A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin,East China

Parameter calibration and sensitivity analysis (SA) are usually not straightforward tasks for distributed hydrological models, owing to the complexity of models and the large number of parameters. A two-step SA approach is proposed for analysing hydrological signatures based on the distributed hydrology–soil–vegetation model (DHSVM) in the Jinhua River Basin, East China. A preliminary SA is conducted to obtain influential parameters via analysis of variance. These parameters are further analysed through a variance-based global sensitivity analysis method to achieve robust rankings and parameter contributions. Parallel computing is designed to reduce the computational burden. The results reveal that only a few parameters are significantly sensitive and that interactions between parameters cannot be ignored. When analysing hydrological signatures, it is found that water yield is simulated very well for most samples. Small and medium floods are simulated very well, while slight underestimations happen for large floods.     

Classified real-time flood forecasting by coupling fuzzy clustering and neural network

This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed.     

Spillway design floods in Sweden: I. New guidelines

Abstract

The new Swedish guidelines for the estimation of design floods for dams and spillways are presented, with emphasis on high-hazard dams. The method is based on a set of regional design precipitation sequences, rescaled for basin area, season and elevation above sea level, and a full hydrological model. A reservoir operation strategy is also a fundamental component of the guidelines. The most critical combination of flood generating factors is searched by systematically inserting the design precipitation sequence into a ten year climatological record, where the initial snowpack has been replaced by a statistical 30-year snowpack. The new guidelines are applicable to single reservoir systems as well as more complex hydroelectric schemes, and cover snowmelt floods, rain floods and combinations of the two. In order to study the probabilities of the computed floods and to avoid regional inconsistencies, extensive comparisons with observed floods and frequency analyses have been carried out.     

An inverse-modelling approach to assess the impacts of climate change in the Seyhan River basin,Turkey / Une approche de modélisation inverse pour évaluer les impacts du changement climatique dans le bassin versant de la Rivière Seyhan,Turquie

Abstract

One of the most significant anticipated consequences of global climate change is the change in frequency of hydrological extremes. Predictions of climate change impacts on the regime of hydrological extremes have traditionally been conducted by a top-down approach that involves a high degree of uncertainty associated with the temporal and spatial characteristics of general circulation model (GCM) outputs and the choice of downscaling technique. This study uses the inverse approach to model hydrological risk and vulnerability to changing climate conditions in the Seyhan River basin, Turkey. With close collaboration with the end users, the approach first identifies critical hydrological exposures that may lead to local failures in the Seyhan River basin. The Hydro-BEAM hydrological model is used to inversely transform the main hydrological exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological conditions is investigated under present and future climate scenarios by means of a weather generator based on the improved K-nearest neighbour algorithm. The weather generator, linked with the output of GCMs in the last step of the proposed methodology, allows for the creation of an ensemble of scenarios and easy updating when improved GCM outputs become available. Two main conclusions were drawn from the application of the inverse approach to the Seyhan River basin. First, floods of 100-, 200- and 300-year return periods under present conditions will have 102-, 293- and 1370-year return periods under the future conditions; that is, critical flood events will occur much less frequently under the changing climate conditions. Second, the drought return period will change from 5.3 years under present conditions to 2.0 years under the future conditions; that is, critical drought events will occur much more frequently under the changing climate conditions.     

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.     

Copula models for frequency analysis what can be learned from a Bayesian perspective?

Large spring floods in the Québec region exhibit correlated peakflow, duration and volume. Consequently, traditional univariate hydrological frequency analyses must be complemented by multivariate probabilistic assessment to provide a meaningful design flood level as requested in hydrological engineering (based on return period evaluation of a single quantity of interest). In this paper we study 47 years of a peak/volume dataset for the Romaine River with a parametric copula model. The margins are modeled with a normal or gamma distribution and the dependence is depicted through a parametric family of copulas (Arch 12 or Arch 14). Parameter joint inference and model selection are performed under the Bayesian paradigm. This approach enlightens specific features of interest for hydrological engineering: (i) cross correlation between margin parameters are stronger than expected , (ii) marginal distributions cannot be forgotten in the model selection process and (iii) special attention must be addressed to model validation as far as extreme values are of concern.     

