Kendall Method Research Articles

Impact of China’s Provincial Government Debt on Economic Growth and Sustainable Development

Macroeconomic stability is the core concept of sustainable development. However, the coronavirus disease (COVID-19) pandemic has caused government debt problems worldwide. In this context, it is of practical significance to study the impact of government debt on economic growth and fluctuations. Based on panel data of 30 provinces in China from 2012 to 2019, we used the Mann–Kendall method and Kernel Density estimation to analyze the temporal and spatial evolution of China’s provincial government debt ratio and adopted a panel model and HP filtering method to study the impact of provincial government debt on economic growth and fluctuation. Our findings indicate that, during the sample period, China’s provincial government debt promoted economic growth and the regression coefficient (0.024) was significant. From different regional perspectives, the promotion effect of the central region (0.027) is higher than that of the eastern (0.020) and western regions (0.023). There is a nonlinear relationship between China’s provincial government debt and economic growth, showing an inverted “U-shaped” curve. Fluctuations in government debt aggravate economic volatility, with a coefficient of 0.009; tax burden fluctuation and population growth rate aggravate economic changes. In contrast, the optimization of the province’s industrial structure and the improvement of the opening level of provinces slow down economic fluctuations.

Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, Hd, characterized by duration d equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, ta, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse ta can lead to significant underestimates of Hd. The effect of ta on the estimation of trends in annual maximum depths with d = 24 h, Hd=24 h, over the last 100 years is examined. We have used a published series of Hd=24 h derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio ta/d, each Hd=24 h value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of Hd=24 h values with coarse ta plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the Hd=24 h values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the Hd values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of Hd series containing potentially underestimated values, especially when d = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, Hd values determined from rainfall data characterized by coarse temporal resolution should always be corrected.

Temporal variations in rainfall patterns in Kilembe, Uganda

AbstractThis study investigated rainfall variabilities in Kilembe. Secondary (monthly rainfall) data for the study were obtained from the Uganda National Meteorological Authority in Kampala, Uganda, for the period 1986–2016. To determine the temporal variation of rainfall, a Mann–Kendall method and Sen's slope estimator test were used. The results showed that March, April, May, October, and November represented the smallest coefficient of variation indicating modest variability of rainfall in these months, whereas June and July had the largest coefficient of variation. The month of July recorded an overall lowest average rainfall and a decreasing trend in rainfall. The study further revealed a historical general upward trend in rainfall between 1986 and 2016 in Kilembe. The trend in the monthly and annual rainfall is partly attributed to the Intertropical Convergence Zone, influx of moisture from Congo Forest, and climate change. The area recorded also two peaks of rainfall in the year, one in April and another in September, revealing a bimodal pattern of rainfall. The findings indicate that the government should adopt policy measures in the form of an adaptive strategy in order to save the people in the impacted areas.

Trend and Seasonal Analysis of Annual One Day Maximum Rainfall

Saurashtra region is characterized by high temporal and spatial rainfall fluctuations. The daily maximum rainfall has a direct impact on the agricultural yield. It is thereby necessary to comprehend the trend changes in the annual daily maximum rainfall (ADMR). The daily rainfall data of 40 years (1981 to 2020), for 11 stations in Saurashtra was utilized for trend analysis of ADMRusing Mann Kendall's method and Sen's slope method. It was revealed from the Mann-Kendall test that significant positive trends were exhibited at Dwarka and Surendranagar. Further, Sen's slope and linear regression indicated that the ADMRat Rajkot and Surendranagar were having highest increasing trend. Trend analysis of ADMRcontribution to annual rainfall showed negative trend indicating better temporal distribution of rainfall. It was also revealed that the extreme events of rainfall usually occurred uniformly on certain days of the year from the results of directional statistics.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

Analysing the impacts of climate variability on the yield of Kharif rice over Punjab, Pakistan

AbstractRainfall and temperature are essential climate parameters that determine crop yield variability at a regional level. The purpose of this study is to analyse the impacts of climate variability on the Kharif rice crop in the Punjab regions, Pakistan for the period of 1979–2018. Skewness and kurtosis, standardization, and Mann–Kendall methods were used to measure the effects of climate variability on Kharif rice yield over the Punjab regions, Pakistan. As compared to other regions, high variability in rice yield is found in the Sargodha region. Results revealed that high maximum and minimum temperatures were noticed in all investigated regions of Punjab. More variation in minimum temperature is detected than the maximum temperature in the Central region. The regions in Southern and Western Punjab were mostly influenced by maximum temperature. Mean temperature shows a significant negative relationship to rice yield during a vegetative phase in the Gujranwala and Rawalpindi regions. The maximum temperature has a negative significant effect on the plant establishment resulting in a reduction in the number of rice plants during the tillering and stem elongates stages in Bahawalpur and Sargodha regions. In the Gujranwala and Multan regions, high maximum temperature increases the floral initiation in the rice plant which reduces the rice grain weight in the heading and flowering stages. The minimum temperature declares a significant negative effect on the Kharif yield of rice in the Gujranwala, Faisalabad, and Multan zones during the re‐plantation stage. Rainfall has a significant positive impact on rice yield during the re‐plantation and tillering stages in the Lahore, Sargodha, and D.G. Khan regions, respectively. In other regions, rainfall indicates an insignificant effect in the re‐plantation stage. Overall, rainfall indicates significant effects in re‐plantation and tillering stages in the Lahore, Sargodha, and D.G. Khan zones. The outcomes of the research indicate how the regional variability in rainfall and temperature affects Kharif rice yield in Punjab, Pakistan.

Spatio-Temporal Patterns of Global Population Exposure Risk of PM2.5 from 2000–2016

A high level of fine particulate matter (PM2.5) has become one of the greatest threats to human health. Based on multi-source remote sensing data, the pollutant population exposure model, accompanied by the Theil–Sen Median and Mann–Kendall methods, was used to analyze the spatio-temporal patterns of global population exposure risk of PM2.5 from 2000 to 2016. The population distribution patterns of high-risk exposure areas have been accurately identified; the variation trend and stability of global population exposure risk of PM2.5 have also been analyzed. According to the results, the average concentration of PM2.5 is correlated with the total population. The average concentration of PM2.5 for countries from high to low are Asia (14.7 μg/m3), Africa (8.1 μg/m3), Europe (8.03 μg/m3), South America (5.69 μg/m3), North America (4.41 μg/m3), and Oceania (1.27 μg/m3). In addition, the global average population exposure risk of PM2.5 is decreasing annually. Specifically, China, India, Southeast Asia, and other regions have higher exposure risks. Less developed mountainous regions, cold regions, deserts and tropical rainforest regions have lower exposure risks. Moreover, Oceania, North America, South America and other regions have relatively stable exposure, whereas areas with relatively unstable exposure risk of PM2.5 are mainly concentrated in Asia, India, and eastern China, followed by Southeast Asia, Europe, and Africa. Furthermore, Asia has the largest population of all the continents, followed by Africa and Europe. Countries with increased populations are mainly distributed in Africa, whereas the countries with a declining population are mainly distributed in Europe. Based on this, it is important to identify the relationship between PM2.5 concentration and population exposure risk to improve human settlements and environmental risk assessment.

АНАЛИЗ ВЗАИМОСВЯЗИ ПАРАМЕТРОВ СОСТАВА ТЕЛА С ПАРАМЕТРАМИ СКОРОСТНЫХ КАЧЕСТВ У ФУТБОЛИСТОВ НА ЭТАПЕ УГЛУБЛЕННОЙ СПЕЦИАЛИЗАЦИИ

Aim: the study aims to reveal the correlation between body composition and control exercises, as well as to receive quantitative data of these correlations in the form of pair coefficients and to find the effective means for developing speed qualities. Materials and methods. Analysis of morphological and functional parameters was conducted by means of bioimpedance measurement. Assessment of speed qualities was conducted by using the Racetime 2 (Microgate) device, which provided measurement accuracy at the stage of sports specialization. Statistical processing was carried out by using the SPSS program for parametric and non-parametric correlation. Control exercises included the following: 10-meter run, 30-meter run, 5×30 shuttle run, 8 test. Results. The Bravais-Pearson method allows to establish that 10-meter run correlates only with body length data. The Kendall method shows that 10-meter run has an average correlation coefficient with the growth rate (r = 0.556) and a weak correlation with total body fat percentage and total body water (r = –0.327, r = 0.266, respectively). The Spearman method demonstrates that 10-meter run has an average corelation with body length data (r = 0.590), an inverse correlation with total body fat percentage and a below average correlation with somatotype (r = –0.443, r = 0.285, respectively). Conclusion. A close correlation was found between the metabolic rate and both body water (r = –0.92) and muscle mass (r = 0.49). A direct correlation was found between the metabolic rate and muscle mass (r = 0.1). As a result of the study, the recommendation can be made to pay attention to those exercises that contribute to athletic development at the stage of sports specialization.

Trend and ENSO-based analysis of last spring frost and chilling in Iran

The frost and chilling phenomena are the important climatic and environmental hazards that cause a lot of damage to the agricultural sector every year. In this respect, the present study intends to highlight the possibility of attributed occurrence to large-scale atmospheric indices. In this study, the probability of last spring frost (LSF) and chilling (LSC) at a critical temperature of + 4.4 to − 3.3 °C and different phases of El Nino Southern Oscillation index (ENSO) were extracted using Weibull Distribution Function (WDF) at 18 synoptic stations in a period of 59 years from 1961 to 2019. Then, the anomaly of LSF and LSC occurrence date was investigated in different ENSO phases (EP) and intensities (EI) and finally the LSF and LSC occurrence trend and the factors affecting this trend were analyzed using Mann–Kendall method and Sen’s slope estimator. Different EP and EI were classified using Oceanic Nino Index (ONI) in Nino 3.4 region. The results showed that the occurrence date and probability of the LSF and LSC with different base and critical temperatures in EP are associated with precedence and latency over the whole period and the anomaly of LSF and LSC occurrence date fluctuates between + 17 to − 12 and + 15 to − 17 days, respectively. In addition, the frequency and anomaly of the LSF and LSC at different base temperatures in different EI have been intensified compared to its different EP; therefore, the occurrence date of LSF and LSC can be predicted based on the EP and EI. On the other hand, the trend analysis of the date of the LSF and LSC indicates its retreat towards early spring or last winter at the main studied stations, which is due to global warming. According to the results obtained in some of the studied areas, the delay of LSF and LSC is also observed, which is due to local topographic characteristics, altitude and proximity to moisture sources.

An Empirical Study of Heterogeneous Cross-Project Defect Prediction Using Various Statistical Techniques

Cross-project defect prediction (CPDP) forecasts flaws in a target project through defect prediction models (DPM) trained by defect data of another project. However, CPDP has a prevalent problem (i.e., distinct projects must have identical features to describe themselves). This article emphasizes on heterogeneous CPDP (HCPDP) modeling that does not require same metric set between two applications and builds DPM based on metrics showing comparable distribution in their values for a given pair of datasets. This paper evaluates empirically and theoretically HCPDP modeling, which comprises of three main phases: feature ranking and feature selection, metric matching, and finally, predicting defects in the target application. The research work has been experimented on 13 benchmarked datasets of three open source projects. Results show that performance of HCPDP is very much comparable to baseline within project defect prediction (WPDP) and XG boosting classification model gives best results when used in conjunction with Kendall's method of correlation as compared to other set of classifiers.

Spatiotemporal changes of terrestrial water storage and possible causes in the closed Qaidam Basin, China using GRACE and GRACE Follow-On data

Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.

Evolution of meteorological factors during 1980–2015 in the Daqing River Basin, North China

Abstract Precipitation and temperature data, such as the homogeneity, trend, abrupt change, and periodicity, obtained at 40 meteorological stations in the Daqing River Basin from 1980 to 2015 are analyzed using the Mann–Kendall method, anomaly accumulation, Rescaled range analysis (R/S analysis) and wavelet transform. The regularity of climate change is studied to provide guidelines for the rational utilization of water resources. The results show that the annual precipitation has an insignificant upward trend and suddenly changes in 2007. The precipitation evolution can be divided into three types of periodicity, that is, 22–32, 8–16, and 3–7 year time scales, where the 28 year scale is the first main period of precipitation change. The annual average temperature shows a notable upward trend, with 1992 as the change year. The annual average temperature can be divided into three types of periodicity, that is, the 25–32, 14–20, and 5–10 year time scales, where the 28 year scale is the first main period of temperature change. In conclusion, the climate of the Daqing River Basin gradually turns into humid and hot climate. The results provide valuable reference for the assessment of the effects of climate change, and the management of water resources.

PROCEDIMIENTO PARA DETERMINAR LA FACTIBILIDAD DE LOS PARQUES SOLARES FOTOVOLTAICOS

The renewable energy sources are the future alternative due to the minimal environmental impact, compared with the current energies. The photovoltaic sector is to the vanguard of renewable energies nowadays. This investigation´s objective is: to make a procedure to determine the economic and technical feasibility of photovoltaic solar parks (PSP) connected to the Cuban National Electro-Energetic System (NES). The proposed procedure has five phases, each of them with several paces, as much as the techniques to be used on them. The first phase and main factor of project’s execution is the meteorological variables’ incidence in the area subjected to study. The procedure is validated using the Kendall method with a concordance factor of 80 % of experts’ discrepancy.

Influence of Sr/Nd partial replacement on fundamental properties of Bi-2223 superconducting system

This comprehensive work aims to examine the change in flux pinning mechanism, physical, mechanical, and structural characteristics of pure and Sr-site Nd-substituted Bi1.8Pb0.35Sr1.9−yNdyCa2.2Cu3Ox (Bi-2223) systems. The magnetoresistivity performances for all the samples are carried out by magnetotransport experiments in the existence of external magnetic field strength intervals 0–7 T. It is found that the increment of Nd/Sr substitution amount in bulk Bi-2223 system retrogrades the pinning capability of thermal flux motions for interlayer Josephson junction between the isolated grains. Similarly, the coupling probabilities of copper pairs and potential energy barriers are significantly diminished by increasing Nd impurity. This is in association with the enhancement of permanent structural problems in the crystal structure. Therefore, the excessive Nd inclusions improve the reattached linear/split pancake-like nature. In this regard, the best magnetic performance quantities are obtained for the pure sample. Besides, the SEM images show that the grain connectivity and surface morphology damage significantly with the Nd impurity. Additionally, the experimental microhardness findings conducted at various external loads (0.245–2.940 N) display that the Nd purity in the superconducting system degrades dramatically the key design mechanical features. Besides, we analyze the mechanical characteristic properties founded on the theoretical approaches with the proportional sample resistance, elastic/plastic deformation, and Hays–Kendall methods. The results obtained show that the Nd purity causes the indentation size effect behavior to decrease dramatically for all the samples. Furthermore, the findings of Hays–Kendall method are noticed to much more agree with the real hardness parameters. Thus, the Hays–Kendall model is the best methods to find the load-independent Vickers hardness values for the Sr-site Nd-substituted Bi1.8Pb0.35Sr1.9−yNdyCa2.2Cu3Ox (Bi-2223) systems. Moreover, in the dynamic microhardness measurements, the contact depth (hc), elastic modulus (Er), and load (Pmax) of all the samples are experimentally recorded for the first time. The results reveal that the mechanical properties depend strongly on the load and Nd impurity level.

Abstract PS4-12: Potential value of FOXA1 expression as genomic predictor of response to docetaxel and carboplatin neoadjuvant (NA) chemotherapy in patients with triple negative breast cancer (TNBC)

Abstract Background: TNBC is a particularly aggressive subtype with high recurrence rates and poor long-term survival. Anthracycline-free regimens based on docetaxel plus carboplatin (TCb) achieve around 50-55% pathologic complete responses (pCR) which correlates with better survival outcomes. Identification of genomic predictors of response is key to individualize therapies for this heterogeneous breast cancer (BC) subgroup. FOXA1 is a pioneer factor for androgen (AR) and estrogen receptors (ER). Its under-expression has been linked to cancer stemness in TNBC and better prognosis in ER-positive BC. The differential expression of AR, FOXA1 and BRCA1 is associated with sensitivity to chemotherapy in TNBC patients. Data support the plasticity role of FOXA1 driving basal to luminal BC cells by inducing luminal genes but also by repressing the basal phenotype and thus, tumor aggressiveness, although its role as a biomarker in TNBC is still controversial. We have analyzed RNAseq data of FOXA1 and AR in a series of TNBC tumors homogenously treated with neoadjuvant TCb to study their correlation with survival and response. Methodology: 300 TNBC patients have been included in a multicenter, prospective, non-randomized trial aimed to identify predictors of response to TCb in the neoadjuvant setting (NCT01560663). 278/300 patients were evaluable. PAM50 subtypes (NanoString nCounter) and RNAseq (HiSeq2500) were analyzed for those patients with both available samples and evaluable pathological response (n=208). Correlations were studied by the Kendall method. Linear or logistic regressions were adjusted for univariant analysis depending on numerical or categorical response variables, respectively. Results: FOXA1 and AR were differentially expressed between basal and non-basal tumor subtypes (PAM50), being overexpressed in non-basal (p<0.001 for both FOXA1 and AR). FOXA1 and AR were significantly overexpressed in non-basal subtypes from the Vanderbilt TNBC-type classification (36/208) as well. Both genes expression showed a significant correlation with several luminal marker genes. AR was significantly associated with ANXA9 (p=0,008), GATA3 (p=0,00002) and XBP1 (p<0.001), while FOXA1 expression was positively associated with ANXA9 (p=0,03), ESR1 (p<0.001),GATA3 (p<0.001) and XBP1 (p<0.001). In terms of response, significant differences in FOXA1 expression levels were found between responders and non-responders by Residual Disease Burden (RCB) classification taking response as binary variable of PCR vs RCB-I/II/III (p=0.003) as well as pCR/RCB-I vs RCB-II/III (p=0.008) with responders showing a lower FOXA1 expression level. The same analysis for AR expression did not show statistically significant differences. Within the PAM50 basal subgroup, neither AR nor FOXA1 individual gene expression (172/208) showed association with response. For survival, with a median follow-up of 36 months, neither FOXA1 nor AR showed significant impact on EFS or OS per univariate Cox regression analysis (EFS: HRFOXA1 1.076; p=0.307; HRAR 1.156, p=0.084 and OS: HRFOXA11.120, p=0.140; HRAR 1.152, p=0.121). Conclusions: Within our cohort, FOXA1 expression is a better predictor of response to NA chemotherapy than AR. FOXA1 is overexpressed in TNBC tumors who do not achieve pCR with neoadjuvant TCb chemotherapy, correlating with non-basal tumor subtypes. This suggests that measuring FOXA1 expression levels in patients with TNBC could be useful to predict response to TCb regimen enabling physicians to select more effective alternative NA schemes in this population. Citation Format: Sara López-Tarruella, María del Monte-Millán, Enrique Alvarez, Yolanda Jerez, José Ángel García Saenz, Isabel Echavarria, Fernando Moreno, Ivan Márquez-Rodas, Coralia Bueno-Muiño, Blanca Herrero, Maria Cebollero, Rocío Ramos-Medina, Nerea Lobato, Ricardo González del Val, Maria Isabel Palomero, Santiago Lizarraga, Uriel Bohn, Ana Isabel Ballesteros, Patricia Rincón, Tatiana Massarrah, Inmaculada Ocaña, Lucía Villarejo, Charles M Perou, Miguel Martin. Potential value of FOXA1 expression as genomic predictor of response to docetaxel and carboplatin neoadjuvant (NA) chemotherapy in patients with triple negative breast cancer (TNBC) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-12.

Necessity of water treatment to meet lake water quality goals in Chitgar Lake

Bayat J, Hashemi SH, Nikzad MF, Talakesh SMR. 2021. Necessity of water treatment to meet lake water quality goals in Chitgar Lake. Lake Reserv Manage. 37:160–169. Managing urban lake water quality to prevent algae blooms is important for tourism at Chitgar Lake, which is a shallow artificial lake created for recreation in northwest Tehran City, Iran. Its water quality is influenced by the Kan River as its water source, as well as by peripheral activities and bird excrement. This study investigates the effectiveness of different water quality management practices including restricted water intake rules and water treatment from April 2013 to April 2020 to improve the water quality of the lake in order to limit algae blooms. The nonparametric Mann–Kendall method and the Sen function were applied to decipher trends and assess impacts of the practices. Despite intake water control, the trend analysis showed that chlorophyll-a and total phosphorus had increased after the opening of the lake in 2013. However, this incremental trend was greatly reduced after commissioning a treatment plant for improving water quality. The treatment system improves the quality of diverted Kan River water during winter lake refill and is used to treat lake water during the rest of the year. The specific characteristics of the lake and the Kan River prevent achievement of target water quality without water treatment.

Study on the dynamic characteristics of shallow groundwater level under the influence of climate change and human activities in Cangzhou, China

Abstract Based on the monitoring data of groundwater level, mining data and meteorological data from 1986 to 2015 in Cangzhou City, the change characteristics of depth of groundwater level were studied. On this basis, the nonparametric statistical test method (Mann–Kendall method) was used to study the trend of depth of groundwater level. Besides In addition, the principal component analysis method and grey correlation method were used to study groundwater influence factors. Finally, the multivariable time series model was used to predict the depth of groundwater level. The results showed that in the past 30 years, influenced by human activities and meteorological changes, the groundwater flow field in 1986–2015 was generally move from southwest to northeast, the depth of groundwater level was increasing from the initial 3.26 to 4.06 m, with an annual increase rate of 0.027 m/a; the contribution rate of exploitation factor, precipitation factor, evaporation factor were 40, 20 and 40%; the fitting figure of the observed values and the predicted values were very good, with an average relative error of 7.73%. According to the prediction schemes, when the evaporation increases by 5%, and the agricultural exploitation decreases by 5%, the depth of groundwater will reach 3.39 m; when the evaporation increases by 10%, and the agricultural exploitation decreases by 10%, the depth of groundwater will reach 3.32 m. This study had important reference significance for regional groundwater treatment and rational utilization.

Water level change of lakes and sinkholes in Central Turkey under anthropogenic effects

Determining water level changes in lakes is significant for sustainable water supply planning, flood control, water resource management, and economic development with ecosystem sustainability. Trend analysis is a commonly used tool for detecting changes in hydrologic time series of quantities such as lake level, precipitation and temperature. Trend analysis of meteorological variables is very important for assessing the long-term changes in lake levels. This study examines long-term changes in the lakes in the Central Anatolia region of Turkey. Kizoren and Timras sinkhole lakes and Lakes Tuz and Beysehir are some of the lakes located in this region. Changes in these lakes were examined along with changes in precipitation trends and human effects. The precipitation stations representing these lakes were determined using Thiessen polygons. In this study, the well-known nonparametric Mann–Kendall trend test (MK) was used. Before performing trend analysis, homogeneity and autocorrelation tests were applied for the time series. The standard normal homogeneity test (SNHT) was used to evaluate homogeneity. Trend analysis was performed before and after the change points the inhomogeneous series for the period of the homogeneous series. In addition, the modified Mann–Kendall method (MMK) was performed to examine the strong autocorrelations seen in the entire time series. The results showed that precipitation station data are homogeneous and lake levels are inhomogeneous. Trend analysis was performed by determining the change points for the inhomogeneous lakes. There were no significant trends after the change points for the Lake Tuz and Timras sinkhole lake. These results are supported by the insignificant trends in precipitation stations. Contrary to the trend analysis results, the increase in the water level of Beysehir Lake was determined to increment from the water transferred to the lake. The decrease in the water level of Kizoren sinkhole was a result of anthropogenic effect rather than precipitation. The results confirm that lake levels have been affected by precipitation trends and anthropogenic effects.

Analyses of Runoff and Sediment Transport and their Drivers in a Rare Earth Mine Drainage Basin of the Yangtze River, China

A comprehensive analysis of the effects of major climate conditions such as El Nino Southern Oscillation (ENSO) and precipitation on changes in runoff and sediment transport in a basin may provide a scientific basis and technical support for regional water resource management and protection of the aquatic ecology. Taking the Taojiang River as an example, a large set of hydrogeographic data on runoff and sediment transport measured on a monthly basis from 1957 to 2015 was analyzed to study the impacts of various correlation factors on runoff and sediment transport in the river, which is located in the middle and lower reaches of the Yangtze River. Besides the conventional Mann–Kendall (M-K) method, cross-wavelet and wavelet coherence analysis methods were also applied in the data analysis. The results showed that: (1) From the M-K mutation tests conducted for the runoff volume and the sediment transport rate from 1957 to 2015, there were no significant changes in runoff. However, a mutation occurred in the sediment transport rate in 2005 and the average annual decrease reached 88.2237 million tons. (2) Precipitation was a dominant factor that controlled the changes in runoff volume and sediment transport rate. It directly influenced the changes in runoff volume, which subsequently caused the changes in sediment transport rate in the study area. Since the year 2005, sediment transport rates have been heavily influenced by the construction of large-scale hydro-power stations (Julongtan), causing a significant rate decline. A comparison between the sediment transport volume during 2005 to 2015 and that during 1980 to 2004 revealed that the annual sediment transport decrease reached 84.4079 million tons, accounting for 95.7% of the total decrease in sediment transport volume. (3) The significant resonance cycle between the sea surface temperature (SST) and the precipitation, runoff volume and sediment transport mainly occurred with a cyclic period between 1.33 and 5.33 years. During an ENSO event, the precipitation, runoff, and sediment transport rates all decreased at the beginning, then increased and reached their maximums, followed by a decline at the end.

Lake area monitoring based on land surface temperature in the Tibetan Plateau from 2000 to 2018

Lake area change in the Tibetan plateau is an important indicator for climate change assessment. To overcome the temporal inconsistency of optical remote sensing-based lake area detections, a land surface temperature (LST)-based detection scheme was proposed by utilizing the big difference between land and water surface temperatures. A trend test conducted by the Mann–Kendall (MK) method was successfully applied to investigate lake area variation from 2000 to 2018 with the use of the annual mean temperature information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST daily product. A comparison with the monitoring results from Landsat images indicates the proposed method can provide spatial distributions of lake area change with high accuracy. More importantly, the temporal variation of annual mean LST provides a special way to detect the abrupt change year (ACY) in lake area.The ACYs of most lakes mainly occur from 2004 to 2012. For an individual lake, the ACY offers vital information about the lake area change process. In summary, this work demonstrates the good potential of the LST-based method for lake area monitoring and assessment.