Assessment of change in design flood frequency under climate change using a multivariate downscaling model and a precipitation-runoff model

Precipitation and runoff are key elements in the hydrologic cycle because of their important roles in water supply, flood prevention, river restoration, and ecosystem management. Global climate change, widely accepted to be happening, is anticipated to have enormous consequences on future hydrologic patterns. Studies on the potential changes in global, regional, and local hydrologic patterns under global climate change scenarios have been an intense area of research in recent years. The present study contributes to this research topic through evaluation of design flood under climate change. The study utilizes a weather state-based, stochastic multivariate model as a conditional probability model for simulating the precipitation field. An important premise of this study is that large-scale climatic patterns serve as a major driver of persistent year-to-year changes in precipitation probabilities. Since uncertainty estimation in the study of climate change is needed to examine the reliability of the outcomes, this study also applies a Bayesian Markov chain Monte Carlo scheme to the widely used SAC-SMA (Sacramento soil moisture accounting) precipitation-runoff model. A case study is also performed with the Soyang Dam watershed in South Korea as the study basin. Finally, a comprehensive discussion on design flood under climate change is made.     

Simulation of future climate scenarios with a weather generator

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.     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

Statistically downscaled probabilistic multi‐model ensemble projections of precipitation change in a watershed

This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Global atmospheric response to specific linear combinations of the main SST modes. Part I: numerical experiments and preliminary results

This article investigates through numerical experiments the controversial question of the impact of El NinCo-Southern Oscillation (ENSO) phenomena on climate according to large-scale and regional-scale interhemispheric thermal contrast. Eight experiments (two considering only inversed Atlantic thermal anomalies and six combining ENSO warm phase with large-scale interhemispheric contrast and Atlantic anomaly patterns) were performed with the Météo-France atmospheric general circulation model. The definition of boundary conditions from observed composites and principal components is presented and preliminary results concerning the month of August, especially over West Africa and the equatorial Atlantic are discussed. Results are coherent with observations and show that interhemispheric and regional scale sea-surface-temperature anomaly (SST) patterns could significantly modulate the impact of ENSO phenomena: the impact of warm-phase ENSO, relative to the atmospheric model intercomparison project (AMIP) climatology, seems stronger when embedded in global and regional SSTA patterns representative of the post-1970 conditions [i.e. with temperatures warmer (colder) than the long-term mean in the southern hemisphere (northern hemisphere)]. Atlantic SSTAs may also play a significant role.     

IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate

Capturing the spatial and temporal correlation of multiple variables in a weather generator is challenging. A new massively multi-site, multivariate daily stochastic weather generator called IMAGE is presented here. It models temperature and precipitation variables as latent Gaussian variables with temporal behaviour governed by an auto-regressive model whose residuals and parameters are correlated through resampling of principle component time series of empirical orthogonal function modes. A case study using European climate data demonstrates the model’s ability to reproduce extreme events of temperature and precipitation. The ability to capture the spatial and temporal extent of extremes using a modified Climate Extremes Index is demonstrated. Importantly, the model generates events covering not observed temporal and spatial scales giving new insights for risk management purposes.     

The predictability of atmospheric and oceanic motions: Retrospect and prospects

This paper reviews the historic understanding of the predictability of atmospheric and oceanic motions, and interprets it in a general framework. On this basis, the existing challenges and unsolved problems in the study of the intrinsic predictability limit(IPL) of weather and climate events of different spatio-temporal scales are summarized. Emphasis is also placed on the structure of the initial error and model parameter errors as well as the associated targeting observation issue. Finally, the predictability of atmospheric and oceanic motion in the ensemble-probabilistic methods widely used in current operational forecasts are discussed.The necessity of considering IPLs in the framework of stochastic dynamic systems is also addressed.     

Evaluation of multiple stochastic rainfall generators in diverse climatic regions

Long term synthetic precipitation data are useful for water resources planning and management. Commonly stochastic weather generator (SWG) models are useful to produce synthetic time series of unlimited length of weather data based on the statistical characteristics of observed weather at a given location. However, it is difficult to find a single model which works best for all weather (climate) patterns. The objective of this study is to evaluate five different SWG models namely CLIGEN, ClimGen, LARS-WG, RainSim and WeatherMan to generate precipitation at three diverse climatic regions: a Mediterranean climate of western USA, temperate climate of eastern Australia and tropical monsoon region in northern Vietnam. The performance of SWG models to generate precipitation characteristics (i.e., precipitation occurrence; wet and dry spell; and precipitation intensity on wet days) varies between three selected climatic regimes. It was observed that the second order Markov chain (ClimGen and WeatherMan) performed well for all three selected regions in generating precipitation occurrence statistics. All models are able to simulate the ratio of wet/dry spell lengths with respect to observed precipitation. The RainSim performed well in reproducing wet/dry spell lengths in comparison to other models for wetter regions in Australia and Vietnam. ClimGen and WeatherMan are the two best models in simulating precipitation in the western USA, followed by CLIGEN and LARS. Similarly, ClimGen and WMAN are the two best models for synthetic precipitation generation for eastern Australian and northern Vietnam stations, but CLIGEN performs poorly over these regions. All SWG model performed differently with respect to climatic regimes, therefore careful validation is required depending on the weather pattern as well as its application in different water resources sectors. Although our findings are preliminary in nature, however, in order to generalize the performance of SWG’s in a given climate type, it is recommended that more number of stations needs to be evaluated in future studies.     

