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1.
A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan 总被引:5,自引:5,他引:0
Hwa-Lung Yu Shang-Jen Yang Hsin-Ju Yen George Christakos 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(4):485-494
Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious
diseases in tropical and sub-tropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was
the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF
patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence
and clustering) and composite space–time effects. The present study proposes a spatio-temporal DF prediction approach based
on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate
and health datasets under conditions of uncertainty, space–time dependence functions, and a Poisson regression model of climatic
variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study
area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required “one-week-ahead” outbreak
warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan
Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across
space–time. 相似文献
2.
Validation and use of rainfall radar data to simulate water flows in the Rio Escondido basin 总被引:2,自引:1,他引:1
Sami Eleuch Alin Carsteanu Khalidou Bâ Ramata Magagi Kalifa Goïta Carlos Diaz 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):559-565
This paper presents a combined validation method of radar-sensed rainfall, using rain gauge data and hydrologic closure, with
an application to the Rio Escondido basin (North-East of Mexico). The space–time scaling behavior of rainfall between rain
gauge and radar scales is compared with the intrinsic variability of rainfall, for a statistical validation of space–time
variability. For hydrological validation purposes, the CEQUEAU model is used to perform rainfall-runoff routing. It provides a basin-wide water balance, to be compared with the measured
water flow at the Villa de Fuentes hydrometric station, for mean-value gauging closure. A good qualitative agreement in terms
of hydrograph shape and timing is obtained between the simulated and the observed water flows, and a multiplicative correction
factor of an initially proposed Z–R relationship is adopted for the watershed under study, which agrees approximately with
other authors’ findings about that relationship. The results are considered particularly useful as a validation-and-correction
methodology of radar rainfall estimates for areas sparsely covered by rain gauges. 相似文献
3.
Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches 总被引:3,自引:1,他引:2
Cosmo S. Ngongondo Chong-Yu Xu Lena M. Tallaksen Berhanu Alemaw Tobias Chirwa 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(7):939-955
Rainfall extremes often result in the occurrence of flood events with associated loss of life and infrastructure in Malawi.
However, an understanding of the frequency of occurrence of such extreme events either for design or disaster planning purposes
is often limited by data availability at the desired temporal and spatial scales. Regionalisation, which involves “trading
time for space” by pooling together observations for stations with similar behavior, is an alternative approach for more accurate
determination of extreme events even at ungauged areas or sites with short records. In this study, regional frequency analysis
of rainfall extremes in Southern Malawi, large parts of which are flood prone, was undertaken. Observed 1-, 3-, 5- and 7-day
annual maximum rainfall series for the period 1978–2007 at 23 selected rainfall stations in Southern Malawi were analysed.
Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. L-moments were applied
to derive regional index rainfall quantiles. The procedure also validated the three rainfall regions identified through homogeneity
and heterogeneity tests based on Monte Carlo simulations with regional average L-moment ratios fitted to the Kappa distribution.
Based on assessments of the accuracy of the derived index rainfall quantiles, it was concluded that the performance of this
regional approach was satisfactory when validated for sites not included in the sample data. The study provides an estimate
of the regional characteristics of rainfall extremes that can be useful in among others flood mitigation and engineering design. 相似文献
4.
Many hydrological and agricultural studies require simulations of weather variables reflecting observed spatial and temporal dependence at multiple point locations. This paper assesses three multi-site daily rainfall generators for their ability to model different spatio-temporal rainfall attributes over the study area. The approaches considered consist of a multi-site modified Markov model (MMM), a reordering method for reconstructing space–time variability, and a nonparametric k-nearest neighbour (KNN) model. Our results indicate that all the approaches reproduce adequately the observed spatio-temporal pattern of the multi-site daily rainfall. However, different techniques used to signify longer time scale observed temporal and spatial dependences in the simulated sequences, reproduce these characteristics with varying successes. While each approach comes with its own advantages and disadvantages, the MMM has an overall advantage in offering a mechanism for modelling varying orders of serial dependence at each point location, while still maintaining the observed spatial dependence with sufficient accuracy. The reordering method is simple and intuitive and produces good results. However, it is primarily driven by the reshuffling of the simulated values across realisations and therefore may not be suited in applications where data length is limited or in situations where the simulation process is governed by exogenous conditioning variables. For example, in downscaling studies where KNN and MMM can be used with confidence. 相似文献
5.
Shiang-Jen Wu Yeou-Koung Tung Jinn-Chuang Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2006,21(2):195-212
Occurrence of rainstorm events can be characterized by the number of events, storm duration, rainfall depth, inter-event time and temporal variation of rainfall within a rainstorm event. This paper presents a Monte-Carlo based stochastic hourly rainfall generation model considering correlated non-normal random rainstorm characteristics, as well as dependence of various rainstorm patterns on rainfall depth, duration, and season. The proposed model was verified by comparing the derived rainfall depth–duration–frequency relations from the simulated rainfall sequences with those from observed annual maximum rainfalls based on the hourly rainfall data at the Hong Kong Observatory over the period of 1884–1990. Through numerical experiments, the proposed model was found to be capable of capturing the essential statistical features of rainstorm characteristics and those of annual extreme rainstorm events according to the available data. 相似文献
6.
Estimation and spatial interpolation of rainfall intensity distribution from the effective rate of precipitation 总被引:1,自引:0,他引:1
Ming Li Quanxi Shao Luigi Renzullo 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(1):117-130
Great emphasis is being placed on the use of rainfall intensity data at short time intervals to accurately model the dynamics
of modern cropping systems, runoff, erosion and pollutant transport. However, rainfall data are often readily available at
more aggregated level of time scale and measurements of rainfall intensity at higher resolution are available only at limited
stations. A distribution approach is a good compromise between fine-scale (e.g. sub-daily) models and coarse-scale (e.g. daily)
rainfall data, because the use of rainfall intensity distribution could substantially improve hydrological models. In the
distribution approach, the cumulative distribution function of rainfall intensity is employed to represent the effect of the
within-day temporal variability of rainfall and a disaggregation model (i.e. a model disaggregates time series into sets of
higher solution) is used to estimate distribution parameters from the daily average effective precipitation. Scaling problems
in hydrologic applications often occur at both space and time dimensions and temporal scaling effects on hydrologic responses
may exhibit great spatial variability. Transferring disaggregation model parameter values from one station to an arbitrary
position is prone to error, thus a satisfactory alternative is to employ spatial interpolation between stations. This study
investigates the spatial interpolation of the probability-based disaggregation model. Rainfall intensity observations are
represented as a two-parameter lognormal distribution and methods are developed to estimate distribution parameters from either
high-resolution rainfall data or coarse-scale precipitation information such as effective intensity rates. Model parameters
are spatially interpolated by kriging to obtain the rainfall intensity distribution when only daily totals are available.
The method was applied to 56 pluviometer stations in Western Australia. Two goodness-of-fit statistics were used to evaluate
the skill—daily and quantile coefficient of efficiency between simulations and observations. Simulations based on cross-validation
show that kriging performed better than other two spatial interpolation approaches (B-splines and thin-plate splines). 相似文献
7.
8.
Jaymie R. Meliker Geoffrey M. Jacquez 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(5):625-634
Our research group recently developed Q-statistics for evaluating space–time clustering in case–control studies with residential histories. This technique relies
on time-dependent nearest-neighbor relationships to examine clustering at any moment in the life-course of the residential
histories of cases relative to that of controls. In addition, in place of the widely used null hypothesis of spatial randomness,
each individual’s probability of being a case is based instead on his/her risk factors and covariates. In this paper, we extend
this approach to illustrate how alternative temporal orientations (e.g., years prior to diagnosis/recruitment, participant’s
age, and calendar year) influence a spatial clustering pattern. These temporal orientations are valuable for shedding light
on the duration of time between clustering and subsequent disease development (known as the empirical induction period), and
for revealing age-specific susceptibility windows and calendar year-specific effects. An ongoing population-based bladder
cancer case–control study is used to demonstrate this approach. Data collection is currently incomplete and therefore no inferences
should be drawn; we analyze these data to demonstrate these novel methods. Maps of space–time clustering of bladder cancer
cases are presented using different temporal orientations while accounting for covariates and known risk factors. This systematic
approach for evaluating space–time clustering has the potential to generate novel hypotheses about environmental risk factors
and provides insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects. 相似文献
9.
