Seasonality and magnitude of floods in Switzerland under future climate change

The flood seasonality of catchments in Switzerland is likely to change under climate change because of anticipated alterations of precipitation as well as snow accumulation and melt. Information on this change is crucial for flood protection policies, for example, or regional flood frequency analysis. We analysed projected changes in mean annual and maximum floods of a 22‐year period for 189 catchments in Switzerland and two scenario periods in the 21st century based on an ensemble of climate scenarios. The flood seasonality was analysed with directional statistics that allow assessing both changes in the mean date a flood occurs as well as changes in the strength of the seasonality. We found that the simulated change in flood seasonality is a function of the change in flow regime type. If snow accumulation and melt is important in a catchment during the control period, then the anticipated change in flood seasonality is most pronounced. Decreasing summer precipitation in the scenarios additionally affects the flood seasonality (mean date of flood occurrence) and leads to a decreasing strength of seasonality, that is a higher temporal variability in most cases. The magnitudes of mean annual floods and more clearly of maximum floods (in a 22‐year period) are expected to increase in the future because of changes in flood‐generating processes and scaled extreme precipitation. Southern alpine catchments show a different signal, though: the simulated mean annual floods decrease in the far future, that is at the end of the 21st century. Copyright © 2013 John Wiley & Sons, Ltd.     

Detecting the impact of climate and reservoirs on extreme floods using nonstationary frequency models

Stochastic Environmental Research and Risk Assessment - The annual maximum flood records of the Danjiangkou reservoir displayed significant decreasing trends. The upper stream of the reservoir was...     

Progress in plant phenology modeling under global climate change

Plant phenology is the study of the timing of recurrent biological events and the causes of their timing with regard to biotic and abiotic forces. Plant phenology affects the structure and function of terrestrial ecosystems and determines vegetation feedback to the climate system by altering the carbon, water and energy fluxes between the vegetation and near-surface atmosphere. Therefore, an accurate simulation of plant phenology is essential to improve our understanding of the response of ecosystems to climate change and the carbon, water and energy balance of terrestrial ecosystems. Phenological studies have developed rapidly under global change conditions, while the research of phenology modeling is largely lagged. Inaccurate phenology modeling has become the primary limiting factor for the accurate simulation of terrestrial carbon and water cycles.Understanding the mechanism of phenological response to climate change and building process-based plant phenology models are thus important frontier issues. In this review, we first summarized the drivers of plant phenology and overviewed the development of plant phenology models. Finally, we addressed the challenges in the development of plant phenology models and highlighted that coupling machine learning and Bayesian calibration into process-based models could be a potential approach to improve the accuracy of phenology simulation and prediction under future global change conditions.     

Copula-based geostatistical modeling of continuous and discrete data including covariates

It is common in geostatistics to use the variogram to describe the spatial dependence structure and to use kriging as the spatial prediction methodology. Both methods are sensitive to outlying observations and are strongly influenced by the marginal distribution of the underlying random field. Hence, they lead to unreliable results when applied to extreme value or multimodal data. As an alternative to traditional spatial modeling and interpolation we consider the use of copula functions. This paper extends existing copula-based geostatistical models. We show how location dependent covariates e.g. a spatial trend can be accounted for in spatial copula models. Furthermore, we introduce geostatistical copula-based models that are able to deal with random fields having discrete marginal distributions. We propose three different copula-based spatial interpolation methods. By exploiting the relationship between bivariate copulas and indicator covariances, we present indicator kriging and disjunctive kriging. As a second method we present simple kriging of the rank-transformed data. The third method is a plug-in prediction and generalizes the frequently applied trans-Gaussian kriging. Finally, we report on the results obtained for the so-called Helicopter data set which contains extreme radioactivity measurements.     

Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima

The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.     

Global projections of changing risks of floods and droughts in a changing climate

Abstract

Simulated daily discharge derived from a relatively high-resolution (approximately 1.1-degree) general circulation model was used to investigate future projections of extremes in river discharge under global warming. The frequency of floods was projected to increase over many regions, except those including North America and central to western Eurasia. The drought frequency was projected to increase globally, while regions such as northern high latitudes, eastern Australia, and eastern Eurasia showed a decrease or no significant changes. Changes in flood and drought are not explained simply by changes in annual precipitation, heavy precipitation, or differences between precipitation and evapotranspiration. Several regions were projected to have increases in both flood frequency and drought frequency. Such regions show a decrease in the number of precipitation days, but an increase in days with heavy rain. Several regions show shifts in the flood season from springtime snowmelt to the summer period of heavy precipitation.     

