首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.  相似文献   

2.
The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

3.
Glacierised basins are significant sources of sediments generated by glacial retreat. Estimation of suspended sediment transfer from glacierised basins is very important in reservoir planning for hydropower projects in Himalaya. The present study indicates that storage and release of sediment in proglacial streams may categorise the pattern of suspended sediment transfer from these basins. Assessment of suspended sediment concentration (SSC), suspended sediment load (SSL) and yield has been undertaken for Dunagiri Glacier basin located in Garhwal Himalaya (30o33'20”N, 79o53'36”E), and its results are compared with the Gangotri and Dokriani glaciers sharing close proximity. Out of the total drainage basin area, about 14.3 % of the area is glacierised. Data were collected for five ablation seasons (1984–1989, barring 1986). The mean daily SSCs for July, August and September were 333.9, 286.0 and 147.15 mg/l, respectively, indicating highest concentration of mean daily suspended sediment in July followed by August. SSL trends were estimated to be 93.0, 57.0 and 21.3 tonnes. About 59% of the total SSL of the melt period was transported during the months of August and September. Sediment yield for the study basin was computed to be 296.3 t km?2 yr ?1. It is observed that the cumulative proportion of SSC precedes the discharge throughout the melt season except in the year 1987. Release of SSL in terms of total load is less in the early part of melt season than in the later stage as compared to that of discharge. Diurnal variations in SSC reach their maximum at 2400 h, and therefore, SSC was found to be high during night (2000–0400 h). There was a good relationship between SSC and SSL with discharge for the ablation seasons (1988 and 1989). Mean monthly SSC and mean monthly SSL provide a good exponentional relationship with mean monthly temperature. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

4.
《水文科学杂志》2013,58(6):1270-1285
Abstract

The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN.  相似文献   

5.
《水文科学杂志》2013,58(1):236-252
Abstract

Suspended sediments are a natural component of aquatic ecosystems, but when present in high concentrations they can become a threat to aquatic life and can carry large amounts of pollutants. Suspended sediment concentration (SSC) is therefore an important abiotic variable used to quantify water quality and habitat availability for some species of fish and invertebrates. This study is an attempt to quantify and predict annual extreme events of SSC using frequency analysis methods. Time series of daily suspended sediment concentrations in 208 rivers in North America were analysed to provide a large-scale frequency analysis study of annual maximum concentrations. Seasonality and the correlation of discharges and annual peak of suspended sediment concentration were also analysed. Peak concentrations usually occur in spring and summer. A significant correlation between extreme SSC and associated discharge was detected only in half of the stations. Probability distributions were fitted to station data recorded at the stations to estimate the return period for a specific concentration, or the concentration for a given return period. Selection criteria such as the Akaike and Bayesian information criterion were used to select the best statistical distribution in each case. For each selected distribution, the most appropriate parameter estimation method was used. The most commonly used distributions were exponential, lognormal, Weibull and Gamma. These four distributions were used for 90% of stations.  相似文献   

6.
In high elevation cold regions of the Tibetan Plateau, suspended sediment transfer from glacier meltwater erosion is one of the important hydrological components. The Zhadang glacier is a typical valley‐type glacier in the Nyainqentanglha Mountains on the Tibetan Plateau. To make frequent and long period records of meltwater runoff and sediment processes in the very high elevation and isolated regions, an automatic system was installed near the glacier snout (5400 m a.s.l) in August 2013, to measure the transient discharge and sediment processes at 5‐min interval, which is shorter than the time span for the water flow to traverse the catchment from the farthest end to the watershed outlet. Diurnal variations of discharge, and suspended sediment concentration (SSC) were recorded at high frequency for the Zhadang glacier, before suspended sediment load (SSL) was computed. Hourly SSC varied from the range of 0.2 kg/m3 to 0.5 kg/m3 (at 8:00–9:00) to the range of 2.0 kg/m3 to 4.0 kg/m3 (at 17:00–18:00). The daily SSL was 32.24 t during the intense ablation period. Hourly SSC was linearly correlated with discharge (r = 0.885**, n = 18, p < 0.01). A digit‐eight hysteresis loop was observed for the sediment transport in the glacier area. Air temperature fluctuations influence discharge, and then result in the sediment variations. The results of this study provide insight into the responses of suspended sediment delivery processes with a high frequency data in the high elevation cold regions. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

7.
For sediment yield estimation, intermittent measurements of suspended sediment concentration (SSC) have to be interpolated to derive a continuous sedigraph. Traditionally, sediment rating curves (SRCs) based on univariate linear regression of discharge and SSC (or the logarithms thereof) are used but alternative approaches (e.g. fuzzy logic, artificial neural networks, etc.) exist. This paper presents a comparison of the applicability of traditional SRCs, generalized linear models (GLMs) and non‐parametric regression using Random Forests (RF) and Quantile Regression Forests (QRF) applied to a dataset of SSC obtained for four subcatchments (0·08, 41, 145 and 445 km2) in the Central Spanish Pyrenees. The observed SSCs are highly variable and range over six orders of magnitude. For these data, traditional SRCs performed inadequately due to the over‐simplification of relating SSC solely to discharge. Instead, the multitude of acting processes required more flexibility to model these nonlinear relationships. Thus, alternative advanced machine learning techniques that have been successfully applied in other disciplines were tested. GLMs provide the option of including other relevant process variables (e.g. rainfall intensities and temporal information) but require the selection of the most appropriate predictors. For the given datasets, the investigated variable selection methods produced inconsistent results. All proposed GLMs showed an inferior performance, whereas RF and QRF proved to be very robust and performed favourably for reproducing sediment dynamics. QRF additionally provides estimates on the accuracy of the predictions and thus allows the assessment of uncertainties in the estimated sediment yield that is not commonly found in other methods. The capabilities of RF and QRF concerning the interpretation of predictor effects are also outlined. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

8.
Remote sensing was used to understand the seasonal and spatial variation of suspended sediment in the Ganges and Brahmaputra Rivers in Bangladesh for two different discharge periods. Suspended sediment concentration (SSC) in these rivers was estimated from the reflectance of Landsat TM band 3. During the high discharge period, SSC in the Ganges is higher than that in the Brahmaputra. But in the low discharge period, this is reversed. Both erosional and depositional processes are active on their flood plains. Significant fluctuations in SSC and in suspended sediment load were observed along their courses because of river bank erosion and deposition and/or scouring and aggradation of river beds. Owing to scouring and turbulence, SSC increases markedly at the confluence of these rivers. Reflectance of AVHRR band 1 data was also analysed to study the distribution of suspended sediment along other reaches of these rivers. Like SSC, reflectance at the confluence zone increases compared with that in the Brahmaputra. However, this increase is not marked compared with the Ganges. The influence of their tributaries on the suspended sediment load could be inferred from the pattern of reflectance. Remote sensing data used in this study was corrected for atmospheric effects. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

9.
ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.  相似文献   

10.
Glaciers are major agents of erosion that increase sediment load to the downstream fluvial system. The Castle Creek Glacier, British Columbia, Canada, has retreated ~1.0 km in the past 70 years. Suspended sediment concentration (SSC) and streamflow (Q) were monitored independently at five sites within its pro‐glacial zone over a 60 day period from July to September 2011, representing part of the ablation season. Meteorological data were collected from two automatic weather stations proximal to the glacier. The time‐series were divided into hydrologic days and the shape and magnitude of the SSC response to hydro‐meteorological conditions (‘cold and wet’, ‘hot and dry’, ‘warm and damp’, and ‘storm’) were categorized using principal component analysis (PCA) and cluster analysis (CA). Suspended sediment load (SSL) was computed and summarized for the categories. The distribution of monitoring sites and results of the multivariate statistical analyses describe the temporal and spatial variability of suspended sediment flux and the relative importance of glacial and para‐glacial sediment sources in the pro‐glacial zone. During the 2011 study period, ~ 60% of the total SSL was derived from the glacial stream and sediment deposits proximal to the terminus of the glacier; during ‘storm’ events, that contribution dropped to ~40% as the contribution from diffuse and point sources of sediment throughout the pro‐glacial zone and within the meltwater channels increased. While ‘storm’ events accounted for just 3% of the study period, SSL was ~600% higher than the average over the monitoring period, and ~20% of the total SSL was generated in that time. Determining how hydro‐meteorological conditions and sediment sources control sediment fluxes will assist attempts to predict how pro‐glacial zones respond to future climate changes. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

11.
The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.  相似文献   

12.
ABSTRACT

Suspended solids are present in every river, but high quantities can worsen the ecological conditions of streams; therefore, effective monitoring and analysis of this hydrological variable are necessary. Frequency, seasonality, inter-correlation, extreme events, trends and lag analyses were carried out for peaks of suspended sediment concentration (SSC) and discharge (Q) data from Slovenian streams using officially monitored data from 1955 to 2006 that were made available by the Slovenian Environment Agency. In total more than 500 station-years of daily Q and SSC data were used. No uniform (positive or negative) trend was found in the SSC series; however, all the statistically significant trends were decreasing. No generalization is possible for the best fit distribution function. A seasonality analysis showed that most of the SSC peaks occurred in the summer (short-term intense convective precipitation produced by thunderstorms) and in the autumn (prolonged frontal precipitation). Correlations between Q and SSC values were generally relatively small (Pearson correlation coefficient values from 0.05 to 0.59), which means that the often applied Q–SSC curves should be used with caution when estimating annual suspended sediment loads. On average, flood peak Q occurred after the corresponding SSC peak (clockwise-positive hysteresis loops), but the average lag time was rather small (less than 1 day).
Editor M.C. Acreman; Associate editor Y. Gyasi-Agyei  相似文献   

13.
The collapse of soil pipes due to internal erosion can result in fully mature gullies. Few studies have measured the rates of sediment detachment and transport through soil pipes in situ. The objectives of this work were to determine suspended sediment concentration (SSC) in soil pipes as a function of pipeflow rate to develop sediment rating curves (SRC) and measure the bedload transport as a function of cumulative flow per storm event. H-flumes were installed in seven discontinuous gullies formed by pipe collapse and instrumented for pipe discharge measurements and suspended sediment sampling. The typical response to pipeflow was an initial flush of high concentration of suspended sediment followed by a decrease as pipeflow increased (rising limb of hydrograph). Pipeflows were often so dynamic that it was difficult to consistently capture the initial flush of sediment, resulting in weak to non-existent SRCs. The falling limb of the hydrograph tended to have a relatively low SSC. Thus, soil pipe SRCs tended to be better represented by hysteretic SRCs, although relationships between SSC and flow rate were poorly represented by SRCs. A power law equation given by SSC = aQb was adopted to represent the SRC relationships. Fitting this equation to data showed a correlation between the offset, a, and the slope, b, with the slope decreasing as the offset increases. Both SRC parameters (a and b) were correlated to the contributing area of the individual pipe. Bedload appeared to be an important contributor to sediment transport, with bedload – expressed as an average event sediment concentration (mg l−1) – decreasing as the volume of the event discharge (m3) increased. A significant portion (11–31%) of the bedload material was gravel and aggregates (>2 mm diameter material). While this work was the first to determine SRCs for soil pipes, refined sampling and measurement techniques are needed. © 2020 John Wiley & Sons, Ltd.  相似文献   

14.
Sediment rating curves are commonly used to estimate the suspended sediment load in rivers and streams under the assumption of a constant relation between discharge (Q) and suspended sediment concentrations (SSC) over time. However, temporal variation in the sediment supply of a watershed results in shifts in this relation by increasing variability and by introducing nonlinearities in the form of hysteresis or a path‐dependent relation. In this study, we used a mixed‐effects linear model to estimate an average SSC–Q relation for different periods of time within the hydrologic cycle while accounting for seasonality and hysteresis. We tested the performance of the mixed‐effects model against the standard rating curve, represented by a generalized least squares regression, by comparing observed and predicted sediment loads for a test case on the Chilliwack River, British Columbia, Canada. In our analyses, the mixed‐effects model reflected more accurate patterns of interpolated SSC from Q data than the rating curve, especially for the low‐flow summer months when the SSC–Q relation is less clear. Akaike information criterion scores were lower for the mixed‐effects model than for the standard model, and the mixed‐effects model explained nearly twice as much variance as the standard model (52% vs 27%). The improved performance was achieved by accounting for variability in the SSC–Q relation within each month and across years for the same month using fixed and random effects, respectively, a characteristic disregarded in the sediment rating curve. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

15.
Suspended sediment concentration (SSC) is a critical parameter in the study of river sediment transport and water quality variation, but traditional measurement methods are costly and time‐consuming. This paper is focused on presenting a methodology that may be useful in estimating SSC which is of key importance in process geomorphology and hydrology. In previous studies, remote sensing has been applied to estimate the SSC of sea waters as well as low turbid inland waters like lakes, reservoirs and short river reaches visible within a single Landsat satellite image coverage. Rivers, especially highly turbid large rivers, have largely been ignored. The dataset used in this paper includes measured SSC and multi‐temporal Landsat ETM+ images covering most part of the Yangtze River. Using an effective easy‐to‐use atmospheric correction method that does not require in situ atmospheric conditions, retrieved water reflectance of Band 4 was found to be a good SSC indicator within the large SSC range 22–2610 mg l–1. The newly developed regression relation between SSC and water reflectance of Band 4 appears to be able to provide a relatively accurate SSC estimate directly from Landsat ETM+ images for the Yangtze River from the upper, the middle to the lower reaches. With the relation it is possible to estimate or map out SSC dynamics of large rivers which lack SSC data due to constraints of conventional measurements. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

