American Journal of Engineering and Applied Sciences, 2019
Unwise and increasing exploitation of various energy carriers, including oil, gas and coal, has m... more Unwise and increasing exploitation of various energy carriers, including oil, gas and coal, has made countries to focus on two major issues, namely running out fossil fuels, as well as environmental pollution. Hence, one of the main priorities of energy policy is to diversification of energy sources, as well as finding a safe, cheap and nonpolluting energy source (devoid of greenhouse gases). Therefore, nuclear energy is important. In this regard, the main purpose of this paper is to examine the effect of energy consumption on the quality of the environment and the introduction of the best economically efficient option for energy production. In this research, we conducted a literature search incorporating keywords of nuclear energy, nuclear fission, fossil fuel, greenhouse effect in Persian and English databases in a structured manner and retrieved the papers and articles dealt with benefits and disadvantages of nuclear energy as well as its environmental impacts and subsequently they analyzed in a descriptive manner. In this study, while studying nuclear energy, fossil fuels and renewable energy, we tried to introduce a suitable replacement of these energies to fossil fuels, taking into account the economic and environmental perspectives resulting from it. Among all types of energy sources, nuclear energy may have the least impact on the environment, including water, soil and air and is a source free from gases. In addition to all of these, this energy is considered as a safe economic option.
American Journal of Engineering and Applied Sciences, 2020
Sistan (Zabol) region is located in Sistan and Baluchestan province and in southeastern part of I... more Sistan (Zabol) region is located in Sistan and Baluchestan province and in southeastern part of Iran. The specific atmospheric condition of this region, especially high-speed and continuous winds, can provide the necessary condition for Electricity generation from wind power. 120-day winds of Sistan region are one of the renewable energy sources in the province, which has been neglected in recent years. This paper examines how to beneficially use this power source, in particular to generate electricity, using field research, written reports, reviewing wind speed atlases and wind energy in the region and interviewing authorities and relevant organizations. The results demonstrate that, considering the climatic features and recent drought in the region, correct use of wind energy can lead to creating new line of employment, preventing the emission of fossil fuels, exporting electricity to neighboring countries in need of electricity and saving on fossil fuel sources. To achieve these goals, more attention and cooperation from related authorities and government and private sector investment is significantly required.
Journal of Experimental Agriculture International, 2018
Evaluation of sediment delivery ratio is important for determining watershed sediment yield. Rate... more Evaluation of sediment delivery ratio is important for determining watershed sediment yield. Rates of both interrill and rill erosion were calculated under shrub and uncovered Inceptisols conditions and were not observed to the presence by ravines and gullies in the watershed of Jacu River, in a semiarid region, Brazil. Direct measurement campaigns of suspended sediment and bedload were also carried out by means of the US DH-48 for collection of suspended sediment samples and US BLH-84used to collect samples bed load. The soil loss due to interril erosion under uncovered conditions was equal to 8.43 t ha-1 and was considered high, and the same was true for the values of rill erosion with erodibility equal to 0.0021142 kg N-1 s-1 and critical shear stress (τc) equal to 2.34 Pa. The mean value of sediment delivery ratio of Jacu watershed was equal to 0.165 and ranged from 0.29 in the year 2008 to 0.026 in 2010. This variation was associated with the natural variability of semiarid environment, indicating the necessity of assessment for a longer period to deepen our knowledge of sediment delivery ratio of the Jacu semiarid watershed.
Suspended sediment and bedload discharges in sand-bed rivers shape semi-arid landscapes and impac... more Suspended sediment and bedload discharges in sand-bed rivers shape semi-arid landscapes and impact sediment delivery from these landscapes, but are still incompletely understood. Suspended sediment and bedload fluxes of the intermittent Exu River, Brazil, were sampled by direct measurements. The highest suspended sediment concentration observed was 4847.4 mg L-1 and this value was possibly associated with the entrainment of sediment that was deposited in the preceding year. The bedload flux was well related to the stream power and the river efficiently transported all available bedload with a mean rate of 0.0047 kg m-1 s-1 , and the percentage of bedload to suspended sediment varied between 4 and 12.72. The bed sediment of Exu River was prone to entrainment and showed a proclivity for transport. Thus, sand-bed and gravel-bed rivers of arid environments seem to exhibit the same mobility in the absence of armour layer.
