scholarly journals Analyzing the Dependence of Major Tanks in the Headwaters of the Aruvi Aru Catchment on Precipitation. Applying Drought Indices to Meteorological and Hydrological Data

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2941
Author(s):  
Robin Saase ◽  
Brigitta Schütt ◽  
Wiebke Bebermeier
Keyword(s):  
Water Level ◽  
Management Practices ◽  
Standardized Precipitation Index ◽  
Drought Indices ◽  
Strong Positive Correlation ◽  
Level Data ◽  
Water Level Data ◽  
Dry Zone ◽  
Catchment Size ◽  
The Standardized Precipitation Index

This study aims to analyze the dependence of reservoirs (locally called tanks or wewas) in the headwaters of the Aruvi Aru catchment on precipitation and thus to evaluate their efficiency. The Aruvi Aru is located in the Dry Zone of Sri Lanka, and numerous human made reservoirs characterize the study area. The methodology is based on the application and correlation of climatic and hydrological drought indices. The Standardized Precipitation Index (SPI) is applied to precipitation data at different time scales and the Standardized Water-Level Index (SWLI) is applied to water-level data of five major tanks in the catchment. The results show that near normal present-day average precipitation is appropriate to fill the investigated tanks. The precipitation of the previous 6–12 months has the highest impact on water-level changes. A moderate to strong positive correlation between SWLI and SPI point to other factors besides precipitation affecting the water level of the tanks. These are: (i) catchment size together with the buffering capacity of the upstream catchment and (ii) management practices. As the overall conclusion of our study shows, the tanks functioned efficiently within their system boundaries.

scholarly journals Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin

2005 ◽  
Vol 9 (5) ◽  
pp. 523-533 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno
Keyword(s):  
Time Scales ◽  
Hydrological Cycle ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Long Time ◽  
The Standardized Precipitation Index ◽  
Hydrological Droughts ◽  
Different Time Scales

Abstract. At present, the Standardized Precipitation Index (SPI) is the most widely used drought index to provide good estimations about the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI in comparison with other indices is the fact that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. It is widely accepted that SPI time scales affect different sub-systems in the hydrological cycle due to the fact that the response of the different water usable sources to precipitation shortages can be very different. The long time scales of SPI are related to hydrological droughts (river flows and reservoir storages). Nevertheless, few analyses empirically verify these statements or the usefulness of the SPI time scales to monitor drought. In this paper, the SPI at different time scales is compared with surface hydrological variables in a big closed basin located in the central Spanish Pyrenees. We provide evidence about the way in which the longer (>12 months) SPI time scales may not be useful for drought quantification in this area. In general, the surface flows respond to short SPI time scales whereas the reservoir storages respond to longer time scales (7–10 months). Nevertheless, important seasonal differences can be identified in the SPI-usable water sources relationships. This suggests that it is necessary to test the drought indices and time scales in relation to their usefulness for monitoring different drought types under different environmental conditions and water demand situations.

Presentation and assessment of tides and water level records for geophysical investigations

1970 ◽  
Vol 7 (2) ◽  
pp. 607-625 ◽  
Author(s):  
G. C. Dohler ◽  
L. F. Ku
Keyword(s):  
Water Level ◽  
Power Spectra ◽  
Rate Of Change ◽  
Regression Technique ◽  
Level Data ◽  
Water Level Data ◽  
Geophysical Investigations ◽  
Water Level Variations ◽  
Mean Water Level ◽  
Change In Mean

The methods and problems involved in collecting water level data are explained, and the processing and formats of the data are illustrated. The trend of the change in mean water level is plotted and the corresponding rate of change is estimated by the regression technique. The power spectra of the water level variations are plotted to illustrate these variations in terms of frequencies.

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