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.     

Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application

Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Regionalization of unit hydrograph Parameters: 2. Uncertainty analysis

Hydrologic model parameters obtained from regional regression equations are subject to uncertainty. Consequently, hydrologic model outputs based on the stochastic parameters are random. This paper presents a systematic analysis of uncertainty associated with the two parameters, N and K, in Nash's IUH model from different regional regression equations. The uncertainty features associated with N and K are further incorporated to assess the uncertainty of the resulting IUH. Numerical results indicate that uncertainty of N and K from the regional regression equations are too significant to be ignored.     

Driving a lumped hydrological model with precipitation output from weather generators of different complexity

ABSTRACT

This paper deals with the question of whether a lumped hydrological model driven with lumped daily precipitation time series from a univariate single-site weather generator can produce equally good results compared to using a multivariate multi-site weather generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Three different weather generators were tested: a univariate “Richardson type” model, an adapted univariate Richardson type model with an improved reproduction of the autocorrelation of precipitation amounts and a semi-parametric multi-site weather generator. The three modelling systems were evaluated in two Alpine study areas by comparing the hydrological output with respect to monthly and daily statistics as well as extreme design flows. The application of a univariate Richardson type weather generator to lumped precipitation time series requires additional attention. Established parametric distribution functions for single-site precipitation turned out to be unsuitable for lumped precipitation time series and led to a large bias in the hydrological simulations. Combining a multi-site weather generator with a hydrological model produced the least bias.     

Review of Chinese atmospheric science research over the past 70 years: Synoptic meteorology

Synoptic meteorology is a branch of meteorology that uses synoptic weather observations and charts for the diagnosis,study,and forecasting of weather.Weather refers to the specific state of the atmosphere near the Earth's surface during a short period of time.The spatial distribution of meteorological elements in the atmosphere can be represented by a variety of transient weather phenomena,which are caused by weather systems of different spatial and temporal scales.Weather is closely related to people's life,and its development and evolution have always been the focus of atmospheric scientific research and operation.The development of synoptic meteorology is closely related to the development of observation systems,dynamical theories and numerical models.In China,observation networks have been built since the early 1950 s.Up to now,a comprehensive meteorological observation systembased on ground,air and space has been established.In particular,the development of a new generation of dense radar networks,the development of the Fengyun satellite series and the implementation of a series of large field experiments have brought our understanding of weather from large-scale environment to thermal dynamics,cloud microphysical structure and evolution characteristics of meso and micro-scale weather systems.The development of observation has also promoted the development of theory,numerical model and simulation.In the early days,China mainly used foreign numerical models.Lately,China has developed numerical model systems with independent intellectual property rights.Based on the results of high-resolution numerical simulations,in-depth understanding of the initiation and evolution mechanism and predictability of weather at different scales has been obtained.Synoptic meteorology has gradually changed from an initially independent development to a multidisciplinary approach,and the interaction between weather and the change of climate and environment has become a hot and frontier topic in atmospheric science.This paper reviews the important scientific and technological achievements made in China over the past 70 years in the fields of synoptic meteorology based on the literatures in China and abroad,from six aspects respectively including atmospheric dynamics,synoptic-scale weather,typhoon and tropical weather,severe convective weather,numerical weather prediction and data assimilation,weather and climate,atmospheric physics and atmospheric environment.     

天气尺度瞬变扰动的物理分解原理

大气变量可以在时空域内物理分解成四个部分.前两个是纬圈-时间平均的对称部分和时间平均的非对称部分,分别由太阳辐射和海陆分布热力调节的季节变化引起,并形成规则的逐日气候.第三部分是由年际和季节内的热带海洋或极地热力强迫引起的纬圈平均瞬变对称扰动,可形成大气变量的行星尺度指数循环.第四部分是一些复杂的天气尺度瞬变非对称扰动.大气变量中的逐日天气尺度瞬变扰动,可以用于指示区域持续性的干旱、暴雨、低温和热浪等极端天气事件.天气尺度瞬变扰动天气图能在极端天气事件的预报中发挥应有的作用.     

Using a rainfall stochastic generator to detect trends in extreme rainfall

An original approach is proposed to estimate the impacts of climate change on extreme events using an hourly rainfall stochastic generator. The considered generator relies on three parameters. These parameters are estimated by average, not by extreme, values of daily climatic characteristics. Since climate changes should result in parameters instability in time, the paper focuses on testing the presence of linear trends in the generator parameters. Maximum likelihood tests are used under a Poisson–Pareto-Peak-Over-Threshold model. A general regionalization procedure is also proposed which offers the possibility to work on both local and regional scales. From the daily information of 139 rain gauge stations between 1960 and 2003, changes in heavy precipitations in France and their impacts on quantile predictions are investigated. It appears that significant changes occur mainly between December and May for the rainfall occurrence which increased during the four last decades, except in the Mediterranean area. Using the trend estimates, one can deduced that these changes, up to now, do not affect quantile estimations.     

