Trend Analysis Of Time Series Research Articles

Effect of Heavy Rainfall Events on the Dry Matter Yield Trend of Whole Crop Maize (Zea mays L.)

The objective of this study was to detect the historical dry matter yield (DMY) trend and to evaluate the effects of heavy rainfall events on the observed DMY trend of whole crop maize (WCM, Zea mays L.) using time-series analysis in Suwon, Republic of Korea. The climatic variables corresponding to the seeding to harvesting period, including the growing degree days, mean temperature, etc., of WCM along with the DMY data (n = 543) during 1982–2011, were used in the analysis. The DMY trend was detected using Autoregressive Integrated Moving Average with the explanatory variables (ARIMAX) form of time-series trend analysis. The optimal DMY model was found to be ARIMAX (1, 1, 1), indicating that the DMY trend follows the mean DMY of the preceding one year and the residual of the preceding one year with an integration level of 1. Furthermore, the SHGDD and SHHR were determined to be the main variables responsible for the observed trend in the DMY of WCM. During heavy rainfall events, the DMY was found to be decreasing by 4745.27 kg/ha (p < 0.01). Our analysis also revealed that both the intensity and frequency of heavy rainfall events have been increasing since 2005. The forecasted DMY indicates the potential decrease, which is expected to be 11,607 kg/ha by 2045. This study provided us evidence for the correlation between the DMY and heavy rainfall events that opens the way to provide solutions for challenges that summer forage crops face in the Republic of Korea.

Analysis of the Characteristics of Monthly Rainfall Pattern in Katsina

Aims: This paper seeks to analyse the characteristics of monthly rainfall pattern in Katsina City in a view to unveiling the trends and describing its dynamics so that adequate recommendations can be made for its modelling.
 Study Design: The analysis involves a complete statistical, trend, spectral and nonlinear analysis of the monthly rainfall time series recorded in Katsina.
 Place and Duration of Study: Location: Katsina City, Katsina State, Nigeria from 1990 to 2015; a period of 26 years.
 Methodology: Secondary data of daily rainfall recorded in Katsina city from 1990 to 2015 was collected from the Nigerian Meteorological Agency (NiMet), and monthly averages were taken to obtain the monthly rainfall data. The data was then subjected to statistical, trend, spectral and nonlinear analysis techniques to reveal the behavioural patterns in the rainfall and also to reveal its underlying dynamics for its future modelling and prediction.
 Results: The outcome of this analysis indicates that the monthly rainfall in Katsina exhibits an increasing trend with high variance and right-skewed distribution requiring a maximum of 6 independent variables to model its dynamics. The correlation exponent plot reached a saturation value of 5.892 confirming that the monthly rainfall in Katsina over the last 26 years exhibits low dimensional chaotic behavior while the largest Lyapunov exponent for the monthly rainfall time series in Katsina was also computed and found to be positive, having a value of 0.006055/month confirming the presence of deterministic chaos dynamics and is predictable for the next 165 months.
 Conclusion: Since from the findings of this work it is confirmed that the rainfall in Katsina exhibits chaotic behavior with an increasing trend, it is recommended that more drainages and dams be built to provide steady supply of water for agricultural and domestic purposes as well as curtail the menace of flooding and drought which may occur as a result of global warming and climate change.

A decade of CH<sub>4</sub>, CO and N<sub>2</sub>O in situ measurements at Lauder, New Zealand: assessing the long-term performance of a Fourier transform infrared trace gas and isotope analyser

