during the observed periods also improved the prediction accuracy, all by stimulating differential soil drying.     

Predicting Extreme Droughts in Savannah Africa: A Comparison of Proxy and Direct Measures in Detecting Biomass Fluctuations,Trends and Their Causes

We monitored pasture biomass on 20 permanent plots over 35 years to gauge the reliability of rainfall and NDVI as proxy measures of forage shortfalls in a savannah ecosystem. Both proxies are reliable indicators of pasture biomass at the onset of dry periods but fail to predict shortfalls in prolonged dry spells. In contrast, grazing pressure predicts pasture deficits with a high degree of accuracy. Large herbivores play a primary role in determining the severity of pasture deficits and variation across habitats. Grazing pressure also explains oscillations in plant biomass unrelated to rainfall. Plant biomass has declined steadily and biomass per unit of rainfall has fallen by a third, corresponding to a doubling in grazing intensity over the study period. The rising probability of forage deficits fits local pastoral perceptions of an increasing frequency of extreme shortfalls. The decline in forage is linked to sedentarization, range loss and herbivore compression into drought refuges, rather than climate change. The results show that the decline in rangeland productivity and increasing frequency of pasture shortfalls can be ameliorated by better husbandry practices and reinforces the need for ground monitoring to complement remote sensing in forecasting pasture shortfalls.     

流域水碳过程耦合模拟——WaSSI-C模型的率定与检验

在复杂的气候变化条件下, 利用水碳耦合模型进行生态水文学研究成为主要的研究手段和途径。该文以杂谷脑河上游流域为例, 在确定生态水文模型WaSSI-C模拟尺度的基础上, 探讨水碳耦合模型在中国西南湿润地区的适用性。杂古脑河上游流域位于岷江上游, 隶属于长江流域。在分析和讨论了模型结构和机理的基础上, 分别对模型蒸散和融雪计算进行了补充改进, 以提高模型的适用性。将1988-1996年作为模型的率定期, 1997-2006年作为模型的验证期, 分别在率定期和验证期利用实测的径流数据和中分辨率成像光谱仪数据的总初级生产力、蒸散(ET)数据, 对模拟结果进行对比验证。并利用决定系数(R²)和Nash-Sutcliffe效率系数(NS)两个指标对模拟效果进行评价。流域总径流率定期和验证期对比验证的R²分别为0.86和0.78; NS分别为0.82和0.67。总生态系统生产力和ET验证期的R²分别为0.89和0.78。可见模型模拟结果的两个评价指标都处于较为理想的区间内, 说明WaSSI-C模型在研究区内具有较好的适用性。并对模型的蒸散计算方法进行了讨论, 在此基础上提出了模型中存在的问题和改进的方向。     

A study on the relationship between Atlantic sea surface temperature and Amazonian greenness

The growth of tropical rainforest in Amazon is critically vulnerable to the change in rainfall and radiation than in temperature, and that amount of rainfall and cloudiness in the northeast region of South American is strongly affected by the Atlantic sea surface temperature (SST). Results from recent model experiments for future climate projection have indicated a reduction of Amazonian greenness by a weakening of tropical vapor circulation system related with the change in SST. Therefore, the observational investigation of the relations between the Amazon greenness and Atlantic SST is fundamental to understand the response of Amazonian tropical forest to climate change. In this study, the effect of Atlantic SST on the spatial and temporal change of the Normalized Difference Vegetation Index (NDVI) in the Amazonian region is examined by using satellite remote sensing data for the period of 1981–2001. A strong correlation between NDVI and SST is found for certain regions in Amazon during the periods of 1980s and 1990s, respectively. In addition, strong correlations with NDVI lagging behind SST for two months and one year, respectively, are also identified from the interannual December-to-February (rain season) variations during 1981–2001. Despite these findings, the mechanisms behind the identified correlation remain unclear. Further analyses using observed precipitation and radiation data are required to understand the potential changes of Amazonian rainforest in the context of global warming.     

