Using the data of “A field experiment on landatmosphere interaction over arid region in Northwest China” carried out in Dunhuang of Gansu Province from May to June 2000;Characteristics of the atmospheric humidity ov...Using the data of “A field experiment on landatmosphere interaction over arid region in Northwest China” carried out in Dunhuang of Gansu Province from May to June 2000;Characteristics of the atmospheric humidity over desert and Gobi near oasis in the Northwest China Arid Region are analyzed. According to the difference of the characteristics in different wind directions, the impacts of oasis on atmospheric hydrological cycle over desert and Gobi near it are revealed. The relation of atmosphere inverse humidity and negative water vapor flux to wind direction and atmospheric stability is studied. It shows that distribution of the atmosphere inverse humidity is inconsistent with that of the negative water vapor flux;sometimes 1-hour-average value demonstrates the characteristic of counter-gradient transfer. And the diurnal variation of distribution of the counter-gradient transfer and the effect of atmospheric stability on the counter-gradient transfer are also given.展开更多
Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the de...Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity.In this study,we proposed a hybrid modeling approach for rapid prediction of urban floods,coupling the physically based model with the light gradient boosting machine(LightGBM)model.A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model(PCSWMM).The variables related to rainfall,tide level,and the location of flood points were used as the input for the LightGBM model.To improve the prediction accuracy,the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation.Taking Haidian Island,Hainan Province,China as a case study,the optimum values of the learning rate,number of estimators,and number of leaves of the LightGBM model are 0.11,450,and 12,respectively.The Nash-Sutcliffe efficiency coefficient(NSE)of the LightGBM model on the test set is 0.9896,indicating that the LightGBM model has reliable predictions and outperforms random forest(RF),extreme gradient boosting(XGBoost),and k-nearest neighbor(KNN).From the LightGBM model,the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area.The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.展开更多
An infinite slope stability numerical model driven by the comprehensive physically-based integrated hydrology model(InHM) is presented.In this approach,the failure plane is assumed to be parallel to the hydraulic grad...An infinite slope stability numerical model driven by the comprehensive physically-based integrated hydrology model(InHM) is presented.In this approach,the failure plane is assumed to be parallel to the hydraulic gradient instead of the slope surface.The method helps with irregularities in complex terrain since depressions and flat areas are allowed in the model.The present model has been tested for two synthetic single slopes and a small catchment in the Mettman Ridge study area in Oregon,United States,to estimate the shallow landslide susceptibility.The results show that the present approach can reduce the simulation error of hydrological factors caused by the rolling topography and depressions,and is capable of estimating spatial-temporal variations for landslide susceptibilities at simple slopes as well as at catchment scale,providing a valuable tool for the prediction of shallow landslides.展开更多
The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM)...The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia.Two other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for comparison.This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data.The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and precipitation.Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations.The performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River Basin.Compared to classic machine learning algorithms,LSTM shows significant advantages over most evaluating indices.XGBoost also has high NS value in the training period,but is prone to overfitting the discharge.Compared with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data inputs.Moreover,LSTM only requires meteorological data rather than physical characteristics of underlying data.As an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at the cost of increasing the complexity of the mod展开更多
Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulti...Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.展开更多
