State Key Laboratory of Water Resources and Hydropower Engineering Science

Abstract This study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized precipitation–evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998–2015 compared with those during 1980–97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the three-dimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity–area–duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEI-detected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km 2 and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km 2 and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

Biochar and compost (or composting) combined amendments had higher efficiency for remediation of heavy metals polluted soils.

SUMMARY This paper aims to propose a procedure for modeling the joint probability distribution of bivariate uncertain data with a nonlinear dependence structure. First, the concept of dependence measures is briefly introduced. Then, both the Akaike Information Criterion and the Bayesian Information Criterion are adopted for identifying the best‐fit copula. Thereafter, simulation of copulas and bivariate distributions based on Monte Carlo simulation are presented. Practical application for serviceability limit state reliability analysis of piles is conducted. Finally, four load–test datasets of load–displacement curves of piles are used to illustrate the proposed procedure. The results indicate that the proposed copula‐based procedure can model and simulate the bivariate probability distribution of two curve‐fitting parameters underlying the load–displacement models of piles in a more general way. The simulated load–displacement curves using the proposed procedure are found to be in good agreement with the measured results. In most cases, the Gaussian copula, often adopted out of expedience without proper validation, is not the best‐fit copula for modeling the dependence structure underlying two curve‐fitting parameters. The conditional probability density functions obtained from the Gaussian copula differ considerably from those obtained from the best‐fit copula. The probabilities of failure associated with the Gaussian copula are significantly smaller than the reference solutions, which are very unconservative for pile safety assessment. If the strong negative correlation between the two curve‐fitting parameters is ignored, the scatter in the measured load–displacement curves cannot be simulated properly, and the probabilities of failure will be highly overestimated. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments.

Abstract The water-short adjacent basins of the Huang (Yellow), Huai and Hai Rivers in North China contain most of China's farmland. This study evaluates water vulnerability there as a function of sensitivity to climate change, people per unit flow, water use-to-availability ratio, and per capita water use. Three scenarios for 2030 are considered: (1) climate change without socio-economic changes; (2) socio-economic changes without climate change; and (3) both climate and socio-economic changes. Results show increases in the already precarious vulnerability, especially in the Hai river basin. Keywords: water securitywater vulnerability evaluationintegrated analysisNorth China Acknowledgements This study was supported by the National Basic Research Program of China (2010CB428406/2012CB956204) and the Natural Science Foundation of China (No. 41071025). Notes

Abstract An approach of deriving the annual runoff distribution using copulas from an annual rainfall‐runoff model is proposed to provide an alternative annual runoff frequency analysis method in case of changing climatic variables. The annual rainfall‐runoff model is established on the basis of the Budyko formula to estimate annual runoff, with annual precipitation and potential evapotranspiration as input variables. The model contains one single parameter that guarantees that annual water balance is satisfied. In the derivation of the annual runoff distribution, annual precipitation, annual potential evapotranspiration, and parameter are treated as three random variables, while the annual runoff distribution is obtained by integrating the joint probability density function of the three random variables over the domain constrained by the annual rainfall‐runoff model using the canonical vine copula. This copula‐based derivation approach is tested for 40 watersheds in two large basins in China. The estimated annual runoff distribution performs well in most watersheds. The performance is mainly related to the accuracy of the marginal distribution of precipitation. The copula‐based derivation approach can also be used in ungauged watersheds where the distribution of at the local site is estimated from the regional information of the variable, and it also has acceptable performance in most watersheds, while poor performance is observed in a few watersheds with low accuracy in the Budyko formula.

