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).     

Improved SWAT Model Performance With Time-Dynamic Voronoi Tessellation of Climatic Input Data in Southern Africa1

Andersson, Jafet C.M., Alexander J.B. Zehnder, Bernhard Wehrli, and Hong Yang, 2012. Improved SWAT Model Performance with Time-Dynamic Voronoi Tessellation of Climatic Input Data in Southern Africa. Journal of the American Water Resources Association (JAWRA) 48(3): 480-493. DOI: 10.1111/j.1752-1688.2011.00627.x Abstract: In this study, we compared two approaches to obtain climatic time series for the Soil and Water Assessment Tool (SWAT), namely the conventional centroid method and time-dynamic Voronoi tessellation, and assessed the performance of SWAT in simulating discharge and smallholder maize yields in Southern Africa. Climatic time series were estimated with each method. The Voronoi method utilized all available precipitation and temperature data, but the centroid method used only 14.5 and 82.5%, respectively. After centroid processing, sub-basin time series were on average 42 and 63% incomplete, respectively. After Voronoi processing, all time series were complete. SWAT was fed with each climate dataset. Each model setup was independently calibrated and validated against discharge and maize yield. Similar model performance was obtained with both methods for yield. The root mean squared error during calibration was 0.26 and 0.27 t ha⁻¹ for the centroid and Voronoi methods, respectively (p-value: 0.80). However, daily discharge simulations improved significantly with the Voronoi method. The coefficient of determination increased from 0.24 to 0.39 in the calibration period (p-value: 9.6 × 10⁻¹³) and from 0.41 to 0.48 in the validation period (p-value: 3.1 × 10⁻³). The Voronoi method improved the simulation of the river flow regime. The largest improvements were obtained in data scarce situations, at high spatial and temporal resolution, and where the centroid method performed the worst.     

Parameter Uncertainty Reduction for SWAT Using Grace,Streamflow, and Groundwater Table Data for Lower Missouri River Basin1

Abstract: This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10^?2 from previous much underestimated level (<10^?3). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning’s coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.     

Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model1

Harshburger, Brian J., Von P. Walden, Karen S. Humes, Brandon C. Moore, Troy R. Blandford, and Albert Rango, 2012. Generation of Ensemble Streamflow Forecasts Using an Enhanced Version of the Snowmelt Runoff Model. Journal of the American Water Resources Association (JAWRA) 48(4): 643‐655. DOI: 10.1111/j.1752‐1688.2012.00642.x Abstract: As water demand increases in the western United States, so does the need for accurate streamflow forecasts. We describe a method for generating ensemble streamflow forecasts (1‐15 days) using an enhanced version of the snowmelt runoff model (SRM). Forecasts are produced for three snowmelt‐dominated basins in Idaho. Model inputs are derived from meteorological forecasts, snow cover imagery, and surface observations from Snowpack Telemetry stations. The model performed well at lead times up to 7 days, but has significant predictability out to 15 days. The timing of peak flow and the streamflow volume are captured well by the model, but the peak‐flow value is typically low. The model performance was assessed by computing the coefficient of determination (R²), percentage of volume difference (Dv%), and a skill score that quantifies the usefulness of the forecasts relative to climatology. The average R² value for the mean ensemble is >0.8 for all three basins for lead times up to seven days. The Dv% is fairly unbiased (within ±10%) out to seven days in two of the basins, but the model underpredicts Dv% in the third. The average skill scores for all basins are >0.6 for lead times up to seven days, indicating that the ensemble model outperforms climatology. These results validate the usefulness of the ensemble forecasting approach for basins of this type, suggesting that the ensemble version of SRM might be applied successfully to other basins in the Intermountain West.     