L. Guenni A. Bárdossy 《Stochastic Environmental Research and Risk Assessment (SERRA)》2002,16(3):188-206
The need for high resolution rainfall data at temporal scales varying from daily to hourly or even minutes is a very important
problem in hydrology. For many locations of the world, rainfall data quality is very poor and reliable measurements are only
available at a coarse time resolution such as monthly. The purpose of this work is to apply a stochastic disaggregation method
of monthly to daily precipitation in two steps: 1. Initialization of the daily rainfall series by using the truncated normal
model as a reference distribution. 2.␣Restructuring of the series according to various time series statistics (autocorrelation
function, scaling properties, seasonality) by using a Markov chain Monte Carlo based algorithm. The method was applied to
a data set from a rainfall network of the central plains of Venezuela, in where rainfall is highly seasonal and data availability
at a daily time scale or even higher temporal resolution is very limited. A detailed analysis was carried out to study the
seasonal and spatial variability of many properties of the daily rainfall as scaling properties and autocorrelation function
in order to incorporate the selected statistics and their annual cycle into an objective function to be minimized in the simulation
procedure. Comparisons between the observed and simulated data suggest the adequacy of the technique in providing rainfall
sequences with consistent statistical properties at a daily time scale given the monthly totals. The methodology, although
highly computationally intensive, needs a moderate number of statistical properties of the daily rainfall. Regionalization
of these statistical properties is an important next step for the application of this technique to regions in where daily
data is not available. 相似文献
10.
Long-term trends in the ocean wave climate because of global warming are of major concern to many stakeholders within the
maritime industries, and there is a need to take severe sea state conditions into account in design of marine structures and
in marine operations. Various stochastic models of significant wave height are reported in the literature, but most are based
on point measurements without exploiting the flexible framework of Bayesian hierarchical space–time models. This framework
allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge,
yet remains intuitive and easily interpreted. This paper presents a Bayesian hierarchical space–time model with a log-transform
for significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined,
and the results from applying the model to data of different temporal resolutions will be discussed. Different model alternatives
have been tried and long-term trends in the data have been identified for all model alternatives. Overall, these trends are
in reasonable agreement and also agree fairly well with previous studies. The log-transform was included in order to account
for observed heteroscedasticity in the data, and results are compared to previous results where a similar model was employed
without a log-transform. Furthermore, a discussion of possible extensions to the model, e.g. incorporating regression terms
with relevant meteorological data, will be presented. 相似文献
11.
Aurélie Muller Jean-Noël Bacro Michel Lang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(1):33-46
Depth–duration–frequency curves estimate the rainfall intensity patterns for various return periods and rainfall durations.
An empirical model based on the generalized extreme value distribution is presented for hourly maximum rainfall, and improved
by the inclusion of daily maximum rainfall, through the extremal indexes of 24 hourly and daily rainfall data. The model is
then divided into two sub-models for the short and long rainfall durations. Three likelihood formulations are proposed to
model and compare independence or dependence hypotheses between the different durations. Dependence is modelled using the
bivariate extreme logistic distribution. The results are calculated in a Bayesian framework with a Markov Chain Monte Carlo
algorithm. The application to a data series from Marseille shows an improvement of the hourly estimations thanks to the combination
between hourly and daily data in the model. Moreover, results are significantly different with or without dependence hypotheses:
the dependence between 24 and 72 h durations is significant, and the quantile estimates are more severe in the dependence
case. 相似文献
12.
Multivariate time series modeling approaches are known as useful tools for describing, simulating, and forecasting hydrologic variables as well as their changes over the time. These approaches also have temporal and cross-sectional spatial dependence in multiple measurements. Although the application of multivariate linear and nonlinear time series approaches such as vector autoregressive with eXogenous variables (VARX) and multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models are commonly used in financial and economic sciences, these approaches have not been extensively used in hydrology and water resources engineering. This study employed VARX and VARX–MGARCH approaches in modeling mean and conditional heteroscedasticity of daily rainfall and runoff records in the basin of Zarrineh Rood Dam, Iran. Bivariate diagonal VECH (DVECH) model, as a main type of MGARCH, shows how the conditional variance–covariance and conditional correlation structure vary over the time between residuals series of the fitted VARX. For this purpose, five model fits, which consider different combinations of twofold rainfall and runoff, including both upstream and downstream stations, have been investigated in the present study. The VARX model, with different orders, was applied to the daily rainfall–runoff process of the study area in each of these model fits. The Portmanteau test revealed the existence of conditional heteroscedasticity in the twofold residuals of fitted VARX models. Therefore, the VARX–DVECH model is proposed to capture the heteroscedasticity existing in the daily rainfall–runoff process. The bivariate DVECH model indicated both short-run and long-run persistency in the conditional variance–covariance matrix related to the twofold innovations of rainfall–runoff processes. Furthermore, the evaluation criteria for the VARX–DVECH model revealed the improvement of VARX model performance. 相似文献
13.