16.
Abstract The suspended sediment load in the middle Yellow River basin (YRB) cannot be well predicted by capacity‐based transport formulas because a large fraction of suspended sediment load is composed of wash load. This study evaluated the spatial variations of sediment rating curves (SRCs) in the middle YRB. Both power and linear SRCs were used to fit daily flow and suspended sediment concentration (SSC) historical data at 49 gauging stations throughout the middle YRB. The spatial variation in regression coefficients was investigated, and the relationship between regression coefficients and the physical characteristics of watersheds was discussed. The results indicate that SRC regression coefficients vary with drainage area and basin slope, but their responses to these parameters are remarkably different in watersheds with different underlying surfaces, which indicates the significance of sediment availability, erodibility, and grain size distribution. For power SRCs representing sediment transport in unsaturated flows, the regression coefficients are more closely correlated with the drainage area in loess regions and with the basin slope in rock mountain regions. For linear SRCs representing sediment transport in saturated flows, saturated SSCs vary with coarse (particle size > 0.05 mm) and fine (particle size < 0.01 mm) fractions in suspended sediment. The maximum saturated SSC among the different gauging stations is associated with the optimal grain size composition of suspended sediment, which has been proposed for loess regions in previous studies. This study provides theoretical support for estimating the regression parameters for sediment transport modelling, especially in ungauged basins.  相似文献   

17.
近岸Ⅱ类水体表层悬浮泥沙浓度遥感模式研究进展   总被引：13，自引：0，他引：13       下载免费PDF全文
因为具有明显的时间与空间分辨率优势,遥感数据成为近岸Ⅱ类水体悬浮泥沙浓度(SSC)信息提取研究的重要数据源之一.悬浮泥沙遥感信息提取的现状可归纳为:(1)建立近岸Ⅱ类水体SSC遥感模式的方法有三种类型,分别是基于地面光谱与SSC测量的反射率反演方法、基于图像信息法和基于大气辐射传输理论模型法;(2)基于地面测量的反射率反演方法属于理论与经验相结合的方法,也是目前用于SSC定量化遥感模式研究的常用方法.其数学表达形式包括线性关系式、对数关系式、负指数关系式、Gordon模式和综合模式等;(3)到目前为止已有的Ⅱ类水体SSC遥感模式适用性方面还不理想,远未达到与试验室分析相匹配的精度.文章认为:加强地面水文光谱实验研究,建立多光谱SSC定量模式,以高分辨率和高光谱遥感融合数据为基础的SSC定量遥感是今后该方向发展趋势.  相似文献   

18.
Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.  相似文献   

19.
This study presents time‐varying suspended sediment‐discharge rating curves to model suspended‐sediment concentrations (SSCs) under alternative climate scenarios. The proposed models account for hysteresis at multiple time scales, with particular attention given to systematic shifts in sediment transport following large floods (long‐term hysteresis). A series of nested formulations are tested to evaluate the elements embedded in the proposed models in a case study watershed that supplies drinking water to New York City. To maximize available data for model development, a dynamic regression model is used to estimate SSC based on denser records of turbidity, where the parameters of this regression are allowed to vary over time to account for potential changes in the turbidity‐SSC relationship. After validating the proposed rating curves, we compare simulations of SSC among a subset of models in a climate change impact assessment using an ensemble of flow simulations generated using a stochastic weather generator and hydrologic model. We also examine SSC estimates under synthetic floods generated using a peaks‐over‐threshold model. Our results indicate that estimates of extreme SSC under new climate and hydrologic scenarios can vary widely depending on the selected model and may be significantly underestimated if long‐term hysteresis is ignored when simulating impacts under sequences of large storm event. Based on the climate change scenarios explored here, average annual maximum SSC could increase by as much as 2.45 times over historical values.  相似文献   

20.
Arthur J. Horowitz 《水文研究》2003,17(17):3387-3409
In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating‐curve method were made using data from long‐term, daily sediment‐measuring sites within large (>1 000 000 km2), medium (<1 000 000 to >1000 km2), and small (<1000 km2) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors. The sediment rating‐curve method tends to underpredict high, and overpredict low SSCs. As such, the range of errors associated with concomitant flux estimates for relatively short time‐frames (e.g. daily, weekly) are likely to be substantially larger than those associated with longer time‐frames (e.g. quarterly, annually) because the over‐ and underpredictions do not have sufficient time to balance each other. Hence, when error limits must be kept under ±20%, temporal resolution probably should be limited to quarterly or greater. The evaluations indicate that over periods of 20 or more years, errors of <1% can be achieved using a single sediment rating curve based on data spanning the entire period. However, somewhat better estimates for the entire period, and markedly better annual estimates within the period, can be obtained if individual annual sediment rating curves are used instead. Relatively accurate (errors <±20%) annual suspended sediment fluxes can be obtained from hydrologically based monthly measurements/samples. For 5‐year periods or longer, similar results can be obtained from measurements/samples collected once every 2 months. In either case, hydrologically based sampling, as opposed to calendar‐based sampling is likely to limit the magnitude of flux estimation errors. Published in 2003 John Wiley & Sons, Ltd.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号