Climate variability and human activity were regarded as two contributors to streamflow alteration. However, the contributions of the two factors were still unclear in Dongting Lake. Therefore, it was crucial to quantify the relative impact of climate variability and human activity on streamflow alteration. The time series (1961–2010) was divided into three periods, namely, natural period (1961–1980), change period I (1981–2002) and change period II (2003–2010). Sensitivity analysis based on Budyko-type equations was applied to reveal the contributions of climate variability and human activity in those two change periods, respectively. The results showed that during the change period I, climate variability was the main factor responsible for streamflow alteration in most parts of Dongting Lake, accounting for 60.07–67.27%. However, the impact of climate variability was slightly smaller than that of human activity in West Dongting Lake (the former accounting for 43.20% while the latter accounting for 56.80%). For the change period II, human activity was the dominate factor for streamflow alteration, accounting for 58.89–78.33%. The impact of climate variability gradually decreased while the impact of human activity gradually increased. Along with the intensification of the human activity, the impact of it became more dominant. The results could provide a reference for water resources planning and management decisions. Under the condition of uncontrollable climatic factor, effective measures should be put forward in controlling human activity, such as reservoir/dam operation, closed management of protected area and so on. Besides, it is essential to study the impact of climate variability on future water resources and water resource management under different climate change scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

ABSTRACT Precipitation in Xinjiang, China, was modelled with covariates, such as time and climate indices, using quantile regressions. Compared to a frequentist quantile regression, a Bayesian quantile regression tended to generate smoother and narrower band confidence intervals of quantile regression coefficients, especially at extremely high and low quantile levels. A full picture of temporal trends at quantile levels from 0.01 to 0.99 indicates that the wet season (May to August) precipitation in Northern Xinjiang and the western regions of Southern Xinjiang showed statistically significant increases with different magnitudes over all quantile levels. However, the wet season precipitation in South‐eastern Xinjiang decreased at some quantile levels. The Eastern Atlantic/Western Russia ( EAWR ) pattern was the most significant large‐scale climate pattern that influenced wet season precipitation when compared to other studied patterns, i.e. the El Niño–Southern Oscillation ( ENSO ), the Atlantic Multidecadal Oscillation ( AMO ), the Pacific Decadal Oscillation ( PDO ), the Northern Oscillation ( NO ), the Arctic Oscillation ( AO ) and the North Atlantic Oscillation ( NAO ). The quantile regression coefficients associated with the EAWR index positively increased from low to high quantile levels. The ENSO significantly affected the extremely high wet season precipitation in Xinjiang. El Niño increased and La Niña decreased wet season precipitation in Northern Xinjiang, with different magnitudes at different quantile levels. Other climate patterns, i.e. the AMO , PDO , NO , NAO and AO , did not evidently affect the wet season precipitation conditional on the ENSO and EAWR . These findings suggest that the predictability of seasonal precipitation over Xinjiang can be improved by incorporating indices associated with the ENSO and EAWR as model predictors.

Abstract The focus of the present study of a concrete‐faced rockfill dam (CFRD) is the time‐dependent deformation problem of rockfill, especially under a high confining pressure. In this paper, a new creep constitutive model for rockfill is introduced. A numerical realization step and algorithm applied to the analysis using the general finite element method is also presented for the creep model. The numerical simulation result of the creep model is compared with data from a laboratory triaxial creep test. It is concluded that the creep model can describe the real creep characteristics and mechanical behavior of rockfill. In studying the stress and deformation behavior of the high rockfill dam, and considering the creep effect, the creep model is applied in performing creep analysis of the Shuibuya CFRD with 200 m high in China, now the highest constructed CFRD in the world. In comparing the creep numerical simulation results with those of previous analyses that did not consider the creep effect, the deformation of the dam exhibits an evident increment, and the rockfill creep has an obvious influence on the deformation and stress of the concrete‐face slab. The prediction of the final maximum settlement was 2.09 m for the Shuibuya Dam, with the rockfill creep effect in accord with the creep parameters of the triaxial creep test. Copyright © 2009 John Wiley & Sons, Ltd.

Dam-break flows over mobile bed are often sharply stratified, comprising a bedload sediment-laden layer and an upper clear-water layer. Double layer-averaged (DL) models are attractive for modelling such flows due to the balance between the computing cost and the ability to represent stratification. However, existing DL models are oversimplified as sediment concentration in the sediment-laden layer is presumed constant, which is not generally justified. Here a new DL model is presented, explicitly incorporating the sediment mass conservation law in lieu of the assumption of constant sediment concentration. The two hyperbolic systems of the governing equations for the two layers are solved separately and simultaneously. The new model is demonstrated to agree with the experimental measurements of instant and progressive dam-break floods better than a simplified double layer-averaged model and a single layer-averaged model. It shows promise for applications to sharply stratified sediment-laden flows over mobile bed.

Determining soil–water characteristic curve (SWCC) at a site is an essential step for implementing unsaturated soil mechanics in geotechnical engineering practice, which can be measured directly through various in-situ and/or laboratory tests. Such direct measurements are, however, costly and time-consuming due to high standards for equipment and procedural control and limits in testing apparatus. As a result, only a limited number of data points (e.g., volumetric water content vs. matric suction) on SWCC at some values of matric suction are obtained in practice. How to use a limited number of data points to estimate the site-specific SWCC and to quantify the uncertainty (or degrees-of-belief) in the estimated SWCC remains a challenging task. This paper proposes a Bayesian approach to determine a site-specific SWCC based on a limited number of test data and prior knowledge (e.g., engineering experience and judgment). The proposed Bayesian approach quantifies the degrees-of-belief on the estimated SWCC according to site-specific test data and prior knowledge, and simultaneously selects a suitable SWCC model from a number of candidates based on the probability logic. To address computational issues involved in Bayesian analyses, Markov Chain Monte Carlo Simulation (MCMCS), specifically Metropolis-Hastings (M-H) algorithm, is used to solve the posterior distribution of SWCC model parameters, and Gaussian copula is applied to evaluating model evidence based on MCMCS samples for selecting the most probable SWCC model from a pool of candidates. This removes one key limitation of the M-H algorithm, making it feasible in Bayesian model selection problems. The proposed approach is illustrated using real data in Unsaturated Soil Database (UNSODA) developed by U.S. Department of Agriculture. It is shown that the proposed approach properly estimates the SWCC based on a limited number of site-specific test data and prior knowledge, and reflects the degrees-of-belief on the estimated SWCC in a rational and quantitative manner.

ABSTRACT The detrended fluctuation analysis ( DFA ) and multifractal DFA , which can detect nonstationarities of time series with trends, were applied to study long‐term persistence ( LTP ) and multifractal behaviour of 100 stations of daily precipitation and 145 stations of streamflow time series of Canada. Results show that all precipitation time series showed LTP at both small and large time scales, while streamflow time series generally showed nonstationary behaviour at small time scales and LTP at large time scales. The significant multifractal behaviour of Canadian precipitation and streamflow data can be accurately described by the universal multifractal model and the modified multiplicative cascade model. Precipitation over central Canada showed stronger multifractality than that of western and eastern Canada, while multifractality of streamflow data is less spatially homogeneous. The multifractal strength of precipitation is generally smaller than that of streamflow. Eleven (9) out of 100 precipitation stations showed positive (negative) temporal trends in parameters derived using the universal multifractal model, and about half of the stations whose streamflow data exhibited statistically significant abrupt change points showed a weakening or strengthening in the multifractal strength moving from the pre‐change to the post‐change periods. Differences in the multifractal strength between Canadian precipitation and streamflow data suggest that the persistence of streamflow was not only because streamflow is more autocorrelated than precipitation but also it is more consistently affected by human activities such as streamflow regulation.

Abstract This paper explores the potential for seasonal prediction of hydrological variables that are potentially useful for reservoir operation of the Three Gorges Dam, China. The seasonal flow of the primary inflow season and the peak annual flow are investigated at Yichang hydrological station, a proxy for inflows to the Three Gorges Dam. Building on literature and diagnostic results, a prediction model is constructed using sea-surface temperatures and upland snow cover available one season ahead of the prediction period. A hierarchical Bayesian approach is used to estimate uncertainty in the parameters of the prediction model and to propagate these uncertainties to the predictand. The results show skill for both the seasonal flow and the peak annual flow. The peak annual flow model is then used to estimate a design flood (50-year flood or 2% exceedence probability) on a year-to-year basis. The results demonstrate the inter-annual variability in flood risk. The predictability of both the seasonal total inflow and the peak annual flow (or a design flood volume) offers potential for adaptive management of the Three Gorges Dam reservoir through modification of the operating policy in accordance with the year-to-year changes in these variables.

Abstract Anthropogenic impacts on terrestrial evapotranspiration (ET) changes during 1980–2020 were evaluated based on newly released observed‐based GLEAM ET and Coupled Model Intercomparison Project Phase 6 (CMIP6) through optional fingerprint method. Global assessments show that anthropogenic forcings dominate the increasing ET trend, other than natural forcing (NAT). On the global scale, anthropogenic forcings explain ∼84.2% of the observed ET trend, while the signal of natural forcing cannot be detected. Among anthropogenic forcings, greenhouse gases (GHG) are the primary driving factor for ET changes, and GHG‐only has already explained ∼78.8% of the observed ET trend. This study for the first time demonstrates that the GHG signal can be detected in the observed‐based ET and can be separable from NAT and aerosol (AER) signals. At the regional scale, CMIP6 simulations work well in North Hemisphere, but are deficient in South Hemisphere. GHG still dominates ET changes in the North Hemisphere, except for Europe, where the influence of AER forcing stands out, and attributable changes to AER is 2.4 times of GHG. Our first quantitative detection and attribution of ET contribute to advance the understanding of anthropogenic activities on hydrological cycle changes.

In the offshore fields, helical piles are increasingly deemed to constitute suitable tools for anchoring floating structures and wind turbines. A large number of studies have been published to explore the installation torque–capacity correlation, and most of them are conducted in a deterministic manner. However, natural soils are inherently spatially varying, and analyses taking such variation into account might be closer to the reality. To address this issue, this paper examines the installation and uplift process of helical piles considering spatially varying soils via three-dimensional large deformation random finite element analyses within a Monte Carlo framework. Computed values of the installation torques and the uplift capacities compare well with the results in existing publications, therefore verifying the applicability of the numerical model. Spatially varying soil strength is mapped through the random field, followed by Monte Carlo simulations conducted to determine the torque–capacity correlation in random soils. The results suggest that the torque–capacity correlation might be misestimated once the spatially random soil properties are overlooked. Besides, probabilistic assessments of the pile torque–capacity correlation are performed, which may be of great interest to engineering practitioners in the design method of the helical pile.

Hydro turbines operating at partial flow conditions usually have vortex ropes in the draft tube that generate large pressure fluctuations. This unsteady flow phenomenon is harmful to the safe operation of hydropower stations. This paper presents numerical simulations of the internal flow in the draft tube of a Francis turbine with particular emphasis on understanding the unsteady characteristics of the vortex rope structure and the underlying mechanisms for the interactions between the air and the vortices. The pressure fluctuations induced by the vortex rope are alleviated by air admission from the main shaft center, with the water-air two phase flow in the entire flow passage of a model turbine simulated based on the homogeneous flow assumption. The results show that aeration with suitable air flow rate can alleviate the pressure fluctuations in the draft tube, and the mechanism improving the flow stability in the draft tube is due to the change of vortex rope structure and distribution by aeration, i.e. a helical vortex rope at a small aeration volume while a cylindrical vortex rope with a large amount of aeration. The preferable vortex rope distribution can suppress the swirl at the smaller flow rates, and is helpful to alleviate the pressure fluctuation in the draft tube. The analysis based on the vorticity transport equation indicates that the vortex has strong stretching and dilation in the vortex rope evolution. The baroclinic torque term does not play a major role in the vortex evolution most of the time, but will much increase for some specific aeration volumes. The present study also depicts that vortex rope is mainly associated with a pair of spiral vortex stretching and dilation sources, and its swirling flow is alleviated little by the baroclinic torque term, whose effect region is only near the draft tube inlet.

