Overall and Partial Trend Identification of High Resolution Gridded Rainfall Data over Vietnam by Applying Innovative-Şen Trend Analysis (ITA) Method

Abstract. This paper investigates the effects of using non-linear, high resolution rainfall, compared to time averaged rainfall on the triggering of hydrologic thresholds and therefore model predictions of infiltration excess and saturation excess runoff at the point scale. The bounded random cascade model, parameterized to three locations in Western Australia, was used to scale rainfall intensities at various time resolutions ranging from 1.875 min to 2 h. A one dimensional, conceptual rainfall partitioning model was used that instantaneously partitioned water into infiltration excess, infiltration, storage, deep drainage, saturation excess and surface runoff, where the fluxes into and out of the soil store were controlled by thresholds. The results of the numerical modelling were scaled by relating soil infiltration properties to soil draining properties, and in turn, relating these to average storm intensities. For all soil types, we related maximum infiltration capacities to average storm intensities (k*) and were able to show where model predictions of infiltration excess were most sensitive to rainfall resolution (ln k*=0.4) and where using time averaged rainfall data can lead to an under prediction of infiltration excess and an over prediction of the amount of water entering the soil (ln k*>2) for all three rainfall locations tested. For soils susceptible to both infiltration excess and saturation excess, total runoff sensitivity was scaled by relating drainage coefficients to average storm intensities (g*) and parameter ranges where predicted runoff was dominated by infiltration excess or saturation excess depending on the resolution of rainfall data were determined (ln g*<2). Infiltration excess predicted from high resolution rainfall was short and intense, whereas saturation excess produced from low resolution rainfall was more constant and less intense. This has important implications for the accuracy of current hydrological models that use time averaged rainfall under these soil and rainfall conditions and predictions of larger scale phenomena such as hillslope runoff and runon. It offers insight into how rainfall resolution can affect predicted amounts of water entering the soil and thus soil water storage and drainage, possibly changing our understanding of the ecological functioning of the system or predictions of agri-chemical leaching. The application of this sensitivity analysis to different rainfall regions in Western Australia showed that locations in the tropics with higher intensity rainfalls are more likely to have differences in infiltration excess predictions with different rainfall resolutions and that a general understanding of the prevailing rainfall conditions and the soil's infiltration capacity can help in deciding whether high rainfall resolutions (below 1 h) are required for accurate surface runoff predictions.

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High-Resolution Historical Record of Plant Protection Product Deposition Documented by Target and Nontarget Trend Analysis in a Swiss Lake under Anthropogenic Pressure

Environmental Science & Technology ◽

10.1021/acs.est.0c04842 ◽

2020 ◽

Vol 54 (20) ◽

pp. 13090-13100

Author(s):

Aurea C. Chiaia-Hernández ◽

Paul D. Zander ◽

Tobias Schneider ◽

Sönke Szidat ◽

Ronald Lloren ◽

...

Keyword(s):

High Resolution ◽

Trend Analysis ◽

Plant Protection ◽

Historical Record ◽

Anthropogenic Pressure ◽

Plant Protection Product ◽

Swiss Lake

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Comparison of Different Analyzing Techniques in Identifying Rainfall Trends for Colombo, Sri Lanka

Advances in Meteorology ◽

10.1155/2020/8844052 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Anushka Perera ◽

Thilini Ranasinghe ◽

Miyuru Gunathilake ◽

Upaka Rathnayake

Keyword(s):

Sri Lanka ◽

Trend Analysis ◽

Water Distribution ◽

Graphical Method ◽

Sustainable Use ◽

Distribution Networks ◽

Rainfall Trends ◽

Slope Estimator ◽

Trend Identification ◽

Annual Scale

Identifying rainfall trends in highly urbanized area is extremely important for various planning and implementation activities, including designing, maintaining and controlling of water distribution networks and sewer networks and mitigating flood damages. However, different available methods in trend analysis may produce comparable and contrasting results. Therefore, this paper presents an attempt in comparing some of the trend analysis methods using one of the highly urbanized areas in Sri Lanka, Colombo. Recorded rainfall data for 10 gauging stations for 30 years were tested using the MannKendall test, Sen’s slope estimator, Spearman’s rho test, and innovative graphical method. Results showcased comparable findings among three trend identification methods. Even though the graphical method is easier, it is advised to use it with a proper statistical method due to its identification difficulties when the data scatter has some outliers. Nevertheless, it was found herein that Colombo is under a downward rainfall trend in the month of July where the area receives its major rainfall events. In addition, the area has several upward rainfall trends over the minor seasons and in the annual scale. Therefore, the water management activities in the area have to be revisited for a sustainable use of water resources.

