Effects of check dams on runoff and sediment load in a semi-arid river basin of the Yellow River

Check dam has become an efficient measure to control sediment transport and soil erosion in the gully areas. It plays an important role in soil erosion control and agricultural production in the Loess Plateau. Due to construction of numerous check dams, it is necessary to assess the impact of check dams on runoff and sediment load at basin scale. This study applied the SWAT model to simulate monthly runoff and sediment load in the Huangfuchuan basin in the middle reaches of the Yellow River. Twenty key check dams are coupled to the SWAT model simulation in the calibration (1978–1984) and validation period (1985–1989). The determination coefficient (R ²) and the Nash–Sutcliffe coefficient (NS) were 0.94 and 0.83 for runoff, and 0.82 and 0.81 for sediment load in the calibration period, respectively. During the validation period, the R ² and NS were 0.93 and 0.80 for runoff, and 0.90 and 0.83 for sediment load respectively. The results showed that the model simulation was acceptable. Subsequently, the calibrated model was used to examine the effect of check dams on runoff and sediment load between 1990 and 2012. It showed that the increasing check dams contributed 24.8 and 27.7% to the decrease of annual runoff and sediment load during the period of 1990–1999, whereas it reached up to 65.2% for runoff decline and 78.3% for sediment load reduction within 2000–2012. Overall, this study illustrated a case study of the dominant role of check dams on variation of runoff and sediment load in the Huangfuchuan basin.     

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.     

Detection and indication of 1,3,4-C27–29 triol in the sediment of northern South China Sea

After the detection of the 1,20,21-C_(29) long-chain triol in some sediments and freshwater pteridophytes, in this study, a new homologous long-chain triol, 1,3,4-C_(27-29), is detected for the first time in the Site4 B core sediment in the northern South China Sea. The hydroxyl location and length of the carbon chain of this newly discovered triol differ from those of 1,20,21-C_(29) triol. The test results of its molecular distribution and individual carbon isotope reveal that 1,3,4-C_(29) triol has a good correlation with n-C_(26-30) even carbon-numbered long-chain fatty alcohols, with R~2(n=68) values of 0.905, 0.929 and 0.903, respectively, and its carbon isotope composition, at –32.3‰±1.9‰, is similar to that of n-C_(26-30), at –29.13‰±0.87‰, –32.98‰±1.28‰, and –32.98‰±1.28‰. 1,3,4-C_(29) triol from the Site4 B core sediment and terrigenous long-chain fatty alcohol(n-C_(26-34)) show highly consistent distribution trends in the entire section; thus, the former could serve as a proxy indicator of the terrigenous input. Considering that the 1,20,21-C_(29) triol in previous research belongs to Azolla, which are fresh water pteridophytes, the 1,3,4-C_(27-29) triol identified in this study might have similar biogenetic derivation. Thus, determination of its biogenic area and growing environment could provide potential organic geochemical evidence supporting the terrigenous input and source in the northern South China Sea.     

Statistics for sample splitting for the calibration and validation of hydrological models

Hydrological models have been widely applied in flood forecasting, water resource management and other environmental sciences. Most hydrological models calibrate and validate parameters with available records. However, the first step of hydrological simulation is always to quantitatively and objectively split samples for use in calibration and validation. In this paper, we have proposed a framework to address this issue through a combination of a hierarchical scheme through trial and error method, for systematic testing of hydrological models, and hypothesis testing to check the statistical significance of goodness-of-fit indices. That is, the framework evaluates the performance of a hydrological model using sample splitting for calibration and validation, and assesses the statistical significance of the Nash–Sutcliffe efficiency index (E_f), which is commonly used to assess the performance of hydrological models. The sample splitting scheme used is judged as acceptable if the E_f values exceed the threshold of hypothesis testing. According to the requirements of the hierarchical scheme for systematic testing of hydrological models, cross calibration and validation will help to increase the reliability of the splitting scheme, and reduce the effective range of sample sizes for both calibration and validation. It is illustrated that the threshold of E_f is dependent on the significance level, evaluation criteria (both regarded as the population), distribution type, and sample size. The performance rating of E_f is largely dependent on the evaluation criteria. Three types of distributions, which are based on an approximately standard normal distribution, a Chi square distribution, and a bootstrap method, are used to investigate their effects on the thresholds, with two commonly used significance levels. The highest threshold is from the bootstrap method, the middle one is from the approximately standard normal distribution, and the lowest is from the Chi square distribution. It was found that the smaller the sample size, the higher the threshold values are. Sample splitting was improved by providing more records. In addition, outliers with a large bias between the simulation and the observation can affect the sample values of E_f, and hence the output of the sample splitting scheme. Physical hydrology processes and the purpose of the model should be carefully considered when assessing outliers. The proposed framework in this paper cannot guarantee the best splitting scheme, but the results show the necessary conditions for splitting schemes to calibrate and validate hydrological models from a statistical point of view.     

