Extension of observed flood series by combining a distributed hydro-meteorological model and a copula-based model

Long flood series are required to accurately estimate flood quantiles associated with high return periods, in order to design and assess the risk in hydraulic structures such as dams. However, observed flood series are commonly short. Flood series can be extended through hydro-meteorological modelling, yet the computational effort can be very demanding in case of a distributed model with a short time step is considered to obtain an accurate flood hydrograph characterisation. Statistical models can also be used, where the copula approach is spreading for performing multivariate flood frequency analyses. Nevertheless, the selection of the copula to characterise the dependence structure of short data series involves a large uncertainty. In the present study, a methodology to extend flood series by combining both approaches is introduced. First, the minimum number of flood hydrographs required to be simulated by a spatially distributed hydro-meteorological model is identified in terms of the uncertainty of quantile estimates obtained by both copula and marginal distributions. Second, a large synthetic sample is generated by a bivariate copula-based model, reducing the computation time required by the hydro-meteorological model. The hydro-meteorological modelling chain consists of the RainSim stochastic rainfall generator and the Real-time Interactive Basin Simulator (RIBS) rainfall-runoff model. The proposed procedure is applied to a case study in Spain. As a result, a large synthetic sample of peak-volume pairs is stochastically generated, keeping the statistical properties of the simulated series generated by the hydro-meteorological model. This method reduces the computation time consumed. The extended sample, consisting of the joint simulated and synthetic sample, can be used for improving flood risk assessment studies.     

Stochastic similarities between the microscale of turbulence and hydro-meteorological processes

ABSTRACT

Turbulence is considered to generate and drive most geophysical processes. The simplest case is isotropic turbulence. In this paper, the most common three-dimensional power-spectrum-based models of isotropic turbulence are studied in terms of their stochastic properties. Such models often have a high order of complexity, lack stochastic interpretation and violate basic stochastic asymptotic properties, such as the theoretical limits of the Hurst coefficient, when Hurst-Kolmogorov behaviour is observed. A simpler and robust model (which incorporates self-similarity structures, e.g. fractal dimension and Hurst coefficient) is proposed using a climacogram-based stochastic framework and tested over high-resolution observational data of laboratory scale as well as hydro-meteorological observations of wind speed and precipitation intensities. Expressions of other stochastic tools such as the autocovariance and power spectrum are also produced from the model and show agreement with data. Finally, uncertainty, discretization and bias related errors are estimated for each stochastic tool, showing lower errors for the climacogram-based ones and larger for power spectrum ones.     

Meta-heuristic maximum likelihood parameter estimation of the mixture normal distribution for hydro-meteorological variables

In the water resources field, there are emerging problems such as temporal changes of data and new additions of water sources. Non-mixture models are not efficient in analyzing these data because these models are developed under the assumption that data do not change and come from one source. Mixture models could successfully analyze these data because mixture models contain more than one modal. The expectation maximization (EM) algorithm has been widely used to estimate parameters of the mixture normal distribution for describing the statistical characteristics of hydro meteorological data. Unfortunately, the EM algorithm has some disadvantages, such as divergence, derivation of information matrices, local maximization, and poor accuracy. To overcome these disadvantages, this study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm—genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the EM algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.     

Evaluation of SMOS soil moisture retrievals over the central United States for hydro-meteorological application

Soil moisture has been widely recognized as a key variable in hydro-meteorological processes and plays an important role in hydrological modelling. Remote sensing techniques have improved the availability of soil moisture data, however, most previous studies have only focused on the evaluation of retrieved data against point-based observations using only one overpass (i.e., the ascending orbit). Recently, the global Level-3 soil moisture dataset generated from Soil Moisture and Ocean Salinity (SMOS) observations was released by the Barcelona Expert Center. To address the aforementioned issues, this study is particularly focused on a basin scale evaluation in which the soil moisture deficit is derived from a three-layer Xinanjiang model used as a hydrological benchmark for all comparisons. In addition, both ascending and descending overpasses were analyzed for a more comprehensive comparison. It was interesting to find that the SMOS soil moisture accuracy did not improve with time as we would have expected. Furthermore, none of the overpasses provided reliable soil moisture estimates during the frozen season, especially for the ascending orbit. When frozen periods were removed, both overpasses showed significant improvements (i.e., the correlations increased from r = −0.53 to r = −0.65 and from r = −0.62 to r = −0.70 for the ascending and descending overpasses, respectively). In addition, it was noted that the SMOS retrievals from the descending overpass consistently were approximately 11.7% wetter than the ascending retrievals by volume. The overall assessment demonstrated that the descending orbit outperformed the ascending orbit, which was unexpected and enriched our knowledge in this area. Finally, the potential reasons were discussed.     

Predicting seasonal and hydro-meteorological impact in environmental variables modelling via Kalman filtering

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section.     

Future hydro-meteorological drought of the Johor River Basin,Malaysia, based on CORDEX-SEA projections

Water scarcity issues in the Johor River Basin (JRB) could affect the populations of Malaysia and Singapore. This study provides an overview of future hydro-meteorological droughts using climate projections from an ensemble of four Coordinated Regional Climate Downscaling Experiments – Southeast Asia (CORDEX-SEA) domain outputs under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios for the 2021–2050 and 2071–2100 periods. The climate projections were bias corrected using the quantile mapping approach before being incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to examine the meteorological and hydrological droughts, respectively. Overall, future annual precipitation, streamflow, and maximum and minimum temperatures are projected to change by about ?44.2 to 24.3%, ?88.7 to 42.2%, 0.8 to 3.7ºC and 0.7 to 4.7ºC, respectively. The results show that the JRB is likely to receive more frequent meteorological droughts in the future.     

Temporal variation in suspended sediment transport: linking sediment sources and hydro-meteorological drivers

Suspended sediment concentrations (SSCs) in rivers are variable in time due to interacting soil erosion and sediment transport processes. While many hydro-meteorological variables are correlated to SSCs, interpretation of these correlations in terms of driving processes requires in-depth knowledge of the catchment. Detailed sediment source information is needed to establish the causal linkages between driving processes and variations in SSC. This study innovatively combined sediment fingerprinting with multivariate statistical analyses of hydro-meteorological data to investigate how differential contributions of sediment sources control SSC in response to hydro-meteorological variables during high-flow events in rivers. Applied to the River Aire (UK), five sediment sources were classified: grassland topsoil in three lithological areas (limestone, millstone grit and coal measures), eroding riverbanks, and street dust. A total of 159 suspended sediment samples were collected during 14 high-flow events (2015–2017). Results show substantial variation in sediment sources during high-flow events. Limestone grassland and street dust, the dominant contributors to the suspended sediment, show temporal variations consistent with variations in total SSC, and are correlated with precipitation and discharge shortly prior and during high-flow events (i.e. fast mobilization to and within river). Contrarily, contributions from millstone and coals grassland appear to be driven by antecedent hydro-meteorological conditions (i.e. lag-time between soil erosion and sediment delivery). Riverbank material is poorly correlated to hydro-meteorological variables, possibly due to weak source discrimination or the infrequent nature of its delivery to the channel. Differences in source-specific drivers and process interactions for sediment transport demonstrate the difficulty in generalizing sediment transport patterns and developing targeted suspended sediment management strategies. While more research is essential to address different uncertainties emerging from the approach, the study demonstrates how empirical data on sediment monitoring, fingerprinting, and hydro-meteorology can be combined and analysed to better understand sediment connectivity and the factors controlling SSC. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.     

Spatio-temporal modelling of hydro-meteorological derived risk using a Bayesian approach: a case study in Venezuela

Extreme environmental events have considerable impacts on society. Preparation to mitigate or forecast accurately these events is a growing concern for governments. In this regard, policy and decision makers require accurate tools for risk estimation in order to take informed decisions. This work proposes a Bayesian framework for a unified treatment and statistical modeling of the main components of risk: hazard, vulnerability and exposure. Risk is defined as the expected economic loss or population affected as a consequence of a hazard event. The vulnerability is interpreted as the loss experienced by an exposed population due to hazard events. The framework combines data of different spatial and temporal supports. It produces a sequence of temporal risk maps for the domain of interest including a measure of uncertainty for the hazard and vulnerability. In particular, the considered hazard (rainfall) is interpolated from point-based measured rainfall data using a hierarchical spatio-temporal Kriging model, whose parameters are estimated using the Bayesian paradigm. Vulnerability is modeled using zero-inflated distributions with parameters dependent on climatic variables at local and large scales. Exposure is defined as the total population settled in the spatial domain and is interpolated using census data. The proposed methodology was applied to the Vargas state of Venezuela to map the spatio-temporal risk for the period 1970–2006. The framework highlights both high and low risk areas given extreme rainfall events.     

Daily streamflow trends in Western versus Eastern Norway and their attribution to hydro-meteorological drivers

