2D Optimized Electrode Arrays for Borehole Resistivity Tomography and CO2 Sequestration Modelling

Modern optimization approaches for electrode configurations can significantly improve the resolution of 2.5D resistivity imaging surveys. This study presents a brief review of the 2.5D optimization approach, particularly for borehole–borehole surveys with applications for mapping virtual CO₂ plumes sequestrated in deep saline reservoir formations. The applied algorithm searches for arrays that maximize the spatial resolution of the survey among the comprehensive dataset of best possible spatial resolution (i.e. least temporal resolution). A main goal of this study is to increase the temporal resolution of ERT borehole–borehole surveys by selecting optimized electrode configurations in order to minimise the required data acquisition time while sustaining a high spatial resolution. The optimized dataset starts with a base set and is iteratively increased based on the model resolution matrix (R ) until the required number of data points is achieved. Among four different optimization methods, the compare R (CR) method of the best resolution is applied to directly calculate R for each new array added to the optimized dataset. Small optimized datasets generated by this technique are only <5% of their comprehensive sets but of an average resolution ratio (R _r) of >0.95 (i.e. almost the same resolution). With increasing the size of the optimized dataset (during its generation), the algorithm progressively enhances R _r values in the central interwell region (of low sensitivities and low resolution) far higher than in the near borehole region (of high sensitivities). Also the inverted tomogram reliability increases by increasing the optimized data size. Briefly, the optimized arrays improve the resolution in the interwell region which is commonly low in borehole–borehole ERT studies. The inverted output model is evaluated quantitatively using the model difference relative to the input model. The results reflect the common smearing effects and artefacts of varying degrees that overpredict volumes, underpredict magnitudes and blur boundaries of the target anomalies. This input model is a synthetic resistivity model that was used to generate synthetic (forward solution) data used during the inversion. Applications on synthetic CO₂ models show that the mapping resolution for optimized datasets is better than that for other highly resolving arrays of the same number of data points. Problems of smeared boundaries and thin layers are less visible in the optimized array than in the other highly resolving arrays.     

Using structure‐from‐motion to create glacier DEMs and orthoimagery from historical terrestrial and oblique aerial imagery

Increased resolution and availability of remote sensing products, and advancements in small‐scale aerial drone systems, allows observations of glacial changes at unprecedented levels of detail. Software developments, such as structure‐from‐motion (SfM), now allow users an easy and efficient method to generate three‐dimensional (3D) models and orthoimages from aerial or terrestrial datasets. While these advancements show promise for current and future glacier monitoring, many regions still suffer a lack of observations from earlier time periods. We report on the use of SfM to extract spatial information from various historic imagery sources. We focus on three geographic regions, the European Alps, high Arctic Norway and the Nepal Himalayas. We used terrestrial field photographs from 1896, high oblique aerial photographs from 1936 and aerial handheld photographs from 1978 to generate digital elevation models (DEMs) and orthophotos of the Rhone glacier, Brøggerhalvøya and the lower Khumbu glacier, respectively. Our analysis shows that applying SfM to historic imagery can generate high quality models using only ground control points. Limited camera/orientation information was largely reproduced using self‐calibrated model data. Using these data, we calculated mean ground sampling distances across each site which demonstrates the high potential resolution of resulting models. Vertical errors for our models are ±5.4 m, ±5.2 m and ±3.3 m. Differencing shows similar patterns of thinning at lower Rhone (European Alps) and Brøggerhalvøya (Norway) glaciers, which have mean thinning rates of 0.31 m a^?1 (1896–2010) to 0.86 m a^?1 (1936–2010) respectively. On these clean ice glaciers thinning is highest in the terminus region and decreasing up‐glacier. In contrast to these glaciers, uneven topography, exposed ice‐cliffs and debris cover on the Khumbu glacier create a highly variable spatial distribution of thinning. The mean thinning rate for the Khumbu study area was found to be 0.54 ± 0.9 m a^?1 (1978–2015). Copyright © 2017 John Wiley & Sons, Ltd.     