Abstract. We present a 10-year (January 2007–December 2016) time series of continuous in situ measurements of methane (CH4), carbon monoxide (CO) and nitrous oxide (N2O) made by an in situ Fourier transform infrared trace gas and isotope analyser (FTIR) operated at Lauder, New Zealand (45.04 S, 169.68 E, 370 m a. m. s. l.). Being the longest continuous deployed operational FTIR system of this type, we are in an ideal position to perform a practical evaluation of the multi-year performance of the analyser. The operational methodology, measurement precision, reproducibility, accuracy and instrument reliability are reported. We find the FTIR has a measurement repeatability of the order of 0.37 ppb (1σ standard deviation) for CH4, 0.31 ppb for CO and 0.12 ppb for N2O. Regular target cylinder measurements provide a reproducibility estimate of 1.19 ppb for CH4, 0.74 ppb for CO and 0.27 ppb for N2O. FTIR measurements are compared to co-located ambient air flask samples acquired at Lauder since May 2009, which allows a long-term assessment of the FTIR data set across annual and seasonal composition changes. Comparing FTIR and co-located flask measurements show that the bias (FTIR minus flask) for CH4 of −1.02 ± 2.61 ppb and CO of −0.43 ± 1.60 ppb are within the Global Atmospheric Watch (GAW)-recommended compatibility goals of 2 ppb. The N2O FTIR flask bias of −0.01 ± 0.77 ppb is within the GAW-recommended compatibility goals of 0.1 ppb and should be viewed as a serendipitous result due to the large standard deviation along with known systematic differences in the measurement sets. Uncertainty budgets for each gas are also constructed based on instrument precision, reproducibility and accuracy. In the case of CH4, systematic uncertainty dominates, whilst for CO and N2O it is comparable to the random uncertainty component. The long-term instrument stability, precision estimates and flask comparison results indicate the FTIR CH4 and CO time series meet the GAW compatibility recommendations across multiple years of operation (and instrument changes) and are sufficient to capture annual trends and seasonal cycles observed at Lauder. The differences between FTIR and flask N2O measurements need to be reconciled. Trend analysis of the 10-year time series captures seasonal cycles and the secular upward trend of CH4 and N2O. The CH4 and CO time series have the required precision and accuracy at a high enough temporal resolution to be used in inversion models in a data-sparse region of the world.

Water provisioning improvement through payment for ecosystem services

We assess whether a Payments for Ecosystem Services (PES) programme met its objectives of reducing soil erosion and yielding water in an environmental protected area, the Guariroba River Basin, Midwestern Brazil. We measured rainfall and water discharge throughout 2012 and 2016. During the same period, soil and water conservation practices were performed in the basin, such as: building level terraces and riparian vegetation recovery. We separated streamflow into baseflow and direct runoff, then we evaluted the baseflow index that indicated that groundwater significantly contributes to total flow. Therefore, to investigate the effects on streamflow, we performed a trend analysis in the baseflow time series using the Mann-Kendall test. In addition, we analysed the efficiency of soil erosion regulation practices over time, considering the total payment and the trends found in the baseflow. Whereas precipitation records present a decreasing trend (1 mm month−1), baseflow tends to increase by 0.018 m3 s−1 in the same period. Our findings show that soil conservation practices performed in the basin increase baseflow and also provide a better resilience to endure extreme events such as drought based on an increase in forest areas and soil conservation practices such as level terrace.

Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation.

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

FOREIGN CAPITAL FLOWS AND ECONOMIC GROWTH OF KENYA.

The purpose of the study was to establish the effects of foreign capital flows on economic growth of Kenya. The study employed a quantitative research design. The target population of this study was Kenya since it is the Center of analysis. Considering that the population is one country, Kenya, secondary data was collected over a period of 25 years from 1993 to 2017. Therefore, the number of observations was X * 25 = 25. The research conducted a census on Kenya using secondary data from Nairobi Securities Exchange (NSE), Capital Markets Authority (CMA), Kenya National Bureau of Statistics (KNBS), Central Bank of Kenya, World Bank and United Nations Conference on Trade and Development (UNCTAD). Data over time was analyzed using a time series model and trend analysis. Model test and correlation analysis were done before conducting regression and univariate regression analysis. The study found that, when external commercial borrowing is increased by one US dollar, annual GDP will increase by 395.990% when all other factors are kept constant. The opposite also applies. But, if external commercial borrowing is zero, annual GDP will decrease by USD 8,151,662,920.94 when all other factors are kept constant. Additionally, when Foreign Portfolio investment is increased by one US dollar, annual GDP will increase by 805.37% when all other factors are kept constant. The opposite also applies. But, if Foreign Portfolio Investment is zero, annual GDP will remain to be USD 25394237979 when all other factors are kept constant. Also, when FDI is increased by one US dollar, annual GDP will increase by 3026.30% when all other factors are kept constant. The opposite also applies. But, if FDI is zero, annual GDP will still increase by USD 18493289187.3 when all other factors are kept constant. Further results revealed that when Non-Resident Kenyan Deposits are increased by one US dollar, annual GDP will increase by 3738.65% when all other factors are kept constant. The opposite also applies. But, if Non-Resident Kenyan Deposits is zero, annual GDP will remain to be USD 4869680695.47 when all other factors are kept constant. The study recommends that the Government pursues policies that will attract and favour net increases in Foreign Direct Investments, Foreign Portfolio Investments, External Commercial Borrowings and Non-Resident Kenyan deposits into the country.

