Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities,such as over-exploitation of groundwater,are exerting unavoidable stress on groundwater resources.This study investigated the spatio-temporal variation of depth to groundwater level(DGWL)and the impacts of climatic(precipitation,maximum temperature,and minimum temperature)and anthropogenic(gross district product(GDP),population,and net irrigated area(NIA))variables on DGWL during 1994-2020.The study considered DGWL in 113 observation wells and piezometers located in arid western plains(Barmer and Jodhpur districts)and semi-arid eastern plains(Jaipur,Ajmer,Dausa,and Tonk districts)of Rajasthan State,India.Statistical methods were employed to examine the annual and seasonal patterns of DGWL,and the generalized additive model(GAM)was used to determine the impacts of climatic and anthropogenic variables on DGWL.During 1994-2020,except for Barmer District,where the mean annual DGWL was almost constant(around 26.50 m),all other districts exhibited increase in DGWL,with Ajmer District experiencing the most increase.The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL.Similarly,32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter;33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon;35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon;and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon.Interestingly,most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts.Furthermore,the GAM analysis revealed that climatic variables,such as precipitation,significantly affected DGWL in Barmer District,and DGWL in all other districts was influenced by anthropogenic variables,including GDP,NIA,and population.As a result,stringent regulations should be implemented to curb excessive groundwater extraction,manage agricultural water demand,initiate proactive aquifer recharge programs,and strengthen sustainable management in these water-scarce regions.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government's monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.

Variation in the distribution of wintering anchovy Engraulis japonicus and its relationship with water temperature in the central and southern Yellow Sea

In the present study,we investigated a shift in the spatial distribution of wintering anchovy(Engraulis japonicus)and its relationship with water temperature,using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea,during 2000–2015.Our results indicate that the latitudinal distribution of wintering anchovy varied between years,but there was no consistent pattern in the direction of change(north or south).Wintering anchovy did not move northward with increasing water temperature.However,the latitudinal distribution of wintering anchovy correlated well with 10°C and 11°C isotherms.The results of both a one-step and a two-step generalized additive model indicated that water temperature was associated with both presence and biomass of wintering anchovy.This paper is the fi rst to systematically examine the relationship between anchovy distribution and water temperature using a variety of techniques.All the fi ndings confi rm the impact of water temperature on wintering anchovy distribution,which has important implications for the continued management of the anchovy resource and the enhancement of marine fi shery resources in the Yellow Sea,especially as the climate changes.However water temperature only partly explains the species distribution of anchovy,and stock characteristics also aff ect fi shery distribution.Therefore,other factors should be considered in future research.

Effects of spatio-temporal and environmental factors on distribution and abundance of wintering anchovy Engraulis japonicus in central and southern Yellow Sea

We investigated the spatio-temporal and environmental factors that affect the distribution and abundance of wintering anchovy and quantifi es the infl uences of these factors. Generalized additive models(GAMs) were developed to examine the variation in species distribution and abundance with a set of spatiotemporal and oceanographic factors, using data collected by bottom trawl surveys and remote sensing in the central and southern Yellow Sea during 2000–2011. The fi nal model accounted for 28.21% and 41.03% of the variance in anchovy distribution and abundance, respectively. The results of a two-step GAM showed that hour, longitude, latitude, temperature gradient(TGR), and chlorophyll a(Chl- a) concentration best explained the anchovy distribution(presence/absence) and that a model including year, longitude, latitude, depth, sea surface temperature(SST), and TGR best described anchovy abundance(given presence). Longitude and latitude were the most important factors affecting both distribution and abundance, but the area of high abundance tended to be east and south of the area where anchovy were most likely to be present. Hour had a signifi cant effect on distribution, but year was more important for anchovy abundance, indicating that the anchovy catch ratio varied across the day but abundance had an apparent interannual variation. With respect to environmental factors, TGR and Chl- a concentration had effects on distribution, while depth, SST, and TGR affected abundance. Changes in SST between two successive years or between any year and the 2000–2011 mean were not associated with changes in anchovy distribution or abundance. This fi nding indicated that short- and long-term water temperature changes during 2000–2011 were not of suffi cient magnitude to give rise to variation in wintering anchovy distribution or abundance in the study area. The results of this study have important implications for fi sheries management.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. Three typical oceanographic factors aff ecting the squid habitat were investigated in this research, including sea surface temperature(SST), sea surface salinity(SSS) and sea surface height(SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive(SAR) model and a generalized additive model(GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas off shore Peru, and off er a new SAR modeling method for advancing fishery science.

Spatio-temporal distribution of Konosirus punctatus spawning and nursing ground in the South Yellow Sea

In recent years,Konosirus punctatus has accounted for a large portion in catch composition and become important economic species in the South Yellow Sea.However,the distribution of K.punctatus early life stages is still poorly understood.In this study,generalized additive models with Tweedie distribution were used to analyze the relationships between K.punctatus ichthyoplankton and environmental factors(longitude and latitude,sea surface temperature(SST),sea surface salinity(SSS)and depth),and predict distribution K.punctatus spawning ground and nursing ground,based on samplings collected in 6 months during 2014–2017.The results showed that K.punctatus’spawning ground were mainly distributed in central and north study area(from 33.0°N to 37.0°N).By comparison,the nursing ground shifted southward,which were approximately located along central and south coast of study area(from 31.7°N to 35.5°N).The optimal models identified that suitable SST,SSS and depth for eggs were 19–26℃,25–30 and 9–23 m,respectively.The suitable SSS for larvae were 29–31.The K.punctatus spawning habit might have changed in the past decades,which was a response to increasing SST and fishing pressure.That needs to be proved in further study.The study provides references of conservation and exploitation for K.punctatus.

Hierarchical responses of soil organic and inorganic carbon dynamics to soil acidification in a dryland agroecosystem,China

Soil acidification is a major global issue of sustainable development for ecosystems. The increasing soil acidity induced by excessive nitrogen （N） fertilization in farmlands has profoundly impacted the soil carbon dynamics. However, the way in which changes in soil pH regulating the soil carbon dynamics in a deep soil profile is still not well elucidated. In this study, through a 12-year field N fertilization experiment with three N fertilizer treatments （0, 120, and 240 kg N/（hm-2·a）） in a dryland agroecosystem of China, we explored the soil pH changes over a soil profile up to a depth of 200 cm and determined the responses of soil organic carbon （SOC） and soil inorganic carbon （SIC） to the changed soil pH. Using a generalized additive model, we identified the soil depth intervals with the most powerful statistical relationships between changes in soil pH and soil carbon dynamics. Hierarchical responses of SOC and SIC dynamics to soil acidification were found. The results indicate that the changes in soil pH explained the SOC dynamics well by using a non-linear relationship at the soil depth of 0-80 cm （P=0.006）, whereas the changes in soil pH were significantly linearly correlated with SIC dynamics at the 100-180 cm soil depth （P=0.015）. After a long-term N fertilization in the experimental field, the soil pH value decreased in all three N fertilizer treatments. Furthermore, the declines in soil pH in the deep soil layer （100-200 cm） were significantly greater （P=0.035） than those in the upper soil layer （0-80 cm）. These results indicate that soil acidification in the upper soil layer can transfer excess protons to the deep soil layer, and subsequently, the structural heterogeneous responses of SOC and SIC to soil acidification were identified because of different buffer capacities for the SOC and SIC. To better estimate the effects of soil acidification on soil carbon dynamics, we suggest that future investigations for soil acidification should be extended to a deeper soil depth, e.g., 200 cm.