Abstract The segmentation of flood seasons has both theoretical and practical importance in hydrological sciences and water resources management. The probability change-point analysis technique is applied to segmenting a defined flood season into a number of sub-seasons. Two alternative sampling methods, annual maximum and peaks-over-threshold, are used to construct the new flow series. The series is assumed to follow the binomial distribution and is analysed with the probability change-point analysis technique. A Monte Carlo experiment is designed to evaluate the performance of proposed flood season segmentation models. It is shown that the change-point based models for flood season segmentation can rationally partition a flood season into appropriate sub-seasons. China's new Three Gorges Reservoir, located on the upper Yangtze River, was selected as a case study since a hydrological station with observed flow data from 1882 to 2003 is located 40 km downstream of the dam. The flood season of the reservoir can be reasonably divided into three sub-seasons: the pre-flood season (1 June–2 July); the main flood season (3 July–10 September); and the post-flood season (11–30 September). The results of flood season segmentation and the characteristics of flood events are reasonable for this region. Citation Liu, P., Guo, S., Xiong, L. & Chen, L. (2010) Flood season segmentation based on the probability change-point analysis technique. Hydrol. Sci. J. 55(4), 540–554.

Aiming at studying the regulation quality of isolated turbine regulating systems under load disturbance and different regulation modes, the complete mathematical model of a turbine regulating system under three regulation modes is established. Then, based on dominant poles and null points, the method of order reduction for a high-order system of time response of the frequency is proposed. By this method, the complete high-order systems are solved and the regulation quality for time response of the frequency is studied. The results indicate that (1) the tail wave, which is the main body of time response of the frequency and the principal factor that determines the regulation quality, is mainly determined by the dominant poles; (2) for the three regulation modes, by deleting the high-order terms, the three equivalent overall transfer functions are fourth order, third order, and third order, respectively, and can be solved; (3) the analytical fluctuation equations of time response of the frequency solved from low-order equivalent overall transfer functions accurately simulate the fluctuation characteristics of time response; and (4) based on damped vibrations decomposed from analytical fluctuation equations, the regulation qualities of three regulation modes are analyzed.

This study explored three techniques for estimating the soil salt content from Landsat data. First, the 127 items of in situ measured hyperspectral reflectance data were collected and resampled to the spectral resolution of the reflectance bands of Landsat 5 and Landsat 8, respectively. Second, 12 soil salt indices (SSI) summarized from previous literature were determined based on the simulated Landsat bands. Third, 127 measurement groups with Landsat bands and SSI were randomly divided into training (102) and testing subgroups (25). Three techniques including partial least square regression (PLSR), support vector machine (SVM), and deep learning (DL) were used to establish a soil salinity model using SSI and the simulated Landsat bands as independent variables (IV), respectively. Results indicated that PLSR with SSI performed best for both simulated Landsat 5 and Landsat 8 data. Compared with PLSR, SVM underestimated soil salt content, whereas DL obtained centralized simulations and failed to capture the lower and upper observations. We recommend the PLSR model with SSI as IV to estimate soil salt content because it can identify >66% moderate-to-high-saline soils, which indicates its great potential for soil salt monitoring in arid or semiarid regions.

Climate variability modulates spatio-temporal variability of dry spells (DSs) and wet spells (WSs) within a river basin and will affect water resources management practices leading to various impacts on the socio-economic development in river basins. In this study, we evaluated spatio-temporal variability of DS and WS in Huai River basin (HRB), China, by developing copula-based severity-duration-frequency (SDF) curves. The result shows that the upper reach and the southern part of middle reach of HRB are prone to both DS and WS; however, the duration and severity of WS are comparatively higher in comparison to DS. It was observed that DS is more frequent in spring and summer, whereas WS in summer and autumn. The choice of copula plays an important role in deriving the SDF curves, and an inappropriately chosen copula function may result in a large bias of SDF estimation. The arch12 copula was found to be the best choice in the majority of stations for deriving the SDF curves. The constructed SDF curves primarily shows two major patterns for DS and WS, i.e. concave down pattern and convex up pattern. The frequency of extreme DS decreases from 1960s to 1990s, and increases after 2000s, while the frequency of extreme WS increases from 1970s to 1990s and then decreases from 1990s to 2000s. The results in this study can provide useful information for designing conservation structures and to develop water allocation strategies at different temporal scales.