基于MODIS的黄土高原土地荒漠化动态监测

以2001年和2009年8月MODIS卫星影像为数据源,基于归一化植被指数和像元二分原理,通过建立科学的荒漠化土地分类系统,对黄土高原地区近8 a的荒漠化土地进行了动态变化监测,分析了2个时期荒漠化土地的空间分布特征和面积变化情况。结果表明:黄土高原荒漠化土地面积整体呈明显的减少态势,但类型转化结构表明荒漠化土地强度却处于不断发展阶段。8 a间,极重度荒漠化土地面积增加了16.53 km²,增长率为28.36%,重度和中度荒漠化土地面积均有不同程度减少,分别减少了1.2×10⁴ km²和7.0×10⁴ km²,变化率分别为32.97%和29.19%;分别有9.0×10^4 km²和1.2×10⁴ k²的轻度和潜在荒漠化土地转化为其他类型荒漠化土地,并分别增加了9.3×10³ km²和7.3×10⁴ km²,增长率分别为4.2%和57.3%。发展区面积为1.9×10⁵ km²,稳定区面积为4.0×10⁵ km²,逆转区面积为2.8×10⁴ km²,发展区面积比逆转区面积大1.6×10⁵ km²,表明黄土高原环境质量不断下降,荒漠化强度不断扩张的趋势。     

洪河湿地植被地上生物量遥感反演研究

在对洪河湿地植被地上生物量实地采样调查的基础上,利用准同步的TM数据建立了洪河湿地地上生物量遥感反演模型。主要研究了洪河湿地植被地上生物量的空间分布情况,并结合研究区的DEM分析生物量空间分布特征和高程的相关关系。并分析了不同生物量范围内,生物量与高程之间相关性存在差异的原因。研究表明:多元回归模型与其他模型相比拟合精度最高,决定系数为0.813,是洪河湿地地上生物量估算的精度最优模型;经估算得到2007年洪河湿地地上生物量主要集中分布于600~1 200 g/m²之间,总生物量为2.4856×10⁸g,平均生物量为934.7105 g/m²。通过生物量与DEM的相关分析得到,在生物量值为0~600 g/m²的低生物量分布区域,生物量与高程之间存在较好的相关性,相关系数为0.79839;在生物量为600~1 200 g/m²和1 200 g/m²以上范围内,生物量与高程值之间相关性较弱。     

基于RS和GIS的无锡市陆地生态系统的碳源汇估算

基于IPCC-LULUCF建议的温室气体计量方法、遥感数据和文献资料,利用RS和GIS分析了1991~2005年无锡市土地利用变化所引起的陆地生态系统碳储量的变化,结果表明,近14 a来,无锡市土地利用变化较明显,尤其是各种土地利用类型的相互转化,使得陆地生态系统碳库储量减少457.18×10⁴ t,是碳源。具体表现在：地上碳储量减少145.91×10⁴ t,地下碳储量减少59.87×10⁴ t,土壤碳储量减少251.40×10⁴ t。无锡市各种生态系统类型中,有林地的固碳潜力最大,为190.71 t/hm²,其次是草地,为121.186 t/hm²,农地最小,为92.6 t/hm²,有林地面积的减少,造成有林地中固定的碳大量释放,使无锡市陆地生态系统成为一个明显碳源。     

高分辨率遥感影像GeoEye-1在黑河下游柽柳生物量估算中的应用

以黑河下游绿洲柽柳为研究对象,利用高分辨率遥感影像GeoEye-1柽柳分类结果,基于典型样点生物学特性调查与生物量试验,建立柽柳冠幅面积与生物量关系模型,计算研究区柽柳地上部分的生物量,分析黑河0~2、2~5、5~10与10~15 km不同缓冲带柽柳生物量空间分布规律。结果显示:研究区柽柳总生物量为4.10×10⁵ t,其中:0~2、2~5、5~10与10~15 km缓冲带内柽柳生物量分别为2.34×10⁵、1.07×10⁵、6.35×10⁴和5.17×10³ t。河流距离对柽柳生物量影响显著,单位面积柽柳生物量随着与河流距离的增加而减少,二者相关系数为-0.97。     

