Daily Series Research Articles

Remote Sensing and Bio-Geochemical Modeling of Forest Carbon Storage in Spain

This study simulates annual net primary production (NPP) of forests over peninsular Spain during the years 2005–2012. The modeling strategy consists of a linked production efficiency model based on the Monteith approach and the bio-geochemical model Biome-BGC. Recently produced databases and data layers over the study area including meteorological daily series, ecophysiological parameters, and maps containing information about forest type, rooting depth, and growing stock volume (GSV), were employed. The models, which simulate forest processes assuming equilibrium conditions, were previously optimized for the study area. The production efficiency model was used to estimate daily gross primary production (GPP), while Biome-BGC was used to simulate growth (RG) and maintenance (RM) respirations. To account for actual forest conditions, GPP, RG, and RM were corrected using the ratio of the remotely-sensed derived actual to potential GSV as an indicator of the actual state of forests. The obtained results were evaluated against current annual increment observations from the Third Spanish Forest Inventory. Coefficients of determination ranged from 0.46 to 0.74 depending on the forest type. A simplified dataset was produced by applying regular increments in air temperature and reductions in precipitation to the original 2005–2012 daily series with the goal of covering the range of variation of the climate projections corresponding to the different climate change scenarios reported in the literature. The modified meteorological series were used to simulate new GPP, RG, and RM through Biome-BGC and corrected using GSV. Precipitation was confirmed as the main limiting factor in the study area. In the regions where precipitation was already a limiting factor during 2005–2012, both the increment in air temperature and the reduction in precipitation contributed to a reduction of NPP. In the regions where precipitation was not a limiting factor during 2005–2012, the increment in air temperature led to an increment of NPP. This study is therefore relevant to characterize the growth of Spanish forests both in current and expected climate conditions.

Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin, India

The study comprehensively assessed the effects of river basin parameters (basin morphology, drainage network, topography, climate, land use land cover (LULC) composition & pattern, and soil properties) on soil erosion (SE) and specific sediment yield (SSY) in the tropical river basin of Konar, India. The revised universal soil loss equation (RUSLE) along with the sediment delivery ratio (SDR) model has been used to quantify the SE and SSY. As the basin parameters are highly codependent with each other, multivariate partial least square regression (PLSR) with variable importance projection (VIP) statistics has been applied to 32 basin parameters to elucidate the linkage with SE and SSY. Multi-spectral LANDSAT-8 image, topographical sheets (1:50,000), gridded (0.25° × 0.25°) daily rainfall data series, laboratory estimates of soil parameters along with other ancillary data have been employed in this regard. Results revealed that wastelands encouraged moderate to very high (5–40.0 t ha−1 yr−1) annual rate of SE compare to other LULC classes. The PLSR studies revealed that the mean slope gradient (S), basin relief (HD), sediment delivery ratio (SDR), patch density (PD), edge density (ED), hypsometric integral (HI), topographic wetness index (TWI) were very strongly influential (b = 0.1–0.16) in SE. Likewise, SDR, mean annual precipitation (P), rainfall-runoff erosivity (R factor) PD, S, and HD were highly (b=0.12–0.22) important variables (VIP = 1.2–2.3) for SSY. Generated spatial information may help in understanding the SE and SSY dynamics in the basin and the knowledge can be used for soil erosion conservation measures in and around the river basins in India.

Nigerian Stock Exchange and Weak Form Efficiency

This study, Nigerian Stock Exchange and Weak Form Efficiency with two intervals of All Share Index collected from the Nigeria Stock Market fact books. This study employed various parametric tools found for daily and annual all share index as follows: a significant relationship between the price series and their lagged values; coefficient of variance equations are statistically significant; price series do not follow a random walk or are abnormally distributed; level series not significant and causality is found in daily price series with no causality found in the annual price series. The finding in this study in balance affirmed that the Nigeria Stock Exchange is not efficient in weak form, by extension is inefficient in any form. As result the researchers suggest to the supervisory and regulatory authorities to promulgate laws that will strengthen the Nigerian Stock Market. Keyword s: NSE, Weak Form, Parametric Tools DOI: 10.7176/RJFA/11-8-09 Publication date: April 30 th 2020

Homogenized Daily Relative Humidity Series in China during 1960\u20132017

Surface relative humidity (RH) is a key element for weather and climate monitoring and research. However, RH is not as commonly applied in studying climate change, partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system. A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017, ChinaRHv1.0, has been developed. Most (685 or 91.82% of the total) station time series were inhomogeneous with one or more break points. The major breakpoints occurred in the early 2000s for many stations, especially in the humid and semi-humid zones, due to the implementation of automated observation across the country. The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records, for most of the biased station series. There are more break points detected by using the MASH (Multiple Analysis of Series for Homogenization) method, with biases mainly around −0.5% and 0.5%. These inhomogeneous biases are adjusted with reference to the most recent observations for each station. Based on the adjusted observations, the regional mean RH series of China shows little long-term trend during 1960–2017 [0.006% (10 yr)−1], contrasting with a false decreasing trend [−0.414% (10 yr)−1] in the raw data. It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China, including the jump in the early 2000s, raising a caveat for its application in studying climate change in the region.

Stock Market Anomaly: Day of the Week Effect in Bombay Stock Exchange with the Application of GARCH Model

It has observed from many stock markets around the world that index value used to vary due to fluctuation in stock prices. One of the most important factors of variation in the stock prices is the day of the week effect, which indicates calendar irregularities in stock markets. Investment in the stock market is the most uncertain; therefore investors get worried regarding the appropriate day to trade in the financial market. The main objective of the present study is to find out the appropriate day of the week effect of developing the stock market of an emergent nation like India from 1st January 2000 to 31st December 2018. For fulfilling the objectives of the study, the daily closing value of four major indices of the Bombay Stock Exchange has been taken into consideration. To test the equality between average returns to different days and to examine the distribution pattern of daily returns series that measure the day of the week analysis, the parametric tools alike Mean and Standard deviation have employed. Apart from the parametric test, t-test has also applied to the daily returns in order to test the hypothesis. In this study, descriptive statistics and the GARCH model has also used with the purpose of measuring the day of the week effect analysis. Conferring to the results, the coefficients express that the return among different days of the week are statistically significant,

Trends and interannual variability of extreme rainfall indices over Ghana, West Africa

Rainfall plays an important role in the socio-economic development of any nation. Below or above normal rainfall amounts have serious consequences on key socio-economic sectors. This is particularly true for countries such as Ghana where rainfall plays a key role in the agriculture and energy sectors. In this study, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)-homogenized daily rainfall series was used to determine the variability of ten extreme rainfall indices recommended by the World Meteorological Organization-Commission for Climatology (WMO-CCL) and the research project on Climate Variability and Predictability (CLIVAR). The trends of these indices and their links to SST anomalies at oceanic basins were also explored in this study. To understand the variability at interannual to decadal time scales and links with SST forcings, the sum of the first three principal components of the indices were correlated with SST anomalies at oceanic basins. The 35 years mean of frequency indices, CDD, CWD, R10mm and R20mm were found to be in a range of 10–125, 6–14, 30–60 and 8–24 days per year, respectively. On the other hand, the mean over the same period of intensity indices PRCPTOT, R95p, R99p, SDII, RX1day and RX5day were in the range of 900–1700 mm, 200–400 mm, 40–130 mm, 9–13 mm, 30–100 mm and 90–180 mm per year, respectively. The maximum of temporally averaged intensity indices covers southwestern Ghana, while the minimum of the indices lies over northwestern and eastern coasts. A significant decreasing trend in wet indices were observed over the Volta Lake and central portions of the country (7.5°N to 9.5°N), whereas low positive trends were observed over the Northern parts of the country. Wet indices over the country showed significant positive correlations with the Atlantic Ocean SST and negative correlations with the Pacific and Indian basins SSTs. Specifically, the NINO3.4 revealed significant negative correlations with the wet indices over the west-central portions of the country. IOD was observed to have a dipole effect on rainfall indices with the central and southern parts generally covered by negative correlations, while Northern and coastal regions showed positive correlations. The impacts of Atlantic SST on wet rainfall indices are significant over most parts of the country. These results have implications on the improvement of seasonal forecast of the Ghanaian rainfall and its extremes and also provide prior knowledge for better understandings of multidecadal modulations of global interannual teleconnections.

Daily GRACE satellite data evaluate short-term hydro-meteorological fluxes from global atmospheric reanalyses

Changes in terrestrial water storage as observed by the satellite gravity mission GRACE (Gravity Recovery and Climate Experiment) represent a new and completely independent way to constrain the net flux imbalance in atmospheric reanalyses. In this study daily GRACE gravity field changes are used for the first time to investigate high-frequency hydro-meteorological fluxes over the continents. Band-pass filtered water fluxes are derived from GRACE water storage time series by first applying a numerical differentiation filter and subsequent high-pass filtering to isolate fluxes at periods between 5 and 30 days corresponding to typical time-scales of weather system persistence at moderate latitudes. By comparison with the latest atmospheric reanalysis ERA5 of the European Centre for Medium-Range Weather Forecasts (ECWMF) we show that daily GRACE gravity field models contain realistic high-frequency water flux information. Furthermore, GRACE-derived water fluxes can clearly identify improvements realized within ERA5 over its direct predecessor ERA-Interim particularly in equatorial and temperate climate zones. The documented improvements are in good agreement with rain gauge validation, but GRACE also identifies three distinct regions (Sahel Zone, Okavango Catchment, Kimberley Plateau) with a slight degradation of net-fluxes in ERA5 with respect to ERA-Interim, thereby highlighting the potentially added value of non-standard daily GRACE gravity series for hydro-meteorological monitoring purposes.

Irrigation and fertilization as tools to boost growth stability of stone pine (Pinus pinea L.) plantations

Pinus pinea is a Mediterranean species that has been found to grow and develop vigorously in Chile. This species is resistant to drought; however, it has been found to be sensitive to increasing aridity. A few studies have reported the effects of irrigation and fertilization on growth, but no detailed studies have considered the effects of both management techniques to improve growth stability. In this work, daily stem diameter variations (SDVs) in stone pine were monitored for a whole growth year using digital dendrometers in a 31-year-old plantation in Chile. Four management practices were assessed as tools to boost growth: fertilization, irrigation, their combination, and control. Daily growth series showed a marked synchronicity among trees of different management schemes. The combined treatment (fertilization and irrigation) resulted in a 25% longer growing period and greater growth than the control. During the fastest stem growth period, daily diameter increments were, on average, 0.06 mm for fertilized and irrigated trees, and 0.02 mm for control trees. Significant correlations between the standardized SDVs and series of climate variables were found, showing that untreated trees had the highest sensitivity to extreme temperature, rainfall, and potential evapotranspiration. Variability in daily stem diameter growth of fertilized and irrigated trees was correlated only to minimum temperature. The results highlight the importance of fertilization and irrigation in stone pine plantations to enhance diameter growth and reduce variability due to the sensitivity to environmental conditions.

Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting

Reliable and accurate streamflow forecasting is vital for water resource management. Many streamflow prediction studies have demonstrated the excellent prediction ability of decomposition ensemble models. Several studies first extracted subsignals from a streamflow series and then split subsignals into training and validation sets to build prediction models, capturing some future information not available in practical streamflow forecasting. Alternatively, dividing a time series into training and validation sets first and later decomposing them into subsignals can expose the subsignals to the boundary effect, making predicting streamflow difficult. Furthermore, building one model for each subsignal is laborious and can cause error accumulation. Therefore, establishing a robust and efficient decomposition ensemble model without future information to predict highly nonstationary and nonlinear streamflow is challenging. Hence, a single-model forecasting (SF) scheme that assesses the validation distribution during the training stage to adapt to the boundary effect was designed. A SF scheme based on variational mode decomposition (VMD) and long short-term memory (LSTM), namely, SF-VMD-LSTM, was proposed to predict daily streamflow 1–7 days ahead. Non-decomposition-based and decomposition-ensemble-based LSTM models established using SF, multi-model ensemble forecasting (MEF) and SF with the most influential subsignals (SFMIS) and the ensemble empirical mode decomposition (EEMD) and discrete wavelet transform (DWT) were compared. Additionally, multi-model ensemble hindcasting (MEH), single-model hindcasting (SH) and SH with the most influential subsignals (SHMIS) were used as benchmarks to evaluate the forecasting schemes’ adaptability to the boundary effect. Two daily streamflow series from Han River and Jing River, China, were investigated. The results indicate that (1) SF is more robust and efficient than MEF and SFMIS; (2) VMD performs better than EEMD and DWT; (3) SF-VMD-LSTM, with NSE values larger than 0.8 for almost all the prediction scenarios, outperformed other comparative models. Therefore, SF-VMD-LSTM is robust and efficient for forecasting highly nonstationary and nonlinear streamflow.

Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models

Scarcity of water temperature data in rivers may limit a diversity of studies considering this property, which regulates many physical, chemical, and biological processes. We present a robust method to generate a consistent, continuous daily river water temperature (RWT) data series for medium and large rivers using the combined techniques of remote sensing and water temperature modelling. In order to validate our approach, we divided this study into two parts: (i) we evaluated methods to derive RWT from Landsat 7 ETM+ and Landsat 8 TIRS imagery; and (ii) we evaluated the calibration and validation of river temperature models, using these data, to generate the continuous RWT data series. A 1.2 km section of the White River located near Hazleton, IN, USA, was selected to assess this method mainly due to river width and data availability. We tested three methods to retrieve RWT from Landsat 7 and four from Landsat 8, and we also applied a simple thermal sharpening technique. For Landsat 7, the methods showed bias and RMSE of 0.01–0.46 °C and 1.32–1.84 °C, while for Landsat 8, the methods showed bias and RMSE of 0.08–1.27 °C and 1.74–2.17 °C, and in both cases, the best results were found applying the radiative transfer equation with NASA's Atmospheric Correction Parameter Calculator. For the second part of the validation process, we compared a stochastic model and a hybrid model, air2stream, using as input two datasets: the RWT data derived from Landsat 7 only, and a combined dataset of both Landsat 7 and 8 derived RWT. The air2stream model outperformed the stochastic model when calibrated with Landsat 7 data only, with RMSE of 1.83 °C, but both models showed similar results when calibrated with the combined Landsat data, when air2stream showed RMSE of 1.58 °C. Due to its physical basis, better calibration procedure, and higher consistency, air2stream was considered the best model for deriving the continuous RWT data series. When compared to the measured daily mean RWT data, there was no observed tendency in under or overestimating the RWT in low or high temperature conditions by the modelled series. While further tests are needed in order to evaluate if our approach can be applied to analyse past behaviour and present trends, and the impacts of climate change on the temperature of rivers, the consistent results indicate that this approach has the potential to be applied in rivers with no measured temperature data, for example, in the spatial modelling of longitudinal profiles of rivers and the modelling of tributary river temperatures.

Gender and age factors affecting the mortality during the COVID-19 epidemic in Italy

the COVID-19 epidemic severely affected Italy among European countries causing a considerable number of deaths across the country, especially in Northern Italy, leading also to serious problems to the Italian healthcare system, in particular the overcrowding of Intensive Care Units (ICU). In literature, the debate on the overall mortality during the COVID-19 epidemic, directly and indirectly, associated with the disease, is still open. to describe the time trend of the mortality in Italy during the COVID-19 pandemic accounting for age, gender, and geographical areas. analysis of mortality trend, by region, age, and gender. the Italian mortality data, released by the Italian National Institute of Statistics (Istat), have been considered for the analyses. The data refer to the first four months of 2015-2019 and 2020, involving 7,270 municipalities, corresponding to 93% of the Italian population. the mortality rates in the first four months of 2015-2019 and 2020, age-adjusted, have been calculated together with the percent variation. The data were analysed by gender, age class (<65; >=65 years), Region, and geographical areas (Northern versus Central-Southern Italy). The overall daily mortality series have been represented as rates over 100,000 resident population. in addition to the geographical location, the age component was a major determinant of the mortality pattern. The greater increase in the overall mortality was evidenced among elderly subjects in the Northern Italian Regions most affected by the epidemic. In these areas, also gender component played an important role in determining the mortality excess: higher mortality rates in the first four months of 2020 are observed for males in comparison to female populations. this research reveals that the population components are an important issue in determining the COVID-19 mortality excess. For this reason, it is of primary importance to monitor mortality (overall and by COVID-19) by age and gender and to consider these components and the related factors (comorbidity, exposures affecting the lung) in the public prevention policies towards the protection of the most fragile population groups.

Climate change effect on the bud break and flowering dates of the apple trees in mountainous and plain regions of Algeria

Global warming is a strongly felt reality in recent years in Algeria. The fruit trees crop is particularly exposed to the impact of this warming, especially apple trees. A comparative study has been realized between a chronological daily temperature series from 1980 to 2016, and phenological data series (budburst and flowering) from 2000 to 2016, regarding the apple tree variety of Golden Delicious in two zones of Northern Algeria, Sidi Lakhdar (town of Ain Defla, in an altitude of 211 m) and Benchicao (town of Medea, in an altitude of 1133 m). Some contrasting tendencies according to sites and periods have been demonstrated: very significant warming at Sidi Lakhdar site in autumn and spring, in particular in October and April, disturbing thus the entrance of the buds in the endodormancy and ecodormancy. The result is a late action of the cold until February, which proved to be insufficient. However, no average warming has been demonstrated at the Benchicao site, where the temperatures between November and January were cold enough to satisfy the need of cold units and raise the endodormancy. It seems that the failure to fulfill the need of cold units at Sidi Lakhdar site has strongly affected the goodness of fit of the classic phenological models, confirming indirectly the existence of more complex physiological processes (not taken in consideration by models), which manifest themselves in limited zones such as Sidi Lakhdar site.

A weekly spatio‐temporal distribution of drought events over the Po Plain (North Italy) in the last five decades

AbstractThe aim of this research is to identify and characterize, in terms of length, intensity, and spatial propagation, the main drought events which took place in the Po Valley (Italy) from 1965 to 2017. Two drought indices were applied, the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI). Daily precipitation and temperature series belonging to the National System for the Collection, Processing, and Dissemination of Climatic Data of Environmental Interest (SCIA) database were collected. Subsequent to an accurate quality control, the converted weekly climatic values were spatialized on a 20 × 20 km cell grid, and for each index, weekly severe and extreme drought episodes at a 12‐, 24‐, and 36‐month time scale were calculated. Results showed that the application of two indices is fundamental in the study of drought episodes, and that different triggering factors act over time. Especially, since the 2001 drought, episodes have become stronger in terms of frequency and length, and they seem to be mostly related to changes in the intra‐annual precipitation distribution. An analysis of the spatial propagation also indicates that two spatial gradients follow each other during the analysed period.

Reference evapotranspiration during rainy and dry seasons in Mossoró, RN, Brazil

Mossoró, RN, Brazil, is considered one of the fruit growing centers of the Northeast region. This municipality has a persistent water deficit, with the need to develop irrigated agriculture with efficient water use and rational management of irrigation. The objective of this study was to evaluate estimation methods of reference evapotranspiration (ETo) for different climate conditions of Mossoró, comparing them with the standard Penman-Monteith-FAO 56 method. A daily data series of two distinct years, a rainy (2011) and a dry year (2012), was used. The data were obtained from the weather station of the Federal Rural University of the Semi-Arid Region (UFERSA). An analysis was performed to identify methods that best fit those of Penman-Monteith-FAO 56, and for this, ten methods were evaluated using statistical indices. The Penman-Original, Radiation-Temperature, and Hargreaves-Original methods stood out and satisfactorily met ETo estimation for the rainy season, while the Jensen-Haise, Radiation-Temperature, and Hargreaves-Original methods achieved satisfactory performance for the dry season. The Hargreaves-Samani method did not demonstrate viability in the use of ETo estimation, as it was the worst method in both studied periods and is not recommended for irrigation management.

Comparison of homogenization methods for daily temperature series against an observation-based benchmark dataset

Homogenization of daily temperature series is a fundamental step for climatological analyses. In the last decades, several methods have been developed, presenting different statistical and procedural approaches. In this study, four homogenization methods (together with two variants) have been tested and compared. This has been performed constructing a benchmark dataset, where segments of homogeneous series are replaced with simultaneous measurements from neighboring homogeneous series. This generates inhomogeneous series (the test set) whose homogeneous version (the benchmark set) is known. Two benchmark datasets are created. The first one is based on series from the Czech Republic and has a high quality, high station density, and a large number of reference series. The second one uses stations from all Europe and presents more challenges, such as missing segments, low station density, and scarcity of reference series. The comparison has been performed with pre-defined metrics which check the statistical distance between the homogenized versions and the benchmark. Almost all homogenization methods perform well on the near-ideal benchmark (maximum relative root mean square error (rRMSE): 1.01), while on the European dataset, the homogenization methods diverge and the rRMSE increases up to 1.87. Analyses of the percentages of non-adjusted inhomogeneous data (up to 39%) and substantial differences in the trends among the homogenized versions helped identifying diverging procedural characteristics of the methods. These results add new elements to the debate about homogenization methods for daily values and motivate the use of realistic and challenging datasets in evaluating their robustness and flexibility.

Research on Pricing of Shanghai 50ETF Options Based on Fractal B-S Model and GARCH Model

A reasonable option trading price will have certain guiding significance for option traders. Fractal B-S model and GARCH model are common pricing methods. This article explores which pricing method is more reasonable based on SSE 50ETF options. Due to the spikes and thick tails, conditional heteroscedasticity, and fractal characteristics of the SSE 50ETF option yield data, this paper performs stationary test, autocorrelation and partial autocorrelation test, ARCH test, and Hurst test on the daily sample rate series of the target sample. The characteristics of the yield sequence are used to construct a GARCH model and predict the daily rate of volatility. Finally, the volatility predicted by the GARCH model is used as the parameter value in the fractal Brownian motion option pricing method to realize the option pricing. At the same time, this paper calculates the pricing results of the BS option pricing method based on historical volatility, and compares the two options pricing results with the closing price of the option transaction price. The results show that the prediction of the Shanghai Securities 50ETF option pricing method based on the GARCH fractal Brownian motion model. The accuracy is significantly higher than the standard BS option pricing method.

Assessing the Regional Concept with Sub-Sampling Approach to Identify Probability Distribution for at-Site Hydrological Frequency Analysis

The framework of regional analysis allows superior discrimination as well as better identification of the shape of a population distribution in a hydrological frequency analysis. The aim of this study is to incorporate the better of regional concept while performing an at-site frequency analysis. The study proposes a new method in the form of sub-sampling technique with the aid of a regional distribution selection procedure to choose an appropriate probability distribution function for frequency analysis. The technique is evaluated against common distribution selection methods: a widely used goodness-of-fit method in Anderson–Darling (AD) and a popular graphical assessment tool in L-moment ratio diagram (LMRD). The performance is evaluated by applying the technique to gauged annual maximum daily precipitation data series of 24 stations located across China. It is found that the technique accomplished a better performance in discriminating among distributions which or else may not be achievable only by the AD or LMRD test. In general, all results indicate that the proposed technique can be an attractive means in discriminating as well as identifying the best distribution for at-site frequency analysis.

Ecological water level and its safeguard measures in Lake Dongting

湖泊生态水位是维持湖泊生态系统健康的重要因素.基于洞庭湖城陵矶、杨柳潭、南咀3个水文站1959—2016年日平均水位序列进行分析，采用Mann-Kendall法、累积距平法和滑动T检验法综合确定洞庭湖水位变异时间节点，结合生态水位年内展布法以及IHA-RVA法，计算分析湖泊最小和适宜生态水位，并且采用Tennant法进行合理验证，在此基础上对水文变异前、后湖泊生态水位保障度进行研究.研究结果表明：（1）洞庭湖城陵矶和杨柳潭水文站年均水位呈上升趋势，而且城陵矶站水位上升趋势显著，南咀站年均水位呈显著下降趋势.（2）洞庭湖3个典型水文站水位年际变化突变年份为2003年，突变年份基本上与三峡工程蓄水时间相符.（3）城陵矶、南咀和杨柳潭年均最小生态水位分别为21.41、28.95和27.84 m，分别占多年平均水位的86.3%、95.9%和95.7%，城陵矶、南咀和杨柳潭年均适宜生态水位分别为23.29、29.51和28.36 m，分别占多年平均水位的93.9%、97.8%和97.5%，生态水位计算结果考虑了天然湖泊水位年内丰枯变化，满足了湖泊生态目标需求.（4）洞庭湖最低生态水位保障程度较高，基本能达到80%以上，但适宜生态水位保障程度相对较低，其中2003年以后洞庭湖10月和11月生态水位保障程度显著下降，与上游水利工程蓄水有关，建议在此期间采取调度措施适当增加洞庭湖水量，以保障湖泊生态系统的健康与生物多样性.;Lake ecological water level (EWL) is an important factor to maintain the health of lake ecosystem. Based on the analysis of the daily average water level series of Chenglingji, Yangliutan and Nanzui hydrological stations in Lake Dongting from 1959 to 2016, the hydrological variation characters of Lake Dongting were determined by Mann-Kendall method, cumulative anomaly method and sliding T test method. Based on the method of annual EWL distribution and the improved method of IHA-RVA, the minimum and optimal EWLs were calculated, and the Tennant method was used for the reasonability verification. Finally, the guaranteed rates before and after the abrupt point were analyzed. The results show that: (1) The average annual water level of Chenglingji and Yangliutan hydrology stations were both on the rise, with much significant trend for the former one. While the average annual water level of Nanzui station exhibited significant decline trend. (2) The time of abrupt changes in water levels of the three stations was 2003, which was basically consistent with the beginning operation time of the Three Gorges Dam. (3) The annual minimum EWLs of Chenglingji, Nanzui and Yangliutan stations were 21.41, 28.95 and 27.84 m, respectively, accounting for 86.3%, 95.9% and 95.7% of the multi-year average level. The optimal EWLs were 23.29, 29.51 and 28.36 m, respectively, accounting for 93.9%, 97.8% and 97.5% of the multi-year average level. Such defined EWLs could meet the requirements of lake ecosystem goals with the consideration on the annual changes in natural lakes. (4) the safeguarding on the minimum ecological water level in Lake Dongting is relatively high, basically reaching more than 80%, but the guarantee degree of the appropriate EWLs is relatively low. After 2003, the guarantee degree of Lake Dongting decreased significantly in October and November, which is related to water storage and conservancy projects in the upstream. The EWL guarantee measures in Lake Dongting were suggested to maintain the health and biodiversity of the lake ecosystem based on the low guarantee degree and the difference of annual EWL distribution.

Modeling Exchange Rate Volatility Dynamics of the Great Britain Pound to Ethiopian Birr Using the Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model

In this paper, a robust analysis of volatility forecasting of the GBP-ETB exchange rate was provided using weekly data spanning the period June 30, 2003-January 24, 2020. To our knowledge, this was the first study that focuses on the GBP-ETB exchange rate using high-frequency data and the Fuzzy-EGARCH-ANN econometric model. The research finds that the best performing model in terms of one-step ahead forecasts based on realized volatility computed from the underlying daily data series is the Fuzzy-EGARCH-ANN(1, 2, 2, 1) with students t-distribution.

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020.

The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru.We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place.Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.96 (95% CI: 0.87, 1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government.While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.