Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling     

Dongkyun?KimEmail author  Huidae?Cho  Christian?Onof  Minha?Choi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(4):1023-1043

We present a web application named Let-It-Rain that is able to generate a 1-h temporal resolution synthetic rainfall time series using the modified Bartlett–Lewis rectangular pulse (MBLRP) model, a type of Poisson stochastic rainfall generator. Let-It-Rain, which can be accessed through the web address http://www.LetItRain.info, adopts a web-based framework combining ArcGIS Server from server side for parameter value dissemination and JavaScript from client side to implement the MBLRP model. This enables any desktop and mobile end users with internet access and web browser to obtain the synthetic rainfall time series at any given location at which the parameter regionalization work has been completed (currently the contiguous United States and Republic of Korea) with only a few mouse clicks. Let-It-Rain shows satisfactory performance in its ability to reproduce observed rainfall mean, variance, auto-correlation, and probability of zero rainfall at hourly through daily accumulation levels. It also shows a reasonably good performance in reproducing watershed runoff depth and peak flow. We expect that Let-It-Rain can stimulate the uncertainty analysis of hydrologic variables across the world.  相似文献   

Paleomagnetism and magnetic fabrics of Mio-Pliocene hypabyssal rocks of the Combia event,Colombia: tectonic implications     

Victor A. Piedrahita  Roberto S. Molina-Garza  Gloria M. Sierra  José F. Duque-Trujillo 《Studia Geophysica et Geodaetica》2017,61(4):772-800

Mio-Pliocene hypabyssal rocks of the Combia event in the Amagá basin (NW Andes-Colombia), contain a deformational record of the activity of the Cauca-Romeral fault system, and the interaction of terranes within the Choco and northern Andean blocks. Previous paleomagnetic studies interpreted coherent counterclockwise rotations and noncoherent modes of rotation about horizontal axes for the Combia intrusives. However, rotations were determined from in-situ paleomagnetic directions and the existing data set is small. In order to better understand the deformational features of these rocks, we collected new paleomagnetic, structural, petrographic and magnetic fabric data from well exposed hypabyssal rocks of the Combia event. The magnetizations of these rocks are controlled by a low-coercivity ferromagnetic phase. Samples respond well to alternatingfield demagnetization isolating a magnetization component of moderate coercivity. These rocks do not have ductile deformation features. Anisotropy of magnetic susceptibility and morphotectonic analysis indicate that rotation about horizontal axes is consistently to the south-east, suggesting the need to apply a structural correction to the paleomagnetic data. The relationships between magnetic foliations and host-rock bedding planes indicate tectonic activity initiated before ~10 Ma. We present a mean paleomagnetic direction (declination D = 342.8°, inclination I = 12.1°, 95% confidence interval α₉₅ = 12.5°, precision parameter k = 8.6, number of specimens n = 18) that incorporates structural corrections. The dispersion S = 27° of site means cannot be explained by secular variation alone, but it indicates a counterclockwise rotation of 14.8° ± 12.7° relative to stable South America. Paleomagnetic data within a block bounded by the Sabanalarga and Cascajosa faults forms a more coherent data set (D = 336.5°, I = 17.4°, α₉₅ = 11.7°, k = 12.5, n = 14), which differs from sites west of the Sabanalarga fault and shows a rotation about a vertical axis of 20.2° ± 10.7°. Deformation in the Amagá basin may be tentatively explained by the obduction of the Cañas Gordas terrane over the northwestern margin of the northern Andean block. However, it can also be related to the local effects of the Cauca-Romeral fault system.  相似文献   

Assessment of a new solution for tracking pebbles in rivers based on active RFID          下载免费PDF全文

