Your search found 36 records
1 Privalsky, V.; Jensen, D. T. 1993. Time series analysis package: Autoregressive time and frequency domains analysis of scalar and multi-variate time series. Logan, UT, USA: Utah State University. Utah Climate Center. 61p.
(Location: IWMI-HQ Call no: 519.55 G000 PRI Record No: H020150)
2 Jenkins, G. M.; Watts, D. G. 1968. Spectral analysis and its applications. San Francisco, CA, USA: Holden-Day. xviii, 525p.
(Location: IWMI-HQ Call no: 519.5 G000 JEN Record No: H027209)
3 Nelson, C. R. 1973. Applied time series analysis for managerial forecasting. San Francisco, CA, USA: Holden-Day. xiv, 231p.
(Location: IWMI-HQ Call no: 519.5 G000 NEL Record No: H027211)
4 Khan, A. R. 2001. Searching evidence for climatic change: Analysis of hydro-meteorological time series in the Upper Indus Basin. Lahore, Pakistan: International Water Management Institute (IWMI) iv, 31p. (IWMI Working Paper 023) [doi: https://doi.org/10.3910/2009.152]
Water resource management ; River basins ; Analysis ; Catchment areas ; Stream flow ; Data collection ; Models ; Time series ; hydrology ; Climate / Pakistan / Upper Indus Basin
(Location: IWMI-PAK Call no: IWMI 631.7.5 G730 KHA Record No: H028687)
(925 KB)
The study examines some of the major components of water cycle in the Upper Indus Basin (UIB) to look for evidence of climate change. An analysis of hydrometeorological data has been performed for UIB. An Additive Decomposition Model was used for analyzing the time series data from ten meteorological stations in the Mangla (Jhelum River) and the Tarbela (Indus River) catchments and the long-term flow data for the three major rivers, the Indus, Jhelum and Chenab. The model decomposes a time series into trend, cyclical or periodic, autoregressive and irregular components. Furthermore, spectral analysis is done in order to display these components of the time series and examine the results of the removal of the components. This approach makes use of the fact that a change in climate, if it has occurred, will have a magnified effect on hydrologic time series. By detecting trends in such series, it should be possible to work backwards and identify the causative climatic change. In case of flow data for the three rivers, which was available for a longer period than the meteorological data, the ‘F’ and ‘t’ tests for stability of variance and mean, respectively, were also performed. The annual cycle dominated all the temperature series i.e., large periodic components, and none explained by the periodic component and a dominant random component. In case of stream- flow data, the annual temperature cycle was dominant and no trend components were found in any of the flow series. The F-test and the t-test indicated the variances and means for different sub- periods of each flow series to be stable at 5% level of significance. The analysis of time series of river flows and associated climatic data did not find any pattern of trends likely to be caused by ‘greenhouse warming’ in the Upper Indus Basin.
5 Smakhtin, V. Y.; Masse, B. 2000. Continuous daily hydrograph simulation using duration curves of a precipitation index. Hydrological Processes, 14:1083-1100.
(Location: IWMI-HQ Call no: P 5874 Record No: H028953)
6 Smakhtin, V. U. 2001. Estimating continuous monthly base flow time series and their possible applications in the contest of the ecological reserve. Water SA, 27(2):213-217.
(Location: IWMI-HQ Call no: P 5880 Record No: H028959)
7 Smakhtin, V. Y. 1999. Generation of natural daily flow time-series in regulated rivers using a non-linear spatial interpolation technique. Regulated Rivers: Research & Management, 15:311-323.
(Location: IWMI-HQ Call no: P 5884 Record No: H028963)
8 Hughes, D. A.; Smakhtin, V. 1996. Daily flow time series patching or extension: A spatial interpolation approach based on flow duration curves. Hydrological Sciences Journal, 41(6):851-871.
(Location: IWMI-HQ Call no: P 5888 Record No: H028968)
9 Hughes, D. A.; Keeffe, J. O.; Smakhtin, V.; King, J. 1997. Development of an operating rule model to simulate time series of reservoir releases for instream flow requirements. Water SA, 23(1):21-30.
(Location: IWMI-HQ Call no: P 5889 Record No: H028969)
(1.22 MB)
10 Chatfield, C. 2000. Time-series forecasting. Boca Raton, FL, USA: Chapman & Hall/CRC. xi, 267p.
(Location: IWMI-HQ Call no: 519.5 G000 CHA Record No: H029633)
11 Kish, L. 1995. Survey sampling. New York, NY, USA: John Wiley & Sons, Inc. xvi, 643p. (Wiley classics library)
(Location: IWMI-HQ Call no: 519.5 G000 KIS Record No: H029636)
12 Asafu-Adjaye, J. 2000. The relationship between energy consumption, energy prices and economic growth: Time series evidence from Asian developing countries. Energy Economics, 22:615-625.
Energy consumption ; Income ; Economic growth ; Models ; Time series ; Developing countries / India / Indonesia / