Spatiotemporal Variability in the Hydrometeorological Time-Series over Upper Indus River Basin of Pakistan

This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.

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An improved framework to predict river flow time series data

PeerJ ◽

10.7717/peerj.7183 ◽

2019 ◽

Vol 7 ◽

pp. e7183 ◽

Cited By ~ 1

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Ishfaq Ahmad ◽

Muhammad Faisal ◽

Ibrahim M. Almanjahie

Keyword(s):

Time Series ◽

Water Resource Management ◽

River Flow ◽

Time Series Data ◽

Flow Time ◽

Indus Basin ◽

Series Data ◽

Percentage Error ◽

Empirical Bayesian ◽

Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

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Customization of the OSSEM model for application to annual data from the Talar aquifer, Northern Iran

Water Science & Technology Water Supply ◽

10.2166/ws.2015.021 ◽

2015 ◽

Vol 15 (3) ◽

pp. 656-666

Author(s):

Nazila Sedaei ◽

Abolghasem Akbari ◽

Leila Sedaei ◽

Jonathan Peter Cox

Keyword(s):

Time Series ◽

Water Resources ◽

Water Deficit ◽

Time Series Data ◽

Driving Forces ◽

Anthropogenic Activities ◽

Government Support ◽

Series Data ◽

Annual Data ◽

Water Resource System

There are several principal driving forces behind the damaging coastal water resources depletion in many countries, including: high population growth, degrading water resources due to overexploitation and contamination, lack of awareness among local beneficiaries regarding sustainable management, and deficient government support and enforcement of conservation programs. To ensure a water resource system is productive in coastal areas, holistic and comprehensive management approaches are required. To address the aforementioned issues, a combined methodology which considers anthropogenic activities, together with environmental problems defined as the Overall Susceptibility Socio-Ecological System Environmental Management (OSSEM) has been investigated. The OSSEM model has been applied successfully in Spain based upon daily time series data. This research is ground breaking in that it integrates the OSSEM model in a geographic information system (GIS) environment to assess the groundwater contamination based on annual time series data and the assessment of system management by means of an overall susceptibility index (OSI). Centered on OSI indicators, the renewal, salinization and water deficit potentials in the Talar aquifer were estimated to be 4.89%, 4.61%, and 3.99%, respectively. This data demonstrates a high susceptibility in terms of environmental pollution, salinization, and water deficit.

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Hydrologic Variations and Stochastic Modeling of Runoff in Zoige Wetland in the Eastern Tibetan Plateau

Advances in Meteorology ◽

10.1155/2015/529354 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Guanghua Qin ◽

Hongxia Li ◽

Zejiang Zhou ◽

Kechao Song ◽

Li Zhang

Keyword(s):

Time Series ◽

Tibetan Plateau ◽

Nearest Neighbor ◽

Time Series Data ◽

Water Source ◽

Annual Runoff ◽

Series Data ◽

Eastern Tibetan Plateau ◽

Double Peaks ◽

Zoige Wetland

Hydrological time series data (1988–2008) of the Hei River, the main water source to Zoige wetland in the Eastern Tibetan Plateau, were investigated. Results showed that the runoff distribution of Hei River varies with the relative change in amplitude (Cm=15.9) and the absolute change in amplitude (ΔQ=37.1 m3/s) during the year. There was a significant decreasing trend since 1988 with annual runoff of 20.0 m3/s (1988–1994), 19.0 m3/s (1995–2000), and 15.2 m3/s (2001–2008). There were double peaks in runoff during the water year: the highest peak in the period of 1988–2000 occurred in July while in the period of 2001–2008 it occurred in October. Shifting peak flow means less water quantity in wetland during growing season. Nearest neighbor bootstrapping regressive method was used to predict daily runoff of the Hei River. Model results show that it was fitted with 94.23% ofR2for daily time series, which can provide a basis for the development and utilization of regional water resources.

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Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

Complexity ◽

10.1155/2019/2782715 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Muhammad Faisal ◽

Alaa Mohamd Shoukry ◽

Showkat Gani ◽

...

Keyword(s):

Time Series ◽

Hybrid Model ◽

Prediction Accuracy ◽

Optimal Allocation ◽

Time Series Data ◽

Hybrid Models ◽

Indus Basin ◽

Series Data ◽

Stage Model ◽

Two Stage

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

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Statistical assessment of rainfall variability and trends in northeastern Algeria

Journal of Water and Land Development ◽

10.2478/jwld-2019-0009 ◽

2019 ◽

Vol 40 (1) ◽

pp. 87-96 ◽

Cited By ~ 2

Author(s):

Noureddine Merniz ◽

Ali Tahar ◽

Amine M. Benmehaia

Keyword(s):

Time Series ◽

Time Series Data ◽

Rainfall Variability ◽

Temporal Trends ◽

Kendall Test ◽

Series Data ◽

Mann Kendall Test ◽

Slope Estimator ◽

Northeastern Algeria ◽

Rainy Days

Abstract In the present study, time series for annual, monthly rainfall and number of rainy days per year were analysed to quantify spatial variability and temporal trends for 22 rainfall stations distributed in northeastern Algeria for the period 1978–2010. The Mann–Kendall test and the Sen’s slope estimator were applied to assess the significance and magnitude of the trend. The results showed that precipitation decreases spatially from North to South and from East to West. The application of the Mann–Kendall test (for 0.05% threshold) to the time series data showed that for annual precipitation, no station showed statistically significant trends, unlike the number of rainy days, where there was a significant negative trend in four stations (Jijel, Constantine, Oum El Bouaghi and Tébessa). For the monthly time series, significant positive trends were observed during the months of September in the coastal stations and July for the plateaus and southern Saharan Atlas stations, while significant negative trends were recorded during the months of February and March for the stations of the extreme East in the study area. These results revealed that for the period analysed, there was no significant climate change in northeastern Algeria but there is a seasonal delay having important agroecological implications.

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CROP AREA MAPPING BY INTELLIGENT PIXEL INFORMATION INFERRED USING 250M MODIS VEGETATION TIMESERIES IN TRANSBOUNDARY INDUS BASIN

Big Data In Water Resources Engineering (BDWRE) ◽

10.26480/bdwre.02.2020.32.35 ◽

2020 ◽

Vol 1 (2) ◽

pp. 32-35

Author(s):

Muhammad Mohsin Khan ◽

Muhammad Jehanzeb Masud Cheema ◽

Talha Mahmood ◽

Saddam Hussain ◽

Muhammad Sohail Waqas ◽

...

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Remote Sensing Data ◽

Secondary Data ◽

Indus Basin ◽

Series Data ◽

Pixel Resolution ◽

Error Matrix ◽

Crop Area

Irrigation water could be managed properly by mapping area of various crops. Remote sensing data can provide useful Land Use Land Cover (LULC) for assessment of different crop area and change detection. The present study was carried out with core objective to map crop area within the Indus Basin’s transboundary. Four major crops (i.e. wheat, rice, cotton and sugarcane) were identified using Normalize Difference Vegetation Index (NDVI) time series that was picked up from MODIS sensors aboard Terra (EOS AM) and Aqua (EOS PM) satellites with 250m pixel resolution. Crop phonological information was used to train each pixel intelligently for interpretation of unanalyzed NDVI data into crops. Eight days of time series data was used for identification and mapping of various crops on the basis of their phenology for the years 2008, 2010 and 2013. Error matrix was prepared to reveal mapping accurateness and ground truthing was also done in particular canal commands within the Indus basin. Furthermore, the temporal variation in cropped area was determined and for accuracy check, secondary data was matched with prepared maps. LULC maps for year 2008, 2010 and 2013 were defined for Rabi and kharif seasons.

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Trend Analysis Variations and Relation Between Discharge and Rainfall: a Study on Kushiyara River

Journal of Environmental Science and Natural Resources ◽

10.3329/jesnr.v10i2.39025 ◽

2018 ◽

Vol 10 (2) ◽

pp. 121-132

Author(s):

ME Uddin ◽

SA Akter ◽

MJ Uddin ◽

MTM Diganta

Keyword(s):

Climate Change ◽

Water Resources ◽

Trend Analysis ◽

Time Series Data ◽

Downward Trend ◽

Series Data ◽

Discharge Data ◽

Sen’S Slope Estimator ◽

Sen’S Slope ◽

Slope Estimator

Water resources and rivers play a very important role in economy and agriculture. But due to climate change and improper management, water resources are losing the natural discharge capacity from upper stream. This study was under taken to find out the trends and variability of rainfall and discharge at two stations of the Kushiyara River. Rainfall and discharge data Daily time series data of stream flow or discharge at Sheola (1976 to 2007) and Sherpur (1982 to 2007) and observed discharge data in certain interval from 2008 to 2016 have been analyzed in this study. For trend analysis, Mann-Kendall Test (MK) and Sen’s Slope Estimator method were used as non-parametric test and relationship assessment was done by undertaking Pearson’s Coefficient of Correlation. Annual discharge at Sheola and Sherpur station showed downward trend and maximum discharge was found downward trend at both stations. In Mannkendall test and Sen’s Slope Estimator method showed downward trend in most cases. Climate change and improper land management may have influenced the present condition.J. Environ. Sci. & Natural Resources, 10(2): 121-132 2017

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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