Coupled modeling of rainfall-induced floods and sediment transport at the catchment scale

Rainfall-induced floods may trigger intense sediment transport on erodible catchments, especially on the Loess Plateau in China, which in turn modifies the floods. However, the role of sediment transport in modifying floods has to date remained poorly understood. Concurrently, traditional hydrodynamic models for rainfall-induced floods typically ignore sediment transport, which may lead to inaccurate results for highly erodible catchments. Here, a two-dimensional (2D) coupled shallow water hydro-sediment-morphodynamic (SHSM) model, based on the Finite Volume Method on unstructured meshes and parallel computing, is proposed and applied to simulate rainfall-induced floods in the Zhidan catchment on the Loess Plateau, Shaanxi Province, China. For six historical floods of return periods up to 2 years, the numerical results compare well with observations of discharge hydrographs at the catchment outlet. The computed runoff-sediment yield relation is quantitatively reasonable as compared with other catchments under similar geographical conditions. It is revealed that neglecting sediment transport leads to underestimation of peak discharge of the flood by 14%–45%, whilst its effect on the timing of the peak discharge varies for different flood events. For 18 design floods with return periods of 10–500 years, sediment transport may lead to higher peak discharge by around 9%–15%. The temporal pattern of concentrated rainfall in a short period may lead to a larger exponent value of the power function for the runoff-sediment yield relation. The current finding leads us to propose that incorporating sediment transport in rainfall-induced flood modeling is warranted. The SHSM model is applicable to flood and sediment modeling at the catchment scale in support of risk management and water and soil management.     

Assessment of regional drought risk under climate change using bivariate frequency analysis

Since water supply failure is one of the primary impacts of drought, drought risk should be quantified in the context of a lack of available water. To assess the drought risk, water supply system performance indices such as reliability, resiliency, and vulnerability are usually introduced as they correspond to primary drought characteristics, i.e., frequency, duration, and magnitude. In this study, we developed a drought risk index (DRI) through weighted averaging the performance indices derived using bivariate drought frequency analysis. We suggested two types of DRI: observed DRI (DRI_O) and designed DRI (DRI_D). DRI_O was calculated using an observed (or synthesized) time series of water shortages. DRI_D was estimated from the bivariate drought frequency curves, which are the probabilistic magnitudes of water shortages corresponding to a particular duration. The historical maximum drought event that represents the maximum DRI_O has generally been used as the target security level. However, we could establish a practically applicable target security level considering that the future water supply failure risk is represented by DRI_D. We defined regional drought safety criteria in this study by comparing DRI_O and DRI_D. Application of the criteria to the Nakdong river basin in South Korea showed that W1 (Byeongseongcheon) and W4 (Hyeongsangang) had the lowest and highest drought risk, respectively, and the drought safety criteria showed an average range of 5–20 years.     

气候变暖下太湖极端洪水的归因探讨

全球增温引起的降水变化是否引起极端洪水的增加,发生在不同气候背景的极端洪水事件可提供不同参照系;而不同驱动因子下气候、水文数值模拟为认识洪水发生和归因提供了有效途径.本文结合机理数值模拟和随机统计模拟两种途径,针对1990s和1880s的太湖流域特大洪水,通过GCM气候模拟驱动的流域水文模拟和不确定性的阈值模拟,分析19世纪末和20世纪末极端洪水的发生强度和频率的变化,从而论证极端洪水发生的风险系数.结果表明,1990s的极端洪水流量(0.1%的极端洪水流量(Q0.1%)为2929~3601 m³/s,0.5%的极端洪水流量(Q0.5%)为1842~1893 m³/s)比工业革命前大气温室气体状况下(Q0.1%为2069~3119 m³/s,Q0.5%为1436~1561 m³/s)显著增大.与19世纪末相比,由于太湖流域人类活动改变的流域下垫面在1999年特大洪水中引起最大增量占35%,本文模拟和分析的20世纪末气候下的洪水最大增量占60%.去除人类活动影响的下垫面变化,估计特大洪水风险的最大增量为25%,因此认为20世纪末气候变化引起的太湖极端洪水风险在增加;这将为认识与全球增温相关联的洪水灾害预测预警提供科学依据.     