Extreme hydrological events,palaeo-information and climate change

Abstract

Extreme flood events have been and continue to be one of the most important natural hazards responsible for deaths and economic losses. Extreme floods result in direct destructive effects during the time of the event, and they also may be followed by a related chain of indirect calamities such as famines and epidemics that produce additional damages and suffering. Extreme hydrological events that have occurred in the historical past may also occur in the future. Knowledge about magnitudes and recurrence frequencies of past extreme hydrological events in most regions are too short to adequately evaluate potential magnitudes and recurrence frequencies of extreme hydrological events. Stationary climate in which the mean and variance do not change over time is a basic underlying assumption of standard methodological procedures for estimating recurrence probabilities of extreme hydrological events. Palaeo-archives contained in river and lake sediments, fossil plant and animal matter, ice layers, and other natural archives show that the assumption of stationary climate is not valid when the time scale is extended beyond centuries and millennia. Records of past extreme floods that occurred long before the period of instrumentation can be reconstructed from the distribution of slackwater flood deposits or from derivation of water depths competent to transport the largest rocks found in flood deposited sediment. Palaeoflood records reconstructed from the Upper Mississippi and Lower Colorado River systems in the United States confirm nonstationary behaviour of the mean and variance in hydrological time series. These stratigraphic records have shown that even very modest climatic changes have resulted in very important changes in the magnitudes and recurrence frequencies of extreme floods. A close relationship was found between the palaeo-flood record of extreme floods in the Upper Mississippi River system and a palaeo-record of stable isotopes of oxygen and carbon preserved in speleothem calcite from a local cave. The relationship suggests that isotopic records elsewhere might be calibrated to provide insight about how future potential climate changes might impact extreme flood magnitudes and recurrence frequencies there. Atmospheric global circulation models (GCMs) mainly simulate average climatic conditions and are presently inadequate sources of information about how future climate changes might be represented at the extreme event scale. Palaeo-flood archives, however, provide basic information about how magnitudes and recurrence frequencies of extreme hydrological events responded to past climate changes and they also provide a reference base against which GCM simulations can be calibrated regionally and be better interpreted to decipher hydrological information at the extreme event scale.     

Predicting storm runoff from different land‐use classes using a geographical information system‐based distributed model

A method is presented to evaluate the storm runoff contributions from different land‐use class areas within a river basin using the geographical information system‐based hydrological model WetSpa. The modelling is based on division of the catchment into a grid mesh. Each cell has a unique response function independent of the functioning of other cells. Summation of the flow responses from the cells with the same land‐use type results in the storm runoff contribution from these areas. The model was applied on the Steinsel catchment in the Alzette river basin, Grand Duchy of Luxembourg, with 52 months of meteo‐hydrological measurements. The simulation results show that the direct runoff from urban areas is dominant for a flood event compared with runoff from other land‐use areas in this catchment, and this tends to increase for small floods and for the dry‐season floods, whereas the interflow from forested, pasture and agricultural field areas contributes to recession flow. It is demonstrated that the relative contribution from urban areas decreases with flow coefficient, that cropland relative contribution is nearly constant, and that the relative contribution from grassland and woodland increases with flow coefficient with regard to their percentage of land‐use class areas within the study catchment. Copyright © 2005 John Wiley & Sons, Ltd.     

Study of hydrological processes by the combination of environmental tracing and hill slope measurements: application on the Haute‐Mentue catchment

The objective of this research is to improve the comprehension of the hydrological behaviour of natural catchments. The main originality of this work is to associate different types of measurement in order to obtain a better vision of hydrological processes responsible for streamflow generation. First, the hydrological behaviour is studied at the catchment scale by the application of environmental tracing. A three‐component mixing model based on the silica and calcium concentrations of water allows one to distinguish the contributions of direct precipitation, soil water and groundwater during flood generation. Despite the different hydrological responses observed between the four subcatchments studied, a common behaviour is apparent. Soil contribution increases with a rise in the basin humidity. The subsurface water dominates the generation of major floods, which occur in wet conditions. In order to discover the processes responsible for the important soil water contributions, a large‐scale time‐domain reflectometry experiment (64 probes) was conducted. On the whole, this experiment indicates that the water flow in soil is spatially quite heterogeneous and depends on local properties. Macropore flows were clearly identified during a rainfall simulator experiment. Preferential flows may be responsible for the important contribution of soil water and the heterogeneity of the soil moisture. In order to test this hypothesis, a dye‐tracing experiment was done. This new investigation confirms that an important part of soil water reaches the stream by preferential flows. So as to synthesize all these observations, a conceptual model is proposed. This model respects both the hydrochemical responses highlighted by the environmental tracing experiment and the observations done at the local scale. This conceptual model suggests that the important contribution of soil water is due to the extent of the hydrographic network and the role of preferential flows. Copyright © 2005 John Wiley & Sons, Ltd.     

Review of Understanding of Earth’s Hydrological Cycle: Observations,Theory and Modelling

Water is our most precious and arguably most undervalued natural resource. It is essential for life on our planet, for food production and economic development. Moreover, water plays a fundamental role in shaping weather and climate. However, with the growing global population, the planet’s water resources are constantly under threat from overuse and pollution. In addition, the effects of a changing climate are thought to be leading to an increased frequency of extreme weather causing floods, landslides and drought. The need to understand and monitor our environment and its resources, including advancing our knowledge of the hydrological cycle, has never been more important and apparent. The best approach to do so on a global scale is from space. This paper provides an overview of the major components of the hydrological cycle, the status of their observations from space and related data products and models for hydrological variable retrievals. It also lists the current and planned satellite missions contributing to advancing our understanding of the hydrological cycle on a global scale. Further details of the hydrological cycle are substantiated in several of the other papers in this Special Issue.     

Assessing the influence of the Merzbacher Lake outburst floods on discharge using the hydrological model SWIM in the Aksu headwaters,Kyrgyzstan/NW China

Glacial lake outburst floods (GLOF) often have a significant impact on downstream users. Including their effects in hydrological models, identifying past occurrences and assessing their potential impacts are challenges for hydrologists working in mountainous catchments. The regularly outbursting Merzbacher Lake is located in the headwaters of the Aksu River, the most important source of water discharge to the Tarim River, northwest China. Modelling its water resources and the evaluation of potential climate change impacts on river discharge are indispensable for projecting future water availability for the intensively cultivated river oases downstream of the Merzbacher Lake and along the Tarim River. The semi‐distributed hydrological model SWIM was calibrated to the outlet station Xiehela on the Kumarik River, by discharge the largest tributary to the Aksu River. The glacial lake outburst floods add to the difficulties of modelling this high‐mountain, heavily glaciated catchment with poor data coverage and quality. The aims of the study are to investigate the glacier lake outburst floods using a modelling tool. Results include a two‐step model calibration of the Kumarik catchment, an approach for the identification of the outburst floods using the measured gauge data and the modelling results and estimations of the outburst flood volumes. Results show that a catchment model can inform GLOF investigations by providing ‘normal’ (i.e. without the outburst floods) catchment discharge. The comparison of the simulated and observed discharge proves the occurrence of GLOFs and highlights the influences of the GLOFs on the downstream water balance. © 2013 The Authors. Hydrological Processes Published by John Wiley & Sons Ltd.     

Impact of climate change on hydrological extremes in the Yangtze River Basin,China

The recent (1970–1999) and future (2070–2099) climates under the SRES A1B scenario, simulated by the regional climate model RegCM4.0 driven with lateral boundary conditions from the ECHAM5 general circulation model, are utilized to force a large-scale hydrological model for assessing the hydrological response to climate changes in the Yangtze River Basin, China. The variable infiltration capacity model (VIC) is utilized to simulate various hydrological components for examining the changes in streamflow at various locations throughout the Yangtze River Basin. In the end of the twenty-first century, most of the Yangtze River Basin stands out as “hotspots” of climate change in China, with an annual temperature increase of approximately 3.5 °C, an increase of annual precipitation in North and a decrease in South. Runoff in the upper reach of Yangtze River is projected to increase throughout the year in the future, especially in spring when the increase will be approximately 30 %. Runoff from the catchments in the northern part of Yangtze River will increase by approximately 10 %, whereas that in the southern part will decrease, especially in the dry season, following precipitation changes. The frequency of extreme floods at three mainstream stations (Cuntan, Yichang, and Datong) is projected to increase significantly. The original extreme floods with return periods of 50, 20, and 10 years will change into floods with return periods of no more than 20, 10, and 5 years. The projected increase in extreme floods will have significant impacts on water resources management and flood control systems in the Yangtze River Basin.     

Integration of remote sensing data into hydrological models for reservoir management

Abstract

The purpose of this paper is to present the methodology set up to derive catchment soil moisture from Earth Observation (EO) data using microwave spaceborne Synthetic Aperture Radar (SAR) images from ERS satellites and to study the improvements brought about by an assimilation of this information into hydrological models. The methodology used to derive EO data is based on the appropriate selection of land cover types for which the radar signal is mainly sensitive to soil moisture variations. Then a hydrological model is chosen, which can take advantage of the new information brought by remote sensing. The assimilation of soil moisture deduced from EO data into hydrological models is based principally on model parameter updating. The main assumption of this method is that the better the model simulates the current hydrological system, the better the following forecast will be. Another methodology used is a sequential one based on Kalman filtering. These methods have been put forward for use in the European AIMWATER project on the Seine catchment upstream of Paris (France) where dams are operated to alleviate floods in the Paris area.     

Estimation of uncertainty sources in the projections of Lithuanian river runoff

Particular attention is given to the reliability of hydrological modelling results. The accuracy of river runoff projection depends on the selected set of hydrological model parameters, emission scenario and global climate model. The aim of this article is to estimate the uncertainty of hydrological model parameters, to perform sensitivity analysis of the runoff projections, as well as the contribution analysis of uncertainty sources (model parameters, emission scenarios and global climate models) in forecasting Lithuanian river runoff. The impact of model parameters on the runoff modelling results was estimated using a sensitivity analysis for the selected hydrological periods (spring flood, winter and autumn flash floods, and low water). During spring flood the results of runoff modelling depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period—the parameter that determines river underground feeding was the most important. The estimation of climate change impact on hydrological processes in the Merkys and Neris river basins was accomplished through the combination of results from A1B, A2 and B1 emission scenarios and global climate models (ECHAM5 and HadCM3). The runoff projections of the thirty-year periods (2011–2040, 2041–2070, 2071–2100) were conducted applying the HBV software. The uncertainties introduced by hydrological model parameters, emission scenarios and global climate models were presented according to the magnitude of the expected changes in Lithuanian rivers runoff. The emission scenarios had much greater influence on the runoff projection than the global climate models. The hydrological model parameters had less impact on the reliability of the modelling results.     

Development of a thresholds approach for real‐time flash flood prediction in complex geomorphological river basins

Flash floods represent one of the deadliest and costliest natural disasters worldwide. The hydrological analysis of a flash flood event contributes in the understanding of the runoff creation process. This study presents the analysis of some flash flood events that took place in a complex geomorphological Mediterranean River basin. The objective of the present work is to develop the thresholds for a real‐time flash flood forecasting model in a complex geomorphological watershed, based on high‐frequency data from strategically located hydrological and meteorological telemetric stations. These stations provide hourly real‐time data which were used to determine hydrological and meteorological parameters. The main characteristics of various hydrographs specified in this study were the runoff coefficients, lag time, time to peak, and the maximum potential retention. The estimation of these hydrometeorological parameters provides the necessary information in order to successfully manage flash floods events. Especially, the time to peak is the most significant hydrological parameter that affects the response time of an oncoming flash flood event. A study of the above parameters is essential for the specification of thresholds which are related to the geomorphological characteristics of the river basin, the rainfall accumulation of an event, the rainfall intensity, the threshold runoff through karstic area, the season during which the rainfall takes place and the time intervals between the rainstorms that affect the soil moisture conditions. All these factors are combined into a real‐time‐threshold flash flood prediction model. Historical flash flood events at the downstream are also used for the validation of the model. An application of the proposed model is presented for the Koiliaris River basin in Crete, Greece. Copyright © 2011 John Wiley & Sons, Ltd.     

A stochastic differential equation model for assessing drought and flood risks

Droughts and floods are two opposite but related hydrological events. They both lie at the extremes of rainfall intensity when the period of that intensity is measured over long intervals. This paper presents a new concept based on stochastic calculus to assess the risk of both droughts and floods. An extended definition of rainfall intensity is applied to point rainfall to simultaneously deal with high intensity storms and dry spells. The mean-reverting Ornstein–Uhlenbeck process, which is a stochastic differential equation model, simulates the behavior of point rainfall evolving not over time, but instead with cumulative rainfall depth. Coefficients of the polynomial functions that approximate the model parameters are identified from observed raingauge data using the least squares method. The probability that neither drought nor flood occurs until the cumulative rainfall depth reaches a given value requires solving a Dirichlet problem for the backward Kolmogorov equation associated with the stochastic differential equation. A numerical model is developed to compute that probability, using the finite element method with an effective upwind discretization scheme. Applicability of the model is demonstrated at three raingauge sites located in Ghana, where rainfed subsistence farming is the dominant practice in a variety of tropical climates.     

Non-linear growth of cumulative flood losses with time

Statistical data over the past 30 years show that the cumulative sum of losses caused by floods S(t) has been increasing with time approximately as t^1·3, i.e. faster than the linear growth expected for a stationary process. (Losses are evaluated by the number of homeless caused by floods, since these data are the most systematically reported.) At the same time, the factors determining flood losses (the rate of floods and single loss distribution) appear to be stationary over the period of observation. An explanation of this paradox is suggested based on a heavy-tail distribution function of losses, i.e. a distribution function with infinite expectation value. The proposed stochastic model predicts a faster than linear growth of the cumulative losses until some limiting time, which corresponds to the recurrence period of the maximal possible single loss. Similar pseudo-non-stationary effects can be observed for other types of catastrophes and hydrological characteristics with heavy-tail distributions © 1998 John Wiley & Sons, Ltd.     

A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions

Some recent research on fluvial processes suggests the idea that some hydrological variables, such as flood flows, are upper-bounded. However, most probability distributions that are currently employed in flood frequency analysis are unbounded to the right. This paper describes an exploratory study on the joint use of an upper-bounded probability distribution and non-systematic flood information, within a Bayesian framework. Accordingly, the current PMF maximum discharge appears as a reference value and a reasonable estimate of the upper-bound for maximum flows, despite the fact that PMF determination is not unequivocal and depends strongly on the available data. In the Bayesian context, the uncertainty on the PMF can be included into the analysis by considering an appropriate prior distribution for the maximum flows. In the sequence, systematic flood records, historical floods, and paleofloods can be included into a compound likelihood function which is then used to update the prior information on the upper-bound. By combining a prior distribution describing the uncertainties of PMF estimates along with various sources of flood data into a unified Bayesian approach, the expectation is to obtain improved estimates of the upper-bound. The application example was conducted with flood data from the American river basin, near the Folsom reservoir, in California, USA. The results show that it is possible to put together concepts that appear to be incompatible: the deterministic estimate of PMF, taken as a theoretical limit for floods, and the frequency analysis of maximum flows, with the inclusion of non-systematic data. As compared to conventional analysis, the combination of these two concepts within the logical context of Bayesian theory, contributes an advance towards more reliable estimates of extreme floods.     

Suspended sediment dynamics in a large drainage basin: the River Rhine

The behaviour of suspended sediment in rivers is often a function of energy conditions, i.e. sediment is stored at low flow and transported under high discharge conditions. The timing of maximum sediment transport can, however, also be related to mixing and routing of water and sediment from different sources. In this study suspended sediment transport was studied in the River Rhine between Kaub and the German–Dutch border. As concentrations decrease over a runoff season and as the relationship between water discharge and suspended sediment concentrations during most floods is characterized by clockwise hysteresis, it is concluded that sediment depletion occurs during a hydrological year and during individual floods. However, analyses of the sediment contribution from the River Mosel indicate that clockwise hysteresis may result from sediment depletion as well as from early sediment supply from a tributary. Thus, although the suspended sediment behaviour in the downstream part of the River Rhine is partly a transport phenomenon related to energy conditions, mixing and routing of water from different sources also plays an important role. Suspended sediment transport during floods was modelled using a ‘supply‐based’ model. Addition of a sediment supply term to the sediment rating curve leads to a model that produces better estimates of instantaneous suspended sediment concentrations during high discharge events. A major constriction of the model is that it cannot be used to predict suspended sediment concentrations as long as the amount of sediment in storage and the timing of sediment supply are unknown. Copyright © 1999 John Wiley & Sons, Ltd.