Abstract:In order to investigate the dynamic variations of parameters in the Xin’anjiang model and improve the model accuracy under changing conditions, a distributed Xin’anjiang model was implemented for the Dapoling Basin in the upper Huai River. The impact of changes in precipitation, temperature, and NDVI (normalized difference vegetation index) on the dynamic patterns of the sensitive parameters KC, SM, and CS was analyzed using the Spearman correlation coefficient. The results indicate that the dynamic variations of parameters KC and SM reflect changes in precipitation, temperature, and vegetation coverage conditions in the study area, while the CS parameter only reflects changes in the total annual precipitation. Compared to static parameters, the simulation results with dynamic parameters exhibit a significant improvement in accuracy, with the mean relative error of runoff depth decreasing from 21.3% to 9.1% and mean Nash efficiency coefficient increasing from 0.75 to 0.81. This suggests that dynamic parameters are more suitable for the current changing climate than static parameters.

Abstract:In order to obtain soil moisture data at deep soil layers and to complete the missing soil moisture measurements at different depths, machine learning technique is used to perform the soil moisture inversion at multi-layers from surface to root zone. Three machine learning algorithms, support vector machine (SVM), back propagation (BP) neural network and random forest (RF), are applied to construct the inversion models for the train and inversion of soil moisture data at different soil layers, and the meteorological factors, which have high correlation coefficient with the soil moisture, are selected as the input factors based on the principal component analysis (PCA) method in each layer of soil. The major findings are: the simulation results of random forest are more stable and the inversion effect is the best; due to the impact of meteorological variables, the three machine learning models perform best for the inversion of surface soil moisture at the surface soil layer within 0 to 10 cm depth; however, the inversion effect for the soil moisture at the deep zone is gradually reduced with the depth of soil layer; the addition of surface soil moisture and soil temperature at different soil layers as driven factors could improve the inversion capacity of machine learning model for the soil moisture at the deep zone.

Abstract:In order to assess the applicability of different precipitation products in the upper Yangtze River Basin, based on meteorological station data, using multiple evaluation indicators, a quantitative assessment was conducted on the application accuracy of satellite retrieval(IMERG-Final and GSMaP-Gauge) and reanalysis (ERA5-Land) precipitation data in the upper Yangtze River Basin, and the error structures of different intensity precipitation in each dataset were analyzed. The results show that all three sets of products are able to accurately capture the spatial pattern of higher precipitation in the east and lower precipitation in the west of the basin, showing good correlation with observed precipitation and stable performance during 2015-2019, which indicates the potential of long-term application. Products with higher temporal resolution perform worse, larger errors occur in the central region with drastic changes in basin topography, and there is a tendency to overestimate weak precipitation and underestimate strong precipitation, with underestimation increasing with precipitation intensity. The overall errors of the two satellite products are small, with good applicability. IMERG-Final demonstrates better error control capability, while GSMaP-Gauge shows more accurate identification of precipitation events and ERA5-Land significantly overestimates basin precipitation, particularly in the southwest.

Abstract:In order to investigate the temporal and spatial variations patterns and trends of runoff and sediment in the northern main reach of Yellow River, by adopting various methods including Mann-Kendall test, abrupt change analysis, wavelet analysis, and coordination analysis of water-sediment relationship, this study analyzed the variations of water and sediment for the hydrological stations of Toudaoguai, Longmen and Tongguan, based on the water and sediment data along the northern reach of Yellow River from 2002 to 2021. The findings revealed that the unevenness and concentration of sediment were greater than those of runoff, and the intra-annual characteristic variations exhibited more significant. Concentrated period for runoff was primarily in September, while sediment was mainly concentrated in August and September. Significant abrupt changes in water-sediment characteristics occurred in 2005 and 2017, which may be related to the regulatory impact of large-scale water conservancy projects in recent years. The dominant periodicities for water-sediment sequences were found to be 13 years, 10 years, and 9 years for Toudaoguai, Longmen, and Tongguan stations, respectively. Before 2008, the water-sediment relationship indicated sediment dominance over water, while after 2008, the trend shifted to water dominance. Particularly, sediment yield coefficient at Toudaoguai remained consistently below 0.009, indicating the persistent water dominance. Overall, the trend showed a continuous increase in runoff while sediment witnessed a noticeable decrease, showing an increasingly significant trend towards runoff dominance over sediment.

Abstract:In order to better apply the Grid-Xin’anjiang(Grid-XAJ)model in the flood simulation and forecasting in areas affected by reservoir discharge, the effect of reservoir discharge on the model confluence calculation was compared between the segmented Muskingum(MK) method, grid-based MK method and grid-based Muskingum-Cunge-Todini (MCT) method, based on the real geographical location of the reservoir in the Hengpaitou Watershed. The results show that the flood peak simulation of the three schemes is good, and the average pass rate of all the floods in the periodic and verification period of each scheme rate is 100%. The average pass rate of runoff depth in the grid-based MK method and segmented MK method is 76.9%, which is lower than that of the grid-based MCT method, 84.6%. Compared with the segmented MK method, grid-based MCT method and grid-based MK method can reflect the influence of upstream reservoir discharge on the spatial and temporal distribution of discharge in the catchment process. Grid-based MCT method further considers the real situation of the underlying river channel on the basis of grid-based MK method. In general,grid-based MCT method has strong adaptability and reliability for flood forecasting in basins affected by reservoir drainage.

Abstract:To obtain high-precision data of key soil hydraulic properties (saturated hydraulic conductivity ( K s) and field capacity ( F c)) in typical humid mountainous areas in southern China, four models were developed for estimating key soil hydraulic properties of the topsoil, including the multiple linear regression (MLR), genetic algorithm-artificial neural network (GA-BP), support vector regression (SVR), and random forest (RF). In addition, three input-variable combination modes were also established with terrain and soil physicochemical properties as inputs that were selected using correlation analysis. Then, four estimation models are compared with the pedotransfer functions (PTFs) to estimate key soil hydraulic properties.These estimation models are selected to predict soil hydraulic properties of the Tunxi Watershed. The results show that the estimation effect of K s ranked in descending order as RF, SVR, MLR, GA-BP and PTFs, while the results of F c ranked as SVR, RF, GA-BP, MLR and PTFs. The spatial variations of K s and F c in the Tunxi Watershed show a consistency with the spatial variation of elevation, which indicates that there is a close nonlinear relationship between key soil hydraulic properties and elevation in humid mountainous areas.The SVR and RF models are more suitable for the regression analysis of small samples, while the GA-BP model requires larger samples to fully capture the features to achieve good results.

Abstract:To address the problem that the parameters of the LSTM model do not transpose well in nested watersheds with inadequate records of hydrological observations, a runoff modulus-based LSTM(RM-LSTM) was developed using the Tunxi Watershed as the study watershed and its nested sub-basins as the reference watersheds, with the consideration of the influence of area factor on the parameter transfer method. The flood simulations in the reference watersheds were carried out using the parameter transfer method. The results showed that the RM-LSTM model was well applied in the study watershed, with a determination coefficient of 0.87 for the simulation results, and a qualification rate of 85.0% or more for flood peak, flood volume and peak present time. In the reference watersheds, the RM-LSTM model showed a marked improvement in terms of the determination coefficient, qualification rate of flood peak, qualification rate of flood volume, and qualification rate of peak time after the parameter transplantation, compared to the results obtained from the LSTM model. The RM-LSTM model can better take into account the influence of watershed area changes on the parameter transfer method.

Abstract:To explore the application of the carbon fiber reinforced polymer (CFRP) strands in concrete structures, the CFRP strands were used as the prestressed reinforcements in this study. The experimental study on the flexural behavior of concrete beams reinforced by hybrid prestressed CFRP strands and steel rebars was conducted. The test results indicate that the plane cross-section assumption is suitable for the hybrid reinforced concrete beams. Application of the prestressed CFRP strands can significantly increase the flexural capacity of concrete beams. The cracking, yielding, and ultimate loads of the hybrid reinforced beams are increased by 74%-146%, 27%-48% and 29%-50%, compared with ordinary reinforced concrete beams. However, the ductility of the hybrid reinforced beams is obviously reduced, while the prestress level and number of prestressed CFRP strands have an insignificantly effect on the ductility under the failure mode of concrete compressive crushing. The calculation equations of the flexural capacity for prestressed fiber reinforced polymer (FRP) bar reinforced concrete beams in GB 50608-2020 Technical Standard for Fiber Reinforced Polymer in Construction can be used for calculating the flexural capacity of the concrete beams reinforced by hybrid prestressed CFRP strands and steel rebars.

Abstract:In order to study the anti-disintegration properties of biological glue modified clay, xanthan gum, guar gum, and mixed composite glue of xanthan gum and guar gum mass with ratio of 1∶1 were used to improve the clay. Through the anti-disintegration test, the effects of biological glue content, type and compaction degree on the anti-disintegration properties of improved clay were investigated, and the improvement mechanism of biological glue was further analyzed by scanning electron microscopy test. The results show that the biological glue enhances the anti-disintegration property of the sample by enhancing the cohesion between soil particles. The collapse failure process can be divided into the pre-stable collapse stage, stable collapse stage and post-stable collapse stage. With the increase of biological glue content, the disintegration stage of the improved clay is prolonged, the disintegration rate slows down, and the disintegration rate decreases. The disintegration resistance of the modified composite adhesive is stronger than that of single biological adhesive when the content is greater than 1.0%. With the increase of compaction degree, the disintegration resistance of the sample is obviously improved.

Abstract:In order to explore the impact of temperature and humidity variations on self-sensing concrete (SSC) efficacy, the functional group of carbon nanotube(CNT) was performed by the low-temperature plasma modification to enhance the dispersion in aqueous systems. The resulting modified carbon nanotubes (P-CNT) were integrated into the concrete to form P-CNT/SSC sensors. Varying water content and temperature conditions were tested to assess the polarization effect of P-CNT/SSC sensor and SSC’s self-sensing capabilities under cyclic loading. Results show that too high or too low water content impaired SSC’s sensing abilities, with the most significant impact observed under full water conditions, leading to an 89.8% decrease in stress sensitivity coefficient and unstable pressure-sensitivity curves. Moreover, during temperature variation tests, P-CNT/SSC resistivity exhibited a negative correlation with temperature. Stress sensitivity coefficients decreased by approximately 35.66% and 44.53% at high and low temperatures, respectively, compared to room temperature specimens, and the pressure-sensitivity curves displayed upward shifts or downward slides. Thus, the study suggests that the optimal testing condition of P-CNT/SSC applications involves room temperature and natural moisture content.

Abstract:The article explores the hydrodynamic characteristics of turbine arrays, summarized the general rules of changes in the wake of turbines with different horizontal and longitudinal spacing, combs the optimization methods of turbine array layout, and reviews the research progress of single-objective optimization and multi-objective optimization of turbine array layout from two aspects of artificial traditional optimization of array layout and computer automatic optimization of array layout. The artificial traditional layout structure is simple and easy to operate, and more practical in engineering, while the constraints of physical tests are limited, and it is difficult to simulate the power generation of turbine array in real sea conditions; compared with the traditional layout, the automatic optimization has more practical significance, but it still faces many challenges, such as the lack of economic module factors and the multi-objective, inconsistent theory and practice, and long preparation time.

Abstract:To investigate the spatial and temporal evolution of salt marsh vegetation in Jiangsu Yancheng Wetland Rare Bird National Nature Reserve, a phenological feature composite method was used to invert the area of four types of salt marsh vegetation ( Spartina alterniflora , Phragmites australis, Suaeda salsa and Imperata cylindrica) in the core area of the Reserve from 1984-2021, and to analyze its spatial and temporal evolution and driving mechanism. The results showed that the total area of salt marsh vegetation in the core area increased from 11 113.2.hm²to 16 528.14.hm², with a compound annual growth rate of 1.11%, mainly due to the ability of Spartina alterniflora to preserve the beach and promote siltation. Spartina alterniflora and Phragmites australis, by occupying a large number of Suaeda salsa beach, Phragmites australis beach and light beach, the area increased by 4 968.62.hm2 and 8 806.95.hm², with compound annual growth rates of 26.67% and 6.88%, respectively. The areas of Suaeda salsa and Imperata cylindrica decreased by 3 494.25.hm² and 4 867.38.hm² respectively by the superimposed effects of natural factors such as ecological niche competition and artificial disturbances such as aquaculture bases, with a compound annual growth rate of -3.14% and -8.11%, resulting in the severe fragmentation of the suitable habitats.

Abstract:In order to explore the response of estuarine salt intrusion to river and tidal forcing as well as water depth under the influence of human activities and climatic changes, a three-dimensional numerical salinity model for three-dimensional ideal estuaries was established. By analyzing the vertical and lateral distributions of longitudinal circulation and salinity, the effects of vertical salt transport induced by the vertical and lateral shear as well as the tidal pumping on saline intrusion, and the response of salt transport to radial tidal dynamics and water depth were studied. The results show that the salt intrusion is sensitive to changes in the river runoff and tidal forcing. In a relatively deep estuary, increasing the runoff or tidal forcing can reduce salt intrusion throughout the estuary. In shallower estuaries, the increase of tidal drive causes the opposite trend of tidal-averaged salinity change in the upstream and downstream of the estuary, which can be explained by the response of longitudinal salt transport to radial tide depth conditions caused by transverse shear, longitudinal shear and tidal pump respectively.