Factors controlling spatial and seasonal distributions of precipitation δ18O in China

Utilising datasets from the Global Network of Isotopes in Precipitation of the International Atomic Energy Agency, and previous isotopic studies, we investigated δ¹⁸O spatial and temporal patterns in Chinese precipitation. Significantly positive relationships existed between precipitation δ¹⁸O and air temperature above the north of 35°N and in high altitude regions above 32°N. Significantly negative relationships between precipitation δ¹⁸O and the precipitation amount existed below south of 35°N. These temperature and precipitation effects became stronger with increasing altitude except in high altitude regions between 32°N and 35°N. The NCEP/NCAR reanalysis data from 1980 to 2004 showed that variations in spatial and seasonal wind fields at 700 hpa and total precipitable water from the ground to the top of the atmosphere were correlated with the monthly spatial distribution of precipitation δ¹⁸O. Basing on this relationship, we established quantitative correlations between the mean monthly precipitation δ¹⁸O and both latitude and temperature in different seasons. We found that spatial variations in precipitation δ¹⁸O could be described well using the Bowen–Wilkinson model and second‐order equations developed during the present study only in winter (from December to February). During the rest of the year, patterns were too complex to predict using simple models. The results suggest that it is difficult to demonstrate variations of precipitation δ¹⁸O throughout the year and for all regions of China using a single model. Moreover, the new models for the relationships among precipitation, latitude, and temperature were better able to depict the variations in precipitation δ¹⁸O than the Bowen–Wilkinson model. Copyright © 2011 John Wiley & Sons, Ltd.     

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R² values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.     

Variability of the spatial structure of intense Mediterranean precipitation

Intense Mediterranean precipitation can generate devastating flash floods. A better understanding of the spatial structure of intense rainfall is critical to better identify catchments that will produce strong hydrological responses. We focus on two intense Mediterranean rain events of different types that occured in 2002. Radar and rain gauge measurements are combined to have a data set with a high spatial (1 × 1 km²) and temporal (5 min) resolution. Two thresholds are determined using the quantiles of the rain rate values, corresponding to the precipitating system at large and to the intense rain cells. A method based on indicator variograms associated with the thresholds is proposed in order to automatically quantify the spatial structure at each time step during the entire rain events. Therefore, its variability within intense rain events can be investigated. The spatial structure is found to be homogeneous over periods that can be related to the dynamics of the events. Moreover, a decreasing time resolution (i.e., increasing accumulation period) of the rain rate data will stretch the spatial structure because of the advection of rain cells by the wind. These quantitative characteristics of the spatial structure of intense Mediterranean rainfall will be useful to improve our understanding of the dynamics of flash floods.     

Detection and extraction of orientation-and-scale-dependent information from two-dimensional GPR data with tuneable directional wavelet filters

The Ground Probing Radar (GPR) is a valuable tool for near surface geological, geotechnical, engineering, environmental, archaeological and other work. GPR images of the subsurface frequently contain geometric information (constant or variable-dip reflections) from various structures such as bedding, cracks, fractures, etc. Such features are frequently the target of the survey; however, they are usually not good reflectors and they are highly localized in time and in space. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is very sensitive to broadband noise from buried small objects, electromagnetic anthropogenic activity and systemic factors, which frequently blurs the reflections from such targets.This paper introduces a method to de-noise GPR data and extract geometric information from scale-and-dip dependent structural features, based on one-dimensional B-Spline Wavelets, two-dimensional directional B-Spline Wavelet (BSW) Filters and two-dimensional Gabor Filters. A directional BSW Filter is built by sidewise arranging s identical one-dimensional wavelets of length L, tapering the s-parallel direction (span) with a suitable window function and rotating the resulting matrix to the desired orientation. The length L of the wavelet defines the temporal and spatial scale to be isolated and the span determines the length over which to smooth (spatial resolution). The Gabor Filter is generated by multiplying an elliptical Gaussian by a complex plane wave; at any orientation the temporal or spatial scale(s) to be isolated are determined by the wavelength. λ of the plane wave and the spatial resolution by the spatial aspect ratio γ, which specifies the ellipticity of the support of the Gabor function. At any orientation, both types of filter may be tuned at any frequency or spatial wavenumber by varying the length or the wavelength respectively. The filters can be applied directly to two-dimensional radargrams, in which case they abstract information about given scales at given orientations. Alternatively, they can be rotated to different orientations under adaptive control, so that they remain tuned at a given frequency or wavenumber and the resulting images can be stacked in the LS sense, so as to obtain a complete representation of the input data at a given temporal or spatial scale.In addition to isolating geometrical information for further scrutiny, the proposed filtering methods can be used to enhance the S/N ratio in a manner particularly suitable for GPR data, because the frequency response of the filters mimics the frequency characteristics of the source wavelet. Finally, signal attenuation and temporal localization are closely associated: low attenuation interfaces tend to produce reflections rich in high frequencies and fine-scale localization as a function of time. Conversely, high attenuation interfaces will produce reflections rich in low frequencies and broad localization. Accordingly, the temporal localization characteristics of the filters may be exploited to investigate the characteristics of signal propagation (hence material properties). The method is shown to be very effective in extracting fine to coarse scale information from noisy data and is demonstrated with applications to noisy GPR data from archaeometric and geotechnical surveys.     