基于多源遥感数据的大豆叶面积指数估测精度对比

近年来遥感技术的革新促使遥感源越来越丰富.为分析多源遥感数据的叶面积指数(LAI)估测精度,本文以大豆为研究对象,利用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合地面实测LAI构建经验回归模型,比较3类遥感数据(地面高光谱数据、无人机多光谱影像以及高分一号WFV影像)对大豆LAI的估测能力,并从传感器几何位置和光谱响应特性以及像元空间分辨率三方面分析讨论了3类遥感数据的LAI反演差异.结果表明： 地面高光谱数据模型和无人机多光谱数据模型都可以准确预测大豆LAI(在α=0.01显著水平下,R²均>0.69,RMSE均<0.40);地面高光谱RVI对数模型的LAI预测能力优于无人机多光谱NDVI线性模型,但两者差异不大(E_A相差0.3%,R²相差0.04,RMSE相差0.006);高分一号WFV数据模型对研究区内大豆LAI的预测效果不理想(R²<0.30,RMSE>0.70).针对星、机、地三类遥感信息源,地面高光谱数据在反演LAI方面较传统多光谱数据有优势但不突出;16 m空间分辨率的高分一号WFV影像无法满足田块尺度作物长势监测的需求;在保证获得高精度大豆LAI预测值和高工作效率的前提条件下,基于无人机遥感的农情信息获取技术不失为一种最佳试验方案.在当今可用遥感信息源越来越多的情况下,农业无人机遥感信息可成为指导田块精细尺度作物管理的重要依据,为精准农业研究提供更科学准确的信息.     

Meteorological influences on algal bloom potential in a nutrient-rich blackwater river

1. The effect of variability in rainfall on the potential for algal blooms was examined for the St Johns River in northeast Florida. Water chemistry and phytoplankton data were collected at selected sites monthly from 1993 through 2003. Information on rainfall and estimates of water turnover rates were used in the analyses of trends in phytoplankton biomass.
2. Major trends in rainfall and runoff within the lower St Johns River catchment over the 10-year study period were marked by both significant drought and flood periods. Autumn and winter rainfall patterns were strongly correlated with the range of Pacific sea surface temperature anomalies associated with El Niño events and La Niña periods. The effect of these major shifts in rainfall was evident in the strong relationship to replacement rates for water within the lower St Johns River.
3. The eutrophic status of the river was reflected in the high concentrations of nitrogen and phosphorus observed at all sampling sites, with total nitrogen concentrations up to 3100  μ g L⁻¹ and total phosphorus concentrations up to 180  μ g L⁻¹.
4. While it is clear that the high phytoplankton biomass and frequent blooms that characterize the freshwater portions of the lower St Johns River are fundamentally based on nutrient status, the expression of that potential was strongly correlated to water replacement rates, as revealed by the inverse relationship between phytoplankton biovolume increase and water turnover rate, with an R ² of 0.80 for the major bloom season. The sensitivity of algal blooms to rainfall patterns over the 10-year study period suggest that longer-term temporal and spatial shifts in rainfall, such as multi-decadal cycles and the global-warming phenomenon, will also influence the frequency and intensity of algal blooms.     

中国陆地植被净初级生产力遥感估算

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。     

An Environmental Data Set for Vector-Borne Disease Modeling and Epidemiology

Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases. Low- and middle-income countries bear a significant portion of the disease burden; but data about weather conditions in those countries can be sparse and difficult to reconstruct. Here, we describe methods to assemble high-resolution gridded time series data sets of air temperature, relative humidity, land temperature, and rainfall for such areas; and we test these methods on the island of Madagascar. Air temperature and relative humidity were constructed using statistical interpolation of weather station measurements; the resulting median 95^th percentile absolute errors were 2.75°C and 16.6%. Missing pixels from the MODIS11 remote sensing land temperature product were estimated using Fourier decomposition and time-series analysis; thus providing an alternative to the 8-day and 30-day aggregated products. The RFE 2.0 remote sensing rainfall estimator was characterized by comparing it with multiple interpolated rainfall products, and we observed significant differences in temporal and spatial heterogeneity relevant to vector-borne disease modeling.     

Fourteen months of seed rain in three Australian semi‐arid communities

The abundance and composition of seed rain was measured over 14 months (February 2004 to March 2005) in Currawinya National Park, western Queensland. The experimental design included four measurement periods, three vegetation communities and two grazing regimes. A total of 12 586 seeds from 104 species were captured. There were significantly more seeds and species captured during the measurement period with the least rainfall, although no significant correlation was found between the amount of rainfall and the number of seeds or species captured. More seeds and species were captured where native and feral grazing pressure was removed, but this was only significant for the number of species. The above‐ground vegetation showed no significant difference between grazing treatments over the study period and exhibited far fewer species than the seed rain. However, the majority of species found in the above‐ground vegetation were represented in the seed rain. Hypotheses are explored as an attempt to understand the apparent lack of a relationship between seed rain and rainfall. The effects of grazing and seed movement and storage are also discussed.     

表观反射率及其在植被遥感中的应用

 由于植被遥感应用定量化和监测等的需求,光学遥感数据的辐射校正更加受到重视。该文论述了辐射校正,辐射定标和大气校正的概念以及它们之间的区别及关系。特别对辐射定标的结果之一,大气层顶表观反射率,简称表观反射率(Apparent reflectance)的定义、概念、计算和它在植被遥感中的应用等方面,进行了详细的论述。     

Predicting species distributions across the Amazonian and Andean regions using remote sensing data

Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1‐km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian–Andean elevational gradients. Within remote sensing subsets, microwave‐based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large‐scale land‐use change.     

多卫星降水产品在环渤海地区的精度评价

降水是全球能量和水循环中的重要变量之一,降水资料的精度对洪旱灾害的监测与预报、水资源管理等至关重要.本文利用环渤海地区1998—2014年55个气象站点的逐日降水数据,分别从日、月尺度对TRMM 3B42、CMORPH、PERSIANN 3种卫星降水产品进行精度评价,并对其季节差异进行分析.结果表明: 总体上,TRMM 3B42 V7的精度最高,PERSIANN精度最低,但CMORPH对日降水事件的探测成功率(0.69)最高;3B42略高估算了日降水量,其他两者则对日降水量存在低估.春、夏、秋季各卫星降水产品对降水的成功探测率高于冬季,且相关系数、均方根误差等指标随季节变化明显.3种卫星降水产品的月尺度估算精度优于日尺度,其中对日降水量的估算精度较低,特别是明显低估了暴雨事件的实际降水量.本研究为卫星降水产品在环渤海地区降水估算及其在气候与水文模拟和预测中的应用提供了依据和支持.     

Population subdivision in Siamese mud carp Henicorhynchus siamensis in the Mekong River basin: implications for management

A molecular approach was employed to investigate stock structure in Siamese mud carp Henicorhynchus siamensis populations collected from 14 sites across mainland south-east Asia, with the major focus being the lower Mekong River basin. Spatial analysis of a mitochondrial DNA fragment (ATP ase 6 and 8) identified four stocks in the Mekong River basin that were all significantly differentiated from a population in the nearby Khlong River, Thailand. In the Mekong River basin, populations in northern Lao People's Democratic Republic and northern Thailand represent two independent stocks, and samples from Thai tributaries group with those from adjacent Mekong sites above the Khone Falls to form a third stock. All sites below the Khone Falls constituted a single vast stock that includes Cambodia and the Mekong Delta in Vietnam. While H. siamensis is considered currently to undertake extensive annual migrations across the Mekong River basin, the data presented here suggest that natural gene flow may occur over much more restricted geographical scales within the basin, and hence populations may need to be managed at finer spatial scales than at the whole-of-drainage-basin level.