Impact of complexity on daily and multi-step forecasting of streamflow with chaotic,stochastic, and black-box models

Despite significant research advances achieved during the last decades, seemingly inconsistent forecasting results related to stochastic, chaotic, and black-box approaches have been reported. Herein, we attempt to address the entropy/complexity resulting from hydrological and climatological conditions. Accordingly, mutual information function, correlation dimension, averaged false nearest neighbor with E1 and E2 quantities, and complexity analysis that uses sample entropy coupled with iterative amplitude adjusted Fourier transform were employed as nonlinear deterministic identification tools. We investigated forecasting of daily streamflow for three climatologically different Swedish rivers, Helge, Ljusnan, and Kalix Rivers using self-exciting threshold autoregressive (SETAR), k-nearest neighbor (k-nn), and artificial neural networks (ANN). The results suggest that the streamflow in these rivers during the 1957–2012 period exhibited dynamics from low to high complexity. Specifically, (1) lower complexity lead to higher predictability at all lead-times and the models’ worst performances were obtained for the most complex streamflow (Ljusnan River), (2) ANN was the best model for 1-day ahead forecasting independent of complexity, (3) SETAR was the best model for 7-day ahead forecasting by means of performance indices, especially for less complexity, (4) the largest error propagation was obtained with the k-nn and ANN and thus these models should be carefully used beyond 2-day forecasting, and (5) higher number input variables except for the dominant variables made insignificant impact on forecasting performances for ANN and k-nn models.     

Injection Effects on Sediment Transport in Closed-Conduit Flows

Experiments were conducted to investigate injection effects on sediment transport in closed-conduit flows. The results show that the sediment transport rate essentially remains unchanged when the ratio of the injection velocity and that at boiling, Vi/Vcr < 10. However, significant sediment transport rate is observed when Vi/Vcr increases beyond this limit. In the literature, three semi-empirical models have been developed to relate seepage effects on the sediment transport rate. The experimentally measured data in the pre- and post-boiling condition (Liu and Chiew 2014, and the present study, respectively) are compared with these models. The results show that the models of Francalanci et al. (2008) and Nielsen et al. (2001) perform poorly in predicting injection effects on the sediment transport. Although Yang’s (2013) model could reasonably predict the influence of injection on the sediment transport rate in the post-boiling condition, it similarly fails when applied to the pre-boiling condition.     

Spherical cap harmonic analysis of regional magnetic anomalies based on CHAMP satellite data

We used CHAMP satellite vector data and the latest IGRF12 model to investigate the regional magnetic anomalies over mainland China. We assumed satellite points on the same surface (307.69 km) and constructed a spherical cap harmonic model of the satellite magnetic anomalies for elements X, Y, Z, and F over Chinese mainland for 2010.0 (SCH2010) based on selected 498 points. We removed the external field by using the CM4 model. The pole of the spherical cap is 36N° and 104°E, and its half-angle is 30°. After checking and comparing the root mean square (RMS) error of ΔX, ΔY, and ΔZ and X, Y, and Z, we established the truncation level at K ^max = 9. The results suggest that the created China Geomagnetic Referenced Field at the satellite level (CGRF2010) is consistent with the CM4 model. We compared the SCH2010 with other models and found that the intensities and distributions are consistent. In view of the variation of F at different altitudes, the SCH2010 model results obey the basics of the geomagnetic field. Moreover, the change rate of X, Y, and Z for SCH2010 and CM4 are consistent. The proposed model can successfully reproduce the geomagnetic data, as other data-fitting models, but the inherent sources of error have to be considered as well.     

