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1.
Spatio-temporal variability of pollutants in the environment is a complex phenomenon that requires a combined approach for its analysis. Whereas data on measured levels of contaminants in various environmental compartments is essential, it is not always possible to monitor at the necessary frequency and with the adequate spatial sampling distribution to capture this variability. Therefore a modelling approach able to complement experimental data and close the gaps in the monitoring programs is useful for assessing the contaminant dynamics occurring at different time scales. In this work a 1D water column fate model has been developed and tested for Polycyclic Aromatic Hydrocarbons (PAHs). The model has been coupled with a simple ecological model that includes a bioaccumulation module. Afterwards, the model has been used to study the temporal variability of contaminant concentrations as well as the fluxes between compartments. The results evidence the complex coupling between spatio-temporal scales and its influence on environmental concentration levels.  相似文献   

2.
时晨燚  刘凤  祝铠  张媛  刘海 《湖泊科学》2024,36(3):670-684
饮用水源地藻类增殖监测和预测对于改善水生态系统环境和保护人类健康具有重要意义。利用多源遥感数据能够获取高时空分辨率的藻类动态信息,结合长时序遥感监测与机器学习算法能够适应藻类生长复杂的影响机制和非线性特征,实现藻类增殖风险时空变化的预测。本文利用Landsat与MODIS长时间序列卫星遥感数据,采用FAI与NDVI两种方法提取2000—2020年丹江口水库藻类浓度的时空变化信息,在此基础上分析藻类增殖对气象因子(气温、气压、相对湿度、风速和累计日照时间)的时间滞后效应。利用支持向量机、朴素贝叶斯与随机森林3种机器学习算法预测藻类增殖风险,并对3种算法的预测性能进行评价和对比。结果表明:丹江口水库藻类浓度年际变化呈现出先增后降的趋势,呈现出明显的季节性周期变化,春末夏初是藻类快速增殖时期。空间上入库支流和库湾等局部地区藻类浓度相对较高,为藻类增殖高风险区,丹江口水库藻类增殖风险预测模型能够较为准确地确定藻类增殖高风险区位置并反映短期内的空间变化情况,3种算法的预测结果呈现出整体上的一致性,其中支持向量机与朴素贝叶斯算法表现出更高的精度,提前4～5天是最佳预测时间。  相似文献   

3.
Stream solute monitoring has produced many insights into ecosystem and Earth system functions. Although new sensors have provided novel information about the fine-scale temporal variation of some stream water solutes, we lack adequate sensor technology to gain the same insights for many other solutes. We used two machine learning algorithms – Support Vector Machine and Random Forest – to predict concentrations at 15-min resolution for 10 solutes, of which eight lack specific sensors. The algorithms were trained with data from intensive stream sensing and manual stream sampling (weekly) for four full years in a hydrologic reference stream within the Hubbard Brook Experimental Forest in New Hampshire, USA. The Random Forest algorithm was slightly better at predicting solute concentrations than the Support Vector Machine algorithm (Nash-Sutcliffe efficiencies ranged from 0.35 to 0.78 for Random Forest compared to 0.29 to 0.79 for Support Vector Machine). Solute predictions were most sensitive to the removal of fluorescent dissolved organic matter, pH and specific conductance as independent variables for both algorithms, and least sensitive to dissolved oxygen and turbidity. The predicted concentrations of calcium and monomeric aluminium were used to estimate catchment solute yield, which changed most dramatically for aluminium because it concentrates with stream discharge. These results show great promise for using a combined approach of stream sensing and intensive stream discrete sampling to build information about the high-frequency variation of solutes for which an appropriate sensor or proxy is not available.  相似文献   