Mathieu Cassel  Thomas Dépret  Hervé Piégay 《地球表面变化过程与地形》2017,42(13):1938-1951

Low‐frequency passive integrated transponders (PIT tags), are commonly used for monitoring pebble mobility in gravel‐bed rivers. Although early studies reported high recovery rates for PIT tags used in small streams, recovery rates in larger systems remain low, substantially limiting the possibilities for their use in such rivers. These low recovery rates are potentially due to missed detections caused by tag signal collision, burial in the sediment layer deeper than the maximum detection range and insufficient (but still exhausting) field effort to cover the concerned areas. A potential solution for addressing these problems is to use active ultra‐high frequency (a‐UHF) transponders as these have a greater detection range and anti‐collision protocols. In order to assess the potential of such transponders for pebble tracking in rivers, we used 433.92 MHz COIN‐ID and COIN‐HC models (ELA Innovation Company, Montpellier, France). We completed several tests to (i) characterize transponder detection ranges in the water and in saturated sediment and (ii) develop field protocols for locating tags by combining global positioning systems (GPS) sites and transponder received signal strength indication (RSSI) levels. The results showed that (i) the maximum detection ranges are about 2.4 m in the water column and more than 2.6 m in a column of saturated gravelly‐sandy sediment, (ii) RSSI spatial interpolation can be used to determine transponder position with good accuracy (< 1 m), (iii) the desired minimal level of accuracy can be adjusted depending on in‐field effort and signal impulse interval, (iv) the RSSI maximal value observed cannot yet be used to determine transponder burial depth because of the multipath propagation of radio frequencies and the semi‐directional emission of the tag signal. Copyright © 2017 John Wiley & Sons, Ltd.  相似文献   

Spectral simulation of vector random fields with stationary Gaussian increments in <Emphasis Type="Italic">d</Emphasis>-dimensional Euclidean spaces     

Daisy?Arroyo  Xavier?EmeryEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(7):1583-1592

This paper addresses the problem of simulating multivariate random fields with stationary Gaussian increments in a d-dimensional Euclidean space. To this end, one considers a spectral turning-bands algorithm, in which the simulated field is a mixture of basic random fields made of weighted cosine waves associated with random frequencies and random phases. The weights depend on the spectral density of the direct and cross variogram matrices of the desired random field for the specified frequencies. The algorithm is applied to synthetic examples corresponding to different spatial correlation models. The properties of these models and of the algorithm are discussed, highlighting its computational efficiency, accuracy and versatility.  相似文献   

Regularized Bayesian estimation for GEV-B-splines model     

Nawres?Yousfi  Salaheddine?El?AdlouniEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(2):535-550

Large observed datasets are not stationary and/or depend on covariates, especially, in the case of extreme hydrometeorological variables. This causes the difficulty in estimation, using classical hydrological frequency analysis. A number of non-stationary models have been developed using linear or quadratic polynomial functions or B-splines functions to estimate the relationship between parameters and covariates. In this article, we propose regularised generalized extreme value model with B-splines (GEV-B-splines models) in a Bayesian framework to estimate quantiles. Regularisation is based on penalty and aims to favour parsimonious model especially in the case of large dimension space. Penalties are introduced in a Bayesian framework and the corresponding priors are detailed. Five penalties are considered and the corresponding priors are developed for comparison purpose as: Least absolute shrinkage and selection (Lasso and Ridge) and smoothing clipped absolute deviations (SCAD) methods (SCAD1, SCAD2 and SCAD3). Markov chain Monte Carlo (MCMC) algorithms have been developed for each model to estimate quantiles and their posterior distributions. Those approaches are tested and illustrated using simulated data with different sample sizes. A first simulation was made on polynomial B-splines functions in order to choose the most efficient model in terms of relative mean biais (RMB) and the relative mean-error (RME) criteria. A second simulation was performed with the SCAD1 penalty for sinusoidal dependence to illustrate the flexibility of the proposed approach. Results show clearly that the regularized approaches leads to a significant reduction of the bias and the mean square error, especially for small sample sizes (n < 100). A case study has been considered to model annual peak flows at Fort-Kent catchment with the total annual precipitations as covariates. The conditional quantile curves were given for the regularized and the maximum likelihood methods.  相似文献   

Intercomparison of multiple statistical downscaling methods: multi-criteria model selection for South Korea     

Hyung-Il?EumEmail author  Alex?J.?Cannon  Trevor?Q.?Murdock 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(3):683-703

A number of statistical downscaling methodologies have been introduced to bridge the gap in scale between outputs of climate models and climate information needed to assess potential impacts at local and regional scales. Four statistical downscaling methods [bias-correction/spatial disaggregation (BCSD), bias-correction/constructed analogue (BCCA), multivariate adaptive constructed analogs (MACA), and bias-correction/climate imprint (BCCI)] are applied to downscale the latest climate forecast system reanalysis (CFSR) data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. All methods are calibrated with observational station data for 19 years from 1973 to 1991 and validated for the more recent 19-year period from 1992 to 2010. We construct a comprehensive suite of performance metrics to inter-compare methods, which is comprised of five criteria related to time-series, distribution, multi-day persistence, extremes, and spatial structure. Based on the performance metrics, we employ technique for order of preference by similarity to ideal solution (TOPSIS) and apply 10,000 different weighting combinations to the criteria of performance metrics to identify a robust statistical downscaling method and important criteria. The results show that MACA and BCSD have comparable skill in the time-series related criterion and BCSD outperforms other methods in distribution and extremes related criteria. In addition, MACA and BCCA, which incorporate spatial patterns, show higher skill in the multi-day persistence criterion for temperature, while BCSD shows the highest skill for precipitation. For the spatial structure related criterion, BCCA and MACA outperformed BCSD and BCCI. From the TOPSIS analysis, we found that MACA is the most robust method for all variables in South Korea, and BCCA and BCSD are the second for temperature and precipitation, respectively. We also found that the contribution of the multi-day persistence and spatial structure related criteria are crucial to ranking the skill of statistical downscaling methods.  相似文献   