Comparing farmers’ perceptions of climate change with meteorological data in three irrigated cropping zones of Punjab, Pakistan

Farmers’ perception about changing climate leads to farm-adaptive responses. Coherence between perceptions and meteorological trends leads to improved farm decisions. The objective of this study was to examine the degree of agreement between farmers’ perception and actual climate trends in three irrigated districts of Punjab province in Pakistan. The study also inquired the role of extension services and other factors affecting farmers’ perception. Using multistage sampling technique, a sample of 300 farmers was taken who were interviewed using pre-structured and field-tested questionnaire about their perceptions of climate change at farm. Climate data for 33 years regarding temperature and for 34 years for precipitation were obtained from meteorological stations in selected districts. It employed Mann–Kendall and Sen’s slope tests for trend analysis in climate time series and tested nonparametric association in cross-sectional data. The results revealed that farmers’ perception about precipitation trends do not match with actual trends mainly because of the way climate information is processed among farmers and scientific community. However, farmers’ perceptions about increase in temperature were in line with meteorological evidence across selected irrigated cropping zones. Extension services along with income and size of land holding were key factors affecting farmers’ perception about climate change. The study recommends bridging information gaps between scientific community and farmers about climate change.

Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of the Teacapan-Agua Brava lagoon system, Mexico

ABSTRACTMonthly time series, from 2001 to 2016, of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) from MOD13Q1 products were analyzed with Seasonal Trend Analysis (STA), assessing seasonal and long-term changes in the mangrove canopy of the Teacapan-Agua Brava lagoon system, the largest mangrove ecosystem in the Mexican Pacific coast. Profiles from both vegetation indices described similar phenological trends, but the EVI was more sensitive in detecting intra-annual changes. We identified a seasonal cycle dominated by Laguncularia racemosa and Rhizophora mangle mixed patches, with the more closed canopy occurring in the early autumn, and the maximum opening in the dry season. Mangrove patches dominated by Avicennia germinans displayed seasonal peaks in the winter. Curves fitted for the seasonal vegetation indices were better correlated with accumulated precipitation and solar radiation among the assessed climate variables (Pearson’s correlation coefficients, estimated for most of the variables, were r ≥ 0.58 p < 0.0001), driving seasonality for tidal basins with mangroves dominated by L. racemosa and R. mangle. For tidal basins dominated by A. germinans, the maximum and minimum temperatures and monthly precipitation fit better seasonally with the vegetation indices (r ≥ 0.58, p < 0.0001). Significant mangrove canopy reductions were identified in all the analyzed tidal basins (z values for the Mann-Kendall test ≤ −1.96), but positive change trends were recorded in four of the basins, while most of the mangrove canopy (approximately 87%) displayed only seasonal canopy changes or canopy recovery (z > −1.96). The most resilient mangrove forests were distributed in tidal basins dominated by L. racemosa and R. mangle (Mann-Kendal Tau t ≥ 0.4, p ≤ 0.03), while basins dominated by A. germinans showed the most evidence of disturbance.

Crypto Economics - The Top 100 Token Models Compared

The article provides an overview of the evolving economic incentive designs in decentralized systems. After introducing the decentralized economic policy tools that influence token design, the author examines the leading token models in a hand-selected dataset comprising the top one hundred cryptocurrencies by market capitalization (N=100). The dataset enables a time series trend analysis of token models, consensus algorithms, and governance mechanisms, among other data points.

Time series trend analysis and prediction of water quality in a managed canal system, Florida (USA)

Water quality in the Everglades canal system discharging into the St. Lucie Estuary in south Florida is greatly influenced by anthropogenic activities. We analyzed the water quality parameters trend from 1979 to 2014 using seasonal trend decomposition and LOESS of four canals (C23, C24, C25, and C44). We also predicted the value of total nitrogen (TN) and total Phosphorus (TP) and turbidity from 2016 to 2020 using exponential smoothing and additive Holt-winters method. The main goals of this paper were to reveal long-term changes trends in water quality and identify the emphasis of management in the near future. We found that the change range of water quality parameters was different, but change rules were similar. The mean of DO at each station was in the range of 5.0∼6.3 mg l-1, and the colour was in the range of 5 ∼ 400PCU. The highest specific conductivity was 2250 μS cm-1 in C24 and the lowest was 125 μS cm-1 in C44, and the minimum turbidity was only 0.4 NTU at C25 and the maximum value was 90.3 NTU at C44. The mean value of TN at each station was C24>C44 >C23>C25, and TP was C24>C23>C44>C25. Nutrient export exhibited an upward trend for TN from all the basins and downward trend was present for TP. Water quality was influenced by rainstorm and non-point source pollution. Our predictions showed that TN and TP concentrations in C23 canal were higher than other three canals every month and turbidity of C44 canal was higher than other three canals. Therefore, improved best management practices should be implemented on land that have high TN and TP concentrations in runoff and leachate flowing into the C23 canal.