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Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India

Comptes Rendus Geoscience ◽

10.1016/j.crte.2012.12.001 ◽

2013 ◽

Vol 345 (1) ◽

pp. 22-27 ◽

Cited By ~ 44

Author(s):

Priya Narayanan ◽

Ashoke Basistha ◽

Sumana Sarkar ◽

Sachdeva Kamna

Keyword(s):

Trend Analysis ◽

Monsoon Rainfall ◽

Rainfall Data ◽

Western India

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Crossing Empirical Trend Analysis (CETA) At Risk Levels In Hydro-Meteorological Time Series

10.21203/rs.3.rs-372087/v1 ◽

2021 ◽

Author(s):

Zekai Sen

Keyword(s):

Time Series ◽

Probability Distribution ◽

Trend Analysis ◽

Danube River ◽

Restrictive Assumption ◽

Monotonic Trend ◽

Risk Levels ◽

Serial Independence ◽

Geographical Regions ◽

Trend Identification

Abstract Trend identification procedures are employed to determine the systematic monotonic trend lines in a given hydro-meteorological time series records for depiction of time dependent changes in the form of increase or decrease. Different methodologies are proposed for such identifications, but most of them require restrictive assumptions such as the normal (Gaussian) probability distribution, serial independence and long sample sizes. In order to relieve especially the serial independence requirement pre-whitening and over-whitening procedures are suggested, but they cannot render a serially dependent series into completely independent structure. In this paper, a new trend methodology is proposed on the basis of crossing features along any given straight-line within the given time series and the one with the maximum crossing number is the searched trend component. This approach does not require any restrictive assumption. Contrary to the previous trend algorithms, the suggested crossing empirical trend analysis (CETA) yields not a single trend, but a set of trends at different levels within the variation range of hydro-meteorological time series records. In this paper for the sake of brevity only three levels are considered at 10%, 50% and 90% risk levels. The comparison of the CETA approach is presented with the classical and frequently used method of Mann-Kendall (MK) trend identification procedure based on the Sen’s slope calculation. For small serial correlation coefficients and normal probability distribution (PDF) function cases CETA and classical technique yield almost the same trend line within +5% error band limits. The application of this methodology is presented for monthly and annual discharge records of Danube River and annual precipitation records from seven geographical regions of Turkey.

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DYNAMICS PATTERNS OF INFLOW IN THE RESERVOIR THAT OPERATED FOR TWO DECADES

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.56.4.10 ◽

2021 ◽

Vol 56 (4) ◽

pp. 92-103

Author(s):

Heriantono Waluyadi ◽

Pitojo Tri Juwono ◽

Widandi Soetopo ◽

Rispiningtati ◽

Lily Montarcih Limantara ◽

...

Keyword(s):

Climate Change ◽

Trend Analysis ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Rainfall Data ◽

Operation Pattern ◽

The Pacific ◽

Ocean Region ◽

Reservoir Watershed ◽

June July

Climate change in the past 20 years brings significant alteration in the earth surface. It affects extremely anomaly temperature, such as the ENSO, IOD, and SOI phenomena. The Pacific Ocean Region, the Indian Ocean Region, and the Darwin – Tahiti Region undergo an increase and a decrease in the sea surface temperatures (SST); thus, it can lead to seasonal change in Indonesia. Due to ENSO, IOD, and SOI, climate change also highly affects the operation pattern of reservoirs, food production, and other commodities. This research used SST data (Nino 1.2, Nino 3, Nino 3.4, Nino 4, IOD West, IOD East, and SOI) from National Oceanic and Atmospheric Administration (NOAA) and rainfall data from 1998 to 2018 of nine stations at Wonogiri Reservoir watershed. Trend analysis of the SST index indicated an increase in trend SST index. Trend analysis of monthly rainfall average at Wonogiri Watershed area indicated a decrease in January, March, April, May, June, July, August, and October, while it increased in February, September, November, and December. Multiple linear regression analysis with the stepwise regression method indicated that during the rainy season, the rainfall at Wonogiri Watershed and Inflow at Wonogiri reservoir were influenced by the SST index (Nino 1.2, Nino 3, Nino 3.4, Nino 4). Meanwhile, during the dry season, the rainfall at Wonogiri Watershed and the Inflow at Wonogiri reservoir were influenced by the SST index (IOD West, IOD East, and SOI). With monthly correlations between SST and rainfall data that have a dynamic characteristic, it can be used to calculate the inflow probability distribution in optimizing reservoir operation patterns.

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Over-whitening or Pre-whitening in Hydro-climatological Trend Analysis?

10.21203/rs.3.rs-1183199/v1 ◽

2022 ◽

Author(s):

Zekai Sen

Keyword(s):

Time Series ◽

White Noise ◽

Trend Analysis ◽

Serial Correlation ◽

Original Time Series ◽

Trend Component ◽

The Difference ◽

Temperature Records ◽

Original Time ◽

Trend Identification

Abstract To meet the basic assumption of classical Mann-Kendall (MK) trend analysis, which requires serially independent time series, a pre-whitening (PW) procedure is proposed to alleviate the serial correlation structure of a given hydro-meteorological time series records for application. The procedure is simply to take the lagged differences in a given time series in the hope that the new time series will have an independent serial correlation coefficient. The whole idea was originally based on the first-order autoregressive AR (1) process, but such a procedure has been documented to damage the trend component in the original time series. On the other hand, the over-whitening procedure (OW) proposes a white noise process superposition of the same length with zero mean and some standard deviation on the original time series to convert it into serially independent series without any damage to the trend component. The stationary white noise addition does not have any trend components. For trend identification, annual average temperature records in New Jersey and Istanbul are presented to show the difference between PW and OW procedures. It turned out that the OW procedure was superior to the PW procedure, which did not cause a loss in the original trend component.

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