Estimation of extreme Component-wise Excess design realization: a hydrological application     

E.?Di?Bernardino  F.?Palacios-RodríguezEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(10):2675-2689

The classic univariate risk measure in environmental sciences is the Return Period (RP). The RP is traditionally defined as “the average time elapsing between two successive realizations of a prescribed event”. The notion of design quantile related with RP is also of great importance. The design quantile represents the “value of the variable(s) characterizing the event associated with a given RP”. Since an individual risk may strongly be affected by the degree of dependence amongst all risks, the need for the provision of multivariate design quantiles has gained ground. In contrast to the univariate case, the design quantile definition in the multivariate setting presents certain difficulties. In particular, Salvadori, G., De Michele, C. and Durante F. define in the paper called “On the return period and design in a multivariate framework” (Hydrol Earth Syst Sci 15:3293–3305, 2011) the design realization as the vector that maximizes a weight function given that the risk vector belongs to a given critical layer of its joint multivariate distribution function. In this paper, we provide the explicit expression of the aforementioned multivariate risk measure in the Archimedean copula setting. Furthermore, this measure is estimated by using Extreme Value Theory techniques and the asymptotic normality of the proposed estimator is studied. The performance of our estimator is evaluated on simulated data. We conclude with an application on a real hydrological data-set.  相似文献   

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model     

Hamid?Moeeni  Hossein?BonakdariEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(8):1997-2010

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.  相似文献   

Improving at-site flood frequency analysis with additional spatial information: a probabilistic regional envelope curve approach     

Daryl?LamEmail authorView author&#;s OrcID profile  Chris?Thompson  Jacky?Croke 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(8):2011-2031

Extreme flood events have detrimental effects on society, the economy and the environment. Widespread flooding across South East Queensland in 2011 and 2013 resulted in the loss of lives and significant cost to the economy. In this region, flood risk planning and the use of traditional flood frequency analysis (FFA) to estimate both the magnitude and frequency of the 1-in-100 year flood is severely limited by short gauging station records. On average, these records are 42 years in Eastern Australia and many have a poor representation of extreme flood events. The major aim of this study is to test the application of an alternative method to estimate flood frequency in the form of the Probabilistic Regional Envelope Curve (PREC) approach which integrates additional spatial information of extreme flood events. In order to better define and constrain a working definition of an extreme flood, an Australian Envelope Curve is also produced from available gauging station data. Results indicate that the PREC method shows significant changes to the larger recurrence intervals (≥100 years) in gauges with either too few, or too many, extreme flood events. A decision making process is provided to ascertain when this method is preferable for FFA.  相似文献   

An assessment of using subsampling method in selection of a flood frequency distribution     

Samiran?DasEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2017,31(8):2033-2045

In flood frequency analysis, a suitable probability distribution function is required in order to establish the flood magnitude-return period relationship. Goodness of fit (GOF) techniques are often employed to select a suitable distribution function in this context. But they have been often criticized for their inability to discriminate between statistical distributions for the same application. This paper investigates the potential utility of subsampling, a resampling technique with the aid of a GOF test to select the best distribution for frequency analysis. The performance of the methodology is assessed by applying the methodology to observed and simulated annual maximum (AM) discharge data series. Several AM series of different record lengths are used as case studies to determine the performance. Numerical analyses are carried out to assess the performance in terms of sample size, subsample size and statistical properties inherent in the AM data series. The proposed methodology is also compared with the standard Anderson–Darling (AD) test. It is found that the methodology is suitable for a longer data series. A better performance is obtained when the subsample size is taken around half of the underlying data sample. The methodology has also outperformed the standard AD test in terms of effectively discriminating between distributions. Overall, all results point that the subsampling technique can be a promising tool in discriminating between distributions.  相似文献