Satellite Precipitation Measurements for Water Resource Monitoring1

Abstract: Satellites offer an unrivaled vantage point to observe and measure Earth system processes and parameters. Observations of meteorological phenomena permit a more holistic view of the weather and climate that is not possible through conventional surface observations. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable: conventional gauge and radar measurements tend to be land‐based with variable coverage. This paper provides an overview of the satellite systems that provide the observations, the techniques used to derive precipitation from the observations, and examples of the precipitation products available for users to access.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

微波消毒技术在医疗废物处理中的应用

本文通过对微波技术的调查,全面总结了医疗废物微波消毒系统的机理、设备组成、影响消毒效果的主要因素、设备的优越性和人体保护注意事项等。     

CLASSIFYING STORM RUNOFF POTENTIAL WITH PASSIVE MICROWAVE MEASUREMENTS1

ABSTRACT: The average microwave temperature of the watershed surface as detected by an airborne Passive Microwave Imaging Scanner (PMIS) was compared with the measured Soil Conservation Service (SCS) watershed storm runoff coefficient (CN). Previous laboratory work suggested that microwave response to the watershed surface is influenced by some of the same surface characteristics that affect runoff, i.e., soil moisture, surface roughness, vegetative cover, and soil texture. In order to field test and develop relations between runoff potentfal and microwave response, several highly instrumented watersheds of approximately 1.5 to 17 km2 were scanned under wet- and dry-soil conditions in April and June 1973. The polarized (horizontal and vertical) scans at 2.8 cm wavelength provided the data base from which other values were calculated. The best relationship between runoff coefficients (CN) and PMIS temperatures was observed when horizontally polarized temperatures from the near-dormant, early-growing season flight were used. Lower SCS runoff coefficients seem to be correlated with the cross-polarized response under dry watershed conditions late in the growing season and the difference in horizontal polarized response between wet conditions early in the growing season and dry conditions late in the growing season. To apply the results, the relationships need to be verified further.     

Tracking Cholera in Coastal Regions Using Satellite Observations1

Jutla, Antarpreet S., Ali S. Akanda, and Shafiqul Islam, 2010. Tracking Cholera in Coastal Regions Using Satellite Observations. Journal of the American Water Resources Association (JAWRA) 46(4): 651-662. DOI: 10.1111/j.1752-1688.2010.00448.x Abstract: Cholera remains a significant health threat across the globe. The pattern and magnitude of the seven global pandemics suggest that cholera outbreaks primarily originate in coastal regions and then spread inland through secondary means. Cholera bacteria show strong association with plankton abundance in coastal ecosystems. This review study investigates the relationship(s) between cholera incidence and coastal processes and explores utility of using remote sensing data to track coastal plankton blooms, using chlorophyll as a surrogate variable for plankton abundance, and subsequent cholera outbreaks. Most studies over the last several decades have primarily focused on the microbiological and epidemiological understanding of cholera outbreaks. Accurate identification and mechanistic understanding of large scale climatic, geophysical, and oceanic processes governing cholera-chlorophyll relationship is important for developing cholera prediction models. Development of a holistic understanding of these processes requires long and reliable chlorophyll datasets, which are beginning to be available through satellites. We have presented a schematic pathway and a modeling framework that relate cholera with various hydroclimatic and oceanic variables for understanding disease dynamics using the latest advances in remote sensing. Satellite data, with its unprecedented spatial and temporal coverage, have potentials to monitor coastal processes and track cholera outbreaks in endemic regions.     

DEVELOPMENT AND TESTING OF A SNOWMELT-RUNOFF FORECASTING TECHNIQUE1

ABSTRACT: The snowmelt-runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April-September) for ten years on the Rio Grande Basin (3419 km²) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi-forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six-month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi-tation data, SRM produced a forecast hydrograph that had a r²= 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R²= 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

The Potential Use of Synthetic Aperture Radar for Estimating Methane Ebullition From Arctic Lakes1

Abstract: Arctic lakes are significant emitters of methane (CH₄), a potent greenhouse gas, to the atmosphere; yet no rigorous quantification of the magnitude and variability of pan‐Arctic lake emissions exists. In this study, we demonstrate the potential for a new method using synthetic aperture radar (SAR) imagery to detect methane bubbles in lake ice to scale up whole‐lake measurements of CH₄ ebullition (bubbling) to regional scales. We estimated ebullition from lakes, which is often the dominant mode of lake emissions, by mapping the distribution of bubble clusters frozen in early winter ice across surfaces of seven tundra lakes and one boreal forest lake in Alaska. Applying previously measured ebullition rates associated with four distinct classes of bubble clusters found in lake ice, we estimated whole‐lake emissions from individual lakes. The percent surface area of lake ice covered with bubbles (R² = 0.68) and CH₄ ebullition rates from lakes (R² = 0.59) and were correlated with radar return values from RADARSAT‐1 Standard Beam mode 3 for the tundra lakes, suggesting that with appropriate scaling and consideration for variability in lake‐ice conditions, this technique has the potential to be used for estimating broader‐scale regional and pan‐Arctic lake methane emissions.     

气候遥感解译法在区域气候研究中的应用

运用遥感手段进行气候资源普查和生态气候类型的划分,是现代应用气候的一种新的研究方法,也是遥感技术应用的一个新领域。研究实践证明,遥感技术对于下垫面气候研究具有独特的功能,特别是对地方气候分析提供了新的内容和手段。本文以内蒙古兴安盟科右中旗和突泉县为列,对气候遥感解译法进行初步分析和概述,以促进该研究法在区域气候研究中更广泛地推广和应用。     

EFFECT OF SIMULATED CLIMATE CHANGE ON SNOWMELT RUNOFF MODELING IN SELECTED BASINS1

ABSTRACT: The projected increase in the concentration of CO₂ and other greenhouse gases in the atmosphere is likely to result in a global temperature increase. This paper reports on the probable effects of a temperature increase and changes in transpiration on basin discharge in two different mountain snowmelt regions of the western United States. The hydrological effects of the climate changes are modeled with a relatively simple conceptual, semi-distributed snowmelt runoff model. Based on the model results, it may be concluded that increased air temperatures will result in a shift of snowmelt runoff to earlier in the snowmelt season. Furthermore, it is shown that it is very important to include the expected change in climate-related basin conditions resulting from the modeled temperature increase in the runoff simulation. The effect of adapting the model parameters to reflect the changed basin conditions resulted in a further shift of streamflow to April and an even more significant decrease of snowmelt runoff in June and July. If the air temperatures increase by approximately 5°C and precipitation and accumulated snow amounts remain about the same, runoff in April and May, averaged for the two basins, is expected to increase by 185 percent and 26 percent, respectively. The runoff in June and July will decrease by about 60 percent each month. Overall, the total seasonal runoff decreases by about 6 percent. If increased CO₂ concentrations further change basin conditions by reducing transpiration by the maximum amounts reported in the literature, then, combined with the 5°C temperature increase, the April, May, June, and July changes would average +230 percent, +40 percent, ?55 percent, and ?45 percent, respectively. The total seasonal runoff change would be +11 percent.     

环境模拟试验室人工降雪装置的研制

采用喷水雾的方式在空调器环境模拟试验室内实现人工降雪功能。对造雪方法、喷头选用、供水系统、供水/供气压力以及管道防冻等方面进行了研制和试验。试验结果表明人工降雪装置可实现稳定连续降雪,降雪洒落分布均匀、积雪蓬松类似自然雪,达到预期效果。     

CBERS-1在国土资源与环境遥感调查中的应用

利用CBBRS－1／CCD中国、巴西地球资源卫星遥感信息源，解决了长期困扰新疆遥感研究的基础信息源问题，并且多专题、多层次、多角度地进行了信息源的处理、信息提取和专题分析，编制了一系列资源环境专题图件，提供了一整套包括土地利用、水资源、森林资源、草地资源、地质矿产资源、以及土地荒漠化等在内的遥感调查数据，为新疆在新的形势下，进行土地规划、生态环境建设、区域经济发展等工作，提供了重要的科学支撑和技术保障。     

Estimating the Variability and Confidence of Land Use and Imperviousness Relationships at a Regional Scale1

Abstract: Impervious cover is a commonly used metric to help explain or predict anthropogenic impacts on aquatic resources; often it is used as a surrogate for intensity of human impacts when evaluating effects on aquatic resources. The most common way to estimate imperviousness is based on relationships with land use. Few studies have evaluated how the relationship between impervious surface and land use varies among geographies with different levels of development and between types of imagery used to assign land use type. In this study, we assess variability in estimates of imperviousness based on two locally available land use datasets: one based on aerial imagery (2‐m resolution) and another based on satellite imagery (30‐m resolution). The ranges and variability in imperviousness within land use categories were assessed at several spatial scales, including within counties, between counties, and between watersheds. Results indicate that there was considerable variability for all developed land use types. Estimated impervious cover often varied over a range of 20‐40% points within a land use category. Furthermore, there were clear spatial patterns both between and within counties, with impervious cover for a given land use type being higher near the urban centers and lower at the margins of development. Estimates of imperviousness for 12 study watersheds indicated that variability increased with increasing watershed development, making it difficult to confidently set management or regulatory targets based on impervious cover. This study suggests that locally derived, high resolution satellite or aerial imagery should be used to estimate imperviousness when a high level of accuracy and precision is required for regulatory or management decisions. Furthermore, the error associated with impervious land use relationships should be accounted for when using impervious cover in runoff or water quality models, or when making management decisions regarding stream health.     