Spatial distribution and temporal trends of rainfall and erosivity in the Eastern Africa region

Soil erosion by water is one of the main environmental concerns in the drought‐prone Eastern Africa region. Understanding factors such as rainfall and erosivity is therefore of utmost importance for soil erosion risk assessment and soil and water conservation planning. In this study, we evaluated the spatial distribution and temporal trends of rainfall and erosivity for the Eastern Africa region during the period 1981–2016. The precipitation concentration index, seasonality index, and modified Fournier index have been analysed using 5 × 5‐km resolution multisource rainfall product (Climate Hazards Group InfraRed Precipitation with Stations). The mean annual rainfall of the region was 810 mm ranging from less than 300 mm in the lowland areas to over 1,200 mm in the highlands being influenced by orography of the Eastern Africa region. The precipitation concentration index and seasonality index revealed a spatial pattern of rainfall seasonality dependent on latitude, with a more pronounced seasonality as we go far from the equator. The modified Fournier index showed high spatial variability with about 55% of the region subject to high to very high rainfall erosivity. The mean annual R‐factor in the study region was calculated at 3,246 ± 1,895 MJ mm ha^?1 h^?1 yr^?1, implying a potentially high water erosion risk in the region. Moreover, both increasing and decreasing trends of annual rainfall and erosivity were observed but spatial variability of these trends was high. This study offers useful information for better soil erosion prediction as well as can support policy development to achieve sustainable regional environmental planning and management of soil and water resources.     

Qualitative and quantitative applications of LiDAR imagery in fluvial geomorphology

The potential for geomorphological mapping and quantitative calculations of light detection and ranging (LiDAR) data within fluvial geomorphology was studied for two river catchments within Belgium (Dijle and Amblève), which differ in physical settings and floodplain morphology. Two commercial, of‐the‐shelf LiDAR datasets with different specifications (horizontal resolution and vertical accuracy) were available for parts of the floodplains of both catchments. Real‐time kinematic (RTK) Global Positioning System (GPS) data were used as ground truth for error calculations. Qualitative analysis of LiDAR data allowed the identification of former channel patterns, levees, colluvial hillslope and fan deposits. These results were confirmed by field data, topographic surveys and historical maps. The pixel resolution proved to be an important factor in the identification of small landforms: only features with a width equal to or larger than LiDAR resolution can be detected. This poses limits on the usability of regionally available LiDAR data, which often have a horizontal resolution of several metres. The LiDAR data were also used in a quantitative analysis of channel dynamics. In the study area, the width of the Dijle River channel increased 3 m on average between 1969 and 2003. A sediment budget of channel processes for the period 1969–2003 indicated a total river bank erosion of 16·1 10³ m³ and a total within channel deposition of 7·1 10³ m³, resulting in a net river erosion of 9·0 10³ m³ or c. 0·4 Mg year^?1 per metre river length. Sequential LiDAR data can in theory be used to calculate vertical sedimentation rates, as long as there is control on the error of the reference levels used. Copyright © 2008 John Wiley and Sons, Ltd.     