Regional warming and modifications in precipitation regimes has large impacts on streamflow in Norway, where both rainfall and snowmelt are important runoff generating processes. Hydrological impacts of recent changes in climate are usually investigated by trend analyses applied on annual, seasonal, or monthly time series. None of these detect sub-seasonal changes and their underlying causes. This study investigated sub-seasonal changes in streamflow, rainfall, and snowmelt in 61 and 51 catchments respectively in Western (Vestlandet) and Eastern (Østlandet) Norway by applying the Mann–Kendall test and Theil–Sen estimator on 10-day moving averaged daily time series over a 30-year period (1983–2012). The relative contribution of rainfall versus snowmelt to daily streamflow and the changes therein have also been estimated to identify the changing relevance of these driving processes over the same period. Detected changes in 10-day moving averaged daily streamflow were finally attributed to changes in the most important hydro-meteorological drivers using multiple-regression models with increasing complexity. Earlier spring flow timing in both regions occur due to earlier snowmelt. Østlandet shows increased summer streamflow in catchments up to 1100 m a.s.l. and slightly increased winter streamflow in about 50% of the catchments. Trend patterns in Vestlandet are less coherent. The importance of rainfall has increased in both regions. Attribution of trends reveals that changes in rainfall and snowmelt can explain some streamflow changes where they are dominant processes (e.g., spring snowmelt in Østlandet and autumn rainfall in Vestlandet). Overall, the detected streamflow changes can be best explained by adding temperature trends as an additional predictor, indicating the relevance of additional driving processes such as increased glacier melt and evapotranspiration.     

Future high- and low-flow estimations for Central Vietnam: a hydro-meteorological modelling chain approach

We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies.     

Power of the Mann–Whitney test for detecting a shift in median or mean of hydro-meteorological data

 The non-parametric Mann–Whitney (MW) statistic test has been popularly used to assess the significance of a shift in median or mean of hydro-meteorological time series. It has been considered that the test is more suitable for non-normally distributed data and it may be not sensitive to the distribution type of sample data. However, no evidence has been provided to demonstrate these. This study investigates the power of the test in various circumstances by means of Monte Carlo simulation. Simulation results demonstrate that the power of the test is very sensitive to various properties of sample data. The power depends on the pre-assigned significance level, magnitude of a shift, sample size, and its occurrence position within a time series; and it is also strongly affected by the variation, skewness, and distribution type of a time series. The bigger the magnitude of a shift, the more powerful the test is; the larger the sample size, the more powerful the test is; and the bigger the variation within a time series, the less power the test has. The test has the highest power if a shift occurs at the midpoint of a time series. For the samples with different distribution types, the power of the test is dramatically different. The test has the highest power for time series with the extreme value type III (EV3) distribution while it indicates the lowest power for time series with the lognormal distribution.     

Identification of the four rearranged hopane series in geological bodies and their geochemical significances

Saturated fractions in a total of 23 oil samples and hydrocarbon source rocks from the Songliao, Tarim,and Ordos Basins have been analyzed by GC–MS and GC–MS–MS. According to the relative retention, mass spectral characteristics, and comparison with existing literature, a complete carbon number distribution ranging from C27 to C35(without C28) in the 17a(H)-diahopane series and early-eluting rearranged hopane series is identified. Compounds in the 18a(H)-neohopane series(Ts and C29Ts) and21-methyl-28-nor-hopane series(29Nsp and 30Nsp) are also noted. These four series of rearranged hopanes seem to occur in both brackish-saline lacustrine and coal measure environments. However, the coal measure and swamp environments being under an oxic condition, compared with brackish-saline lacustrines, are presumably more helpful to the formation of 30 E. Diversity in the content and distribution patterns indicate that rearranged hopanes could serve as good indicators of organic facies, depositional environment and maturity in petroleum geology.     

滇西地区GPS时间序列中陆地水载荷形变干扰的GRACE分辨与剔除

利用“中国大陆构造环境监测网络”在云南西部地区的13个连续GPS观测站和法国空间大地测量研究组Space Geodesy Research Group）的GRACE时变重力场资料,定量分析了该区域陆地水载荷所产生的非构造形变的量值和变化特点,探讨了利用GRACE分辨和剔除GPS观测中陆地水负荷所引起的非构造形变干扰的依据和模型.结果表明：滇西地区GPS坐标变化时间序列的垂向分量中,普遍包含有明显的年周期非构造形变波动,高值可达12mm,其中约42%源于陆地水迁徙变化所引起的负荷形变;通过主成份分析方法所获取的区域GPS共模误差与GRACE陆地水载荷形变序列的相关性高达0.87,若以GRACE扣除陆地水负荷形变,则滇西地区GPS网共模误差可消除约64%,且物理机制明确.然而,由于目前的GRACE只能有效分辨大约400km范围内陆地水载荷的整体变化,所以对于各GPS站点更加局部化的陆地水负荷非构造形变干扰,尚无法进行有效分辨.     

On reconstruction of the Ito-like equation from persistent time series

The Langevin equation with finite-range persistence was introduced as a macroscopic model of various geophysical phenomena. The modified histogram procedure (MHP) of reconstruction of the equation from time series was proposed. An efficiency of MHP was tested on artificial persistent time series (with short and long-tail distributions) generated by different Ito-like equations. For an exemplary geophysical time series, the appropriate Ito-like equation was reconstructed.     

Assessment of temporal dynamics of snow cover and its validation with hydro-meteorological data in parts of Chenab Basin,western Himalayas

The present investigation was conducted to analyze the temporal patterns of snow cover area%(SCA%), air temperature, snowfall and river discharge in parts of Chenab basin, western Himalayas. The relationship of mean SCA% with mean air temperature and river discharge was also tested using Pearson's product-moment correlation at 95% confidence limit and further sensitivity analysis of river discharge to SCA and SCA to air temperature was performed. Moderate Resolution Imaging Spectroradiometer(MODIS) 8-day surface reflectance product MOD09A1 was used to delineate SCA during the period 2000–2013. Moreover, variation in the lowest elevation from where snow cover area starts(LESCA) was also analyzed and its relationship with mean air temperature was also studied. Non-parametric method, Mann-Kendall test was employed to determine the trend in the SCA%, air temperature, snowfall and river discharge. The investigation carried out for three meteorological stations i.e. Batote, Reasi and Tandi revealed significant findings. At Batote and Reasi, statistically significant decreasing trends were observed over the period 2000 to 2012, for maximum, minimum and mean air temperature. Mean minimum SCA% exhibited a significant upward trend during 2000–2013 which is corroborated by the significantly increasing trend of mean annual snowfall(Tandi station) from 2000 to 2010. Further, significant decreasing trend of river discharge for the winter season at Batote station from 2000 to 2011 and decreasing trends in the maximum, minimum and mean air temperature at Batote and Reasi stations are also consistent with the increasing trend of SCA%. At both Batote and Reasi stations, mean SCA% exhibited significant negative relationship with the mean air temperature. On the other hand, LESCA exhibited positive correlation with the mean air temperature except in a few months, where negative relationship was seen. Sensitivity analysis of river discharge to SCA exhibited very low values of sensitivity coefficient in most of the months, indicating less sensitivity of river discharge to SCA. On the other hand, sensitivity coefficient of SCA to air temperature exhibited comparatively higher values which indicate SCA is more sensitive to air temperature.     

Paleomagnetic results from Mio-Pliocene marine sedimentary series in Crete

Paleomagnetic measurements have been conducted on Mio-Pliocene marine sedimentary series from Crete in order to detect any eventual rotation of this island. The results obtained from 14 stable sites of Tortonian age (～ 7 m.y.) yield a paleomagnetic pole not significantly different from either the European or the African ones, showing that Crete has not undergone any significant rotation since that epoch.     

Bootstrap confidence intervals for design storms from exceedance series

Abstract

An approach for better understanding of the physical implication of estimated aquifer parameters is demonstrated by analysing the pumping test data at Cottam in the Nottingham aquifer, UK. A sensitivity analysis showed that the area represented by the estimated parameters was much smaller than the area covered by the depression cone. After parameters are estimated, further research should be carried out to understand what portions of the aquifer the parameters represent. The parameters estimated at Cottam represented mainly aquifer features between roughly 100 and 2000 m. The sensitivity analysis also showed that the observed drawdown being satisfactorily matched by a model with uniform parameters does not prove that the aquifer is homogeneous. Slightly anomalous data may imply the existence of large anomalous zones. Although the drawdowns at Cottam could be ‘satisfactorily’ fitted by a model with uniform parameters, the fit could be improved by a model using a more permeable aquifer but with a zone about 700 m wide and with 42% less transmissivity.     

Intra-seasonal variability and wavelet characteristics of lake evaporation in comparison with hydro-meteorological data at Lake Keban (Turkey)

ABSTRACT

This study focuses on the variability of lake evaporation and also the periodic relationships among hydro-meteorological variables. The monthly hydro-meteorological data of Lake Keban were investigated by wavelet transforms. The results show that the main periodicity is on an annual scale. This periodicity is weaker for precipitation and wind speed but higher for evaporation, temperature, runoff and relative humidity. In addition to this, the continuous wavelet figures show some weak periodicities on the almost 10-year scale level but they are not continuous over time. Also, strong events on a short-term monthly scale are seen for evaporation, precipitation and runoff in 1988. This event in 1988 may be explained by the 1988 La Niña event, which was one of the strongest on record. Also, the periodicities on the 2–8-month scales in the precipitation data can be interpreted as being connected with the strong El Niño events of 1982 and 1992.

Editor D. Koutsoyiannis; Associate editor A. Carsteanu     

Multi-site bias correction of climate model outputs for hydro-meteorological impact studies: An application over a watershed in China

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre-processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi-variate bias correction (MBC) methods—initially proposed to correct the inter-variable correlation or multi-variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution-based single-site bias correction methods are used for comparison. The effects of multi-site correction on hydro-meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi-site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi-site methods consistently reproduce watershed-averaged meteorological variables better than single-site methods, especially for extreme values. In terms of representing hydrological extremes, the multi-site methods generally perform better than the single-site methods, although the benefits vary according to the hydrological index. However, when applying the multi-site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi-site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies.