Identification of sub-trends from hydro-meteorological series

In hydro-meteorological trend analysis, an alteration in the given variable is detected by considering the long-term series as a whole. Whereas the long-term trend may be absent, the significance of hidden (short-durational) sub-trends in the series may be important for environmental management practices. In this paper, a graphical approach of identifying trend or sub-trends using nonparametric cumulative rank difference (CRD) was proposed. To confirm the significance of the visualized trend, the CRD was translated from the graphical to a statistical metric. To assess its capability, the performance of the CRD method was compared with that of the well-known Mann–Kendall (MK) test. The graphical and statistical CRD techniques were applied to detect trends and sub-trends in the annual rainfall of 10 River Nile riparian countries (RNRCs). The co-occurrence of the trend evolutions in the rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. The power of the CRD method was shown to closely agree with that of the MK test under the various circumstances of sample sizes, variations, linear trend slopes, and serial correlations. At the level of significance α = 5 %, the long-term trends were found present in 30 % of the RNRCs. However at α = 5 %, the main downward (upward) sub-trends were found significant in 30 (60 %) of the RNRCs. Generally at α = 1 %, linkages of the trend evolutions in the rainfall of the RNRCs were found to those of the influences from the Atlantic and Indian Oceans. At α = 5 %, influences from the Pacific Ocean on the rainfall trends of some countries were also evident.     

Probabilistic calibration of a distributed hydrological model for flood forecasting

Abstract

The complexity of distributed hydrological models has led to improvements in calibration methodologies in recent years. There are various manual, automatic and hybrid methods of calibration. Most use a single objective function to calculate estimation errors. The use of multi-objective calibration improves results, since different aspects of the hydrograph may be considered simultaneously. However, the uncertainty of estimates from a hydrological model can only be taken into account by using a probabilistic approach. This paper presents a calibration method of probabilistic nature, based on the determination of probability functions that best characterize different parameters of the model. The method was applied to the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model using the Manzanares River basin in Spain as a case study. The proposed method allows us to consider the uncertainty in the model estimates by obtaining the probability distributions of flows in the flood hydrograph.

Citation Mediero, L., Garrote, L. & Martín-Carrasco, F. J. (2011) Probabilistic calibration of a distributed hydrological model for flood forecasting. Hydrol. Sci. J. 56(7), 1129–1149.     

Basinwide flood control system by combining prediction and reservoir operation

Japan has traditionally performed flood prevention through the construction and use of dikes, storage reservoirs, and basins which are costly and time consuming options. Another non-structural option is to operate the flood control system appropriately with a view to reducing flood damage. In this paper, a flood control system combining the runoff prediction model in the whole river basin with the reservoir operation is discussed. Different models of the runoff process are introduced in order to compare their accuracies and the computational time for the flood forecasting system. The reservoir operational rule is formulated in terms of fuzzy inference theory. Historical data are applied in a case study for verification of the proposed theories.     

Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data

Monte-Carlo simulations of a two-dimensional finite element model of a flood in the southern part of Sicily were used to explore the parameter space of distributed bed-roughness coefficients. For many real-world events specific data are extremely limited so that there is not only fuzziness in the information available to calibrate the model, but fuzziness in the degree of acceptability of model predictions based upon the different parameter values, owing to model structural errors. Here the GLUE procedure is used to compare model predictions and observations for a certain event, coupled with both a fuzzy-rule-based calibration, and a calibration technique based upon normal and heteroscedastic distributions of the predicted residuals. The fuzzy-rule-based calibration is suited to an event of this kind, where the information about the flood is highly uncertain and arises from several different types of observation. The likelihood (relative possibility) distributions predicted by the two calibration techniques are similar, although the fuzzy approach enabled us to constrain the parameter distributions more usefully, to lie within a range which was consistent with the modellers' a priori knowledge of the system.     

Stochastic analysis of flood series

A stochastic model based on the renewal process was developed and used to analyse the characteristics of floods: the volume exceedence, the duration of the flood and the maximum annual flow. The model contains a method for determination of total annual volume exceedence and total annual duration of floods, as well as a method for calculation of maximum annual exceedence, maximum flood duration and maximum flow. The subset of the flood occurrence number in a given time interval is common for all analysed phenomena (volume exceedence, flood duration, maximum flow). The subset of given exceedences is common for total annual volume exceedence, as well as for maximum annual volume exceedence. The same holds for durations of individual floods. The model was then applied to analyse the floods on the Drina River at the Paunci hydrological station and on the Danube River at the Bezdan station.     

Vertical mixing of gravel over a long flood series

Vertical sediment exchange is a fundamental component of bedload transport in gravel‐bed channels. This paper describes the characteristic depth of exchange achieved over a long flood series. Analysis is based on 11 recoveries of magnetically tagged gravels deployed in Carnation Creek, Canada, completed between 1990 and 2008. Vertical grain exchange mixes gravels throughout the streambed relatively rapidly. Within one to eight floods the mean burial depth approaches two times the surface layer thickness, quantified by the 90th percentile of the size distribution. Finer gravels are mixed more rapidly into the bed than coarser gravels. Both active and passive grain exchanges throughout most of the bed produce the overall vertical distribution of marked grains. Gravel exchanges exhibit fairly consistent patterns once tracers are well mixed by large floods. Results highlight the role of flood sequence in determining exchange depths, support the notion of an upper limit to exchange, and underscore the importance of passive grain exchange. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessment on the function of reservoirs for flood control during typhoon seasons based on a distributed hydrological model

A grid‐based distributed hydrological model, PDTank model, is used to simulate hydrological processes in the upper Tone River catchment. The Tone River catchment often suffers from heavy rainfall events during the typhoon seasons. The reservoirs located in the catchment play an important role in flood regulation. Through the coupling of the PDTank model and a reservoir module that combines the storage function and operation function, the PDTank model is used for flood forecasting in this study. By comparing the hydrographs simulated using gauging and radar rainfall data, it is found that the spatial variability of rainfall is an important factor for flood simulation and the accuracy of the hydrographs simulated using radar rainfall data is slightly improved. The simulation of the typhoon flood event numbered No. 9 shows that the reservoirs in the catchment attenuate the peak flood discharge by 423·3 m³/s and validates the potential applicability of the distributed hydrological model on the assessment of function of reservoirs for flood control during typhoon seasons. Copyright © 2011 John Wiley & Sons, Ltd.     

Semi‐distributed flood runoff model at the subcontinental scale for southwestern Iran

Rainfall–runoff modelling, as a surface hydrological process, on large‐scale data‐poor basins is currently a major topic of investigation that requires the model parameters be identified by using basin physical characteristics rather than calibration. This paper describes the application of the TOPMODEL framework accompanied by a kinematic wave model to the Karun River sub‐basins in southwestern Iran with just one conceptual parameter for calibration. ISLSCP1, HYDRO1K and Reynolds data sets are presented in a geographical information system and used as data sources for meteorological information, hydrological features and soil characteristics of the study area respectively. The results show that although the model developed can adequately predict flood runoff in the catchment with only one calibrated parameter, it is suggested that the effect of surface reservoirs be considered in the proposed model. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluation of a distributed model for urban catchments using a 7‐year continuous data series

The documentation existing on both land use and the delineation of pervious and impervious zones in urban areas tends to be rather complete. In addition, topographical information (altitudes, slopes) is generally available, although contours are not drawn in detail on urban‐area maps. The development of urban databases has provided a convenient means of accessing this information for the purpose of hydrological modelling. The objective of this paper is to evaluate a recent model, ‘SURF’ (semi‐urbanized runoff flow), specifically developed for coupling with a GIS based on a digital terrain representation. This model was evaluated by use of an original approach from the field of urban hydrology. A 7‐year continuous data series, which includes the dry periods, has been used as input to run the model. The principles behind the SURF model are briefly described herein. A sensitivity analysis is then performed in order to select the most influential parameters. Following the calibration stage, the model's validation is discussed. This validation is conducted not only by comparing observed and simulated hydrographs, but also by comparing the SURF model with a more conventional model in urban hydrology, called the URBAN model. It is demonstrated that the SURF model provides useful simulation results and does outperform the URBAN model. Copyright © 2000 John Wiley & Sons, Ltd.     

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain‐averaged (zero‐dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero‐dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n‐dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd.     

A copula-based model for air pollution portfolio risk and its efficient simulation

This paper introduces a portfolio approach for quantifying pollution risk in the presence of PM\(_{2.5}\) concentration in cities. The model used is based on a copula dependence structure. For assessing model parameters, we analyze a limited data set of PM\(_{2.5}\) levels of Beijing, Tianjin, Chengde, Hengshui, and Xingtai. This process reveals a better fit for the t-copula dependence structure with generalized hyperbolic marginal distributions for the PM\(_{2.5}\) log-ratios of the cities. Furthermore, we show how to efficiently simulate risk measures clean-air-at-risk and conditional clean-air-at-risk using importance sampling and stratified importance sampling. Our numerical results show that clean-air-at-risk at 0.01 probability level reaches up to \(352\,{\mu \hbox {gm}^{-3}}\) (initial PM\(_{2.5}\) concentrations of cities are assumed to be \(100\,{\mu \hbox {gm}^{-3}}\)) for the constructed sample portfolio, and that the proposed methods are much more efficient than a naive simulation for computing the exceeding probabilities and conditional excesses.     

Understanding hydrological trends by combining the Budyko hypothesis and a stochastic soil moisture model

Abstract

The actual evapotranspiration and runoff trends of five major basins in China from 1956 to 2000 are investigated by combining the Budyko hypothesis and a stochastic soil moisture model. Based on the equations of Choudhury and Porporato, the actual evapotranspiration trends and the runoff trends are attributed to changes in precipitation, potential evapotranspiration, rainfall depth and water storage capacity which depends on the soil water holding capacity and the root depth. It was found that the rainfall depth increased significantly in China during the past 50 years, especially in southern basins. Contributions from changes in the water storage capacity were significant in basins where land surface characteristics have changed substantially due to human activities. It was also observed that the actual evapotranspiration trends are more sensitive to precipitation trends in water-limited basins, but more sensitive to potential evapotranspiration trends in energy-limited basins.

Editor D. Koutsoyiannis; Associate editor A. Porporato     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

On the distribution of flood volume in partial duration series analysis of flood phenomena

A methodology based on the theory of stochastic processes is applied to the analysis of floods. The approach will be based on some results of the theory of extreme values over a threshold. In this paper, we focus on the estimation of the distribution of the flood volume in partial duration series analysis of flood phenomena, by using a bivariate exponential distribution of discharge exceedances and durations over a base level.     

Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed,reconstructed and projected streamflow

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.     

Pooled flood frequency analysis: a comparison based on peaks-over-threshold and annual maximum series

Despite some theoretical advantages of peaks-over-threshold (POT) series over annual maximum (AMAX) series, some practical aspects of flood frequency analysis using AMAX or POT series are still subject to debate. Only minor attention has been given to the POT method in the context of pooled frequency analysis. The objective of this research is to develop a framework to promote the implementation of pooled frequency modelling based on POT series. The framework benefits from a semi-automated threshold selection method. This study introduces a formalized and effective approach to construct homogeneous pooling groups. The proposed framework also offers means to compare the performance of pooled flood estimation based on AMAX or POT series. An application of the framework is presented for a large collection of Canadian catchments. The proposed POT pooling technique generally improved flood quantile estimation in comparison to the AMAX pooling scheme, and achieved smaller uncertainty associated with the quantile estimates.     

The strategy of building a flood forecast model by neuro‐fuzzy network

A methodology is proposed for constructing a flood forecast model using the adaptive neuro‐fuzzy inference system (ANFIS). This is based on a self‐organizing rule‐base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall‐runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self‐constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back‐propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model. Copyright © 2005 John Wiley & Sons, Ltd.