Appropriate temporal resolution of precipitation data for discharge modelling in pre-alpine catchments

Precipitation time series with high temporal resolution are desired for hydrological modelling and flood studies. Yet the choice of an appropriate resolution is not straightforward because the use of too high a temporal resolution increases the data requirements, computational costs and, presumably, associated uncertainty, while performance improvement may be indiscernible. In this study, the effect of averaging hourly precipitation on model performance and associated uncertainty is investigated using two data sources: station network precipitation (SNP) and radar-based precipitation (RBP). From these datasets, time series of different temporal resolutions were generated, and runoff was simulated for 13 pre-alpine catchments with a bucket-type model. Our results revealed that different temporal resolutions were required for an acceptable model performance depending on the catchment size and data source. These were 1–12 h for small (16–59 km²), 3-21 h for medium (60–200 km²), and 24 h for large (200–939 km²) catchments.     

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

Impacts of Urbanization and Station-relocation on Surface Air Temperature Series in Anhui Province, China

Since the 1990s, many meteorological stations in China have passively “entered” cities, which has led to frequent relocation and discontinuity in observational records at many stations. To study the impacts of urbanization on surface air temperature series, 52 meteorological stations in Anhui Province were chosen based firstly on a homogeneity test of the time series, and then their surrounding underlying surfaces during different decades were identified utilizing Landsat Multispectral Scanner images from the 1970s, Landsat Thematic Mapper images from 1980s and 1990s, and Enhanced Thematic Mapper images after 2000, to determine whether or not the station “entered” city, and then these stations were categorized into three groups: urban, suburban, and rural using Landsat-measured land use/land cover (LULC) around the station. Finally, variations in annual mean air temperature (T _mean), maximum air temperature (T _max), and minimum air temperature (T _min) were analyzed in urban-type stations and compared to their surrounding rural-type stations. The results showed that, in Anhui Province over the past two decades, many rural stations experienced urbanization and changed into urban or suburban locations. This process is referred as the “city-entering” phenomena of stations. Consequently, many of the latest stations were relocated and moved to currently rural and suburban areas, which significantly influenced the continuity of observational records and the homogeneity of long-term trends. Based on homogeneous data series, the averaged annual T _mean, T _max, and T _min over Anhui Province increased at a rate of 0.407, 0.383 and 0.432 °C decade^?1 from 1970 to 2008. The strongest effect of urbanization on annual T _mean, T _max, and T _min trends occurred at urban stations, with corresponding contributions of 35.824, 14.286, and 45.161 % to total warming, respectively. This work provides convincing evidences that (1) urban expansion has important impacts on the evaluation of regional climate change, (2) high spatial resolution images of Landsat are very useful for selecting reference climate stations for evaluating the potential urban bias in the surface air temperature data in certain regions of the continents, and (3) meteorological observation adjustments of station-relocation-induced inhomogeneities are essential for the study of regional or global climate change.     

Impacts of human activities on recharge in a multilayered semiarid aquifer (Campo de Cartagena,SE Spain)

The development of intense agriculture in semiarid areas modifies intensity and spatial distribution of groundwater recharge by summing irrigation return flow to limited rainfall infiltration. Environmental tracers provide key information, but their interpretation is complicated by more complex groundwater flow patterns. In multilayered aquifers, the real origin of the groundwater samples is hard to assess because of local mixing processes occurring inside long‐screened boreholes. We use environmental tracers (¹⁴C, ¹³C, ²H, ¹⁸O, ³H) to investigate the long‐term evolution of recharge in the five‐layer Campo de Cartagena aquifer in South‐Eastern Spain, in addition to high‐resolution temperature loggings to identify the depth of origin of groundwater. Despite the complex background, this methodology allowed a reliable interpretation of the geochemistry and provided a better understanding of the groundwater flow patterns. The tritium method did not give good quantitative results because of the high variability of the recharge signal but remained an excellent indicator of recent recharge. Nonetheless, both pre‐anthropization and post‐anthropization recharge regime could be identified and quantified by radiocarbon. Before the development of agriculture, recharge varied from 17 mm.year^‐1 at the mountain ranges to 6 mm.year^‐1 in the plain, whereas the mean annual rainfall is about 300 mm. In response to the increase of agricultural activity, recharge fluxes to the plain were amplified and nowadays reach up to 210 mm.year^‐1 in irrigated areas. These values are strengthened by global water budget and local unsaturated zone studies. Copyright © 2013 John Wiley & Sons, Ltd.     

基于多源卫星数据的小型水体蓝藻水华联合监测——以天津于桥水库为例

近年来水体富营养化呈扩张趋势,蓝藻水华不仅在太湖等大型湖泊频发,水面面积较小的天津于桥水库等也形势严峻,亟需加强卫星遥感监测.但是,以往在太湖等业务化使用非常成功的MODIS等卫星数据(约500 m),由于空间分辨率较低,难以满足小型水体的监测要求;而Landsat-8等空间分辨率较高的卫星数据(30 m),通常重返周期较长,无法满足水华高频监测需求.本文以天津市于桥水库(面积约80 km2)为研究区,针对常用的卫星数据,从空间、时间、光谱范围和数据可获取性共4个方面,评价不同卫星数据蓝藻水华监测能力和算法,同时对不同卫星监测结果一致性进行评估.结果表明:(1)筛选出国产HJ-1A\B CCD、GF-1 WFV和美国Landsat-8 OLI这3种卫星波段合适,空间分辨率较高,适用于桥水库蓝藻水华监测,但考虑到其重返周期较长,建议多星联合观测;(2)各个卫星监测结果与卫星影像目视解译结果基本一致,均方根误差和相对误差均分别控制在0.78 km2和4.9%以内;(3)不同卫星监测结果一致性良好,一致性精度达到99.5%;(4)根据历史影像结果,发现于桥水库2016年水质开始呈富营养化,藻华现象在夏、秋两季最为严重.研究表明,针对小型水面水体蓝藻水华监测,利用较高分辨率数据联合监测,是一种有效的替代策略,今后可在更多小型水域推广.     

Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area

The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be downscaled before being used. This study developed a statistical spatial downscaling scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The downscaled precipitation data were compared with ground observations and exhibited good agreement with r² values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.     

Use of Remotely-Derived Bathymetry for Modelling Biomass in Marine Environments

The paper presents results on the influence of geometric attributes of satellite-derived raster bathymetric data, namely the General Bathymetric Charts of the Oceans, on spatial statistical modelling of marine biomass. In the initial experiment, both the resolution and projection of the raster dataset are taken into account. It was found that, independently of the equal-area projection chosen for the analysis, the calculated areas are very similar, and the differences between them are insignificant. Likewise, any variation in the raster resolution did not change the computed area. Although the differences were shown to be insignificant, for the subsequent analysis we selected the cylindrical equal area projection, as it implies rectangular spatial extent, along with the automatically derived resolution. Then, in the second experiment, we focused on demersal fish biomass data acquired from trawl samples taken from the western parts of ICES Sub-area VII, near the sea floor. The aforementioned investigation into processing bathymetric data allowed us to build various statistical models that account for a relationship between biomass, sea floor topography and geographic location. We fitted a set of generalised additive models and generalised additive mixed models to combinations of trawl data of the roundnose grenadier (Coryphaenoides rupestris) and bathymetry. Using standard statistical techniques—such as analysis of variance, Akaike information criterion, root mean squared error, mean absolute error and cross-validation—we compared the performance of the models and found that depth and latitude may serve as statistically significant explanatory variables for biomass of roundnose grenadier in the study area. However, the results should be interpreted with caution as sampling locations may have an impact on the biomass–depth relationship.     

Block removal and step backwearing as erosion processes on rock shore platforms: a preliminary case study of the chalk shore platforms of south‐east England

Shore platforms frequently exhibit steps or risers facing seaward, landwards or obliquely across‐shore. A combination of soft copy photogrammetry, ortho‐rectification, geo referencing and field measurement of step height are linked in a GIS environment to measure step retreat on chalk shore platforms at sample sites in the south of England over two periods, 1973–2001, 2001–2007. The methods used allow for the identification, delineation and measurement of historic change at high spatial resolution. The results suggest that while erosion of chalk shore platforms by step backwearing is highly variable, it appears to be of similar magnitude to surface downwearing of the same platforms measured by micro‐erosion meters (MEMs) and laser scanning, in a range equivalent to 0·0006 – 0·0050 m y^?1 of surface downwearing. This equates to annual chalk volume loss from the platforms, by the two erosion processes combined, of between 0·0012 m³ m^?2 and 0·0100 m³ m^?2. Results from the more recent years' data suggests that step retreat has variability in both space and time which does not relate solely to climatic variability. The results must be viewed with caution until much larger numbers of measurements have been made of both downwearing and step erosion at higher spatial and temporal resolution. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial patterns of submerged macrophytes and heavy metals in the hypertrophic, contaminated, shallow reservoir Lake Qattieneh/Syria