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.     

A Calibrated,High‐Resolution GOES Satellite Solar Insolation Product for a Climatology of Florida Evapotranspiration1

Paech, Simon J., John R. Mecikalski, David M. Sumner, Chandra S. Pathak, Quinlong Wu, Shafiqul Islam, and Taiye Sangoyomi, 2009. A Calibrated, High‐Resolution GOES Satellite Solar Insolation Product for a Climatology of Florida Evapotranspiration. Journal of the American Water Resources Association (JAWRA) 45(6):1328‐1342. Abstract: Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10‐year period (1995‐2004). These insolation estimates were developed into well‐calibrated half‐hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2‐week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground‐based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three‐step process: (1) comparison with ground‐based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station‐averaged model error of 2.2 MJ m^?2/day (13%). Calibration reduced errors to 1.7 MJ m^?2/day (10%), and also removed temporal‐related, seasonal‐related, and satellite sensor‐related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2‐km resolution maps of estimated daily reference and potential evapotranspiration for water management‐related activities.     

微波辐射在烟秆废料制造活性炭中的应用

文章介绍了微波辐射烟秆废料制造活性炭技术,其中包括氯化锌法和水蒸气法,并采用该技术进行了试验,取得了较好的效果。     

遥感技术在小城镇发展规划中的应用

对卫星遥感技术在小城镇发展规划中的应用进行了分析，包括小城镇空间布局信息分析、小城镇区位分析和小城镇环境污染分析，介绍了遥感技术在小城镇变化监测中的应用。分析表明，遥感技术能为小城镇发展规划提供众多基础数据，具有广阔的应用前景，但不能完全取代地面调查。     

遥感技术在矿区生态环境评价中的应用

利用遥感技术对矿区生态环境进行评价是一种新兴的、快速有效的方法.选取肥城矿区为研究区,利用Landsat ETM+遥感影像,通过计算机自动计算与手工屏幕矢量化相结合的方式进行遥感信息提取,统计出肥城矿区各土地利用类型的面积和河流长度.根据统计信息计算各类生态指数,对肥城矿区进行生态环境评价.评价结果显示,肥城矿区生态环境等级为良,基本适合人类生存.     

遥感在荒地资源调查及开发利用中的应用

利用先进的遥感技术对荒地资源进行调查,可以直接、准确地圈定荒地的范围;确定荒地的土壤性质,这一手段不仅精度高,而且省时省力。另外,本文根据遥感解译,还提出了荒地资源开发利用的构想,并针对性地提出了相应的生产模式。     

Trace Gas Emissions from Biomass Burning from Northeast Region in India—Estimates from Satellite Remote Sensing Data and GIS

Biomass burning associated with shifting cultivation areas from the northeastern region of India is an important source of trace gas emissions in the Southeast Asian region. In the present study, satellite data pertaining to IRS-P4 OCM data and DMSP-OLS has been used to quantify the intensity, areal extent and amount of biomass burnt in the northeastern region states at district level. Trace gas emissions have been quantified both by using IPCC based emission ratios and ground based emission ratios obtained from field based studies. Areal estimates with respect to shifting cultivation areas from IRS-P4 OCM satellite data of 4th April 2000 suggested nearly 112.99 km² of the northeastern region of India affected due to shifting cultivation. In the study, DMSP OLS nighttime data has been used to capture the real time fires during the dry season. The results suggested high amount of fires during the March season when compared to April and May. Using the emission ratios obtained from the ground-based studies and IPCC emission ratios, the emissions for the individual non-CO₂ trace gases have been computed in a GIS framework using the biomass data, combustion factors and emission ratios. Results suggested emissions of 2.063 Mt CH₄, 17.94 Mt CO, 1.419 Mt N₂O, and 51.28 Mt NO _x and 2.643 Mt release of CH₄, 3.7204 Mt CO, 0.145 Mt N₂O, and 8.477 Mt NO _x , respectively, from biomass burning due to shifting cultivation for the year 2000, from the northeastern region in India. The study highlights the importance of Satellite Remote sensing data and GIS in quantifying the trace gas emissions from biomass burning.