Aims We aimed at determining differences in the leaf spectral signatures of plant species groups growing in habitats along the hydrological gradient of an intermittent wetland and to define leaf traits that explain th...Aims We aimed at determining differences in the leaf spectral signatures of plant species groups growing in habitats along the hydrological gradient of an intermittent wetland and to define leaf traits that explain their variability.We want to contribute to the understanding of the causes for plant spectrum variability at leaf and community levels.Methods We measured leaf reflectance spectra(300-887 nm)of representative plant species from different habitats and analyzed spectral differences among species groups.To explain leaf spectra variability within a group,we performed detailed analyses of leaf morphological and biochemical traits in selected species.Important FindingsThe reflectance spectra of the different species groups differed most in the green,yellow and red spectral ranges.The reflectance spectra of submerged leaves of hydrophytes with simple structures were explained by their biochemical traits(carotenoids),while for more complex aerial leaves,morphological traits were more important.In submerged and natant leaves of amphiphytes,total mesophyll and spongy tissue thickness were the most important traits,and these explained 44%and 47%,respectively,of the spectrum variability of each plant group.In general,the redundancy analysis biplots show that samples of different plant species colonizing the same habitat form separate clusters and are related to the explanatory variables in different ways.The redundancy analysis biplots of helophytes and wet meadow species show clustering of graminoids and dicots into two distinct groups.Leaf encrustation(prickle hair properties and epidermis thickness)is important for graminoids,while leaf thickness and specific leaf area have more important roles in dicots.Our results show that knowledge of the species composition and leaf traits is necessary to interpret the reflectance spectra of such plant communities.展开更多
文摘Using the data of “A field experiment on landatmosphere interaction over arid region in Northwest China” carried out in Dunhuang of Gansu Province from May to June 2000;Characteristics of the atmospheric humidity over desert and Gobi near oasis in the Northwest China Arid Region are analyzed. According to the difference of the characteristics in different wind directions, the impacts of oasis on atmospheric hydrological cycle over desert and Gobi near it are revealed. The relation of atmosphere inverse humidity and negative water vapor flux to wind direction and atmospheric stability is studied. It shows that distribution of the atmosphere inverse humidity is inconsistent with that of the negative water vapor flux;sometimes 1-hour-average value demonstrates the characteristic of counter-gradient transfer. And the diurnal variation of distribution of the counter-gradient transfer and the effect of atmospheric stability on the counter-gradient transfer are also given.
基金supported by the State Key Laboratory of Hydraulic Engineering Simulation and Safety(Tianjin University)(Grant Number HESS-2106),Scientific and Technological Projects of Henan Province(Grant Number 222102320025)Key Scientific Research Project in Colleges and Universities of Henan Province of China(Grant Number 22B570003)+2 种基金National Natural Science Foundation of China(Grant Number 52109040,51739009)Excellent Youth Fund of Henan Province of China(212300410088)Science and Technology Innovation Talents Project of Henan Education Department of China(21HASTIT011).
文摘Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity.In this study,we proposed a hybrid modeling approach for rapid prediction of urban floods,coupling the physically based model with the light gradient boosting machine(LightGBM)model.A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model(PCSWMM).The variables related to rainfall,tide level,and the location of flood points were used as the input for the LightGBM model.To improve the prediction accuracy,the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation.Taking Haidian Island,Hainan Province,China as a case study,the optimum values of the learning rate,number of estimators,and number of leaves of the LightGBM model are 0.11,450,and 12,respectively.The Nash-Sutcliffe efficiency coefficient(NSE)of the LightGBM model on the test set is 0.9896,indicating that the LightGBM model has reliable predictions and outperforms random forest(RF),extreme gradient boosting(XGBoost),and k-nearest neighbor(KNN).From the LightGBM model,the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area.The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.
基金Project supported by the National Basic Research Program (973) of China (No 2011CB409901-01)the Foundation of Science and Technology Department of Zhejiang Province (No 2009C33117), China
文摘An infinite slope stability numerical model driven by the comprehensive physically-based integrated hydrology model(InHM) is presented.In this approach,the failure plane is assumed to be parallel to the hydraulic gradient instead of the slope surface.The method helps with irregularities in complex terrain since depressions and flat areas are allowed in the model.The present model has been tested for two synthetic single slopes and a small catchment in the Mettman Ridge study area in Oregon,United States,to estimate the shallow landslide susceptibility.The results show that the present approach can reduce the simulation error of hydrological factors caused by the rolling topography and depressions,and is capable of estimating spatial-temporal variations for landslide susceptibilities at simple slopes as well as at catchment scale,providing a valuable tool for the prediction of shallow landslides.