中亚地区植被净初级生产力时空动态及其与气候因子关系

植被净初级生产力(Net Primary Productivity,NPP)及其对气候变化的响应是全球变化的核心研究内容之一,研究中亚地区NPP的时空格局变化对理解植被—环境的作用机理以及应对全球变化具有重要的意义。基于MOD17A3数据集、气象数据结合GIS分析方法研究中亚地区2000~2014年的植被NPP时空动态特征及其与气候因子的关系。结果表明：①中亚地区空间上NPP的变化范围在0~874 gC/m²·a之间,平均值为151.90 gC/m²·a,NPP年总量平均值为482.41TgC (1 Tg=10¹² g),NPP平均值与总量均呈现出下降趋势;②中亚地区NPP的高值区主要分布在高纬度地区和东南部高山地区,中部和南部荒漠区则为NPP的低值区;③中亚地区2000~2014年间NPP在空间上总体呈现下降趋势,达到显著下降的区域总体面积的39.89%。NPP呈下降趋势的区域主要集中在哈萨克斯坦的大部分区域,不同分区内以典型草原区最为显著;④中亚地区NPP受降水量的影响作用高于气温,荒漠草原区、典型草原区以及荒漠区主要受到降水量的控制,高山草甸区与高山林地区则受到降水和气温的共同作用。     

时间序列冰面湖水深反演及储水量变化监测

针对缺少实测数据导致格陵兰冰面湖水深反演及精度验证较难开展问题，提出了一种基于交叉比对的冰面湖水深反演及精度评价方法，实现了格陵兰消融期内9期时间序列WorldView影像上的冰面湖水深反演和储水量变化监测。引入北极数字高程模型开展基于形态拟合法的时间序列冰面湖水深反演，基于非线性物理模型拟合参数实现基于物理模型的水深反演，两种方法水深反演结果R²平均可达0.728。9期冰面湖水深反演结果平均相对误差优于20.43%,均方根误差平均优于0.69 m。经冰面湖储水量计算，整个消融期内冰面湖总储水量累计可达5 331.35×10⁴ m³。通过冰面湖面积、最大水深和储水量变化监测，发现了冰面湖的融水输送现象，为研究格陵兰冰盖表面融水的存储、输送和释放机制提供有益参考。     

生态系统模型模拟中国叶面积指数变化趋势及驱动因子的不确定性

为了厘清中国近30 a来植被生长趋势及其对不同环境变化的响应,使用了3套长时间序列遥感叶面积指数（Leaf area index, LAI）数据集以及8套生态系统模型,对LAI变化趋势从总量、空间分布以及不同植被类型进行了分析与归因。总量上,1982~2015年遥感观测的LAI趋势（9.8×10^-3m²/m²·a）高于生态系统模型模拟的趋势（4.2×10^-3m²/m²·a）,大气二氧化碳浓度上升是主要驱动因素（（3.5×10^-3m²/m²·a）;遥感观测到全国79.5%的区域LAI都呈现显著增长的趋势,而生态系统模型模拟LAI的增长面积占比为33.1%;除草地外,生态系统模型低估了其他植被类型的LAI变化趋势。模型对降雨变化的响应过于敏感以及对人为活动模拟能力不足是模型模拟中国LAI变化趋势不确定性的重要来源。本研究定量分析了近30 a中国各种植被变化情况及其驱动因子,并对模型低估中国植被生长进行了解释,为后续中国地区植被相关研究提供了参考。     

卫星微波遥感结合可见光遥感估算黄河源区土壤湿度研究

利用欧洲环境卫星(ENVISAT)搭载的高级合成孔径雷达ASAR(Advanced Synthesis Aperture Radar)交叉极化模式(APP)2009年8月9日和10月6日的数据对青藏高原东北部玛曲地区土壤湿度进行了估算。对于裸土区域采用表层微波后向散射几何光学模型GOM(Geometry Optics Model),对于植被覆盖度大的区域利用“水-云”模型处理植被层对后向散射系数的影响,取得了较好的结果:遥感估算的土壤湿度值和地面实测值之间的均方根误差RMSE<0.05,决定系数R²>0.82,表明该方法适合反演玛曲地区的土壤水分。从遥感估算的总体结果可以看出:山谷和陡峭山坡的反演结果相对较差,而在相对平坦的地区反演结果较好,估算的土壤湿度值在0.20~0.50 m³/m³之间。     

基于遥感反演河套平原区域蒸发蒸腾量研究

采用SEBS模型,结合地面站点观测的温度、湿度、风速、日照时数等气象数据,利用MODIS/TERRA卫星遥感影像,反演河套平原2006年日蒸散发量,对河套平原蒸散发量进行分析研究。并结合彭曼公式计算的理论蒸散发量的年内分布规律进行分析,对河套平原实际蒸散发量进行时间尺度推演,对河套平原区域蒸散发量的时间分布特点进行了分析。     

Sentinel-1和Sentinel-2协同反演地表土壤水分

土壤水分是水文循环、生态环境、气候变化等研究中的关键参数,获取高分辨率长时间序列的土壤水分信息对农业管理、作物生长监测等具有重要的意义,同时也是研究的难点。基于时间序列(2019年至2020年)的Sentinel-1雷达数据和Sentinel-2光学数据,构建了地表土壤水分的雷达与光学数据协同反演模型,即裸土条件下地表土壤水分的变化检测方法,并利用归一化植被指数对植被影响进行校正,实现了青藏高原多年冻土区(五道梁)100 m空间分辨率的土壤水分反演。与地面实际观测的土壤水分进行对比验证,结果表明土壤水分反演结果与地面实测数据的相关系数介于0.672与0.941之间,无偏均方根误差介于0.031 m³/m³与0.073 m³/m³之间,土壤水分变化与区域降水事件和特征密切相关,验证了本文提出的考虑植被物候的变化检测方法在地势平坦、植被稀疏的青藏高原地区具有极高的适用性。     

Loss of terrestrial water storage in the Tianshan mountains from 2003 to 2015

ABSTRACT

The Tianshan Mountains region in Central Asia is covered with a large mass of glaciers and seasonal snow cover. This region supplies the main freshwater resources for Central Asia but has been severely affected by climate change over the past decades. In this study, we use the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets to analyze the spatiotemporal variability of the terrestrial water storage (TWS) in the Tianshan Mountains from 2003 to 2015. The analysis shows that the TWS in the Tianshan Mountains has declined during the past decade. Seasonal changes in the water storage are caused by seasonal differences in the combined precipitation and temperature conditions. The results of TWS variations (TWSVs) in the Tianshan Mountains in 2003–2015 indicated that there is a declining rate of the TWS of the TWS-Mascon, TWS-Gaussian, and TWS-Noah is ?0.72, ?0.48, and ?0.41 cm year^?1, respectively. This suggests that the water storage loss in Tianshan Mountains has been about ?4.32 × 10⁹, ?2.88 × 10⁹, and ?2.46 × 10⁹ m³ year^?1 during 2003–2015, respectively. Glaciers and seasonal snow cover shrinkage obviously are the main factors governing the spatial difference in the TWSV. Annual mean temperature stays in a high state from the mid-1990s has been a predominant factor affecting the TWSV in the mountains during the past decade. A significant temperature increase in the middle region (Chinese part) accelerated the glacier and snow-cover shrinkage, which resulted in TWS loss.     

Impact and consequences of evapotranspiration changes on water resources availability in the arid Zhangye Basin,China

Evapotranspiration (ET) plays an important role in the hydrological cycle and it is essential to estimate ET accurately for the evaluation of available water resources. This is most important in arid and semi‐arid regions. In this paper, the long‐term changes in daily ET in the semi‐arid Zhangye Basin in northwest China and its impact factors were studied. The spatial distribution of ET was assessed by using the Surface Energy Balance System (SEBS). Cloud‐free National Oceanic and Atmospheric Administration Advanced (NOAA) Very High Resolution Radiometer (AVHRR) September images over the Zhangye Basin from 1990 to 2004 were used in combination with SEBS to estimate ET at a spatial resolution of 1.1 km. This daily ET was converted to a monthly ET (for September) using daily pan evaporation values from a meteorological station in the study area. Spatial aggregation of all pixels yielded the total monthly ET for the whole study area. Subsequently, the monthly ET was extrapolated to annual ET values using the pan evaporation data. The results were validated with ground‐based measurements on the water balance for the whole Zhangye Basin. The annual ET increased gradually from 23.7×10⁸ m³ in 1990 to 26.9×10⁸ m³ in 2004 for the Zhangye Basin. The main cause appeared to be change in vegetation.     

Development of a global evapotranspiration algorithm based on MODIS and global meteorology data

The objective of this research is to develop a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.'s [Cleugh, H.A., R. Leuning, Q. Mu, S.W. Running (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment 106, page 285-304- 2007 (doi: 10.1016/j.rse.2006.07.007).] Penman-Monteith based ET (RS-PM). Our algorithm considers both the surface energy partitioning process and environmental constraints on ET. We use ground-based meteorological observations and remote sensing data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to estimate global ET by (1) adding vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) using leaf area index as a scalar for estimating canopy conductance; (3) replacing the Normalized Difference Vegetation Index with the Enhanced Vegetation Index thereby also changing the equation for calculation of the vegetation cover fraction (F_C); and (4) adding a calculation of soil evaporation to the previously proposed RS-PM method.We evaluate our algorithm using ET observations at 19 AmeriFlux eddy covariance flux towers. We calculated ET with both our Revised RS-PM algorithm and the RS-PM algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Results indicate that our Revised RS-PM algorithm substantially reduces the root mean square error (RMSE) of the 8-day latent heat flux (LE) averaged over the 19 towers from 64.6 W/m² (RS-PM algorithm) to 27.3 W/m² (Revised RS-PM) with tower meteorological data, and from 71.9 W/m² to 29.5 W/m² with GMAO meteorological data. The average LE bias of the tower-driven LE estimates to the LE observations changed from 39.9 W/m² to − 5.8 W/m² and from 48.2 W/m² to − 1.3 W/m² driven by GMAO data. The correlation coefficients increased slightly from 0.70 to 0.76 with the use of tower meteorological data. We then apply our Revised RS-PM algorithm to the globe using 0.05° MODIS remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for 2001. As expected, the spatial pattern of the MODIS ET agrees well with that of the MODIS global terrestrial gross and net primary production (MOD17 GPP/NPP), with the highest ET over tropical forests and the lowest ET values in dry areas with short growing seasons. This MODIS ET product provides critical information on the regional and global water cycle and resulting environment changes.     

Remote sensing and GIS for estimation of irrigation crop water demand

Satellite images supported by global positioning systems (GPS) and field visits were used to identify the cropping pattern of a large irrigation scheme in Central Asia. Two methods were used to estimate the crop evapotranspiration (ET). In the first, the ETs of the different crops were calculated from local field climatic data using the Penman–Monteith method of calculating crop water requirements as used in the Food and Agriculture Organization (FAO) CropWat programme. The satellite data were transferred to a geographical information system (GIS) and the area of each crop type was identified. Combining the two sets of data gave an estimate of ET and total evaporative water demand for each crop. ET was also calculated directly from the satellite data using a modified sensible heat flux approach (SEBAL). The Penman–Monteith approach estimated the ET to be 5.7, 3.3, 4.4 and 6.3?mm?d^?1 for cotton, mixed crop, alfalfa and rice respectively, whereas the ET estimated from the satellite data were 4.4, 3, 3.2 and 5.3?mm?d^?1, respectively. The possible causes of these differences are discussed. The FAO Penman–Monteith methodology for estimating crop water requirements is best for planning purposes but the SEBAL approach is potentially more useful for management in that it establishes the amount of water being used by the crop and can help identify where water is being wasted.     

Improvements to a MODIS global terrestrial evapotranspiration algorithm

MODIS global evapotranspiration (ET) products by Mu et al. [Mu, Q., Heinsch, F. A., Zhao, M., Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111, 519-536. doi: 10.1016/j.rse.2007.04.015] are the first regular 1-km² land surface ET dataset for the 109.03 Million km² global vegetated land areas at an 8-day interval. In this study, we have further improved the ET algorithm in Mu et al. (2007a, hereafter called old algorithm) by 1) simplifying the calculation of vegetation cover fraction; 2) calculating ET as the sum of daytime and nighttime components; 3) adding soil heat flux calculation; 4) improving estimates of stomatal conductance, aerodynamic resistance and boundary layer resistance; 5) separating dry canopy surface from the wet; and 6) dividing soil surface into saturated wet surface and moist surface. We compared the improved algorithm with the old one both globally and locally at 46 eddy flux towers. The global annual total ET over the vegetated land surface is 62.8 × 10³ km³, agrees very well with other reported estimates of 65.5 × 10³ km³ over the terrestrial land surface, which is much higher than 45.8 × 10³ km³ estimated with the old algorithm. For ET evaluation at eddy flux towers, the improved algorithm reduces mean absolute bias (MAE) of daily ET from 0.39 mm day⁻¹ to 0.33 mm day⁻¹ driven by tower meteorological data, and from 0.40 mm day⁻¹ to 0.31 mm day⁻¹ driven by GMAO data, a global meteorological reanalysis dataset. MAE values by the improved ET algorithm are 24.6% and 24.1% of the ET measured from towers, within the range (10-30%) of the reported uncertainties in ET measurements, implying an enhanced accuracy of the improved algorithm. Compared to the old algorithm, the improved algorithm increases the skill score with tower-driven ET estimates from 0.50 to 0.55, and from 0.46 to 0.53 with GMAO-driven ET. Based on these results, the improved ET algorithm has a better performance in generating global ET data products, providing critical information on global terrestrial water and energy cycles and environmental changes.     

Regional-scale analysis of carbon and water cycles on managed grassland systems

Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m⁻² yr⁻¹, which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m⁻² yr⁻¹ on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m⁻² yr⁻¹, respectively, in agreement with observed average GPP (1230 g C m⁻² yr⁻¹) and RECO (1046 g C m⁻² yr⁻¹). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was ∼5–16 g C week⁻¹ across the study sites, while the goodness of fit (R²) was ∼0.4–0.9. For evapotranspiration (ET), the average value of simulated ET (415 mm yr⁻¹) for all sites and years is close to the average value of the observed ET (451 mm yr⁻¹) by flux towers (on a weekly basis, RMSE∼2–8 mm week⁻¹; R² = 0.3–0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system.     

Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations

Evapotranspiration (ET) is a major pathway for water loss from many ecosystems, and its seasonal variation affects soil moisture and net ecosystem CO₂ exchange. We developed an algorithm to estimate ET using a semi-empirical Priestley-Taylor (PT) approach, which can be applied at a range of spatial scales. We estimated regional net radiation (R_net) at monthly time scales using MODerate resolution Imaging Spectroradiometer (MODIS) albedo and land surface temperature. Good agreement was found between satellite-based estimates of monthly R_net and field-measured R_net, with a RMSE of less than 30 W m^− 2. An adjustable PT coefficient was parameterized as a function of leaf area index and soil moisture based on observations from 27 AmeriFlux eddy covariance sites. The biome specific optimization using tower-based observations performed well, with a RMSE of 17 W m^− 2 and a correlation of 0.90 for predicted monthly latent heat. We implemented the approach within the hydrology module of the CASA biogeochemical model, and used it to estimate ET at a 1 km spatial resolution for the conterminous United States (CONUS). The RMSE of modeled ET was reduced to 21.1 mm mon^− 1, compared to 27.1 mm mon^− 1 in the original CASA model. The monthly ET rates averaged over the Mississippi River basin were similar to those derived using GRACE satellite measurements and river discharge data. ET varied substantially over the CONUS, with annual mean values of 110 ± 76 mm yr^− 1 in deserts, 391 ± 176 mm yr^− 1 in savannas and grasslands, and 840 ± 234 mm yr^− 1 in broadleaf forests. The PT coefficient was the main driver for the spatial variation of ET in arid areas, whereas R_net controlled ET when mean annual precipitation was higher than approximately 400 mm yr^− 1.     

Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea

Estimating the evapotranspiration (ET) is a requirement for water resource management and agricultural productions to understand the interaction between the land surface and the atmosphere. Most remote-sensing-based ET is estimated from polar orbiting satellites having low frequencies of observation. However, observing the continuous spatio-temporal variation of ET from a geostationary satellite to determine water management usage is essential. In this study, we utilized the revised remote-sensing-based Penman–Monteith (revised RS-PM) model to estimate ET in three different timescales (instantaneous, daily, and monthly). The data from a polar orbiting satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS), and a geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), were collected from April to December 2011 to force the revised RS-PM model. The estimated ET from COMS and MODIS was compared with measured ET obtained from two different flux tower sites having different land surface characteristics in Korea, i.e. Sulma (SMC) with mixed forest and Cheongmi (CFC) with rice paddy as dominant vegetation. Compared with flux tower measurements, the estimated ET on instantaneous and daily timescales from both satellites was highly overestimated at SMC when compared with the flux tower ET (Bias of 41.19–145.10 W m^?2 and RMSE of 69.61–188.78 W m^?2), while estimated ET results were slightly better at the CFC site (Bias of –27.28–13.24 W m^?2 and RMSE of 45.19–71.82 W m^?2, respectively). These errors in results were primarily caused due to the overestimated leaf area index that was obtained from satellite products. Nevertheless, the satellite-based ET indicated reasonable agreement with flux tower ET. Monthly average ET from both satellites showed nearly similar patterns during the entire study periods, except for the summer season. The difference between COMS and MODIS estimations during the summer season was mainly propagated due to the difference in the number of acquired satellite images. This study showed that the higher frequency of COMS than MODIS observations makes it more ideal to continuously monitor ET as a geostationary satellite with high spatio-temporal coverage of a geostationary satellite.     

Application of remote sensing for estimating crop water requirements,yield and water productivity of wheat in the Gezira Scheme

An accurate estimate of the evapotranspiration (ET) and crop water productivity (WP) at the regional scale plays a significant role in managing water-saving irrigation in the dry region. Three sites in the Gezira scheme representing research field (Gezira Research Station, GRS), farmers' fields with participatory water management approach (Abdelhakam block) and ordinary farmers' fields (Madina block) were selected to estimate spatial ET, crop yield and WP of wheat using remote sensing data coupled with ground observations. The methodology is based on surface energy balance to estimate sensible and latent heat fluxes by combining remotely sensed data from Landsat 7 ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) with common meteorological data. A comparison between Surface Energy Balance Algorithm for Land (SEBAL) estimated ET and ET calculated based on reference evapotranspiration (ET_o)-crop coefficient (k _c) approach over the growing season exhibited a good agreement. The average SEBAL-based ET of wheat for the entire season at GRS was 396 mm. SEBAL estimated k _c were 0.46, 1.07 and 0.3 at the initial, mid season and late season stages, respectively, whereas the k _c values determined from water depletion measurements were 0.5, 1.15 and 0.5, respectively. The average crop yield at GRS, Abdelhakam block and Madina block were 2.4, 1.9 and 1.4 t ha^?1, respectively. The mean values of WP for the three locations were 0.52 (coefficient of variation, CV?=?0.31), 0.50 (CV?=?0.33) and 0.43 (CV?=?0.45) kg m^?3, respectively. Achieving a coefficient of variation (CV?=?0.15), water savings at the three locations could reach 21%, 42% and 53%, respectively.     

AVHRR satellite remote sensing and shipboard measurements of the thermal plume from the Daya Bay, nuclear power station, China

The 1800 MW Daya Bay Nuclear Power Station (DNPS), China's first nuclear power station, is located on the coast of the South China Sea. DNPS discharges 29 10×10⁵ m³ year⁻¹ of warm water from its cooling system into Daya Bay, which could have ecological consequences. This study examines satellite sea surface temperature data and shipboard water column measurements from Daya Bay. Field observations of water temperature, salinity, and chlorophyll a data were conducted four times per year at 12 sampling stations in Daya Bay during January 1997 to January 1999. Sea surface temperatures were derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites during November 1997 to February 1999. A total of 2905 images with 1.1×1.1 km resolution were examined; among those images, 342 have sufficient quality for quantitative analysis. The results show a seasonal pattern of thermal plumes in Daya Bay. During the winter months (December to March), the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 °C. During the summer and fall months (May to November), there is a larger thermal plume extending 8-10 km south along the coast from DNPS, and the temperature change is about 1.0 °C. Monthly variation of SST in the thermal plume is analyzed. AVHRR SST is higher in daytime than in nighttime in the bay during the whole year. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Further investigations are needed to determine any other ecological effects of the Daya Bay thermal plume.