Learning distance effect on lignite quality variables at global and local scales

Determining scale and variable effects have critical importance in developing an energy resource policy.This study aims to explore the relationships in heterogeneous lignite sites using different scale models,spatial weighting as well as error-based pair-wise identification.From a statistical learning framework,the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models.In this way,the critical roles of spatial weights provided by the coordinates have been specified at a global scale.The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency.Because relationships among lignite quality variables and sampling locations are spatially non-stationary,the local structure and interdependencies among the variables were analyzed by geographically weighting regression.The local analyses including spatial patterns of bandwidths,search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning.The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.

Indoor air particles in research vessel from Shanghai to Antarctic: Characteristics, influencing factors, and potential controlling pathway

Despite millions of seafarers and passengers staying on ships each year, few studies have been conducted on the indoor air quality inside ship hulls. In this study, we investigated the levels and size distribution of indoor particulate matter during two cruises of the research vessel “Xuelong” from Shanghai to Antarctica. The results showed that the particle size less than 2.5 μm(PM_(2.5)), and particle size less than 10 μm(PM_(10)) concentrations in different rooms of the ship widely varied. We observed high particulate matter(PM) levels in some of the rooms. The mass concentration distribution was dominated by 1–4 μm particles, which may have been caused by the hygroscopic growth of fine particles. The dominant factors influencing PM concentrations were indoor temperature, relative humidity, and human activity. We quantified contributions of these factors to the levels of indoor particles using a generalized additive model. In clean rooms, the levels of indoor particles were controlled by temperature and relative humidity, whereas in polluted rooms, the levels of indoor particles were mainly influenced by temperature and human activity, which implied that controlling temperature and human activity would efficiently reduce the levels of indoor particles.

Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some challenges.The crowding among residential customers is one of such challenges.The scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed limits.In order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy communities.In the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor.The non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena.Since the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global optimum.The proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

Climatic Control on Forests and Tree Species Distribution in the Forest Region of Northeast China

North-east （NE） China covers considerable climatic gradients and all major forests types of NE Asia. in the present study, 10 major forest types across the forest region of NE China were sampled to Investigate forest distribution in relation to climate. Canonical correspondence analysis （CCA） revealed that growing season precipitation and energy availability were primary climatic factors for the overall forest pattern of NE China, accounting for 66% of the explanatory power of CCA. Conversely, annual precipitation and winter coldness had minor effects. Generalized additive models revealed that tree species responded to climatic gradients differently and showed three types of response curve： （i） monotonous decline; （ii） monotonous Increase; and （iii） a unimodai pattern. Furthermore, tree species showed remarkable differences in limiting climatic factors for their distribution. The power of climate in explaining species distribution declined significantly with decreasing species dominance, suggesting that the distribution of dominant species was primarily controlled by climate, whereas that of subordinate species was more affected by competition from other species.

A static and dynamic factorscoupled forecasting model of regional rainfall-induced landslides:A case study of Shenzhen

Most rainfall-induced landslide forecasting models focus on the relation between landslides and rainfall,which is one of the dynamic factors,and seldom consider the stacitc factors,such as geological and geograpical factors.Landslide susceptibility,however,is determinded by both static and dynamic factors.This article proposes a static and dynamic factors-coupled forecasting model(SDFCFM) of regional rainfall-induced landslides,which quantitatively considers both the static and dynamic factors that affect landslides.The generalized additive model(GAM) is applied to coupling both factors to get the landslide susceptibility.In the case study,SDFCFM is applied to forecast the landslide occurrences in Shenzhen during a rainfall process in 2008.Compared with the rainfall logistic regression model,the resulting landslide susceptibility map illustrates that SDFCFM can reduce the forecast redundancy and improve the hit ratio.It is both applicable and practical.The application of SDFCFM in landslide warning and prevention system will improve its efficiency and also cut down the cost of human and matreial resources.

Energy hub modeling to minimize residential energy costs considering solar energy and BESS

This paper aims to optimize total energy costs in an operational model of a novel energy hub(EH) in a residential area. The optimization problem is set up based on daily load demand(such as electricity, heat, and cooling) and time-of-use(TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic(PV) generation, solar heat exchanger(SHE), and a battery energy storage system(BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price,capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads.