Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical‐based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash‐Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three‐day period) with NS values of (0.60‐0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ?16%; NS = 0.38‐0.94; RMSE = 0.07‐0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite‐estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground‐based radar networks (i.e., much of the third world).     

FLOOD INUNDATION IN THE SOUTHEASTERN UNITED STATES FROM AIRCRAFT AND SATELLITE IMAGERY1

ABSTRACT: Panchromatic black and white, color, and color infrared photographs and thermal infrared imagery are compared for a capability to show flood boundaries. In open agricultural and urban areas, these boundaries are easily delineated on all types of am. Boundaries are more difficult to see in wooded areas. In March, hardwood trees are dormant, but black and white photographs and color photographs show only the tops of these trees. Color infrared photographs in January and March have a distinctive color or tone in inundated woods; the limit of this tone is the flood boundary. Daytime thermal infrared imagery in March shows that inundated woods are cooler than dry land but warmer than open water. After about April 1, both color infrared photography and thermal infrared imagery show only the top of the tree canopy and do not reflect underlying flood water. Inundated areas can be delineated easily on ERTS satellite imagery from December through March. On imagery from May 4–5, 1973, however, most inundation boundaries had to be drawn as dashed lines; the tree canopy obscures flood waters in wooded areas. Despite this problem, the results of mapping flood boundaries on May imagery are believed to be reasonable for the scale of the imagery.     

LANDSAT MID-INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL-MOISTURE ASSESSMENT1

ABSTRACT: Landsat satellite Thematic Mapper (TM) data were used to assess regional soil moisture conditions. The mid-infrared (MIR) data of TM band 7 were overlain onto four principal land-use categories (Agricultural/Irrigated, Urban/Clearings, Forest/ Wetlands, Water) using a geographic information system (GIS). M data were used to assess four qualitative surface soil-moisture conditions (water/very wet, wet, moist, and dry) within each land-use category of a 208,354 ha southwestern Florida study area. The MIR response was inversely related to the qualitative surface soil-moisture content. Integration of Landsat TM MIR data with land use through GIS appears to be a useful technique for high-resolution regional soil moisture assessment, and further research to reline this technique is recommended.     

THE RELATIONSHIP BETWEEN SUMMER-SEASON RAINFALL EVENTS AND LAKE-SURFACE AREA1

ABSTRACT: Research is presented that statistically analyzes the relationship between lake area and precipitation. These hydrologic variables are assessed in part using LANDSAT MSS satellite data and digital-image processing techniques. Results show dramatic regional hydrologic differences in lake area fluctuations and in lake area response to short term climatic variation.     

Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States1

Tobin, Kenneth J. and Marvin E. Bennett, 2012. Validation of Satellite Precipitation Adjustment Methodology From Seven Basins in the Continental United States. Journal of the American Water Resources Association (JAWRA) 48(2): 221‐234. DOI: 10.1111/j.1752‐1688.2011.00604.x Abstract: The precipitation science community has expressed concern regarding the ability of satellite‐based precipitation products to accurately capture rainfall values over land. There has been some work that has focused on addressing the deficiencies of satellite precipitation products, particularly on the adjustment of bias. This article outlines a methodology that adjusts satellite products utilizing ground‐based precipitation data. The approach is not a simple bias adjustment, but is a three‐step process that transforms a satellite product based on a ground‐based precipitation product (NEXRAD‐derived Multisensor Precipitation Estimator [MPE] product or rain‐gauge data). The developed methodology was successfully applied to seven moderate‐to‐large sized watersheds from continental United States (CONUS) and northern Mexico over a spectrum of climatic regimes ranging from dry to humid settings. Methodology validation is based on comparison of observed and simulated streamflow generated with SWAT (Soil and Water Assessment Tool) model using unadjusted and adjusted precipitation products as input. Streamflow comparison is based on mass balance error and Nash‐Sutcliffe efficiency coefficient. Finally, the contribution of how adjustment to correct misses, false alarms, and bias impacts adjusted datasets and the potential impact that the adjustment methodology can have on hydrological applications such as water resource monitoring and flood prediction are explored.     

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.     

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.     

“十二五”期间秦皇岛市加强环境应急能力建设的对策

结合秦皇岛市沿海旅游城市的实际情况,指出了环境应急管理中存在的机构不健全、人员不固定、设备和经费缺乏等问题,并从队伍、装备、预案、演习、应急指挥、危险化学品运输等10个方面提出了对策,以期为环境应急管理工作提供参考。     

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.     

浅谈遥感监测水环境

目前，遥感监测能有效解决水环境监测的大范围性、连续性、动态性以及高效性等技术问题。遥感监测越来越受到环保部门的重视。     

基于不同数据源的福建省沿海防护林碳储量对比研究

在全球气候变化背景下,准确估算森林碳储量对评估沿海防护林固碳能力及制定适应性管理策略至关重要。本研究围绕福建省沿海防护林体系建设工程实施区域,利用森林资源清查样地、二类调查图斑及遥感数据,采用生物量法,结合平均含碳系数法,估算了2014年和2022年的森林植被碳密度和碳储量及其变化,并分析了不同数据源的估算差异。研究结果表明：①不同数据源估算结果存在差异,其中2014年的碳密度表现为图斑（ 31.29Mg/hm²） <样地（ 33.34 Mg/hm²） <遥感（ 50.48 Mg/hm²）,以样地数据为参考值,图斑偏低6.64%,而遥感数据偏高51.41%,差异可能源于图斑数据蓄积量测量误差及遥感生物量数据准确性不足; 2014年和2022年的碳储量表现为图斑（ 35.33 Tg和44.93 Tg）低于遥感（ 60.53 Tg和65.84 Tg）,但碳汇量表现为图斑（ 9.60 Tg）高于遥感（ 5.31 Tg）,遥感数据源下生物量密度的误差和森林面积识别偏差是主要影响因素;②从不同森林类型来看,常绿阔叶林和常绿针叶林的面积、碳密度、碳储量均最高,是福建省森林碳汇的主要贡献者;遥感估算的大部分类型森林的碳密度都高于图斑估算结果,再次反映遥感生物量数据可能存在偏差;图斑和遥感数据估算的不同类型森林的碳储量也存在显著差异,这部分差距除了来自上述遥感估算的较高碳密度,还再次印证碳储量的估算差异与不同森林类型的面积识别有关。建议未来建立多源数据融合机制,优化碳汇监测体系,并通过差异化林分管理提升密闭林比例以增强碳汇功能。