Critical frequencies <Emphasis Type="Italic">foF</Emphasis>2 as an indicator of trends in thermospheric dynamics

The time variations in three parameters during the last decades are considered. R(foF2) is the correlation coefficient between the nighttime and daytime values of foF2 for the same day. Stable trends are found for the minimum (R(foF2)(max)) and maximum (R(foF2)(min)) values of R(foF2) during a year. The foF2(night)/foF2(day) ratio demonstrates both, negative and positive trends, and the trend sign depends on the inclination I and declination D of the magnetic field. The correlation coefficient r(h, fo) between foF2 and the 100 hP level in the stratosphere demonstrates a decrease (in the years of maximum and minimum solar activity) from the 1980s to the 1990s. The trends in all three groups of data are considered under the assumption of long-term changes in the circulation in the upper atmosphere.     

A novel non-iterative direct displacement-based seismic design procedure for self-centering buckling-restrained braced frame structures

Self-centering buckling-restrained braces (SCBRBs) were proposed recently to minimize residual deformation of the braces induced by yielding or buckling. Although earthquake resilience of structures equipped with the SCBRBs can be well achieved using displacement based designs (DBDs), previously proposed DBD procedures generally involve iterations. In this study, a novel direct displacement-based design method with a non-iterative procedure, named R–C_R DDBD, is proposed and applied to design of steel braced frame structures with SCBRBs. Unlike previously adopted DBD, the yield displacement does not need to be assumed initially in the proposed procedure. Instead, the yield strength and yield displacement are determined directly by the predetermined objective drift (ratio), using the relation of the strength reduction factor (R) and constant-strength inelastic displacement ratio spectra (C_R spectra), i.e. the R–C_R relation. Since the derived R–C_R relation is independent with the peak ground acceleration of the earthquake records when stiffness and strength degradation are not considered, the proposed procedure can be accurate for any seismic level. The R–C_R DDBD is supposed to begin with the knowledge of the seismic excitation level (according to the structure category, site classification and owner’s requirements) and the corresponding target drift; the end of the design is to obtain the cross sections of main frame members and all the bracing parameters. The result of two 7-story buildings designed according to the R–C_R DDBD procedure demonstrates that this procedure can be effective and fairly simple for practical seismic design.     

Comparison between gene expression programming and traditional models for estimating evapotranspiration under hyper arid Conditions

Gene Expression Programming (GEP) was used to develop new mathematical equations for estimating daily reference evapotranspiration (ET _ref) for the Kingdom of Saudi Arabia. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith model was used as a reference target for evapotranspiration (ET) values, with h _c varies from 5 to 105 cm with increment of a centimetre. Eight GEP models have been compared with four locally calibrated traditional models (Hargreaves-Samani, Irmak, Jensen-Haise and Kimberly-Penman). The results showed that the statistical performance criteria values such as determination coefficients (R ²) ranged from as low as 64.4% for GEP-MOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEP-MOD8 with which all climatic parameters included (maximum, minimum and mean temperature; maximum, minimum and mean humidity; solar radiation; wind speed; and crop height). Moreover, an interesting founded result is that the solar radiation has almost no effect on ET _ref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating ET _ref. Furthermore, eight GEP models have obtained better results than the locally calibrated traditional ET _ref equations.     

The partitioning of radiated energy and the largest aftershock of seismic sequences occurred in the northeastern Italy and western Slovenia

We analyzed the most relevant seismic sequences that occurred from 1977 to 2007 in the Friuli-Venezia Giulia region (northeastern Italy) and western Slovenia. The eight aftershock sequences were triggered by low- to moderate-magnitude earthquakes with mainshock duration magnitude ranging from 3.7 to 5.6. The b-value of the Gutenberg–Richter law varies from 0.8 to 1.1. The modified Omori’s modeling of the sequences evidences values of the p exponent ranging from 0.8 to 1.0. Using the Reasenberg and Jones (Science 243:1173–1176, 1989; Science 265:1251–1252, 1994) approach, we computed the probabilistic estimate of the aftershock rates and the largest aftershock in given time intervals. The difference in magnitude between the mainshock and the largest aftershock is calculated according to the modified Båth law and using an approach that considers the partitioning of the radiated seismic energy between mainshock and aftershocks. The partitioning of the radiated seismic energy appears to play a significant role in the evolution of the sequences. We define the parameter R _ES as the ratio between the radiated seismic energy of the mainshock and the summation of the seismic energy radiated by the aftershocks. The difference in magnitude between the mainshock and the largest aftershock, calculated with the parameter R _ES, agrees well with the observed difference. In most sequences, the parameter R _ES decreases very quickly until the occurrence of the largest aftershock and then becomes constant. By analyzing the values of R _ES during the early hours following the mainshock, we found that the R _ES values after 24 h are well related to the final ones, calculated on the whole sequence, and to the differences in magnitude between the mainshock and the largest aftershock.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

Quantitative Analysis of Seismicity Before Large Taiwanese Earthquakes Using the G-R Law

Seismicity has been identified as an example of a natural, nonlinear system for which the distribution of frequency and event size follow a power law called the “Gutenberg–Richter (G-R) law.” The parameters of the G-R law, namely b- and a-values, have been widely used in many studies about seismic hazards, earthquake forecasting models, and other related topics. However, the plausibility of the power law model and applicability of parameters were mainly verified by statistical error σ of the b-value, the effectiveness of which is still doubtful. In this research, we used a newly defined p value developed by Clausetet al. (Power-Law Distributions in Empirical Data, SIAM Rev. 51, 661–703, 2009) instead of the statistical error σ of the b-value and verified its effectiveness as a plausibility index of the power-law model. Furthermore, we also verified the effectiveness of K–S statistics as a goodness-of-fit test in estimating the crucial parameter \(M_{\text{c}}\) of the power-law model.     

Parameter Uncertainty Propagation in a Rainfall–Runoff Model; Case Study: Karoon-III River Basin

Conceptual hydrological models are popular tools for simulating land phase of hydrological cycle. Uncertainty arises from a variety of sources such as input error, calibration and parameters. Hydrologic modeling researches indicate that parametric uncertainty has been considered as one of the most important source. The objective of this study was to evaluate parameter uncertainty and its propagation in rainfall-runoff modeling. This study tried to model daily flows and calculate uncertainty bounds for Karoon-III basin, Southwest of Iran, using HEC-HMS (SMA). The parameters were represented by probability distribution functions (PDF), and the effect on simulated runoff was investigated using Latin Hypercube Sampling (LHS) on Monte Carlo (MC). Three chosen parameters, based on sensitivity analysis, were saturated-hydraulic-conductivity (Ks), Clark storage coefficient (R) and time of concentration (t _c ). Uncertainty associated with parameters were accounted for, by representing each with a probability distribution. Uncertainty bounds was calculated, using parameter sets captured from LHS on parameters PDF of sub-basins and propagating to the model. Results showed that maximum reliability (11%) resulted from Ks propagating. For three parameters, underestimation was more than overestimation. Maximum sharpness and standard deviation (STD) was resulted from propagating Ks. Cumulative Distribution Function (CDF) of flow and uncertainty bounds showed that as flow increased, the width of uncertainty bounds increased for all parameters.     

Thermoremanent and chemical magnetization of exsolution products of nanosized titanomagnetites

Based on the theory of two-phase interacting nanoparticles, the formation of thermoremanent and chemical remanent magnetization in nanosized titanomagnetites is modeled. It is shown that the value of thermoremanent magnetization barely depends on the degree of titanomagnetite exsolution whereas, chemical remanent magnetization which emerges during the exsolution increases up to at most the value of thermoremanent magnetization. The values of the ratio of thermoremanent to ideal magnetization, R _t , fall within the limits 0.8 ≤ R _t ≤ 1. The analogous ratio of chemical remanent magnetization to the ideal R _c are below R _t at all stages of the exsolution. Besides, the magnetic interaction between the nanoparticles reduces the values of thermoremanent and chemical magnetization but barely affects the ratio.     

Sedimentary and geochemical evidence of Eocene climate change in the Xining Basin,northeastern Tibetan Plateau

The northeastern Tibetan Plateau began to grow during the Eocene and it is important to understand the climatic history of Asia during this period of so-called ‘doubthouse' conditions. However, despite major advances in the last few decades,the evolutionary history and possible mechanisms of Eocene climate change in the northeastern Tibetan Plateau remain unclear.The Xining Basin in the northeastern Tibetan Plateau contains a continuous sequence of Early to Late Eocene non-marine sediments which provides the opportunity to resolve long-term climate changes during this period. In this study, we report the results of analyses of lithofacies, sediment color and geochemistry of bulk samples collected from the Xijigou section of the Xining Basin. An abrupt lithofacies change between the Early(~52–40 Ma) and Late Eocene(~40–34 Ma) indicates a change in the depositional environment from a shallow lake to a playa lake in response to a significant climatic shift. During ~52–40 Ma,higher values of sediment redness(a*), redness/lightness(a*/L*) and higher modified Chemical Index of Weathering(CIW′)indicate a relatively warm and humid climate, while from ~40–34 Ma the lower values of a*, a*/L*and lower CIW′ imply subhumid to semi-arid climatic conditions. The paleoclimatic records indicate a long-term(~52–34 Ma) trend of decreasing chemical weathering, consistent with global climate change. An abrupt sharp excursion of the proxy records during ~42–40 Ma suggests a relatively brief warm interval, corresponding to the Middle Eocene Climatic Optimum(MECO). We suggest that global cooling substantially reduced humidity in inner Asia, resulting in sub-humid to semi-arid climatic conditions after 40 Ma in the Xining Basin, which may have been responsible for the long-term trend of decreasing chemical weathering during the Eocene.     

Long-term trends in the ratio of the daytime and nighttime values of <Emphasis Type="Italic">foF</Emphasis>2

The consideration of the relation between the daytime and nighttime values of the critical frequency F2, foF2 of the ionospheric F2 layer, started in the previous publication of the authors, is continued. The main regularities in variations in the correlation coefficient R(foF2) characterizing this relation are confirmed using larger statistical material (more ionospheric stations and longer observational series). Long-term trends in the R(foF2) value are found: at all stations the negative value of R(foF2) increases with time after 1980.     

Diagnostics of Turbulent Dynamo from the Flux Emergence Rate in Solar Active Regions

Line-of-sight magnetograms acquired by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamic Observatory (SDO) and by the Michelson Doppler Imager (MDI) onboard the Solar and Heliospheric Observatory (SOHO) for 14 emerging ARs were used to study the derivative of the total unsigned flux–the flux emergence rate, R(t). We found that the emergence regime is not universal: each AR displays a unique emergence process. Nevertheless, two types of the emergence process can be identified. First type is a “regular” emergence with quasi-constant behavior of R(t) during a 1–3 day emergence interval with a rather low magnitude of the flux derivative, R_max = (0.57 ± 0.22) × 10²² Mx day^–1. The second type can be described as “accelerated” emergence with a long interval (>1 day) of the rapidly increasing flux derivative R(t) that result in a rather high magnitude of R_max= (0.92 ± 0.29) × 10²² Mx day^–1, which later changes to a very short (about a one third of day) interval of R(t) = const followed by a monotonous decrease of R(t). The first type events might be associated with emergence of a flux tube with a constant amount of flux that rises through the photosphere with a quasi-constant speed. Such events can be explained by the traditional largescale solar dynamo generating the toroidal flux deep in the convective zone. The second-type events can be interpreted as a signature of sub-surface turbulent dynamo action that generates additional magnetic flux (via turbulent motions) as the magnetic structure makes its way up to the solar surface.     

Influence of Solar Wind Plasma Parameters on the Intensity of Isolated Magnetospheric Substorms

Parameters of the interplanetary magnetic field and solar wind plasma during periods of 163 isolated substorms have been studied. It is shown that the solar wind velocity V and plasma density N remain approximately constant for at least 3 h before substorm onset Т _o and 1 h after Т _o . On average, the velocity of the solar wind exhibits a stable trend toward anticorrelation with its density over the whole data array. However, the situation is different if the values of V and N are considered with respect to the intensity of substorms observed during that period. With the growth of substorm intensity, quantified as the maximum absolute value of AL index, an increase in both the solar wind plasma velocity and density, at which these substorms appear, is obsreved. It has been found that the magnitude of the solar wind dynamic pressure P is closely related to the magnetosphere energy load defined as averaged values of the Kan–Lee electric field E_KL and Newell parameter dΦ/dt averaged for 1 h interval before Т _o . The growth of the dynamic pressure is accompanied by an increase in the load energy necessary for substorm generation. This interrelation between P and values of EKL and dΦ/dt is absent in other, arbitrarily chosen periods. It is believed that the processes accompanying increasing dynamic pressure of the solar wind result in the formation of magnetosphere conditions that increasingly impede substorm generation. Thus, the larger is P, the more solar wind energy must enter the Earth’s magnetosphere during the period of the growth phase for substorm generation. This energy is later released during the period of the substorm expansion phase and creates even more intense magnetic bays.