Coupled Vis-NIR spectroscopy with chemometrics strategy for soil organic carbon prediction in the Agro-pastoral Transitional zone of northwest China.

Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg-1, 1.457 g kg-1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg-1 and 1.967 g kg-1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China.

Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment.

Urban rainstorm and waterlogging occurred more frequently in recent years, causing huge economic losses and serious social harms. Accurate rainstorm and waterlogging simulation is of significant value for disaster prevention and mitigation. This paper proposed a numerical model for urban rainstorm and waterlogging based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS), and the model was applied in Lianhu district of Xi'an city of China. Furthermore, the effects of rainfall characteristics, pipe network implementation level and urbanization level on waterlogging were explored from the perspectives of spatial distribution of waterlogging points, drainage capacity of pipe network and surface runoff generation and confluence. The results show that: (1) with the increase of rainfall recurrence period, the peak of total water accumulating volume, the average decline rate of water accumulating volume and the number of waterlogging nodes increase; (2) optimizing the pipe diameter can shorten the average overload time of the pipe network from the entire pipe network, but for a single pipe, optimizing the pipe diameter may lead to overloading of unoptimized downstream pipeline; (3) the lower the imperviousness, the less the number of waterlogging nodes and average time of water accumulating, and (4) the west, northwest and southwest areas are relatively affected by the imperviousness, only improving the underlying surface conditions has limited influence on waterlogging in the study area. This study can provide reference for urban waterlogging prevention and reduction and pipe network reconstruction.

Study on hydrological response of runoff to land use change in the Jing River Basin, China.

Land use change greatly affects the runoff characteristics of the basin, which in turn affects the distribution of surface water and groundwater in the region. Quantitative analyses of the hydrological response of watershed runoff to land use change are conducive to the formulation of sustainable water resource strategies. In this paper, the impact of land use change on runoff characteristics in the Jing River Basin was evaluated using the SWAT model, the land use pattern of the Jing River Basin in 2040 was predicted using CA-Markov model, and five land use change scenarios were set up in combination with the trend of land use transfer, and the response relationship between land use change and runoff hydrological characteristics in the basin was studied. The results show that the land use changes reduce runoff and change the hydrological cycle process of the basin. The hydrological response of different land use types varies significantly, but only has a less impact on annual runoff. Farmland has a promoting effect on production flow; woodland and grassland are not conducive to the formation of surface runoff and will increase underground runoff and evapotranspiration in the basin. The increase in vegetation coverage after returning farmland to woodlands and grasslands has reduced surface runoff, increased the recharge of groundwater, and played a positive role in ecological restoration in the river basin. The research results are of great significance for understanding the hydrological consequences of land use change and the rational planning of land use patterns in river basins.

Comparison of hybrid machine learning models to predict short-term meteorological drought in Guanzhong region, China.

Reliable drought prediction plays a significant role in drought management. Applying machine learning models in drought prediction is getting popular in recent years, but applying the stand-alone models to capture the feature information is not sufficient enough, even though the general performance is acceptable. Therefore, the scholars tried the signal decomposition algorithm as a data pre-processing tool, and coupled it with the stand-alone model to build 'decomposition-prediction' model to improve the performance. Considering the limitations of using the single decomposition algorithm, an 'integration-prediction' model construction method is proposed in this study, which deeply combines the results of multiple decomposition algorithms. The model tested three meteorological stations in Guanzhong, Shaanxi Province, China, where the short-term meteorological drought is predicted from 1960 to 2019. The meteorological drought index selects the Standardized Precipitation Index on a 12-month time scale (SPI-12). Compared with stand-alone models and 'decomposition-prediction' models, the 'integration-prediction' models present higher prediction accuracy, smaller prediction error and better stability in the results. This new 'integration-prediction' model provides attractive value for drought risk management in arid regions.

Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China.

Terrestrial vegetation growth activity plays pivotal roles on regional development, which has attracted wide attention especially in water resources shortage areas. The paper investigated the spatiotemporal change characteristics of vegetation growth activity using satellite-based Vegetation Health Indices (VHIs) including smoothed Normalized Difference Vegetation Index (SMN), smoothed Brightness Temperature (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and VHI, based on 7-day composite temporal resolution and 16 km spatial resolution gridded data, and also estimated the drought conditions for the period of 1982-2016 in Jing-Jin-Ji region of China. The Niño 3.4 was used as a substitution of El Niño Southern Oscillation (ENSO) to reveal vegetation sensitivity to ENSO using correlation and wavelet analysis. Results indicated that monthly SMN has increased throughout the year especially during growing season, starts at approximate April and ends at about October. The correlation analysis between SMN and SMT, SMN and precipitation indicated that the vegetation growth was affected by joint effects of temperature and precipitation. The VCI during growing season was positive trends dominated and vice versa for TCI. The relationships between VHIs and drought make it possible to identify and quantify drought intensity, duration and affected area using different ranges of VHIs. Generally, the intensity and affected area of drought had mainly decreased, but the trends varied for different drought intensities, regions and time periods. Large-scale global climate anomalies such as Niño 3.4 exerted obvious impacts on the VHIs. The Niño 3.4 was mainly negatively correlated to VCI and positively correlated to TCI, and the spatial distributions of areas with positive (negative) correlation coefficients were mainly opposite. The linear relationships between Niño 3.4 and VHIs were in accordance with results of nonlinear relationships revealed using wavelet analysis. The results are of great importance to assess the vegetation growth activity, to monitor and quantify drought using satellite-based VHIs in Jing-Jin-Ji region.

Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area.

The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resources in the irrigation area is constructed. With the goal of maximizing comprehensive benefits, the optimal allocation of water quality and quantity of water resources is determined. Finally, the actual effect of the model is verified through specific instance data in a province. Results demonstrate that the proposed irrigation area ecological prediction model based on edge computing and GIS images can provide better performance than other state of the art models on water resources prediction. Specifically, the accuracy can remain above 90%. The proposed model for ecological water demand prediction in the irrigation area and optimal allocation of water resources is based on the principle of quality water supply. The optimal allocation of water resources reveals the sustainable development ideas and the requirements of the optimal allocation model, which is very reasonable. The improvement of the system is effective and feasible, and the optimal allocation results are reasonable. This allocation model aims at the water quality and quantity conditions, water conservancy project conditions, and specific water demand requirements in the study area. The calculation results have great practicability and a strong guiding significance for the sustainable utilization and management of the irrigation area.

Estimation of soil organic matter content based on CARS algorithm coupled with random forest.

Soil organic matter (SOM) is an important index used to evaluate soil fertility and nutrient availability, and it is also an important component of precision agriculture. In this study, in order to quickly and efficiently estimate the SOM content of farmland soil, we took 190 farmland soil samples in Jingbian County and measureed the SOM content of the samples in the lab and collected the corresponding Vis-NIR spectroscopy data. Based on the six pretreatment methods, a competitive adaptive weighting algorithm (CARS) is used for characteristic wavelength selection. Random forest (RF) regression is used to establish the predictive SOM model. The results indicate that after the CARS algorithm screens the different spectral variables, the optimal variable sets of the seven spectral variables are 15, 40, 30, 23, 20, 26, and 23, respectively. The accuracy of the model is improved after the CARS algorithm screens the different spectral variables. A total of 15 characteristic variables from the 2151 spectral wavelengths were used as the optimal spectral variable subset; RF shortened the training time required during the SOM modeling process and dramatically improved the model's accuracy and predictive ability, and the R2 of the validation set increased from 0.21 to 0.96, and the RPD increased from 0.46 to 3.02. The RPIQ increased from 1.25 to 4.41. Among the tested models, the CR-RF model produced the best results. The R2 and RMSE values of the calibration set are 0.91 and 0.49, and the R2, RMSE, RPD, and RPIQ values of the validation set are 0.96, 0.51, 3.02, and 4.41, respectively. Accurate prediction of the SOM of the cultivated layer in the study area was realized.

Hyperspectral characteristics and quantitative analysis of leaf chlorophyll by reflectance spectroscopy based on a genetic algorithm in combination with partial least squares regression.

The precise and nondestructive detection of leaf chlorophyll content is one key to assessing the health status of crops. The objective of this study was to develop a precision method for determining the leaf chlorophyll content in rape. A genetic algorithm (GA) combined with the partial least squares (PLS) method was used to establish a chlorophyll content PLS regression estimation model based on screening the characteristic spectral regions of chlorophyll. The results show that the characteristic bands of chlorophyll in rape are 510-535, 675-695, 905-965, 1025-1225, 1165-1175, 1295-1385, 1495-1765, 1875-1895, 1970-2145, and 2179-2185 nm. Based on the characteristics of each input spectrum, the Rv2 and RPD values of the best model reached 0.97 and 5.41, respectively. This represented an increase of 0.20 and 3.42, respectively, over these values for the original full-spectrum model. The best model also achieved an RMSEP of 2.63 mg g-1, which was only 3.59% of the total sample average and was 3.78 mg g-1 less than that of the original full-spectrum model. Therefore, the best model provided good prediction accuracy for the chlorophyll content of rape. The model based on the Log (1/R) spectral transformation performed best in terms of prediction accuracy. The genetic algorithm combined with the partial least squares method (GA-PLS) can effectively screen the characteristic bands of rape chlorophyll, reduce the number of variables in the model, and produce high estimation accuracy.

Dynamic simulation of river water environmental capacity based on the subsection summation model.

There are noteworthy problems in current strategies to calculate river water environmental capacity (WEC), including the generalization of tributaries and water intakes, which results in inaccurate calculation results of the WEC, and the difficulty in adapting to dynamic changes in demands and hydrological conditions in terms of practical application. To address these flaws, the subsection summation model (SSM) was built for river WEC calculation. The SSM increases the number of control sections according to drain outlets, water intakes, and tributaries and acquires the WEC of the functional area section by section. The Wei River was taken as the study area for verification and application of the SSM. Supported by a comprehensive integration platform, the WEC simulation system of the Wei River was constructed. The results show that the SSM enhances the accuracy of the WEC calculation, and the results are closer to the actual situation. The simulation system could obtain the WEC according to the demands and changes in the hydrological conditions, thus providing technical means for policymakers. PRACTITIONER POINTS: The subsection summation model provides a more accurate water environmental capacity (WEC) calculation method considering tributaries and water intakes avoiding generalization. The simulation system should be established to make the WEC calculation adapt to the demands or changes in the hydrological conditions. The model and system could supply the basis and technical means for decision-making.

A bi-level multiobjective optimization model for waste load allocation in rivers.

It is often difficult to apply existing waste load allocation (WLA) models to management institutions at all levels of the river basin because the existing WLA models do not consider the principles of fairness and efficiency at each management level of the basin. The implementation of environmental protection tax law has also greatly impacted WLA. This paper proposes the bi-level multiobjective allocation model under an environmental protection tax law to solve the WLA problem for different management levels. The upper allocation targets the minimal environmental Gini coefficient and the minimal unit pollutant emission cost. The impact of the environmental protection tax is also considered. The targets of the lower-level allocation are the maximal industrial output value and the minimal unevenness of reduction rates. The proposed model was applied to the case of the Wei River basin, and the results demonstrated that the bi-level multiobjective allocation model could solve the problem of WLA under an environmental protection tax law. Each level of the bi-level multiobjective allocation model considers the principles of fairness and efficiency to distribute the load in the basin, thereby offering a better reference for decision-makers at both levels.

Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics.

Soil visible and near infrared (Vis-NIR) has become an applicable and interesting technique to predict soil properties because it is a fast, cost-effective, and non-destruction technique. This study presents an application of diffuse reflectance spectroscopy (DRS) and chemometric techniques for evaluating concentrations of heavy metals in earth-cumulic-orthic-anthrosols soils. 44 soil samples of 0-30 cm were collected from three representative agriculture areas (Fufeng, Yangling, and Wugong transects with 16, 10, and 18 samples, respectively) and analyzed for Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb by Vis-NIR spectroscopy (350-2500 nm). Average levels of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb were 17.95, 274, 12.77, 7.29, 15.81, 7.51, 0.40, 12.58, and 21.05 mg kg-1, respectively. Twenty-four preprocessing methods were extracted sensitive bands. Partial least squares regression (PLSR) used to obtain effective bands and predict soil heavy metals concentrations. The accuracy of the predictive models were assessed in terms of coefficient of determination (R2), the root mean squared error (RMSE), standard error (SE) and the ratio of performance to deviation (RPD). The results revealed that excellent predictions for Hg(Rv2 = 0.99, RPD = 8.59, RMSEP = 0.12, SEP = 0.13), Cr (Rv2 = 0.97, RPD = 5.96, RMSEP = 0.10, SEP = 0.10), Ni (Rv2 = 0.93, RPD = 3.74, RMSEP = 0.13, SEP = 0.13), Pb (Rv2 = 0.97, RPD = 5.57, RMSEP = 0.10, SEP = 0.01), and Cu (Rv2 = 0.92, RPD = 3.38, RMSEP = 0.08, SEP = 0.08). Models for As (Rv2 = 0.87, RPD = 2.58), Mn (Rv2 = 0.80, RPD = 2.09), and Cd (RPD = 2.77) had Rv2 < 0.9 and RPD<3.0, not excellent predictions. For the element of Zn, although Rv2 = 0.91, RPD = 3.13, the offset had too much deviation, and it cannot be considered an excellent model. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.

Iodine-enhanced ultrasound degradation of sulfamethazine in water.

This study investigated sulfamethazine (SMT) ultrasound degradation, enhanced by iodine radicals, generated by potassium iodide (KI) and hydrogen peroxide (H2O2) in situ. The results showed that the ultrasound/H2O2/KI (US/H2O2/KI) combination treatment achieved an 85.10 ±â€¯0.45% SMT removal (%) in 60 min under the following conditions: pH = 3.2, ultrasound power of 195 W, initial SMT concentration of 0.04 mmol·L-1, H2O2 concentration of 120 mmol·L-1, and KI concentration of 2.4 mmol·L-1. UV-Vis spectrophotometric monitoring of molecular iodine (I2) and triiodide (I3-) revealed a correlation between the SMT degradation and the iodine change in the solution. Quenching experiments using methanol, t-butanol and thiamazole as radical scavengers indicated that iodine radicals, such as I and I2-, were more important than hydroxyl radicals (HO) for SMT degradation. SMT degradation under the US/H2O2/KI treatment followed pseudo-first order reaction kinetics. The activation energy (Ea) of SMT degradation was 7.75 ±â€¯0.61 kJ·mol-1, which suggested the reaction was controlled by the diffusion step. Moreover, TOC removal was monitored, and the obtained results revealed that it was not as effective as SMT degradation under the US/H2O2/KI system.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.