基金supported by the National Natural Science Foundation of China(U1903208,41630859,42071046)。
文摘The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources.In this study,long shortterm memory(LSTM),a state-of-the-art artificial neural network algorithm,is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia.Two other classic machine learning methods,namely extreme gradient boosting(XGBoost)and support vector regression(SVR),along with a distributed hydrological model(Soil and Water Assessment Tool(SWAT)and an extended SWAT model(SWAT_Glacier)are also employed for comparison.This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data.The two typical basins in this study are the main tributaries(the Kumaric and Toxkan rivers)of the Aksu River in the south Tianshan Mountains,which are dominated by snow and glacier meltwater and precipitation.Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations.The performance metrics Nash-Sutcliffe efficiency coefficient(NS)and correlation coefficient(R^(2))of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin,and NS and R^(2) are also higher than 0.70 in the Toxkan River Basin.Compared to classic machine learning algorithms,LSTM shows significant advantages over most evaluating indices.XGBoost also has high NS value in the training period,but is prone to overfitting the discharge.Compared with the widely used hydrological models,LSTM has advantages in predicting accuracy,despite having fewer data inputs.Moreover,LSTM only requires meteorological data rather than physical characteristics of underlying data.As an extension of SWAT,the SWAT_Glacier model shows good adaptability in discharge simulation,outperforming the original SWAT model,but at the cost of increasing the complexity of the mod
文摘Heavy floods occur frequently in the Senegal River Basin, causing catastrophic flooding downstream the river rating station of Bakel. Anticipating the occurrence of such phenomena is the only way to reduce the resulting damages. Flood forecasting is a necessity. Flood forecasting plays also an important role in the implementation of flood management scenarios and in the protection of hydro electric structures. Many methods are applied. The most complete are based on the conservation laws of physics governing the free surface flow. These methods need a complete description of the geometry of the river and their implementation requires also huge investments. In practice the river basin can be considered as a system of inputs-outputs related by a transfer function. In this paper the authors first used a multiple linear regression model with constant parameters estimated by the ordinary least square method to simulate the propagation of the floods in the upstream part of the Senegal river basin. The authors then apply statistical and graphical criteria of goodness-of-fit to test the suitability of this model. Three procedures of parameters updating have then been added to this linear model： the Kalman filter method, the recursive least square method, and the stochastic gradient method The criteria of goodness-of-fit used above have shown that the stochastic gradient method, although more rudimentary, represents better the flood propagation in the head basin of the Senegal river upstream Bakel. This result is particularly interesting because data influenced by Manantali Dam are used.
基金Ministry of Education,Science and Sport,Republic of Slovenia,through the programmes“Biology of Plants”(P1-0212)“Young Researchers”(33135).
文摘Aims We aimed at determining differences in the leaf spectral signatures of plant species groups growing in habitats along the hydrological gradient of an intermittent wetland and to define leaf traits that explain their variability.We want to contribute to the understanding of the causes for plant spectrum variability at leaf and community levels.Methods We measured leaf reflectance spectra(300-887 nm)of representative plant species from different habitats and analyzed spectral differences among species groups.To explain leaf spectra variability within a group,we performed detailed analyses of leaf morphological and biochemical traits in selected species.Important FindingsThe reflectance spectra of the different species groups differed most in the green,yellow and red spectral ranges.The reflectance spectra of submerged leaves of hydrophytes with simple structures were explained by their biochemical traits(carotenoids),while for more complex aerial leaves,morphological traits were more important.In submerged and natant leaves of amphiphytes,total mesophyll and spongy tissue thickness were the most important traits,and these explained 44%and 47%,respectively,of the spectrum variability of each plant group.In general,the redundancy analysis biplots show that samples of different plant species colonizing the same habitat form separate clusters and are related to the explanatory variables in different ways.The redundancy analysis biplots of helophytes and wet meadow species show clustering of graminoids and dicots into two distinct groups.Leaf encrustation(prickle hair properties and epidermis thickness)is important for graminoids,while leaf thickness and specific leaf area have more important roles in dicots.Our results show that knowledge of the species composition and leaf traits is necessary to interpret the reflectance spectra of such plant communities.