Development of a Quantitative Pasture Phosphorus Management Tool Using the SWAT Model1

Abstract: Assessment tools to evaluate phosphorus loss from agricultural lands allow conservation planners to evaluate the impact of management decisions on water quality. Available tools to predict phosphorus loss from agricultural fields are either: (1) qualitative indices with limited applicability to address offsite water quality standards, or (2) models which are prohibitively complex for application by most conservation planners. The purpose of this research was to develop a simple interface for a comprehensive hydrologic/water quality model to allow its usage by farmers and conservation planners. The Pasture Phosphorus Management (PPM) Calculator was developed to predict average annual phosphorus (P) losses from pastures under a variety of field conditions and management options. PPM Calculator is a vastly simplified interface for the Soil and Water Assessment Tool (SWAT) model that requires no knowledge of SWAT by the user. PPM Calculator was validated using 33 months of data on four pasture fields in northwestern Arkansas. This tool has been extensively applied in the Lake Eucha/Spavinaw Basin in northeastern Oklahoma and northwestern Arkansas. PPM Calculator allows conservation planners to take advantage of the predictive capacity of a comprehensive hydrologic water quality model typically reserved for use by hydrologists and engineers. This research demonstrates the applicability of existing water quality models in the development of user friendly P management tools.     

Modeling of Sediment Yield From Anjeni-Gauged Watershed,Ethiopia Using SWAT Model1

Setegn, Shimelis G., Bijan Dargahi, Ragahavan Srinivasan, and Assefa M. Melesse, 2010. Modeling of Sediment Yield From Anjeni-Gauged Watershed, Ethiopia Using SWAT Model. Journal of the American Water Resources Association (JAWRA) 46(3):514-526. DOI: 10.1111/j.1752-1688.2010.00431.x Abstract: The Soil and Water Assessment Tool (SWAT) was tested for prediction of sediment yield in Anjeni-gauged watershed, Ethiopia. Soil erosion and land degradation is a major problem on the Ethiopian highlands. The objectives of this study were to evaluate the performance and applicability of SWAT model in predicting monthly sediment yield and assess the impacts of subbasin delineation and slope discretization on the prediction of sediment yield. Ten years monthly meteorological, flow and sediment data were used for model calibration and validation. The annual average measured sediment yield was 24.6 tonnes/ha. The annual average simulated sediment yield was 27.8 and 29.5 tones/ha for calibration and validation periods, respectively. The study found that the observed values showed good agreement with the simulated sediment yield with Nash-Sutcliffe efficiency (NSE) = 0.81, percent bias (PBIAS) = 28%, RMSE-observations standard deviation ratio (RSR) = 0.23, and coefficient of determination (R²) = 0.86 for calibration and NSE = 0.79, PBIAS = 30%, RSR = 0.29, and R² = 0.84 for validation periods. The model can be used for further analysis of different management scenarios that could help different stakeholders to plan and implement appropriate soil and water conservation strategies.     

Impact of Temporal Resolution of Flow‐Duration Curve on Sediment Load Estimation1

Chen, Li, Rina Schumer, Anna Knust, and William Forsee, 2011. Impact of Temporal Resolution of Flow‐Duration Curve on Sediment Load Estimation. Journal of the American Water Resources Association (JAWRA) 48(1): 145‐155. DOI: 10.1111/j.1752‐1688.2011.00602.x Abstract: Estimates of a channel’s annual sediment transport capacity typically incorporate annualized flow‐duration curves. Average daily flow data, commonly used to develop flow‐duration curves, may not adequately describe sediment‐transporting flows in arid and semiarid ephemeral streams. In this study, we examined impacts of varied temporal resolution flow data on annual sediment load estimation. We derived flow‐duration curves for eight sites in the Southwestern United States based on both 15‐min and daily‐averaged flow data. We then estimated sediment loads for both flow‐duration curves using the Sediment Impact Analysis Method, implemented in HEC‐RAS. When average daily flow is used to generate flow‐duration curves, sediment load estimation is lower by up to an order of magnitude. This trend is generally unaffected by uncertainty associated with sediment particle size or hydraulic roughness. The ratio of sediment loads estimated by 15‐min versus daily‐averaged flow‐duration curves is strongly correlated with channel slope, being greater on steep‐slope channels. Sediment loads estimated by the two types of flow‐duration curves are closely correlated, suggesting possible relationships for improving predictions when high‐temporal resolution data are unavailable. Results also suggest that the largest flow contributes significantly to total sediment load, and thus will greatly impact ephemeral stream geomorphology in arid and semiarid regions.     

Runoff Simulation of the Headwaters of the Yellow River Using The SWAT Model With Three Snowmelt Algorithms1

Abstract: The Soil and Water Assessment Tool (SWAT) model combined with different snowmelt algorithms was evaluated for runoff simulation of an 114,345 km² mountainous river basin (the headwaters of the Yellow River), where snowmelt is a significant process. The three snowmelt algorithms incorporated into SWAT were as follows: (1) the temperature‐index, (2) the temperature‐index plus elevation band, and (3) the energy budget based SNOW17. The SNOW17 is more complex than the temperature‐based snowmelt algorithms, and requires more detailed meteorological and topographical inputs. In order to apply the SNOW17 in the SWAT framework, SWAT was modified to operate at the pixel scale rather than the normal Hydrologic Response Unit scale. The three snowmelt algorithms were evaluated under two parameter scenarios, the default and the calibrated parameters scenarios. Under the default parameters scenario, the parameter values were determined based on a review of the current literature. The purpose of this type of evaluation was to assess the applicability of SWAT in ungauged basins, where there is little observed data available for calibration. Under the calibrated parameters scenario, the parameters were calibrated using an automatic calibration program, the Shuffled Complex Evolution (SCE‐UA). The purpose of this type of evaluation was to assess the applicability of SWAT in gauged basins. Two time periods (1975‐1985 and 1986‐1990) of monthly runoff data were used in this study to evaluate the performance of SWAT with different snowmelt algorithms. Under the default parameters scenario, the SWAT model with complex energy budget based SNOW17 performed the best for both time periods. Under the calibrated parameters scenario, the parameters were calibrated using monthly runoff from 1975‐1985 and validated using monthly runoff from 1986‐1990. After parameter calibration, the performance of SWAT with the three snowmelt algorithms was improved from the default parameters scenario. Further, the SWAT model with temperature‐index plus elevation band performed as well as the SWAT model with SNOW17. The SWAT model with temperature‐index algorithm performed the poorest for both time periods under both scenarios. Therefore, it is suggested that the SNOW17 model be used for modeling ungauged basins; however, for gauged basins, the SNOW17 and simple temperature‐index plus elevation band models could provide almost equally good runoff simulation results.     

Reducing Streamflow Forecast Uncertainty: Application and Qualitative Assessment of the Upper Klamath River Basin,Oregon1

Abstract: The accuracy of streamflow forecasts depends on the uncertainty associated with future weather and the accuracy of the hydrologic model that is used to produce the forecasts. We present a method for streamflow forecasting where hydrologic model parameters are selected based on the climate state. Parameter sets for a hydrologic model are conditioned on an atmospheric pressure index defined using mean November through February (NDJF) 700‐hectoPascal geopotential heights over northwestern North America [Pressure Index from Geopotential heights (PIG)]. The hydrologic model is applied in the Sprague River basin (SRB), a snowmelt‐dominated basin located in the Upper Klamath basin in Oregon. In the SRB, the majority of streamflow occurs during March through May (MAM). Water years (WYs) 1980‐2004 were divided into three groups based on their respective PIG values (high, medium, and low PIG). Low (high) PIG years tend to have higher (lower) than average MAM streamflow. Four parameter sets were calibrated for the SRB, each using a different set of WYs. The initial set used WYs 1995‐2004 and the remaining three used WYs defined as high‐, medium‐, and low‐PIG years. Two sets of March, April, and May streamflow volume forecasts were made using Ensemble Streamflow Prediction (ESP). The first set of ESP simulations used the initial parameter set. Because the PIG is defined using NDJF pressure heights, forecasts starting in March can be made using the PIG parameter set that corresponds with the year being forecasted. The second set of ESP simulations used the parameter set associated with the given PIG year. Comparison of the ESP sets indicates that more accuracy and less variability in volume forecasts may be possible when the ESP is conditioned using the PIG. This is especially true during the high‐PIG years (low‐flow years).     

Effectiveness of Multi‐Site Weather Generator for Hydrological Modeling1

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi‐site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00514.x Abstract: A multi‐site weather generator has been developed using the concept of spatial autocorrelation. The multi‐site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi‐site weather generator compared to a uni‐site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in‐depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi‐site and uni‐site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi‐site weather generator significantly outperformed that using the uni‐site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.     

Impact of Watershed Subdivision and Soil Data Resolution on SWAT Model Calibration and Parameter Uncertainty1

Abstract: Impact of watershed subdivision and soil data resolution on Soil Water Assessment Tool (SWAT) model calibration and parameter uncertainty is investigated by creating 24 different watershed model configurations for two study areas in northern Indiana. SWAT autocalibration tool is used to calibrate 14 parameters for simulating seven years of daily streamflow records. Calibrated parameter sets are found to be different for all 24 watershed configurations, however in terms of calibrated model output, their effect is minimal. In some cases, autocalibration is followed by manual calibration to correct for low flows, which were underestimated during autocalibration. In addition to normal validation using four years of streamflow data for each calibrated parameter set, cross‐validation (using a calibrated parameter set from one model configuration to validate observations using another configuration) is performed to investigate the effect of different model configurations on streamflow prediction. Results show that streamflow output during cross‐validation is not affected, thus highlighting the non‐unique nature of calibrated parameters in hydrologic modeling. Finally, parameter uncertainty is investigated by extracting good parameter sets during the autocalibration process. Parameter uncertainty analysis suggests that significant parameters show very narrow range of uncertainty across different watershed configurations compared with nonsignificant parameters. Results from recalibration of some configurations using only six significant parameters were comparable to that from calibration using 14 parameters, suggesting that including fewer significant parameters could reduce the uncertainty arising from model parameters, and also expedite the calibration process.     

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010 Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.     

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.     

Modeling of Escherichia coli Fluxes on a Catchment and the Impact on Coastal Water and Shellfish Quality1

Bougeard, Morgane, Jean‐Claude Le Saux, Nicolas Pérenne, Claire Baffaut, Marc Robin, and Monique Pommepuy, 2011. Modeling of Escherichia coli Fluxes on a Catchment and the Impact on Coastal Water and Shellfish Quality. Journal of the American Water Resources Association (JAWRA) 1‐17. DOI: 10.1111/j.1752‐1688.2011.00520.x Abstract: The simulation of the impact of Escherichia coli loads from watersheds is of great interest for assessing estuarine water quality, especially in areas with shellfish aquaculture or bathing activities. For this purpose, this study investigates a model association based on the Soil and Water Assessment Tool (SWAT) coupled with a hydrodynamic model (MARS 2D; IFREMER). Application was performed on the catchment and estuary of Daoulas area (France). The daily E. coli fluxes simulated by SWAT are taken as an input in the MARS 2D model to calculate E. coli concentrations in estuarine water and shellfish. Model validation is based on comparison of frequencies: a strong relationship was found between calculated and measured E. coli concentrations for river quality (r² = 0.99) and shellfish quality (r² = 0.89). The important influence of agricultural practices and rainfall events on the rapid and large fluctuations in E. coli fluxes from the watershed (reaching three orders of magnitude in <24 hours) is one main result of the study. Response time in terms of seawater quality degradation ranges from one to two days after any important rainfall event (greater than 10 mm/day) and the time for estuary to recover good water quality also mainly depends on the duration of the rainfall. In the estuary, three effects (rainfall, tidal dilution, and manure spreading) have been identified as important influences.     

浅析攀枝花市2015 ～ 2017年气象要素对空气污染的影响

利用2015～2017年攀枝花市污染因子和气象要素实测数据,运用GIS技术、相关分析以及统计分析等方法,分析攀枝花市空气污染因子和气象要素的时空特征及相关性。结果表明:攀枝花市首要污染物为PM₁₀,其次是NO₂;秋冬以PM₁₀和NO₂污染为主,春夏PM₁₀和O₃为主。不同季节,各气象要素对本地空气质量的影响程度及空间分布存在明显差异。弄弄坪一带PM_2.5和SO₂、CO浓度偏高,市中心炳草岗一带PM₁₀、O₃和NO₂浓度偏高。结合本地发展规划和实际情况,根据气象要素分析,为攀枝花市分区分季节的防污减排决策提供气象参考。     

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.     

遂宁市环境空气中SO2时空分布特征研究

本文采取环境质量监测工作中被动采样法,具有适用性强、地域范围广、使用周期长的特点,按照遂宁市辖区内16km×16km均匀网格布点,开展了为期一年的大气中SO2研究,分析区域内SO2浓度时空变化,以图形化方式表达.研究表明:整个遂宁市辖区范围内SO2浓度整体水平不高;不同时间、不同区域内的SO2浓度差异较大,秋冬高、春夏低;工业集中区浓度较高;受常年主导风向西北风以及污染源整体布局的综合影响,辖区范围内SO2浓度呈现西北低、东南高的趋势.     

上海市空气污染指数API的时空分布特征

空气污染指数(API)是一种综合反应和评价空气质量的指标,分级表征空气污染程度和空气质量状况.利用上海市环境监测中心收集的上海市2006 ～2011年各个区的API指数,对API指数进行时间序列分析和空间分析,包括近6年来API的年变化、季节变化以及比较上海市市区和郊区的API值.结果表明:上海市的空气污染状况在2006～2011年期间得到改善;夏季的空气质量最好,春季最差;空间上存在城郊差异,郊区API指数低于市区,金山区空气质量最好,普陀区和宝山区质量较差.     

Effect of the Spatial Variability of Land Use,Soil Type,and Precipitation on Streamflows in Small Watersheds1

Abstract: The spatial variability of the data used in models includes the spatial discretization of the system into subsystems, the data resolution, and the spatial distribution of hydrologic features and parameters. In this study, we investigate the effect of the spatial distribution of land use, soil type, and precipitation on the simulated flows at the outlet of “small watersheds” (i.e., watersheds with times of concentration shorter than the model computational time step). The Soil and Water Assessment Tool model was used to estimate runoff and hydrographs. Different representations of the spatial data resulted in comparable model performances and even the use of uniform land use and soil type maps, instead of spatially distributed, was not noticeable. It was found that, although spatially distributed data help understand the characteristics of the watershed and provide valuable information to distributed hydrologic models, when the watershed is small, realistic representations of the spatial data do not necessarily improve the model performance. The results obtained from this study provide insights on the relevance of taking into account the spatial distribution of land use, soil type, and precipitation when modeling small watersheds.     

我国非点源污染的基本特征与时空分布规律研究综述

随着点源污染控制的逐步完善,非点源污染对环境造成的危害日益突出,成为了目前我国水质环境恶化的又一重大因素。为有效控制非点源污染,本研究对非点源污染的基本特征进行了概述,分析了非点源污染的研究方法。根据国内学者对非点源污染负荷时空分布的调查、计算及研究成果,总结分析了我国非点源污染负荷时空分布规律的特征,并针对性地提出了非点源污染的控制需从源头减量、过程控制及末端治理等方面进行,从而为非点源污染的预防和治理提供相应的指导。