Impact of climate change on water resources in Yongdam Dam Basin,Korea

The main purpose of this study is to investigate and evaluate the impact of climate change on the runoff and water resources of Yongdam basin, Korea. First, we construct global climate change scenarios using the YONU GCM control run and transient experiments, then transform the YONU GCM grid-box predictions with coarse resolution of climate change into the site-specific values by statistical downscaling techniques. The downscaled values are used to modify the parameters of a stochastic weather generator model for the simulation of the site-specific daily weather time series. The weather series is fed into a semi-distributed hydrological model called SLURP to simulate the streamflows associated with other water resources for the condition of 2CO₂. This approach is applied to the Yongdam dam basin in the southern part of Korea. The results show that under the condition of 2CO₂, about 7.6% of annual mean streamflow is reduced when it is compared with the current condition. Seasonal streamflows in the winter and autumn are increased, while streamflow in the summer is decreased. However, the seasonality of the simulated series is similar to the observed pattern An erratum to this article can be found at     

Pressure effects on regional mean sea level trends in the German Bight in the twenty-first century

The effect of large-scale atmospheric pressure changes on regional mean sea level projections in the German Bight in the twenty-first century are considered. A developed statistical model is applied to climate model data of sea level pressure for the twenty-first century to assess the potential contribution of large-scale atmospheric changes to future sea level changes in the German Bight. Using 78 experiments, an ensemble mean of 1.4-cm rise in regional mean sea level is estimated until the end of the twenty-first century. Changes are somewhat higher for realisations of the special report on emission scenarios (SRES) A1B and A2, but generally do not exceed a few centimeters. This is considerably smaller than the changes expected from steric and self-gravitational effects. Large-scale changes in sea level pressure are thus not expected to provide a substantial contribution to twenty-first century sea level changes in the German Bight.     

Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall–runoff model / Estimation des crues extrêmes de la Meuse à l'aide d'un générateur stochastique de variables météorologiques et d'un modèle pluie–débit

Abstract

A stochastic weather generator has been developed to simulate long daily sequences of areal rainfall and station temperature for the Belgian and French sub-basins of the River Meuse. The weather generator is based on the principle of nearest-neighbour resampling. In this method rainfall and temperature data are sampled simultaneously from multiple historical records with replacement such that the temporal and spatial correlations are well preserved. Particular emphasis is given to the use of a small number of long station records in the resampling algorithm. The distribution of the 10-day winter maxima of basin-average rainfall is quite well reproduced. The generated sequences were used as input for hydrological simulations with the semi-distributed HBV rainfall–runoff model. Though this model is capable of reproducing the flood peaks of December 1993 and January 1995, it tends to underestimate the less extreme daily peak discharges. This underestimation does not show up in the 10-day average discharges. The hydrological simulations with the generated daily rainfall and temperature data reproduce the distribution of the winter maxima of the 10-day average discharges well. Resampling based on long station records leads to lower rainfall and discharge extremes than resampling from the data over a shorter period for which areal rainfall was available.     

Vulnerability of water resources,vegetation productivity and soil erosion to climate change in Mediterranean watersheds

Climate change is expected to increase temperatures and lower rainfall in Mediterranean regions; however, there is a great degree of uncertainty as to the amount of change. This limits the prediction capacity of models to quantify impacts on water resources, vegetation productivity and erosion. This work circumvents this problem by analysing the sensitivity of these variables to varying degrees of temperature change (increased by up to 6·4 °C), rainfall (reduced by up to 40%) and atmospheric CO₂ concentrations (increased by up to 100%). The SWAT watershed model was applied to 18 large watersheds in two contrasting regions of Portugal, one humid and one semi‐arid; incremental changes to climate variables were simulated using a stochastic weather generator. The main results indicate that water runoff, particularly subsurface runoff, is highly sensitive to these climate change trends (down by 80%). The biomass growth of most species showed a declining trend (wheat down by 40%), due to the negative impacts of increasing temperatures, dampened by higher CO₂ concentrations. Mediterranean species, however, showed a positive response to milder degrees of climate change. Changes to erosion depended on the interactions between the decline in surface runoff (driving erosion rates downward) and biomass growth (driving erosion rates upward). For the milder rainfall changes, soil erosion showed a significant increasing trend in wheat fields (up to 150% in the humid watersheds), well above the recovery capacity of the soil. Overall, the results indicate a shift of the humid watersheds to acquire semi‐arid characteristics, such as more irregular river flows and increasingly marginal conditions for agricultural production. Copyright © 2007 John Wiley & Sons, Ltd.