E. Porcu P. Gregori J. Mateu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):683-693
There is a great demand for statistical modeling of phenomena that evolve in both space and time, and thus, there is a growing
literature on correlation function models for spatio-temporal processes. In particular, various properties of these correlation
functions have been studied only for the merely spatial or temporal case, fact that constitutes a strong motivation for our
work. The goal of this paper is to inspect some properties, obtained with respect to partial differentiation and integration,
of stationary spatio-temporal correlation functions for which anisotropy is obtained through isotropy between components as
in Fernández-Casal et al. (Stat Comput 13(2):127–136, 2003). We show that through partial differentiation and integration it is possible to obtain permissible spatio-temporal correlation
functions in the space–time domain. Other new results regard specific classes of space–time correlations introduced in recent
literature. A curious result arises by differentiating scale mixtures of Euclid’s hat.
Work partially funded by grant MTM2004-06231 from the Spanish Ministery of Science and Education. 相似文献
14.
Seasonal and spatial variability in scaling, correlation and wavelet variance parameter of daily streamflow data were investigated using 56 gauging stations from five basins located in two different climate zones. Multifractal temporal scaling properties were detected using a multiplicative cascade model. The wavelet variance parameter yielded persistence properties of the streamflow time series. Seasonal variations were found to be significant in that winter and spring seasons where large‐scale frontal events are dominant showed higher long‐term correlations and less multifractality than did summer and fall seasons. Coherent spatial variations were apparent. The Neches River basin located in a subtropic humid climate zone exhibited high persistence and long‐term correlation as well as less multifractality as compared with other basins. It is found that larger drainage areas tend to have smaller multifractality and higher persistence structure, and this tendency becomes apparent in regions that receive large amounts of precipitation and decreases towards arid regions. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
15.
Sébastien Blaise Benjamin de Brye Anouk de Brauwere Eric Deleersnijder Eric J. M. Delhez Richard Comblen 《Ocean Dynamics》2010,60(3):535-554
At high Peclet number, the residence time exhibits a boundary layer adjacent to incoming open boundaries. In a Eulerian model,
not resolving this boundary layer can generate spurious oscillations that can propagate into the area of interest. However,
resolving this boundary layer would require an unacceptably high spatial resolution. Therefore, alternative methods are needed
in which no grid refinement is required to capture the key aspects of the physics of the residence time boundary layer. An
extended finite element method representation and a boundary layer parameterisation are presented and tested herein. It is
also explained how to preserve local consistency in reversed time simulations so as to avoid the generation of spurious residence
time extrema. Finally, the boundary layer parameterisation is applied to the computation of the residence time in the Scheldt
Estuary (Belgium/The Netherlands). This timescale is simulated by means of a depth-integrated, finite element, unstructured
mesh model, with a high space–time resolution. It is seen that the residence time temporal variations are mainly affected
by the semi-diurnal tides. However, the spring–neap variability also impacts the residence time, particularly in the sandbank
and shallow areas. Seasonal variability is also observed, which is induced by the fluctuations over the year of the upstream
flows. In general, the residence time is an increasing function of the distance to the mouth of the estuary. However, smaller-scale
fluctuations are also present: they are caused by local bathymetric features and their impact on the hydrodynamics. 相似文献
16.
Abstract The spatio-temporal variability of daily precipitation series was investigated in a semiarid region of central Macedonia in northern Greece, Ten years of daily rainfall records for seven stations in the region constituted the data base. The spatial characteristics were examined by drawing composite correlation diagrams for the cool (October-March) season and the warm (April-September) season, and the results confirmed the regional homogeneity of the data sets. Furthermore, the temporal analysis indicated that the non-rainy days constituted the major portion of days throughout the year at all the stations. Similarly, light rainfall represented the majority of rainy days. Moreover, the annual rainfall variation showed high values in March, April and November with low values occurring in the summer and autumn. A sharp increase of rainfall between the 185th and the 195th day of the year must be taken into account when the harvest is scheduled. Harmonic and Power Spectrum analyses applied to the annual variation of rain depths using 5-day intervals revealed significant periodicities of 26, 122, 365 and 55 days. Finally the analysis of the annual variation of rain occurrences. revealed periodicities of 365 and 122 days. 相似文献
17.
Stochastic multi-site generation of daily weather data 总被引:1,自引:1,他引:0
Malika Khalili François Brissette Robert Leconte 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(6):837-849
Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations.
This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol
8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction
of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the
generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions
have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed,
in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the
case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar
radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly
stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial
autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation
correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate
that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation
amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated,
while maintaining efficient daily spatial autocorrelations and monthly interstation correlations. 相似文献
18.
Tae-Woong Kim Hosung Ahn 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(3):367-376
Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper
estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall
model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional
daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the
accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls
separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques
for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining
the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily
rainfall amounts. 相似文献
19.
Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed 总被引:1,自引:0,他引:1
This paper reports on an evaluation of the use of artificial neural network (ANN) models to forecast daily flows at multiple gauging stations in Eucha Watershed, an agricultural watershed located in north‐west Arkansas and north‐east Oklahoma. Two different neural network models, the multilayer perceptron (MLP) and the radial basis neural network (RBFNN), were developed and their abilities to predict stream flow at four gauging stations were compared. Different scenarios using various combinations of data sets such as rainfall and stream flow at various lags were developed and compared for their ability to make flow predictions at four gauging stations. The input vector selection for both models involved quantification of the statistical properties such as cross‐, auto‐ and partial autocorrelation of the data series that best represented the hydrologic response of the watershed. Measured data with 739 patterns of input–output vector were divided into two sets: 492 patterns for training, and the remaining 247 patterns for testing. The best performance based on the RMSE, R2 and CE was achieved by the MLP model with current and antecedent precipitation and antecedent flow as model inputs. The MLP model testing resulted in R2 values of 0·86, 0·86, 0·81, and 0·79 at the four gauging stations. Similarly, the testing R2 values for the RBFNN model were 0·60, 0·57, 0·58, and 0·56 for the four gauging stations. Both models performed satisfactorily for flow predictions at multiple gauging stations, however, the MLP model outperformed the RBFNN model. The training time was in the range 1–2 min for MLP, and 5–10 s for RBFNN on a Pentium IV processor running at 2·8 GHz with 1 MB of RAM. The difference in model training time occurred because of the clustering methods used in the RBFNN model. The RBFNN uses a fuzzy min‐max network to perform the clustering to construct the neural network which takes considerably less time than the MLP model. Results show that ANN models are useful tools for forecasting the hydrologic response at multiple points of interest in agricultural watersheds. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
20.
Spatial-temporal rainfall modelling for flood risk estimation 总被引:4,自引:6,他引:4
H. S. Wheater R. E. Chandler C. J. Onof V. S. Isham E. Bellone C. Yang D. Lekkas G. Lourmas M.-L. Segond 《Stochastic Environmental Research and Risk Assessment (SERRA)》2005,19(6):403-416
Some recent developments in the stochastic modelling of single site and spatial rainfall are summarised. Alternative single
site models based on Poisson cluster processes are introduced, fitting methods are discussed, and performance is compared
for representative UK hourly data. The representation of sub-hourly rainfall is discussed, and results from a temporal disaggregation
scheme are presented. Extension of the Poisson process methods to spatial-temporal rainfall, using radar data, is reported.
Current methods assume spatial and temporal stationarity; work in progress seeks to relax these restrictions. Unlike radar
data, long sequences of daily raingauge data are commonly available, and the use of generalized linear models (GLMs) (which
can represent both temporal and spatial non-stationarity) to represent the spatial structure of daily rainfall based on raingauge
data is illustrated for a network in the North of England. For flood simulation, disaggregation of daily rainfall is required.
A relatively simple methodology is described, in which a single site Poisson process model provides hourly sequences, conditioned
on the observed or GLM-simulated daily data. As a first step, complete spatial dependence is assumed. Results from the River
Lee catchment, near London, are promising. A relatively comprehensive set of methodologies is thus provided for hydrological
application. 相似文献