Our investigation on macrophytes in Lake Qattieneh, a large, shallow reservoir in western Syria, is a first assessment of ecological status of this lake. We studied spatial distribution patterns of aquatic macrophyte vegetation and heavy metal concentrations to determine if they can be used as indicators of point sources of pollution. Industrial and municipal point sources at the lake shore increase nutrient load and contamination by heavy metals. Water analyses revealed high concentrations of some heavy metals at some littoral sites: Ni 88.7 μg L⁻¹, Cr 49.99 μg L⁻¹, Co 14.38 μg L⁻¹, and Cu 11.65 μg L⁻¹. Despite hypertrophic conditions and high heavy metal contamination, we recorded several submerged macrophyte species with heterogeneous spatial distribution patterns. Whereas Potamogeton pectinatus L. dominates in the eastern part of the lake, near industrial point sources, both Myriophyllum spicatum L. and Potamogeton lucens L. form extended patches in the western part of the lake. The shallow, littoral areas near villages are dominated by Ranunculus trichophyllus Chaix in Villars and Ceratophyllum demersum L. The west-east gradient in nutrient and heavy metal concentrations in waters and sediments are reflected by the spatial distribution of submersed species. While the heavy metal concentrations of the water body vary considerably in different seasons, the contents in submersed macrophytes integrate seasonal variations of longer time periods. Spatial distribution and tissue accumulation of littoral macrophyte species reflect the environmental conditions at respective sites such as heavy metal contents in water and sediment.     

基于HJ-CCD和MODIS的吉林省中西部湖泊透明度反演对比

水体透明度能够反映光在水体中的穿透程度,影响水生植被及以光为依赖条件的水生生物的分布,获取透明度的传统方法是采用透明度盘进行测量,但也可以通过遥感方法获得.环境减灾卫星是专门用于环境与灾害监测预报的小卫星星座,影像覆盖范围广,空间、时相分辨率较高,可以为水环境遥感提供较好的数据源.MODIS数据在近岸水体和内陆大型湖泊水环境监测中也有广泛应用,它的时相分辨率也很高,但空间分辨率低.利用HJ-1A卫星CCD数据和MODIS日反射率产品(MOD09GA),以2012年9月吉林省石头口门水库、二龙湖、查干湖、月亮泡等地的实测透明度为基础(实测点数74个,最小值为0.134 m,最大值为1.410 m,平均值为0.488 m),根据灰色关联度选取构建模型的波段组合,建立水体透明度反演模型.HJ1A-CCD数据与MOD09GA数据建立的模型R2分别为0.639和0.894,均方根误差(RMSE)分别为0.248和0.135,模型验证的平均相对误差(MRE)分别为17.1%和9.5%,RMSE分别为0.207和0.089.MODIS数据以其较高的辐射分辨率使模型精度较高,但是HJ数据在应用于透明度小于1 m的水体时精度也较高(MRE=13.5%,RMSE=0.066).HJ-CCD数据在空间分辨率上的优势使其能够获得透明度空间分布的细节信息.比较两者反演得到的湖泊平均透明度,结果较为一致.     

Patuxent Landscape Model. III. Model calibration

Using the LHEM/SME the Patuxent Landscape Model (PLM) was built to simulate fundamental ecological processes in the watershed scale driven by temporal (nutrient loadings, climatic conditions) and spatial (land use patterns) forcings. The model addresses the effects of both the magnitude and spatial patterns of land use change and agricultural practices on hydrology, plant productivity, and nutrient cycling in the landscape. The spatial resolution for the full Patuxent watershed is 1 km², while subwatersheds are analyzed at a 200 × 200 m resolution to allow adequate depiction of the pattern of ecosystems and human settlement on the landscape. The temporal resolution is different for various components of the model. We used a modular, multiscale approach to calibrate and test the model. Model results show good agreement with data.