Prediction of hydrological drought durations based on Markov chains: case of the Canadian prairies     

T.C. Sharma 《水文科学杂志》2013,58(4):705-722

Abstract

Hydrological drought durations (lengths) in the Canadian prairies were modelled using the standardized hydrological index (SHI) sequences derived from the streamflow series at annual, monthly and weekly time scales. The rivers chosen for the study present high levels of persistence (as indicated by values exceeding 0.95 for lag-1 autocorrelation in weekly SHI sequences), because they encompass large catchment areas (2210–119 000 km²) and traverse, or originate in, lakes. For such rivers, Markov chain models were found to be simple and efficient tools for predicting the drought duration (year, month, or week) based on annual, monthly and weekly SHI sequences. The prediction of drought durations was accomplished at threshold levels corresponding to median flow (Q50) (drought probability, q?=?0.5) to Q95 (drought probability, q?=?0.05) exceedence levels in the SHI sequences. The first-order Markov chain or the random model was found to be acceptable for the prediction of annual drought lengths, based on the Hazen plotting position formula for exceedence probability, because of the small sample size of annual streamflows. On monthly and weekly time scales, the second-order Markov chain model was found to be satisfactory using the Weibull plotting position formula for exceedence probability. The crucial element in modelling drought lengths is the reliable estimation of parameters (conditional probabilities) of the first- and second-order persistence, which were estimated using the notions implicit in the discrete autoregressive moving average class of models. The variance of drought durations is of particular significance, because it plays a crucial role in the accurate estimation of persistence parameters. Although, the counting method of the estimation of persistence parameters was found to be unsatisfactory, it proved useful in setting the initial values and also in subsequent adjustment of the variance-based estimates of persistence parameters. At low threshold levels corresponding to q < 0.20, even the first-order Markov chain can be construed as a satisfactory model for predicting drought durations based on monthly and weekly SHI sequences.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Sharma, T.C. and Panu, U.S., 2012. Prediction of hydrological drought durations based on Markov chains in the Canadian prairies. Hydrological Sciences Journal, 57 (4), 705–722.  相似文献   

Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference   总被引：4，自引：0，他引：4  

Jing Yang  Peter Reichert  Karim C. Abbaspour  Hong Yang 《Journal of Hydrology》2007,340(3-4):167-182

Calibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box–Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)–(iv) mentioned above.  相似文献   

On the value of isotope-enabled hydrological model calibration     

Tricia A. Stadnyk  T. L. Holmes 《水文科学杂志》2020,65(9):1525-1538

ABSTRACT

Calibration of hydrological models is challenging in high-latitude regions where hydrometric data are minimal. Process-based models are needed to predict future changes in water supply, yet often with high amounts of uncertainty, in part, from poor calibrations. We demonstrate the utility of stable isotopes (¹⁸O, ²H) as data employed for improving the amount and type of information available for model calibration using the isoWATFLOOD^TM model. We show that additional information added to calibration does not hurt model performance and can improve simulation of water volume. Isotope-enabled calibration improves long-term validation over traditional flow-only calibrated models and offers additional feedback on internal flowpaths and hydrological storages that can be useful for informing internal water distribution and model parameterization. The inclusion of isotope data in model calibration reduces the number of realistic parameter combinations, resulting in more constrained model parameter ranges and improved long-term simulation of large-scale water balance.  相似文献   

Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed,China   总被引：2，自引：1，他引：1  

Jing-Cheng Han  Guo-He Huang  Hua Zhang  Zhong Li  Yong-Ping Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(4):973-989

Uncertainty analysis in hydrological modeling would help to better implement decision-making related to water resources management, which relies heavily on hydrologic simulations. However, an important concern will be raised over the uncertainty associated with watershed subdivision broadly applied in distributed/semi-distributed hydrological models since scale issues would significantly affect model performance, and thus, lead to dramatic variations in simulations. To fully understand the uncertainty associated with watershed subdivision level, however, is still a tough work confronting researchers because of complex modeling processes and high computation requirements. In this study, we analyzed this uncertainty within a formal Bayesian framework using a Markov Chain Monte Carlo method based on Metropolis–Hastings algorithm. In a case study using the semi-distributed land use-based runoff processes hydrologic model in the Xiangxi River watershed, results showed that the variation in the simulated discharges due to parameter uncertainty was much smaller than that due to parameter and model uncertainty under different watershed subdivision levels defined using aggregated simulation areas (ASAs). However, the posterior probability distribution of model parameters varied in response to subdivision levels, and four parameters (i.e. maximum infiltration rate, retention constant for slow store, maximum capacity for slow store, and retention constant for fast store) were identified with smaller uncertainty. Although the uncertainty in the simulated discharge due to parameter and model uncertainty varied little across subdivisions, the simulation uncertainty only due to parameter uncertainty was found to be reduced through increasing the subdivisions. In addition, the coarsest subdivision level (7 ASAs) was not sufficient for obtaining satisfying simulations in the Xiangxi River watershed, but inappreciable improvement was achieved through increasing the level among finer subdivisions. Moreover, it was demonstrated that increasing subdivision level would have no advantage of improving the reliability of hydrological simulations beyond the threshold (45 ASAs). The findings of this research may shed light on the design of operational hydrological forecasting in the Three Gorges Reservoir region with profound socio-economic implications.  相似文献   

Multi-year drought frequency analysis at multiple sites by operational hydrology - A comparison of methods     

Claudio Arena  Mario Rosario Mazzola 《Physics and Chemistry of the Earth》2006,31(18):1146-1163

This paper compares two generators of yearly water availabilities from sources located at multiple sites with regard to their ability to reproduce the characteristics of historical critical periods and to provide reliable results in terms of the return period of critical sequences of different length. The two models are a novel multi-site Markov mixture model explicitly accounting for drought occurrences and a multivariate ARMA. In the case of the multisite Markov mixture model parameter estimation is limited to a search in the parameter space guided by the value of parameter λ to show the sensitivity of the model to this parameter. Application to two of the longest time series of streamflows available in Sicily (Italy) shows that the models can provide quite different results in terms of estimated return periods of historic droughts, although they seem to perform more uniformly when it comes to simulate drought-related statistics such as drought length, severity and intensity. The role of parameter selection for the multisite Markov mixture model and of the marginal probability of generated flows in providing results consistent with the characteristics of the observed series is discussed. Both models are applied to the system of sources supplying the city of Palermo (Sicily) and its environs showing the applicability of the newly developed multisite Markov mixture model to medium-to-large scale water resources systems.  相似文献   

Estimating hydrologic model uncertainty in the presence of complex residual error structures   总被引：1，自引：1，他引：0  

S.?SamadiEmail authorView author&#;s OrcID profile  D.?L.?Tufford  G.?J.?Carbone 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(5):1259-1281

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Simulation of future climate scenarios with a weather generator   总被引：4，自引：0，他引：4  

Simone Fatichi  Valeriy Y. Ivanov 《Advances in water resources》2011,34(4):448-467

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.  相似文献   

Assessing hydrological model predictive uncertainty using stochastically generated geological models          下载免费PDF全文

Xin He  Anker Lajer Højberg  Flemming Jørgensen  Jens Christian Refsgaard 《水文研究》2015,29(19):4293-4311

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

A computationally efficient method for uncertainty analysis of SWAT model simulations     

P.?Athira  C.?Nanda  K.?P.?SudheerEmail author 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(6):1479-1492

The physically based distributed hydrological models are ideal for hydrological simulations; however most of such models do not use the basic equations pertaining to mass, energy and momentum conservation, to represent the physics of the process. This is plausibly due to the lack of complete understanding of the hydrological process. The soil and water assessment tool (SWAT) is one such widely accepted semi-distributed, conceptual hydrological model used for water resources planning. However, the over-parameterization, difficulty in its calibration process and the uncertainty associated with predictions make its applications skeptical. This study considers assessing the predictive uncertainty associated with distributed hydrological models. The existing methods for uncertainty estimation demand high computational time and therefore make them challenging to apply on complex hydrological models. The proposed approach employs the concepts of generalized likelihood uncertainty estimation (GLUE) in an iterative procedure by starting with an assumed prior probability distribution of parameters, and by using mutual information (MI) index for sampling the behavioral parameter set. The distributions are conditioned on the observed information through successive cycles of simulations. During each cycle of simulation, MI is used in conjunction with Markov Chain Monte Carlo procedure to sample the parameter sets so as to increase the number of behavioral sets, which in turn helps reduce the number of cycles/simulations for the analysis. The method is demonstrated through a case study of SWAT model in Illinois River basin in the USA. A comparison of the proposed method with GLUE indicates that the computational requirement of uncertainty analysis is considerably reduced in the proposed approach. It is also noted that the model prediction band, derived using the proposed method, is more effective compared to that derived using the other methods considered in this study.  相似文献   

半湿润流域水文模型比较与集合预报   总被引：1，自引：0，他引：1  

霍文博  李致家  李巧玲 《湖泊科学》2017,29(6):1491-1501

选择7种水文模型分别在中国北部3个半湿润流域做模拟对比,分析不同水文模型在各流域的适用性,并使用贝叶斯模型平均法对不同模型集合,比较各种集合方法的优势,研究贝叶斯模型平均法的应用效果.研究结果表明,以蓄满产流模式为主的模型在半湿润流域应用效果较好,针对不同流域特点对传统模型进行改进可以提高模拟精度.贝叶斯模型平均法能提供较好的确定性预报结果和概率预报结果,仅对少数模拟效果好的模型进行集合,并不能有效提高预报精度,适当增加参与集合的模型数量能使贝叶斯模型平均法更好地综合各模型优势,提高预报结果的精度.  相似文献   

Assessing effects of model complexity and structure on predictions of hydrological responses using serial and parallel model design     

Huicheng Chien  D. Scott Mackay 《水文研究》2020,34(2):404-419

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub-model with a water quality sub-model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25-year high-resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial-based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub-model with one set of scale assumptions onto the front end of another sub-model with a different set of scale assumptions.  相似文献   

Bayesian dynamic modelling for nonstationary hydroclimatic time series forecasting along with uncertainty quantification     

D. Nagesh Kumar  Rajib Maity 《水文研究》2008,22(17):3488-3499

Forecasting of hydrologic time series, with the quantification of uncertainty, is an important tool for adaptive water resources management. Nonstationarity, caused by climate forcing and other factors, such as change in physical properties of catchment (urbanization, vegetation change, etc.), makes the forecasting task too difficult to model by traditional Box–Jenkins approaches. In this paper, the potential of the Bayesian dynamic modelling approach is investigated through an application to forecast a nonstationary hydroclimatic time series using relevant climate index information. The target is the time series of the volume of Devil's Lake, located in North Dakota, USA, for which it was proved difficult to forecast and quantify the associated uncertainty by traditional methods. Two different Bayesian dynamic modelling approaches are discussed, namely, a constant model and a dynamic regression model (DRM). The constant model uses the information of past observed values of the same time series, whereas the DRM utilizes the information from a causal time series as an exogenous input. Noting that the North Atlantic Oscillation (NAO) index appears to co‐vary with the time series of Devil's Lake annual volume, its use as an exogenous predictor is explored in the case study. The results of both the Bayesian dynamic models are compared with those from the traditional Box–Jenkins time series modelling approach. Although, in this particular case study, it is observed that the DRM performs marginally better than traditional models, the major strength of Bayesian dynamic models lies in the quantification of prediction uncertainty, which is of great value in hydrology, particularly under the recent climate change scenario. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

A new bivariate Gamma distribution generated from functional scale parameter with application to drought data   总被引：1，自引：0，他引：1  

Muhammad Mohsin  Albrecht Gebhardt  Jürgen Pilz  Gunter Spöck 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(5):1039-1054

Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA.  相似文献   

The accuracy of drainage network delineation as a function of environmental factors: A case study in Central and Northern Sweden     

Yanzi Yan  William Lidberg  David E. Tenenbaum  Petter Pilesjö 《水文研究》2020,34(26):5489-5504

Drainage networks delineated from Digital Elevation Models (DEMs), are the basis for the modelling of geomorphological and hydrological processes, biogeochemical cycling, and water resources management. Besides providing effective models of water flows, automatically extracted drainage networks based on topography can diverge from reality to varying degrees. The variability of such disagreement within catchments has rarely been examined as a function of the heterogeneity of land cover, soil type, and slope in the catchment of interest. This research gap might not only substantially limit our knowledge of the uncertainty of hydrological prediction, but can also cause problems for users attempting to use the data at a local scale. Using 1:100000 scale land cover maps, Quaternary deposits maps, and 2 m resolution DEMs, it is found that the accuracy of delineated drainage networks tends to be lower in areas with denser vegetation, lower hydraulic conductivity, and higher erodibility. The findings of this study could serve as a guide for the more thoughtful usage of delineated drainage networks in environmental planning, and in the uncertainty analysis of hydrological and biochemical predictions. Therefore, this study makes a first attempt at filling the knowledge gap described above.  相似文献   

Analysis of uncertainties in the hydrological response of a model‐based climate change impact assessment in a subcatchment of the Spree River,Germany          下载免费PDF全文

Anne Gädeke  Herwig Hölzel  Hagen Koch  Ina Pohle  Uwe Grünewald 《水文研究》2014,28(12):3978-3998

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV‐light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long‐term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

The Sources of Uncertainty in Forecasting Caspian Sea Level and Estimating the Inundation Risk of Coastal Areas     

M. V. Bolgov  M. K. Filimonova 《Water Resources》2005,32(6):605-610

Stochastic variations in the climate and hydrological regime, both natural and anthropogenic, are the main cause of uncertainty in long-term hydrological forecasts and hence increase the estimated risk of economic activity in the coastal zone of internal seas. Some sources of uncertainty, which appear during the hydrological analysis, are considered with the purpose to assess this risk. Digital relief models were used to determine the morphological characteristics (as functions of the sea level) and assess their contribution to variations in the level regime. To take into account the sample uncertainty in the parameter estimates of stochastic models of the “impellent” processes, it is proposed to use the existing methodology of probabilistic-deterministic prediction of water level variations in a closed water body in combination with the Bayesian approach.  相似文献   

The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments   总被引：5，自引：0，他引：5  

Satish Bastola  Conor MurphyJohn Sweeney 《Advances in water resources》2011,34(5):562-576

This study attempts to assess the uncertainty in the hydrological impacts of climate change using a multi-model approach combining multiple emission scenarios, GCMs and conceptual rainfall-runoff models to quantify uncertainty in future impacts at the catchment scale. The uncertainties associated with hydrological models have traditionally been given less attention in impact assessments until relatively recently. In order to examine the role of hydrological model uncertainty (parameter and structural uncertainty) in climate change impact studies a multi-model approach based on the Generalised Likelihood Uncertainty Estimation (GLUE) and Bayesian Model Averaging (BMA) methods is presented. Six sets of regionalised climate scenarios derived from three GCMs, two emission scenarios, and four conceptual hydrological models were used within the GLUE framework to define the uncertainty envelop for future estimates of stream flow, while the GLUE output is also post processed using BMA, where the probability density function from each model at any given time is modelled by a gamma distribution with heteroscedastic variance. The investigation on four Irish catchments shows that the role of hydrological model uncertainty is remarkably high and should therefore be routinely considered in impact studies. Although, the GLUE and BMA approaches used here differ fundamentally in their underlying philosophy and representation of error, both methods show comparable performance in terms of ensemble spread and predictive coverage. Moreover, the median prediction for future stream flow shows progressive increases of winter discharge and progressive decreases in summer discharge over the coming century.  相似文献   

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment     

Haykel Sellami  Sihem Benabdallah  Isabelle La Jeunesse  Marnik Vanclooster 《水文科学杂志》2013,58(8):1415-1429

ABSTRACT

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.