4.
The terms ‘downward’ and ‘upward’ (synonymous with ‘top‐down’ and ‘bottom‐up’ respectively) are sometimes used when describing methods for developing hydrological models. A downward approach is used here to develop a lumped catchment‐scale model for subsurface stormflow at the 0·94 km2 Slapton Wood catchment. During the development, as few assumptions as possible are made about the behaviour of subsurface stormflow at the catchment scale, and no assumptions are made about its behaviour at smaller scales. (In an upward approach, in contrast, the modelling would be based on assumptions about, and data for, the behaviour at smaller scales, such as the hillslope, plot, and point scales.) The model has a single store with a relatively simple relationship between discharge and storage, based on equations describing hysteretic patterns seen in a graph of discharge against storage. Double‐peaked hydrographs have been observed at the catchment outlet. Rainfall on the channel and infiltration‐excess and saturation‐excess runoff give a rapid response, and shallow subsurface stormflow gives a delayed response. Hydrographs are successfully simulated for the large delayed responses observed in 1971–1980 and 1989–1991, then a lumped model for the rapid response is coupled to the lumped hysteretic model and some double‐peaked hydrographs simulated. A physical interpretation is developed for the lumped hysteretic model, making use of information on patterns of perched saturation observed in 1982 on a hillslope at the Slapton Wood catchment. Downward and upward approaches are complementary, and the most robust way to develop and improve lumped catchment models is to iterate between downward and upward steps. Possible next steps are described. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

5.
Many researchers have examined the impact of detailed soil spatial information on hydrological modelling due to the fact that such information serves as important input to hydrological modelling, yet is difficult and expensive to obtain. Most research has focused on the effects at single scales; however, the effects in the context of spatial aggregation across different scales are largely missing. This paper examines such effects by comparing the simulated runoffs across scales from watershed models based on two different levels of soil spatial information: the 10‐m‐resolution soil data derived from the Soil‐Land Inference Model (SoLIM) and the 1:24000 scale Soil Survey Geographic (SSURGO) database in the United States. The study was conducted at three different spatial scales: two at different watershed size levels (referred to as full watershed and sub‐basin, respectively) and one at the model minimum simulation unit level. A fully distributed hydrologic model (WetSpa) and a semi‐distributed model (SWAT) were used to assess the effects. The results show that at the minimum simulation unit level the differences in simulated runoff are large, but the differences gradually decrease as the spatial scale of the simulation units increases. For sub‐basins larger than 10 km2 in the study area, stream flows simulated by spatially detailed SoLIM soil data do not significantly vary from those by SSURGO. The effects of spatial scale are shown to correlate with aggregation effect of the watershed routing process. The unique findings of this paper provide an important and unified perspective on the different views reported in the literature concerning how spatial detail of soil data affects watershed modelling. Different views result from different scales at which those studies were conducted. In addition, the findings offer a potentially useful basis for selecting details of soil spatial information appropriate for watershed modelling at a given scale. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

6.
Over the past two decades there have been repeated calls for the collection of new data for use in developing hydrological science. The last few years have begun to bear fruit from the seeds sown by these calls, through increases in the availability and utility of remote sensing data, as well as the execution of campaigns in research catchments aimed at providing new data for advancing hydrological understanding and predictive capability. In this paper we discuss some philosophical considerations related to model complexity, data availability and predictive performance, highlighting the potential of observed patterns in moving the science and practice of catchment hydrology forward. We then review advances that have arisen from recent work on spatial patterns, including in the characterisation of spatial structure and heterogeneity, and the use of patterns for developing, calibrating and testing distributed hydrological models. We illustrate progress via examples using observed patterns of snow cover, runoff occurrence and soil moisture. Methods for the comparison of patterns are presented, illustrating how they can be used to assess hydrologically important characteristics of model performance. These methods include point-to-point comparisons, spatial relationships between errors and landscape parameters, transects, and optimal local alignment. It is argued that the progress made to date augers well for future developments, but there is scope for improvements in several areas. These include better quantitative methods for pattern comparisons, better use of pattern information in data assimilation and modelling, and a call for improved archiving of data from field studies to assist in comparative studies for generalising results and developing fundamental understanding.  相似文献   

7.
Excessive soil erosion and deposition is recognised as a significant land degradation issue. Quantifying soil erosion and deposition is a non-trivial task. One of these methods has been the mathematical modelling of soil erosion and deposition patterns and the processes that drive them. Here we examine the capability of a landscape evolution model to predict both soil erosion rate and pattern of erosion and deposition. This numerical model (SIBERIA) uses a Digital Elevation Model (DEM) to represent the landscape and calculates erosion and deposition at each grid point in the DEM. To assess field soil redistribution rates (SRR) and patterns the distribution of the environmental tracer 137Cs has been analysed. Net hill slope SRR predicted by SIBERIA (a soil loss rate of 1.7 to 4.3 t ha-1 yr-1) were found to be in good agreement with 137Cs based estimates (2.1 – 3.4 t ha-1 yr-1) providing confidence in the predictive ability of the model at the hillslope scale. However some differences in predicted erosion/deposition patterns were noted due to historical changes in landscape form (i.e. the addition of a contour bank) and possible causes discussed, as is the finding that soil erosion rates are an order of magnitude higher than likely soil production rates. The finding that SIBERIA can approximate independently quantified erosion and deposition patterns and rates is encouraging, providing confidence in the employment of DEM based models to quantify hillslope erosion rates and demonstrating the potential to upscale for the prediction of whole catchment erosion and deposition. The findings of this study suggest that LEMs can be a reliable alternative to complex and time consuming methods such as that using environmental tracers for the determination of erosion rates. The model and approach demonstrates a new approach to assessing soil erosion that can be employed elsewhere. © 2018 John Wiley & Sons, Ltd.  相似文献   

8.
This study estimates the environmental Kuznets curve (EKC) relationship at the province level in China. We apply empirical methods to test three industrial pollutants—SO2 emission, wastewater discharge, and solid waste production—in 29 Chinese provinces in 1994–2010. We use the geographically weighted regression (GWR) approach, wherein the model can be fitted at each spatial location in the data, weighting all observations by a function of distance from the regression point. Hence, considering spatial heterogeneity, the EKC relationship can be analyzed region-specifically through this approach, rather than describing the average relationship over the entire area examined. We also investigate the spatial stratified heterogeneity to verify and compare risk factors that affect regional pollution with statistical models. This study finds that the GWR model, aimed at considering spatial heterogeneity, outperforms the OLS model; it is more effective at explaining the relationships between environmental performance and economic growth in China. The results indicate a significant variation in the existence of the EKC relationship. Such spatial patterns suggest province-specific policymaking to achieve balanced growth in those provinces.  相似文献   

9.
In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kernel‐based machine learning approaches have been found to be more popular due to their inherent advantages over traditional modelling techniques including artificial neural networks(ANNs ). In this paper, a kernel‐based learning approach is investigated for its suitability to capture the monthly variation of streamflow time series. Its performance is compared with that of the traditional approaches. Support vector machines (SVMs) are one such kernel‐based algorithm that has given promising results in hydrology and associated areas. In this paper, the application of SVMs to regression problems, known as support vector regression (SVR), is presented to predict the monthly streamflow of the Mahanadi River in the state of Orissa, India. The results obtained are compared against the results derived from the traditional Box–Jenkins approach. While the correlation coefficient between the observed and predicted streamflows was found to be 0·77 in case of SVR, the same for different auto‐regressive integrated moving average (ARIMA) models ranges between 0·67 and 0·69. The superiority of SVR as compared to traditional Box‐Jenkins approach is also explained through the feature space representation. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

10.
This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.  相似文献   

11.
Geomorphology interacts with surface‐ and ground‐water hydrology across multiple spatial scales. Nonetheless, hydrologic and hydrogeologic models are most commonly implemented at a single spatial scale. Using an existing hydrogeologic computer model, we implemented a simple hierarchical approach to modeling surface‐ and ground‐water hydrology in a complex geomorphic setting. We parameterized the model to simulate ground‐ and surface‐water ?ow patterns through a hierarchical, three‐dimensional, quantitative representation of an anabranched montane alluvial ?ood plain (the Nyack Flood Plain, Middle Fork Flathead River, Montana, USA). Comparison of model results to ?eld data showed that the model provided reasonable representations of spatial patterns of aquifer recharge and discharge, temporal patterns of ?ood‐water storage on the ?ood plain, and rates of ground‐water movement from the main river channel into a large lateral spring channel on the ?ood plain, and water table elevation in the alluvial aquifer. These results suggest that a hierarchical approach to modeling ground‐ and surface‐water hydrology can reproduce realistic patterns of surface‐ and ground‐water ?ux on alluvial ?ood plains, and therefore should provide an excellent ‘quantitative laboratory’ for studying complex interactions between geomorphology and hydrology at and across multiple spatial scales. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

12.
The current earthquake forecast algorithms are not free of shortcomings due to inherent limitations. Especially, the requirement of stationarity in the evaluation of earthquake time series as a prerequisite, significantly limits the use of forecast algorithms to areas where stationary data is not available. Another shortcoming of forecast algorithms is the ergodicity assumption, which states that certain characteristics of seismicity are spatially invariant. In this study, a new earthquake forecast approach is introduced for the locations where stationary data are not available. For this purpose, the spatial activity rate density for each spatial unit is evaluated as a parameter of a Markov chain. The temporal pattern is identified by setting the states at certain spatial activity rate densities. By using the transition patterns between the states, 1- and 5-year forecasts were computed. The method is suggested as an alternative and complementary to the existing methods by proposing a solution to the issues of ergodicity and stationarity assumptions at the same time.  相似文献   

13.
Soil erosion is one of the most important environmental problems distributed worldwide. In the last decades, numerous studies have been published on the assessment of soil erosion and the related processes and forms using empirical, conceptual and physically based models. For the prediction of the spatial distribution, more and more sophisticated stochastic modelling approaches have been proposed – especially on smaller spatial scales such as river basins. In this work, we apply a maximum entropy model (MaxEnt) to evaluate badlands (calanchi) and rill–interrill (sheet erosion) areas in the Oltrepo Pavese (Northern Apennines, Italy). The aim of the work is to assess the important environmental predictors that influence calanchi and rill–interrill erosion at the regional scale. We used 13 topographic parameters derived from a 12 m digital elevation model (TanDEM-X) and data on the lithology and land use. Additional information about the vegetation is introduced through the normalized difference vegetation index based on remotely sensed data (ASTER images). The results are presented in the form of susceptibility maps showing the spatial distribution of the occurrence probability for calanchi and rill–interrill erosion. For the validation of the MaxEnt model results, a support vector machine approach was applied. The models show reliable results and highlight several locations of the study area that are potentially prone to future soil erosion. Thus, coping and mitigation strategies may be developed to prevent or fight the soil erosion phenomenon under consideration. © 2020 John Wiley & Sons, Ltd.  相似文献   

14.
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15.
A methodological approach for modelling the occurrence patterns of species for the purpose of fisheries management is proposed here. The presence/absence of the species is modelled with a hierarchical Bayesian spatial model using the geographical and environmental characteristics of each fishing location. Maps of predicted probabilities of presence are generated using Bayesian kriging. Bayesian inference on the parameters and prediction of presence/absence in new locations (Bayesian kriging) are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation ( INLA ) software (which has been seen to be quite a bit faster than the well-known MCMC methods). In particular, the spatial effect has been implemented with the stochastic partial differential equation (SPDE) approach. The methodology is evaluated on Mediterranean horse mackerel (Trachurus mediterraneus) in the Western Mediterranean. The analysis shows that environmental and geographical factors can play an important role in directing local distribution and variability in the occurrence of species. Although this approach is used to recognize the habitat of mackerel, it could also be for other different species and life stages in order to improve knowledge of fish populations and communities.  相似文献   

16.
PSYCHIC is a process-based model of phosphorus (P) and suspended sediment (SS) mobilisation in land runoff and subsequent delivery to watercourses. Modelled transfer pathways include release of desorbable soil P, detachment of SS and associated particulate P, incidental losses from manure and fertiliser applications, losses from hard standings, the transport of all the above to watercourses in underdrainage (where present) and via surface pathways, and losses of dissolved P from point sources. The model can operate at two spatial scales, although the scientific core is the same in both cases. At catchment scale, the model uses easily available national scale datasets to infer all necessary input data whilst at field scale, the user is required to supply all necessary data. The model is sensitive to a number of crop and animal husbandry decisions, as well as to environmental factors such as soil type and field slope angle. It is envisaged that the catchment-scale model would provide the first tier of a catchment characterisation study, and would be used as a screening tool to identify areas within the catchment which may be at elevated risk of P loss. This would enable targeted data collection, involving farm visits and stakeholder discussion, which would then be followed up with detailed field-scale modelling. Both tiers allow the effects of possible mitigation options at catchment scale (Tier 1) and field scale (Tier 2) to be explored. The PSYCHIC model framework therefore provides a methodology for identifying critical source areas of sediment and P transfer in catchments and assessing what management changes are required to achieve environmental goals.  相似文献   

17.
Despite its environmental and scientific significance, predicting gully erosion remains problematic. This is especially so in strongly contrasting and degraded regions such as the Horn of Africa. Machine learning algorithms such as random forests (RF) offer great potential to deal with the complex, often non-linear, nature of factors controlling gully erosion. Nonetheless, their applicability at regional to continental scales remains largely untested. Moreover, such algorithms require large amounts of observations for model training and testing. Collecting such data remains an important bottleneck. Here we help to address these gaps by developing and testing a methodology to simulate gully densities across Ethiopia, Eritrea and Djibouti (total area: 1.2 million km2). We propose a methodology to quickly assess the gully head density (GHD) for representative 1 km2 study sites by visually scoring the presence of gullies in Google Earth and then converting these scores to realistic estimates of GHD. Based on this approach, we compiled GHD observations for 1,700 sites. We used these data to train sets of RF regression models that simulate GHD at a 1 km2 resolution, based on topographic/geomorphic, land cover, soil and rainfall conditions. Our approach also accounts for uncertainties in GHD observations. Independent validations showed generally acceptable simulations of regional GHD patterns. We further show that: (i) model performance strongly depends on the amount of training data used, (ii) large prediction errors mainly occur in areas where also the predicted uncertainty is large and (iii) collecting additional training data for these areas results in more drastic model performance improvements. Analyses of the feature importance of predictor variables further showed that patterns of GHD across the Horn of Africa strongly depend on NDVI and annual rainfall, but also on normalized steepness index (ksn) and distance to rivers. Overall, our work opens promising perspectives to assess gully densities at continental scales. © 2020 John Wiley & Sons, Ltd.  相似文献   

18.
Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.  相似文献   

19.
High-resolution rockfall inventories captured at a regional scale are scarce. This is partly owing to difficulties in measuring the range of possible rockfall volumes with sufficient accuracy and completeness, and at a scale exceeding the influence of localized controls. This paucity of data restricts our ability to abstract patterns of erosion, identify long-term changes in behaviour and assess how rockfalls respond to changes in rock mass structural and environmental conditions. We have addressed this by developing a workflow that is tailored to monitoring rockfalls and the resulting cliff retreat continuously (in space), in three-dimensional (3D) and over large spatial scales (>104 m). We tested our approach by analysing rockfall activity along 20.5 km of coastal cliffs in North Yorkshire (UK), in what we understand to be the first multi-temporal detection of rockfalls at a regional scale. We show that rockfall magnitude–frequency relationships, which often underpin predictive models of erosion, are highly sensitive to the spatial extent of monitoring. Variations in rockfall shape with volume also imply a systemic shift in the underlying mechanisms of detachment with scale, leading us to question the validity of applying a single probabilistic model to the full range of rockfalls observed here. Finally, our data emphasize the importance of cliff retreat as an episodic process. Going forwards, there will a pressing need to understand and model the erosional response of such coastlines to rising global sea levels as well as projected changes to winds, tides, wave climates, precipitation and storm events. The methodologies and data presented here are fundamental to achieving this, marking a step-change in our ability to understand the competing effects of different processes in determining the magnitude and frequency of rockfall activity and ultimately meaning that we are better placed to investigate relationships between process and form/erosion at critical, regional scales. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd  相似文献   

20.
ABSTRACT

The MUSLE is used within hydrological models to estimate sediment yields from catchments of various sizes, but the spatial scale dependency issues associated with estimating the MUSLE parameters have not been adequately addressed. In the absence of detailed observed data on both hydrological response and sediment yield, some analytical approaches and hypothetical examples are presented to identify the key issues. The results suggest that methods used to estimate both the erosivity and topographic factors are scale dependent, particularly if a lumped or semi-distributed modelling approach is used. The conclusion is that spatial scale dependencies will add to the uncertainties inherent in the use of the MUSLE if not carefully understood and appropriately addressed. One suggested approach is to apply the erosivity equation to a fixed (small) representative area and then scale up to the total catchment, an approach that recognizes the variability of averaged parameters across different spatial scales.  相似文献   

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