Emergency Department Visits for Inflammatory Bowel Disease Patients Are Increasing at a Steady Rate

Introduction: Inflammatory bowel disease (IBD) is a chronic condition & often leads to significant utilization of healthcare resources, including emergency department (ED) visits. With the increasing use of biologic therapy over the past decade, one may expect to see a decrease or plateau of the rate of ED visits for IBD. We evaluated whether the rate of ED visits for IBD have changed overtime. Methods: Using claims data from the Healthcare Cost and Utilization Project, we identified a cohort of patients who presented to the ED w/ a primary diagnosis code of either Crohn's disease (CD) or ulcerative colitis (UC), from 2006-2014. We calculated rates of presentation to the ED for CD and UC separately. We also conducted a time series trend analysis to estimate if the rate of change in ED presentation was statistically significant over time. Results: For CD, the total # of ED visits increased each year, from 63000 visits in 2006 to 93277 in 2014, w/ a corresponding increase in the rate of ED visit from 19.76 per 100000, to 29.25 per 100000 (see figure 1). Although the rate increased each year, the rate of change overtime remained steady & wasn't statistically significant (P value 0.87). For CD, the disposition from the ED did vary overtime, w/ an increase in discharges & a slowing of the rate w/ regard to admissions (figure 2), although the trends between the two lines weren't significant, (P value 0.2). For UC, the total # & rate of ED visits were less than that which we saw for CD. For UC, the total # of ED visits increased from 27846 in 2006, to 44670 in 2014, w/ a corresponding increase in the rate of ED visits from 8.7 per 100000, to 14.0 per 100000 (see figure 1). For UC, although the rate of ED visit increased each year, the rate of increase remained stable overtime & wasn't statistically significant (P value 0.98). The rates of discharge from the ED vs admission to the hospital (figure 3), & the rates of the 2 dispositions overtime weren't statistically significant (P value 0.5). The difference between the rate of increase between CD & UC wasn't statistically significant (P value 0.88) Conclusion: The rate of ED visits for CD & UC appears to be increasing overtime, although the rate of increase appears to be steady--neither increasing or decreasing. Further studies evaluating trends in health care utilization are necessary to design initiatives to improve patient care & try to decrease the use of potentially unnecessary hospital based resources.714_A Figure 1. Rate of ED visits in patients with CD & UC714_B Figure 2. Disposition in ED in CD patients714_C Figure 3. Disposition from ED in UC patients

Spatio - temporal changes in NDVI and rainfall over Western Rajasthan and Gujarat region of India

This study examines the MODIS time series NDVI datasets to detect greenness regeneration over Western Rajasthan and the Gujarat region of India. Time series analysis was applied to 17-years (2000-2016). MODIS NDVI satellite data product. Rainfall data for the same period were also analyzed to understand its impact over vegetation. NDVI time series datasets of MODIS 16-day composite provedsufficient for deriving statistically significant trend values for identifying areas of change in vegetation cover. Areas showing positive changes in NDVI trend was clearly correlated with areas which were brought under irrigational network over these areas, indicating an increase in vegetation, due to availability of water supply. Trends in NDVI were also compared with the trends in rainfall over the selected locations from Gujarat and Western Rajasthan. NDVI was positively correlated with the rainfall in both the regions. The NDVI time series trend analysis successfully detected the changes in greenness regeneration areas in Gujarat and western Rajasthan.

Establishing a land degradation neutrality national baseline through trend analysis of GIMMS NDVI Time‐series

AbstractThe land degradation‐neutrality (LDN) national baseline for Kenya in 2015 was established in terms of the three LDN indicators (land cover, land productivity, and carbon stocks), and using trends in GIMMS NDVI and land cover datasets over the 24‐year period from 1992 to 2015. Human‐induced land degradation was separated from degradation driven by climate factors using soil moisture data and the residual trend method. On the basis of Kendall's tau of the NDVI residuals computed using annual mean data of the NDVI and soil moisture relationship, the country has experienced persistent negative trends (browning) over 21.6% of the country, and persistent positive trends (greening) in 8.9% of the country. The land cover change map for the period 1992–2015 showed that in 5.6% of the area there was a change from one land cover class to another. Pronounced changes in terms of land area were the increase in grasslands by 12,171 km2, the decrease of bare land by 9,877 km2, and the decrease in forests by 7,182 km2. Browning and greening trends account for 13% and 12%, respectively, of the land cover change areas. By establishing the LDN national baseline, the LDN concept is now operational. As a first step, targeted field level assessments, alongside the collection of data for the computation of soil organic carbon stocks, should be undertaken in selected browning, greening, and land cover change sites. These field studies will provide decision makers with key information on how to plan for the implementation and monitoring of LDN interventions.

Modeling Hydroclimatic Change in Southwest Louisiana Rivers

We applied the newly developed WRF-Hydro model to investigate the hydroclimatic trend encompassing the three basins in Southwest Louisiana as well as their connection with large-scale atmospheric drivers. Using the North American Land Data Assimilation System Phase 2 (NLDAS-2), we performed a multi-decadal model hindcast covering the period of 1979–2014. After validating the model’s performance against available observations, trend and wavelet analysis were applied on the time series of hydroclimatic variables from NLDAS-2 (temperature and precipitation) and model results (evapotranspiration, soil moisture, water surplus, and streamflow). Trend analysis of model-simulated monthly and annual time series indicates that the regional climate is warming and drying over the past decades, specifically during spring and summer (growing season). Wavelet analysis reveals that, since the late 1990s, the anomaly of evapotranspiration, soil moisture, and streamflow exhibits high coherency with that of precipitation. Pettitt’s test detects a possible change-point around the year 2004, after which the monthly precipitation decreased from 140 to 120 mm, evapotranspiration slightly increased from 80 to 83 mm, and water surplus decreased from 60 to 38 mm. Changes in regional climate conditions are closely correlated with large-scale climate dynamics such as the Atlantic Multidecadal Oscillation (AMO) and El Niño Southern Oscillation (ENSO).

FREQUENCY ANALYSIS OF MODIS NDVI TIME SERIES FOR DETERMINING HOTSPOT OF LAND DEGRADATION IN MONGOLIA

Abstract. This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

Impact of climate change on potential evapotranspiration (case study: west and NW of Iran)

Potential evapotranspiration (ET0) is one of the main elements when computing agricultural irrigation requirements and scheduling. All climatic parameters as well as ET0 are influenced by climate change. The aim of this study is trend analysis of monthly and annual ET0 time series in the west and NW of Iran. Values of ET0 are estimated at 36 selected stations, using the FAO-56 Penman-Monteith (FAO-56 PM) method. Then, the non-parametric Mann-Kendall (MK) method was used to detect trends. The slopes of trend lines are estimated using Sen’s estimator approach. Results showed that about 86%of the monthly ET0 time series had upward trends of which 35.6 and 43% were significant at 0.01 and 0.05 levels, respectively, 47.4% exhibited a significant upward trend at the 10% level. In contrast, less than 0.7% of the whole monthly ET0 time series showed a significant downward trend (α < 0.01). Only 1.1% of the monthly ET0 time series had significant downward trends (α < 0.10). The strongest positive upward trend (significant at the 0.01 level) was detected in August at the Kermanshah station. However, the strongest negative downward trend belonged to the Khodabandeh station. The steepest upward and downward monthly ET0 slopes were observed at Maragheh and Khodabandeh stations. The magnitude of trends for these two stations are estimated as 2.33 and − 2.01 mm/year, respectively. On an annual timescale, above 94% of the stations had upward trend slopes. About 67% of the total stations exhibited significant trends at the 10% level. Very few sites (2.7%) showed downward annual ET0 trends (α < 0.10). At the annual scale, the first three strongest upward trends belonged to the Kermanshah, Urmia, and Tabriz stations, respectively (α < 0.01). It can be concluded that ET0 in the west and NW of Iran have an increasing trend for most of the stations. Therefore, it is important to use water in a prudent manner in this area.

The impact of urban expansion on the regional environment in Myanmar: a case study of two capital cities

This study examines the spatio-temporal patterns of urban expansion for Yangon and Nay Pyi Taw, the former and new national capitals of Myanmar, and its impact on the regional environment between 2000 and 2013. The objective is to examine different driving forces of urban expansion for Yangon and Nay Pyi Taw, and their environmental consequences during Myanmar’s transitional economy. Classified time-series Landsat images are used to evaluate urban expansion processes. Environmental parameters being evaluated in this study include five sets of remotely sensed MODIS land products that are land surface temperature (LST), percent tree cover (PTC), evapotranspiration (ET), terrestrial ecosystem net primary productivity (NPP), and aerosol optical depth (AOD). A time-series trend analysis technique is used to examine the environmental consequences. The built-up areas in Nay Pyi Taw and Yangon exhibit exponential and polynomial increase, respectively. A 1% increase of built-up area could potentially cause an increase of daytime LST of 0.7 °C, a PTC loss of 2.3%, a decrease in NPP of 34.3 kg/m2, and an ET decrease of 42.2 mm for Yangon. Similarly, for Nay Pyi Taw, a 1% increase in built-up area could potentially cause a daytime LST increase of 0.3 °C, a nighttime LST increase of 0.06 °C, a PTC loss of 2.5%, a decrease in NPP of 15.1 kg/m2, and a decrease of 19.2 mm ET. No significant change was observed for AOD for either city. Both cities have experienced extensive urban expansion but with different spatial and temporal characteristics, and their effects on the regional environment are different. Urban expansion of Nay Pyi Taw mainly was government-induced municipal infrastructure development. Yangon’s expansion is mainly caused by population pressure and migration from rural areas. The urban expansion in Yangon was mainly due to reconstruction and renovation, as well as infill development during the study period.

Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann–Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil–Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevsehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

Research of behaviors of continuous GNSS station by signal analysis methods

Continuously Operating Reference Station (CORS) networks consisting of multiple GNSS systems have been set up and operating in many developed countries. In Turkey, CORS-TR has been operating. The aim of CORS-TR is a fast, correct, and reliable collection of all kinds of geographic data, thus, speeding up the activities of cadastre, assuring organized urbanization, constituting the spatial infrastructure for relevant works of e-government, and monitoring plate tectonics. Therefore, in this paper, we investigated 788 days of linear and periodic of AKHR, BEYS, CIHA, KAMN, YUNA, AKSI and KNYA CORS-TR stations by linear trend analysis and Wavelet Transform in located in the Central Anatolia Region of Turkey. As a result of trend analyses of time series, it was determined that except a single station (KAMN), other stations were moving in the Southwest direction (17.78 mm/year). This finding is consistent with the region’s tectonic plate movements. In the periodical analyses, stations’ daily, monthly, seasonal, annual and even biennial movements resulting from atmospheric and hydrologic loading, climatic and other effects were revealed. Moreover, Detail (D) component, which protects the largest percentage of energy, was determined by calculating the energy spectrum. Energies of detail components are similar in horizontal, and especially D7-D8 have the highest energy. However, the components obtained the maximum energy was varied in Up coordinate time series. These results indicate that stations of CORS-TR network located in Central Anatolia were rather stable and able to be providing reliable, accurate and continuous data for national and international studies to be conducted in many fields.

Variation Trend Analysis of Runoff and Sediment Time Series Based on the R/S Analysis of Simulated Loess Tilled Slopes in the Loess Plateau, China

The objective of this study was to illustrate the temporal variation of runoff and sediment of loess tilled slopes under successive rainfall conditions. Loess tilled slopes with four microtopography types (straight cultivated slope, artificial backhoe, artificial digging, and contour tillage) under five slope gradients (5°, 10°, 15°, 20°, 25°) were simulated and a rainfall intensity of 60 mm/h was adopted. The temporal trends of runoff and sediment yield were predicted based on the Rescaled Range (R/S) analysis method. The results indicate that the Hurst indices of runoff time series and sediment time series are higher than 0.5, and a long-term positive correlation exists between the future and the past. This means that runoff and sediment of loess tilled slopes in the future will have the same trends as in the past. The results obtained by the classical R/S analysis method were the same as those of the modified R/S analysis method. The rationality and reliability of the R/S analysis method were further identified and the method can be used for predicting the trend of runoff and sediment yield. The correlation between the microtopography and the Hurst indices of the runoff and sediment yield time series, as well as between the slopes and the Hurst indices, were tested, and the result was that there was no significant correlation between them. The microtopography and slopes cannot affect the correlation and continuity of runoff and sediment yield time series. This study provides an effective method for predicting variations in the trends of runoff and sediment yield on loess tilled slopes.