Journal of Experimental Agriculture International, 2018
Studies assessing technologies to design riparian strips using plant covers, based on sediment yi... more Studies assessing technologies to design riparian strips using plant covers, based on sediment yield in river basins, are required for environmental protection. The removal of semi-shrubby, native vegetation in the Brazilian semiarid region, has contributed to the degradation of semiarid basins. The aim of this study was to design a riparian strip for the Jacu River in the semiarid region of Pernambuco as a function of sediment yield. Experiments were conducted during the years 2008
The copula-entropy theory combines the entropy theory and the copula theory. The entropy theory h... more The copula-entropy theory combines the entropy theory and the copula theory. The entropy theory has been extensively applied to derive the most probable univariate distribution subject to specified constraints by applying the principle of maximum entropy. With the flexibility to model nonlinear dependence structure, parametric copulas (e.g., Archimedean, extreme value, meta-elliptical, etc.) have been applied to multivariate modeling in water engineering. This study evaluates the copula-entropy theory using a sample dataset with known population information and a flood dataset from the experimental watershed at the Walnut Gulch, Arizona. The study finds the following: (1) both univariate and joint distributions can be derived using the entropy theory. (2) The parametric copula fits the true copula better using empirical marginals than using fitted parametric/entropy-based marginals. This suggests that marginals and copula may be identified separately in which the copula is investigated with empirical marginals. (3) For a given set of constraints, the most entropic canonical copula (MECC) is unique and independent of the marginals. This allows the universal solution for the proposed analysis. (4) The MECC successfully models the joint distribution of bivariate random variables. (5) Using the "AND" case return period analysis as an example, the derived MECC captures the change of return period resulting from different marginals.
Multivariate hydrologic frequency analysis has been widely studied using: (1) commonly known join... more Multivariate hydrologic frequency analysis has been widely studied using: (1) commonly known joint distributions or copula functions with the assumption of univariate variables being independently identically distributed (I.I.D.) random variables; or (2) directly applying the entropy theory-based framework. However, for the I.I.D. univariate random variable assumption, the univariate variable may be considered as independently distributed, but it may not be identically distributed; and secondly, the commonly applied Pearson's coefficient of correlation () is not able to capture the nonlinear dependence structure that usually exists. Thus, this study attempts to combine the copula theory with the entropy theory for bivariate rainfall and runoff analysis. The entropy theory is applied to derive the univariate rainfall and runoff distributions. It permits the incorporation of given or known information, codified in the form of constraints and results in a universal solution of univariate probability distributions. The copula theory is applied to determine the joint rainfall-runoff distribution. Application of the copula theory results in: (i) the detection of the nonlinear dependence between the correlated random variables-rainfall and runoff, and (ii) capturing the tail dependence for risk analysis through joint return period and conditional return period of rainfall and runoff. The methodology is validated using annual daily maximum rainfall and the corresponding daily runoff (discharge) data collected from watersheds near Riesel, Texas (small agricultural experimental watersheds) and Cuyahoga River watershed, Ohio.
The purpose of this paper was to choose an appropriate information dissimilarity measure for hier... more The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant ...
This study analyzed extreme precipitation events, using daily rainfall data for 1966–2015. A Mann... more This study analyzed extreme precipitation events, using daily rainfall data for 1966–2015. A Mann–Kendall trend test was used to evaluate trends in extreme precipitation, copula functions were applied to compute the joint return periods of extreme events, and univariate and bivariate distributions were used to determine risk. The results showed that the decrease in consecutive wet days (CWD) was significant in the west and the northwest of Iran, while the consecutive dry days (CDD) index was increasing therein. The precipitation on more than the 90th percentile (P90) very wet days and annual number of days with precipitation less than the 90th percentile threshold (D90) indices followed similar patterns, with no significant trend in most parts of Iran, but at several stations in the north, west, and northwest, their decline was extreme. Furthermore, the increase of D10 (annual number of days with precipitation less than the 90th percentile threshold) and P10 (total precipitation of ...
Soil erosion affects agricultural production, and industrial and socioeconomic development. Chang... more Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran’s climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of ...
River flow regulations and thermal regimes have been altered by human-induced interventions (such... more River flow regulations and thermal regimes have been altered by human-induced interventions (such as dam construction) or climate change (such as air temperature variations). It is of great significance to adopt a well-performed data-driven model to accurately quantify the impact of human-induced interventions or climate change over river water temperature (WT), which can help understand the underlying evolution mechanism of the river thermal regimes by dam operation or climate change. This research applied the Bayesian network-based model (BNM), which can easily identify inherently stronger associated variables with a target variable from multiple influencing variables to predict the daily WT and make a quantitative assessment of the effect produced by Three Gorges Reservoir (TGR) construction in the Yangtze River, China. A comparative study between the proposed model and two other models was implemented to verify the predicted accuracy of the BNM. With the help of the BNM model, t...
The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections fo... more The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections for the Southern Hills-Gulf region, USA, considering emission pathways and a global climate model (GCM) as two main sources of uncertainty. Forty projections of downscaled daily air temperature and precipitation from 2010 to 2099 under four emission pathways and ten CMIP5 GCMs are adopted for hydroclimate modeling via the HELP3 hydrologic model. This study focuses on evapotranspiration (ET), surface runoff, and groundwater recharge projections in this century. Climate projection uncertainty is characterized by the hierarchical Bayesian model averaging (HBMA) method, which segregates emission pathway uncertainty and climate model uncertainty. HBMA is able to derive ensemble means and standard deviations, arising from individual uncertainty sources, for ET, runoff, and recharge. The model results show that future recharge in the Southern Hills-Gulf region is more sensitive to different clima...
The objective of this research was to analyze the temporal patterns of monthly and annual precipi... more The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and Sept...
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for perf... more Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs...
This study presents three new hybrid artificial intelligence optimization models—namely, adaptive... more This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating c...
Flood frequency analysis (FFA) is needed for the design of water engineering and hydraulic struct... more Flood frequency analysis (FFA) is needed for the design of water engineering and hydraulic structures. The choice of an appropriate frequency distribution is one of the most important issues in FFA. A key problem in FFA is that no single distribution has been accepted as a global standard. The common practice is to try some candidate distributions and select the one best fitting the data, based on a goodness of fit criterion. However, this practice entails much calculation. Sometimes generalized distributions, which can specialize into several simpler distributions, are fitted, for they may provide a better fit to data. Therefore, the generalized gamma (GG) distribution was employed for FFA in this study. The principle of maximum entropy (POME) was used to estimate GG parameters. Monte Carlo simulation was carried out to evaluate the performance of the GG distribution and to compare with widely used distributions. Finally, the T-year design flood was calculated using the GG and compared with that with other distributions. Results show that the GG distribution is either superior or comparable to other distributions.
Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate c... more Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate change. This study investigated the relationships between vegetation pattern and hydro-meteorological elements. The joint entropy method was employed to evaluate the dependence between the normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of the Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased at a rate of 0.005/a over the past 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and a high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of the groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. The outcomes of this study are suitable for ecosystem monitoring, especially in the realm of climate change. Further studies are necessary and should consider more factors, such as runoff and irrigation.
This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow simul... more This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow simulation. MRS is motivated from the acknowledgment that typical nonparametric models tend to simulate sequences exhibiting too close a resemblance to historical records and parametric models have limitations in capturing complex distributional and dependence characteristics, such as multimodality and nonlinear autocorrelation. The aim of MRS is to generate streamflow sequences with rich scenarios while properly capturing complex distributional and dependence characteristics. The basic assumptions of MRS include: (1) streamflow of a given month depends on a feature vector consisting of streamflow of the previous month and the dynamic aggregated flow of the past 12 months and (2) streamflow can be multiplicatively decomposed into a deterministic expectation term and a random residual term. Given a current feature vector, MRS first relates the conditional expectation to the feature vector through an ensemble average of multiple regression models. To infer the conditional distribution of the residual, MRS adopts the k-nearest neighbor concept. More precisely, the conditional distribution is estimated by a gamma kernel smoothed density of historical residuals inside the k-neighborhood of the given feature vector. Rather than obtaining the residuals from the averaged model only, MRS retains all residuals from all the original regression models. In other words, MRS perceives that the original residuals put together would better represent the covariance structure between streamflow and the feature vector. By doing so, the benefit is that a kernel smoothed density of the residual with reliable accuracy can be estimated, which is hardly possible in a single-model framework. It is the smoothed density that ensures the generation of sequences with rich scenarios unseen in historical record. We evaluated MRS at selected stream gauges and compared with several existing models. Results show that (1) compared with typical nonparametric models, MRS is more apt at generating sequences with richer scenarios and (2) in contrast to parametric models, MRS can reproduce complex distributional and dependence characteristics. Since MRS is flexible at incorporating different covariates, it can be tailored for other potential applications, such as hydrologic forecasting, downscaling, as well as postprocessing deterministic forecasts into probabilistic ones.
The objective of this study is to develop artificial neural network (ANN) models, including multi... more The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg-Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg-Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.
American Journal of Engineering and Applied Sciences, 2019
Unwise and increasing exploitation of various energy carriers, including oil, gas and coal, has m... more Unwise and increasing exploitation of various energy carriers, including oil, gas and coal, has made countries to focus on two major issues, namely running out fossil fuels, as well as environmental pollution. Hence, one of the main priorities of energy policy is to diversification of energy sources, as well as finding a safe, cheap and nonpolluting energy source (devoid of greenhouse gases). Therefore, nuclear energy is important. In this regard, the main purpose of this paper is to examine the effect of energy consumption on the quality of the environment and the introduction of the best economically efficient option for energy production. In this research, we conducted a literature search incorporating keywords of nuclear energy, nuclear fission, fossil fuel, greenhouse effect in Persian and English databases in a structured manner and retrieved the papers and articles dealt with benefits and disadvantages of nuclear energy as well as its environmental impacts and subsequently they analyzed in a descriptive manner. In this study, while studying nuclear energy, fossil fuels and renewable energy, we tried to introduce a suitable replacement of these energies to fossil fuels, taking into account the economic and environmental perspectives resulting from it. Among all types of energy sources, nuclear energy may have the least impact on the environment, including water, soil and air and is a source free from gases. In addition to all of these, this energy is considered as a safe economic option.
American Journal of Engineering and Applied Sciences, 2020
Sistan (Zabol) region is located in Sistan and Baluchestan province and in southeastern part of I... more Sistan (Zabol) region is located in Sistan and Baluchestan province and in southeastern part of Iran. The specific atmospheric condition of this region, especially high-speed and continuous winds, can provide the necessary condition for Electricity generation from wind power. 120-day winds of Sistan region are one of the renewable energy sources in the province, which has been neglected in recent years. This paper examines how to beneficially use this power source, in particular to generate electricity, using field research, written reports, reviewing wind speed atlases and wind energy in the region and interviewing authorities and relevant organizations. The results demonstrate that, considering the climatic features and recent drought in the region, correct use of wind energy can lead to creating new line of employment, preventing the emission of fossil fuels, exporting electricity to neighboring countries in need of electricity and saving on fossil fuel sources. To achieve these goals, more attention and cooperation from related authorities and government and private sector investment is significantly required.
Journal of Experimental Agriculture International, 2018
Evaluation of sediment delivery ratio is important for determining watershed sediment yield. Rate... more Evaluation of sediment delivery ratio is important for determining watershed sediment yield. Rates of both interrill and rill erosion were calculated under shrub and uncovered Inceptisols conditions and were not observed to the presence by ravines and gullies in the watershed of Jacu River, in a semiarid region, Brazil. Direct measurement campaigns of suspended sediment and bedload were also carried out by means of the US DH-48 for collection of suspended sediment samples and US BLH-84used to collect samples bed load. The soil loss due to interril erosion under uncovered conditions was equal to 8.43 t ha-1 and was considered high, and the same was true for the values of rill erosion with erodibility equal to 0.0021142 kg N-1 s-1 and critical shear stress (τc) equal to 2.34 Pa. The mean value of sediment delivery ratio of Jacu watershed was equal to 0.165 and ranged from 0.29 in the year 2008 to 0.026 in 2010. This variation was associated with the natural variability of semiarid environment, indicating the necessity of assessment for a longer period to deepen our knowledge of sediment delivery ratio of the Jacu semiarid watershed.
Suspended sediment and bedload discharges in sand-bed rivers shape semi-arid landscapes and impac... more Suspended sediment and bedload discharges in sand-bed rivers shape semi-arid landscapes and impact sediment delivery from these landscapes, but are still incompletely understood. Suspended sediment and bedload fluxes of the intermittent Exu River, Brazil, were sampled by direct measurements. The highest suspended sediment concentration observed was 4847.4 mg L-1 and this value was possibly associated with the entrainment of sediment that was deposited in the preceding year. The bedload flux was well related to the stream power and the river efficiently transported all available bedload with a mean rate of 0.0047 kg m-1 s-1 , and the percentage of bedload to suspended sediment varied between 4 and 12.72. The bed sediment of Exu River was prone to entrainment and showed a proclivity for transport. Thus, sand-bed and gravel-bed rivers of arid environments seem to exhibit the same mobility in the absence of armour layer.
Journal of Experimental Agriculture International, 2018
Studies assessing technologies to design riparian strips using plant covers, based on sediment yi... more Studies assessing technologies to design riparian strips using plant covers, based on sediment yield in river basins, are required for environmental protection. The removal of semi-shrubby, native vegetation in the Brazilian semiarid region, has contributed to the degradation of semiarid basins. The aim of this study was to design a riparian strip for the Jacu River in the semiarid region of Pernambuco as a function of sediment yield. Experiments were conducted during the years 2008
The copula-entropy theory combines the entropy theory and the copula theory. The entropy theory h... more The copula-entropy theory combines the entropy theory and the copula theory. The entropy theory has been extensively applied to derive the most probable univariate distribution subject to specified constraints by applying the principle of maximum entropy. With the flexibility to model nonlinear dependence structure, parametric copulas (e.g., Archimedean, extreme value, meta-elliptical, etc.) have been applied to multivariate modeling in water engineering. This study evaluates the copula-entropy theory using a sample dataset with known population information and a flood dataset from the experimental watershed at the Walnut Gulch, Arizona. The study finds the following: (1) both univariate and joint distributions can be derived using the entropy theory. (2) The parametric copula fits the true copula better using empirical marginals than using fitted parametric/entropy-based marginals. This suggests that marginals and copula may be identified separately in which the copula is investigated with empirical marginals. (3) For a given set of constraints, the most entropic canonical copula (MECC) is unique and independent of the marginals. This allows the universal solution for the proposed analysis. (4) The MECC successfully models the joint distribution of bivariate random variables. (5) Using the "AND" case return period analysis as an example, the derived MECC captures the change of return period resulting from different marginals.
Multivariate hydrologic frequency analysis has been widely studied using: (1) commonly known join... more Multivariate hydrologic frequency analysis has been widely studied using: (1) commonly known joint distributions or copula functions with the assumption of univariate variables being independently identically distributed (I.I.D.) random variables; or (2) directly applying the entropy theory-based framework. However, for the I.I.D. univariate random variable assumption, the univariate variable may be considered as independently distributed, but it may not be identically distributed; and secondly, the commonly applied Pearson's coefficient of correlation () is not able to capture the nonlinear dependence structure that usually exists. Thus, this study attempts to combine the copula theory with the entropy theory for bivariate rainfall and runoff analysis. The entropy theory is applied to derive the univariate rainfall and runoff distributions. It permits the incorporation of given or known information, codified in the form of constraints and results in a universal solution of univariate probability distributions. The copula theory is applied to determine the joint rainfall-runoff distribution. Application of the copula theory results in: (i) the detection of the nonlinear dependence between the correlated random variables-rainfall and runoff, and (ii) capturing the tail dependence for risk analysis through joint return period and conditional return period of rainfall and runoff. The methodology is validated using annual daily maximum rainfall and the corresponding daily runoff (discharge) data collected from watersheds near Riesel, Texas (small agricultural experimental watersheds) and Cuyahoga River watershed, Ohio.
The purpose of this paper was to choose an appropriate information dissimilarity measure for hier... more The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant ...
This study analyzed extreme precipitation events, using daily rainfall data for 1966–2015. A Mann... more This study analyzed extreme precipitation events, using daily rainfall data for 1966–2015. A Mann–Kendall trend test was used to evaluate trends in extreme precipitation, copula functions were applied to compute the joint return periods of extreme events, and univariate and bivariate distributions were used to determine risk. The results showed that the decrease in consecutive wet days (CWD) was significant in the west and the northwest of Iran, while the consecutive dry days (CDD) index was increasing therein. The precipitation on more than the 90th percentile (P90) very wet days and annual number of days with precipitation less than the 90th percentile threshold (D90) indices followed similar patterns, with no significant trend in most parts of Iran, but at several stations in the north, west, and northwest, their decline was extreme. Furthermore, the increase of D10 (annual number of days with precipitation less than the 90th percentile threshold) and P10 (total precipitation of ...