Assessing groundwater sustainability under changing climate using isotopic tracers and climate modelling,southwest Ohio,USA

Groundwater, an essential resource, is likely to change with global warming because of changes in the CO₂ levels, temperature and precipitation. Here, we combine water isotope geochemistry with climate modelling to examine future groundwater recharge in southwest Ohio, USA. We first establish the stable isotope profiles of oxygen and deuterium in precipitation and groundwater. We then use an isotope mass balance model to determine seasonal groundwater recharge from precipitation. Climate model output is used to project future changes in precipitation and its seasonal distribution under medium and high climate change scenarios. Finally, these results are combined to examine future changes in groundwater recharge. We find that 76% of the groundwater recharge occurs in the cool season. Climate models project precipitation increase in the cool season and decrease in the warm season. The total groundwater recharge is expected to increase by 3.2% (8.8%) under the medium (high) climate change scenarios.     

Geochemical aspects of some Japanese lavas

K, Rb, Sr, Ba and rare-earth concentrations in some Japanese lavas have been determined by mass-spectrometric stable-isotope dilution. The samples fall into three rare-earth groups corresponding to tholeiitic, high alumina and alkali basalts. Japanese tholeiites have trace element characteristics similar to those of oceanic ridge tholeiites except for distinctly higher relative concentrations of Ba. Japanese lavas may result from various degrees of partial fusion of amphibole eclogite.     

经验方法研究下垫面变化对洪水的影响

1980s以来,人类活动对下垫面的影响加剧,研究下垫面变化对流域产汇流规律的影响具有重要意义.通过对海河流域中紫荆关、阜平两个流域分别用经验方法进行洪水模拟演算,并把洪水资料按照年代不同分时段,建立P+Pa～Rs相关关系图,分析流域下垫面变化对降雨径流相关关系的影响.建立产流量R与洪峰Qm之间的相关关系,通过统计比较不同年代的趋势线发现,在产流量相同情况下,1980s后的洪峰流量较1980s前有所减少,说明海河流域的调蓄作用有增强的趋势.同时建立流域下垫面条件改变后产流量与洪峰变化幅度的相关关系,发现流域产流量变化与洪峰变化呈正相关关系.     

Probabilistic estimation of irrigation requirement under climate uncertainty using dichotomous and marked renewal processes

This study addresses estimation of net irrigation requirement over a growing season under climate uncertainty. An ecohydrological model, building upon the stochastic differential equation of soil moisture dynamics, is employed as a basis to derive new analytical expressions for estimating seasonal net irrigation requirement probabilistically. Two distinct irrigation technologies are considered. For micro irrigation technology, probability density function of seasonal net irrigation depth (SNID) is derived assessing transient behavior of a stochastic process which is time integral of dichotomous Markov process. Probability mass function of SNID which is a discrete random variable for traditional irrigation technology is also presented using a marked renewal process with quasi-exponentially-distributed time intervals. Comparing the results obtained from the presented models with those resulted from a Monte Carlo approach verified the significance of the probabilistic expressions derived and assumptions made.     

长江巨洪的强信号探讨

分析了1900年以来长江3次巨洪的3个强信号：(1)太阳黑子活动，(2)厄尔尼诺事件，(3)青藏高原南部大震，它们对大气环流异常的影响分别称为日气作用、海气作用、地气作用，依据长江巨洪和3个强信号的基本事实，讨论了长江发生巨洪的统计规律，指出当3个强信号的出现时间互相重叠时，长江很可能发生巨洪，如果再叠加其它信号，长江发生巨洪的量级更大，这对长江巨洪的超长期预期具有重要的指示作用。     

The State of knowledge about wetlands and their future under aspects of global climate change: the situation in Russia

About two-thirds of Russia’s land area are flat lands, which contributes to the development of conditions favouring wetland formation. Wetlands cover vast areas, especially in the north. Wetlands in the former Soviet Union were not recognized as separate or distinct ecosystems and this is still the situation in Russia today. Bogs are one of the most abundant and typical wetlands and were treated as worthless wastelands. Beginning in the 17th century and continuing under the Soviet government there was an enforced policy to drain wetlands and reclaim the land, mainly for farming. After the collapse of the USSR, this practice was discontinued along with the Soviet model of agriculture and an end to the forced and unnecessary use of pesticides and fertilizers with the result that the toxic load on Russian aquatic systems decreased drastically. Industrial production was also greatly curtailed. While it is now recovering, many of these are turning to environmentally-friendly technologies. The intensity of land-use related impacts upon Russian wetlands is negligible compared to that in more densely populated countries and therefore the environmental conservation of wetlands in Russia may not currently be an urgent problem. There is currently no consensus on what the overall direct and indirect impacts of climate change on the number of Russian wetlands will be—in some areas they may increase but decrease in others. In Russia, the most urgent issue is not the preservation of wetlands but the development of proper wetland management practices. For effective plans, data and information on wetland status, trends and characteristics are required that are not currently available.     

Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

In the context of climate change and rapid urbanization, urban pluvial floods pose an increasing threat to human wellbeing and security in the cities of China. A valuable aid to managing this problem lies in understanding the roles of environmental factors in influencing the occurrence of pluvial floods. This study presents a spatial analysis of records of inundated streets in the inner city of Shanghai during 1997–2013. A geographically weighted regression (GWR) is employed to examine the spatially explicit relationships between inundation frequency and spatial explanatory factors, and an ordinary least squares regression (OLS) is used to validate the GWR results. Results from the GWR model show that the inundation frequency is negatively related to elevation, pipeline density, and river density, and is positively related to road/square ratio and shantytown ratio. The green ratio is another significant explanatory factor for inundation frequency, and its coefficients range from ?1.11 to 0.81. In comparison with the OLS model, the GWR model has better performance as it has higher R², and lower corrected Akaike information criterion and mean square error values, as well as insignificant spatial autocorrelation of the model residuals. Additionally, the GWR model reveals detailed site-specific roles of the related factors in influencing street inundation. These findings demonstrate that the GWR model is a useful tool for investigating spatially explicit causes of disasters. The results also provide guidance for policy makers aiming to mitigate urban pluvial flood risks.     

Describing the recurrence interval of extreme floods using nonextensive thermodynamics and Tsallis statistics

This paper introduces Tsallis statistics and the q-exponential distribution as a means of analysing hydrological phenomena. The basic framework is introduced and then the method is used to derive a distribution for flood recurrence intervals using recently published data from the River Po, Italy. This fits the data much more effectively than the simple power-law applied in a previous study. Hence, a distribution derived from power-law considerations is more appropriate than the power-law itself. Nonextensive statistical mechanics has the potential for much broader utility in hydrology than is demonstrated here. Some potential avenues for future study are introduced.     

On the prediction of the Toce alpine basin floods with distributed hydrologic models

With the objective of improving flood predictions, in recent years sophisticated continuous hydrologic models that include complex land‐surface sub‐models have been developed. This has produced a significant increase in parameterization; consequently, applications of distributed models to ungauged basins lacking specific data from field campaigns may become redundant. The objective of this paper is to produce a parsimonious and robust distributed hydrologic model for flood predictions in Italian alpine basins. Application is made to the Toce basin (area 1534 km²). The Toce basin was a case study of the RAPHAEL European Union research project, during which a comprehensive set of hydrologic, meteorological and physiographic data were collected, including the hydrologic analysis of the 1996–1997 period. Two major floods occurred during this period. We compare the FEST04 event model (which computes rainfall abstraction and antecedent soil moisture conditions through the simple Soil Conservation Service curve number method) and two continuous hydrologic models, SDM and TDM (which differ in soil water balance scheme, and base flow and runoff generation computations). The simple FEST04 event model demonstrated good performance in the prediction of the 1997 flood, but shows limits in the prediction of the long and moderate 1996 flood. More robust predictions are obtained with the parsimonious SDM continuous hydrologic model, which uses a simple one‐layer soil water balance model and an infiltration excess mechanism for runoff generation, and demonstrates good performance in both long‐term runoff modelling and flood predictions. Instead, the use of a more sophisticated continuous hydrologic model, the TDM, that simulates soil moisture dynamics in two layers of soil, and computes runoff and base flow using some TOPMODEL concepts, does not seem to be advantageous for this alpine basin. Copyright © 2006 John Wiley & Sons, Ltd.