Pan evaporation and reference evapotranspiration trend detection in western Iran with consideration of data persistence

It is important to identify the spatiotemporal trends of evaporation and evapotranspiration under the changing climate for use in regional water resources planning. This work aimed to investigate the trends of the Hargreaves reference evapotranspiration (ETo), pan evaporation (Epan) and pan coefficient (Kpan) series at 12 stations in the west of Iran by using the sequential Mann–Kendall, Kendall and Spearman tests after eliminating the influence of the significant lag-1 serial correlation from the time series by the pre-whitening method for the period 1982–2003. The approximate year of the beginning of the significant trends was detected by using the Mann–Kendall rank statistic. The spatial distribution of the trend magnitudes was obtained from the Inverse-Distance-Weighted (IDW) interpolation method. No significant trends were found in the ETo time series, while an upward trend of 16 mm/year was observed in the Epan series which began in 1998. Moreover, a downward trend was obtained in the Kpan series which started in 1994.

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Spatiotemporal Trends and Detection of Changes in Hydrological and Climatic Variables of Modjo River Watershed, Ethiopia

10.21203/rs.3.rs-945709/v1 ◽

2021 ◽

Author(s):

Kokeb Zena Besha ◽

Tamene Adugna Demissie ◽

Fekadu Fufa Feyessa

Keyword(s):

Time Series ◽

River Flow ◽

Interpolation Method ◽

Resource Planning ◽

Kendall Test ◽

Precipitation Series ◽

Weighted Interpolation ◽

Spatiotemporal Trends ◽

Distance Weighted ◽

Inverse Distance Weighted Interpolation

Abstract Understanding hydro-climatic trends in space and time is crucial for water resource planning and management, agricultural productivity and climate change mitigation of a region. This study examined the spatiotemporal variations in precipitation, reference evapotranspiration (ETo) and streamflow in a tropical watershed located in the central highlands of Ethiopia. Temporal trend implications were analyzed using the Mann-Kendall test, and Theil-Sen approach, whereas the inverse distance weighted interpolation method was applied for spatial trend variability analysis. The result showed that a significant decreasing trends in streamflow for the major rainy (Kiremt: Jun - Sept) season and annual time scales. At the same time, the annual and monthly ETo followed significantly increasing trends, but there has been a trendless time series for most of the months and annual mean precipitation series for the period 1986 - 2015. The study indicated that the spatial variability of annual and seasonal precipitation series decreased from north to south and west to east, while this was increased for ETo both for annual and seasonal time series over the study watershed. The contribution of rainfall and mean temperature to streamflow decline was insignificant. It is pointed out that river flow regime is weakly affected by climate changes, hence human activities are stronger in explaining the river flow trends of the watershed. Therefore, urgent calls on the needs for reducing human-induced impacts, and implementing appropriate watershed management, conservation measures and an efficient use of water resources.

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Time Series Modelling of Monthly Temperature and Reference Evapotranspiration for Navsari (Gujarat), India

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v35i630209 ◽

2019 ◽

pp. 1-13

Author(s):

D. K. Dwivedi ◽

P. K. Shrivastava

Keyword(s):

Time Series ◽

Reference Evapotranspiration ◽

Kendall Test ◽

Trend Detection ◽

Positive Trend ◽

Monthly Temperature ◽

Mean Temperature ◽

Time Series Modelling ◽

Mann Kendall Test ◽

Past Data

Time series modelling has been proved its usefulness in various fields including meteorology, hydrology and agriculture. It utilizes past data and extracts useful information from them to build up a model which could simulate various processes. The prior knowledge of evapotranspiration could help in estimating the amount of water required by the crops that is useful for optimizing design of irrigation systems. In this study, the time series modelling of monthly temperature and reference evapotranspiration has been carried out utilizing past data of 35 years (1983-2017) to assist decision makers related to agriculture and meteorology. 30 years (1983-2012) of temperature and evapotranspiration data were used for training and remaining 5 years of data (2013-2017) were used for validation. The monthly evapotranspiration was estimated using Penman-Monteith FAO-56 method. Mann-Kendall test was used at 5% significant level for identifying trend component in mean temperature. The time series of temperature and evapotranspiration was made stationary for modelling the stochastic components using ARIMA (Autoregressive Integrated Moving Average) model. In order to check the normality of residuals, the Portmantaeu test was applied. The time series models for temperature and evapotranspiration which were validated for 5 years (2013-2017) and further deployed for forecasting of 5 years (2018-2022). It was found that for modelling temperature and reference evapotranspiration for Navsari, seasonal ARIMA (1,0,0)(0,1,1)12 and seasonal ARIMA (1,0,1)(1,1,2)12 were found to be appropriate models respectively. Mann Kendall test used for trend detection in monthly mean temperature revealed that October and November months had significant positive trend. Negative trend was observed only in the month of June.

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Simplified Estimation of Reference Evapotranspiration from Pan Evaporation Data in California

Journal of Irrigation and Drainage Engineering ◽

10.1061/(asce)0733-9437(2005)131:3(249) ◽

2005 ◽

Vol 131 (3) ◽

pp. 249-253 ◽

Cited By ~ 44

Author(s):

Richard L. Snyder ◽

Morteza Orang ◽

Scott Matyac ◽

Mark E. Grismer

Keyword(s):

Reference Evapotranspiration ◽

Pan Evaporation

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PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

Jurnal Gaussian ◽

10.14710/j.gauss.v8i4.26722 ◽

2019 ◽

Vol 8 (4) ◽

pp. 418-427

Author(s):

Eko Siswanto ◽

Hasbi Yasin ◽

Sudarno Sudarno

Keyword(s):

Time Series ◽

Time Series Data ◽

Space Time ◽

Series Data ◽

Inverse Distance Weighted ◽

Modeling And Forecasting ◽

Distance Weighted ◽

The Relationship ◽

Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

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Generation of Time Series Data from Octave Bandwidth SPL of Acoustic Loading Using Interpolation Method

Journal of the Korea Institute of Military Science and Technology ◽

10.9766/kimst.2021.24.1.001 ◽

2021 ◽

Vol 24 (1) ◽

pp. 1-11

Author(s):

Eun-Su Go ◽

In-Gul Kim ◽

Minhyeok Jeon ◽

Hyun-Jun Cho ◽

Jae-Sang Park ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Interpolation Method ◽

Series Data

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Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0080 ◽

2016 ◽

Vol 16 (6) ◽

pp. 98-110

Author(s):

Gao Xuedong ◽

Gu Kan

Keyword(s):

Time Series ◽

Piecewise Linear ◽

Negative Influence ◽

Trend Detection ◽

Computational Accuracy ◽

Starting Point ◽

Time Series Studies ◽

Definition Of ◽

Stage Characteristic

Abstract The traditional time series studies consider the time series as a whole while carrying on the trend detection; therefore not enough attention is paid to the stage characteristic. On the other hand, the piecewise linear fitting type methods for trend detection are lacking consideration of the possibility that the same node belongs to multiple trends. The above two methods are affected by the start position of the sequence. In this paper, the concept of overlapping trend is proposed, and the definition of milestone nodes is given on its base; these way not only the recognition of overlapping trend is realized, but also the negative influence of the starting point of sequence is effectively reduced. The experimental results show that the computational accuracy is not affected by the improved algorithm and the time cost is greatly reduced when dealing with the processing tasks on dynamic growing data sequence.

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Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Atmospheric Chemistry and Physics ◽

10.5194/acp-20-9915-2020 ◽

2020 ◽

Vol 20 (16) ◽

pp. 9915-9938

Author(s):

Kai-Lan Chang ◽

Owen R. Cooper ◽

Audrey Gaudel ◽

Irina Petropavlovskikh ◽

Valérie Thouret

Keyword(s):

Time Series ◽

Tropospheric Ozone ◽

Sampling Frequency ◽

Basic Trend ◽

Trend Detection ◽

Percentage Error ◽

Statistical Regularization ◽

Vertical Correlation ◽

Accurate Quantification

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

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Trend detection of atmospheric time series

Elem Sci Anth ◽

10.1525/elementa.2021.00035 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Kai-Lan Chang ◽

Martin G. Schultz ◽

Xin Lan ◽

Audra McClure-Begley ◽

Irina Petropavlovskikh ◽

...

Keyword(s):

Time Series ◽

Atmospheric Chemistry ◽

Surface Ozone ◽

High Surface ◽

Effective Means ◽

Environmental Data ◽

Model Specification ◽

Trend Detection ◽

Detection Methods ◽

Sources Of Uncertainty

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to (1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry, (2) describe a range of trend-detection methods, and (3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short and long term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to (1) review trend detection methods for addressing different levels of data complexity in different chemical species, (2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability, (3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates, and (4) present an advanced method of quantifying regional trends based on the inter-site correlations of multisite data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

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