Use of Hydrologic Time Series Data for Analysis the Karstic Drought Characteristic

2014 ◽  
Vol 641-642 ◽  
pp. 127-131
Author(s):  
Li Hong Liu ◽  
Da Sheng Wang ◽  
He Huang ◽  
Guang Quan Xu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Water Volume ◽  
Significant Heterogeneity ◽  
Drought Duration ◽  
Hydrologic Time Series ◽  
Drought Hazard ◽  
Groundwater Drought ◽  
Drought Frequency Analysis

Karstification creates significant heterogeneity of hydraulic conductivity within the aquifer, where flows are organized to a hierarchical structure, from the surface to the spring. A karstic aquifer subjected to groundwater flood and drought, as a site for the occurrence of karst groundwater, is the main or unique focus for groundwater development and utilization in southwestern China. The present paper introduces a methodology devoted to groundwater drought hazard assessment. It focuses on groundwater drought by applying of the spring time series for an estimate and categorization of operating resources of groundwater. The results show that a permit for use of water for ER1+ER2 up to 0.48 m3/s, with the exceeding probability 80% selected for representing dry. The longest drought duration time was happened in the year 1993 with the 2.9×106m3 shortage of water volume. Groundwater drought frequency analysis provides a useful tool for water management.

Congo Basin's Shrinking Watersheds

2020 ◽  
pp. 1452-1468
Author(s):  
Bila-Isia Inogwabini
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Democratic Republic Of Congo ◽  
Congo Basin ◽  
Series Data ◽  
Water Volume ◽  
Rainfall Time Series ◽  
Aquatic Resources ◽  
Basin Water

Rainfall time series data from three sites (Kinshasa, Luki, and Mabali) in the western Democratic Republic of Congo were analyzed using regression analysis; rainfall intensities decreased in all three sites. The Congo Basin waters will follow the equation y = -20894x + 5483.16; R2 = 0.7945. The model suggests 18%-loss of the Congo Basin water volume and 7%-decrease for fish biomasses by 2025. Financial incomes generated by fishing will decrease by 11% by 2040 compared with 1998 levels. About 51% of women (N= 408,173) from the Lake Tumba Landscape fish; their revenues decreased by 11% between 2005 and 2010. If this trend continues, women's revenues will decrease by 59% by 2040. Decreased waters will severely impact women (e.g. increasing walking distances to clean waters). Increasing populations and decreasing waters will lead to immigrations to this region because water resources will remain available and highly likely ignite social conflicts over aquatic resources.

Congo Basin's Shrinking Watersheds

2017 ◽  
pp. 211-226
Author(s):  
Bila-Isia Inogwabini
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Democratic Republic Of Congo ◽  
Congo Basin ◽  
Series Data ◽  
Water Volume ◽  
Rainfall Time Series ◽  
Aquatic Resources ◽  
Basin Water

Rainfall time series data from three sites (Kinshasa, Luki, and Mabali) in the western Democratic Republic of Congo were analyzed using regression analysis; rainfall intensities decreased in all three sites. The Congo Basin waters will follow the equation y = -20894x + 5483.16; R2 = 0.7945. The model suggests 18%-loss of the Congo Basin water volume and 7%-decrease for fish biomasses by 2025. Financial incomes generated by fishing will decrease by 11% by 2040 compared with 1998 levels. About 51% of women (N= 408,173) from the Lake Tumba Landscape fish; their revenues decreased by 11% between 2005 and 2010. If this trend continues, women's revenues will decrease by 59% by 2040. Decreased waters will severely impact women (e.g. increasing walking distances to clean waters). Increasing populations and decreasing waters will lead to immigrations to this region because water resources will remain available and highly likely ignite social conflicts over aquatic resources.

scholarly journals Time Series Outlier Detection Based on Sliding Window Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yufeng Yu ◽  
Yuelong Zhu ◽  
Shijin Li ◽  
Dingsheng Wan
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Hydrologic Time Series ◽  
Prediction Confidence ◽  
Evaluation Of Data ◽  
Operation And Management

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

scholarly journals Extending HydroShare to enable hydrologic time series data as social media

2015 ◽  
Vol 18 (2) ◽  
pp. 198-209 ◽  
Author(s):  
Jeffrey M. Sadler ◽  
Daniel P. Ames ◽  
Shaun J. Livingston
Keyword(s):  
Social Media ◽  
Time Series ◽  
Web Services ◽  
Data Management ◽  
Data Storage ◽  
Time Series Data ◽  
Series Data ◽  
Design Development ◽  
Scientific Discussion ◽  
Hydrologic Time Series

The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) hydrologic information system (HIS) is a widely used service oriented system for time series data management. While this system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of ‘Web 2.0’ collaboration and social-networking capabilities being used in other fields. This paper presents the design, development, and testing of a software extension of CUAHSI's newest product, HydroShare. The extension integrates the existing CUAHSI HIS into HydroShare's social hydrology architecture. With this extension, HydroShare provides integrated HIS time series with efficient archiving, discovery, and retrieval of the data, extensive creator and science metadata, scientific discussion and collaboration around the data and other basic social media features. HydroShare provides functionality for online social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The extension is expected to enable scientists to access and share both national- and laboratory-scale hydrologic time series datasets in a standards-based web services architecture combined with social media functionality developed specifically for the hydrologic sciences.

Multiscale Analysis of Hydrologic Time Series Data using the Hilbert-Huang Transform

2010 ◽  
Vol 9 (4) ◽  
pp. 925-942 ◽  
Author(s):  
J. Rudi ◽  
R. Pabel ◽  
G. Jager ◽  
R. Koch ◽  
A. Kunoth ◽  
...  
Keyword(s):  
Time Series ◽  
Multiscale Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Hilbert Huang Transform ◽  
Hydrologic Time Series

scholarly journals Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 66 ◽  
Author(s):  
Wade Roberts ◽  
Gustavious P. Williams ◽  
Elise Jackson ◽  
E. James Nelson ◽  
Daniel P. Ames
Keyword(s):  
Time Series ◽  
Data Storage ◽  
Time Series Data ◽  
Series Data ◽  
Hydrologic Models ◽  
Storage And Retrieval ◽  
Hydrologic Time Series ◽  
Error Metrics ◽  
Skill Scores ◽  
Python Package

Hydrologists use a number of tools to compare model results to observed flows. These include tools to pre-process the data, data frames to store and access data, visualization and plotting routines, error metrics for single realizations, and ensemble metrics for stochastic realizations to calibrate and evaluate hydrologic models. We present an open-source Python package to help characterize predicted and observed hydrologic time series data called hydrostats which has three main capabilities: Data storage and retrieval based on the Python Data Analysis Library (pandas), visualization and plotting routines using Matplotlib, and a metrics library that currently contains routines to compute over 70 different error metrics and routines for ensemble forecast skill scores. Hydrostats data storage and retrieval functions allow hydrologists to easily compare all, or portions of, a time series. For example, it makes it easy to compare observed and modeled data only during April over a 30-year period. The package includes literature references, explanations, examples, and source code. In this note, we introduce the hydrostats package, provide short examples of the various capabilities, and provide some background on programming issues and practices. The hydrostats package provides a range of tools to make characterizing and analyzing model data easy and efficient. The electronic supplement provides working hydrostats examples.

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