Soil erosion affects agricultural production, and industrial and socioeconomic development. Chang... more Soil erosion affects agricultural production, and industrial and socioeconomic development. Changes in rainfall intensity lead to changes in rainfall erosivity (R-factor) energy and consequently changes soil erosion rate. Prediction of soil erosion is therefore important for soil and water conservation. The purpose of this study is to investigate the effect of changes in climatic parameters (precipitation) on soil erosion rates in the near future (2046–2065) and far future (2081–2100). For this purpose, the CMIP5 series models under two scenarios RCP2.6 and RCP8.5 were used to predict precipitation and the R-factor using the Revised Universal Soil Loss Equation (RUSLE) model. Rainfall data from synoptic stations for 30 years were used to estimate the R- factor in the RUSLE model. Results showed that Iran’s climate in the future would face increasing rainfall, specially in west and decreasing rainfall in the central and northern parts. Therefore, there is an increased possibility of ...
River flow regulations and thermal regimes have been altered by human-induced interventions (such... more River flow regulations and thermal regimes have been altered by human-induced interventions (such as dam construction) or climate change (such as air temperature variations). It is of great significance to adopt a well-performed data-driven model to accurately quantify the impact of human-induced interventions or climate change over river water temperature (WT), which can help understand the underlying evolution mechanism of the river thermal regimes by dam operation or climate change. This research applied the Bayesian network-based model (BNM), which can easily identify inherently stronger associated variables with a target variable from multiple influencing variables to predict the daily WT and make a quantitative assessment of the effect produced by Three Gorges Reservoir (TGR) construction in the Yangtze River, China. A comparative study between the proposed model and two other models was implemented to verify the predicted accuracy of the BNM. With the help of the BNM model, t...
The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections fo... more The study investigates the hierarchical uncertainty of multi-ensemble hydroclimate projections for the Southern Hills-Gulf region, USA, considering emission pathways and a global climate model (GCM) as two main sources of uncertainty. Forty projections of downscaled daily air temperature and precipitation from 2010 to 2099 under four emission pathways and ten CMIP5 GCMs are adopted for hydroclimate modeling via the HELP3 hydrologic model. This study focuses on evapotranspiration (ET), surface runoff, and groundwater recharge projections in this century. Climate projection uncertainty is characterized by the hierarchical Bayesian model averaging (HBMA) method, which segregates emission pathway uncertainty and climate model uncertainty. HBMA is able to derive ensemble means and standard deviations, arising from individual uncertainty sources, for ET, runoff, and recharge. The model results show that future recharge in the Southern Hills-Gulf region is more sensitive to different clima...
The objective of this research was to analyze the temporal patterns of monthly and annual precipi... more The objective of this research was to analyze the temporal patterns of monthly and annual precipitation at 36 weather stations of Aguascalientes, Mexico. The precipitation trend was determined by the Mann–Kendall method and the rate of change with the Theil–Sen estimator. In total, 468 time series were analyzed, 432 out of them were monthly, and 36 were annual. Out of the total monthly precipitation time series, 42 series showed a statistically significant trend (p ≤ 0.05), from which 8/34 showed a statistically significant negative/positive trend. The statistically significant negative trends of monthly precipitation occurred in January, April, October, and December. These trends denoted more significant irrigation water use, higher water extractions from the aquifers in autumn–winter, more significant drought occurrence, low forest productivity, higher wildfire risk, and greater frost risk. The statistically significant positive trends occurred in May, June, July, August, and Sept...
Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for perf... more Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs...
This study presents three new hybrid artificial intelligence optimization models—namely, adaptive... more This study presents three new hybrid artificial intelligence optimization models—namely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithms—for flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating c...
Flood frequency analysis (FFA) is needed for the design of water engineering and hydraulic struct... more Flood frequency analysis (FFA) is needed for the design of water engineering and hydraulic structures. The choice of an appropriate frequency distribution is one of the most important issues in FFA. A key problem in FFA is that no single distribution has been accepted as a global standard. The common practice is to try some candidate distributions and select the one best fitting the data, based on a goodness of fit criterion. However, this practice entails much calculation. Sometimes generalized distributions, which can specialize into several simpler distributions, are fitted, for they may provide a better fit to data. Therefore, the generalized gamma (GG) distribution was employed for FFA in this study. The principle of maximum entropy (POME) was used to estimate GG parameters. Monte Carlo simulation was carried out to evaluate the performance of the GG distribution and to compare with widely used distributions. Finally, the T-year design flood was calculated using the GG and compared with that with other distributions. Results show that the GG distribution is either superior or comparable to other distributions.
Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate c... more Terrestrial vegetation dynamics are closely influenced by both hydrological process and climate change. This study investigated the relationships between vegetation pattern and hydro-meteorological elements. The joint entropy method was employed to evaluate the dependence between the normalized difference vegetation index (NDVI) and coupled variables in the middle reaches of the Hei River basin. Based on the spatial distribution of mutual information, the whole study area was divided into five sub-regions. In each sub-region, nested statistical models were applied to model the NDVI on the grid and regional scales, respectively. Results showed that the annual average NDVI increased at a rate of 0.005/a over the past 11 years. In the desert regions, the NDVI increased significantly with an increase in precipitation and temperature, and a high accuracy of retrieving NDVI model was obtained by coupling precipitation and temperature, especially in sub-region I. In the oasis regions, groundwater was also an important factor driving vegetation growth, and the rise of the groundwater level contributed to the growth of vegetation. However, the relationship was weaker in artificial oasis regions (sub-region III and sub-region V) due to the influence of human activities such as irrigation. The overall correlation coefficient between the observed NDVI and modeled NDVI was observed to be 0.97. The outcomes of this study are suitable for ecosystem monitoring, especially in the realm of climate change. Further studies are necessary and should consider more factors, such as runoff and irrigation.
This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow simul... more This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow simulation. MRS is motivated from the acknowledgment that typical nonparametric models tend to simulate sequences exhibiting too close a resemblance to historical records and parametric models have limitations in capturing complex distributional and dependence characteristics, such as multimodality and nonlinear autocorrelation. The aim of MRS is to generate streamflow sequences with rich scenarios while properly capturing complex distributional and dependence characteristics. The basic assumptions of MRS include: (1) streamflow of a given month depends on a feature vector consisting of streamflow of the previous month and the dynamic aggregated flow of the past 12 months and (2) streamflow can be multiplicatively decomposed into a deterministic expectation term and a random residual term. Given a current feature vector, MRS first relates the conditional expectation to the feature vector through an ensemble average of multiple regression models. To infer the conditional distribution of the residual, MRS adopts the k-nearest neighbor concept. More precisely, the conditional distribution is estimated by a gamma kernel smoothed density of historical residuals inside the k-neighborhood of the given feature vector. Rather than obtaining the residuals from the averaged model only, MRS retains all residuals from all the original regression models. In other words, MRS perceives that the original residuals put together would better represent the covariance structure between streamflow and the feature vector. By doing so, the benefit is that a kernel smoothed density of the residual with reliable accuracy can be estimated, which is hardly possible in a single-model framework. It is the smoothed density that ensures the generation of sequences with rich scenarios unseen in historical record. We evaluated MRS at selected stream gauges and compared with several existing models. Results show that (1) compared with typical nonparametric models, MRS is more apt at generating sequences with richer scenarios and (2) in contrast to parametric models, MRS can reproduce complex distributional and dependence characteristics. Since MRS is flexible at incorporating different covariates, it can be tailored for other potential applications, such as hydrologic forecasting, downscaling, as well as postprocessing deterministic forecasts into probabilistic ones.
The objective of this study is to develop artificial neural network (ANN) models, including multi... more The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg-Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg-Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts.
Wells are excavations into the earth for developing groundwater, oil, brine, gas, and engineering... more Wells are excavations into the earth for developing groundwater, oil, brine, gas, and engineering geology operations (Driscoll 1986; Hunt 2005). Water wells are dug, driven, bored, or drilled into the saturated zone of water-bearing formations (aquifers) to extract groundwater for drinking and irrigation (Barrocu 2014; Campbell and Lehr 1973). Shallow water wells are excavated into lose and soft rocks by hand, or mechanically, mainly in unconfined aquifers. They are rarely deeper than 50–60 m, normally with a circular section of 1–3.5 m in diameter. One supports their walls with masonry or a precast concrete ring lining to prevent collapsing. The lining often extends above the ground to prevent people from falling into the well and to reduce contamination. In dug wells, if the water table lowers below their bottom, one can use the existing well as the fore shafts of new wells drilled down below the new water level (Fig. 1). In the ancient past, water was brought to the...
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Papers by Vijay P. Singh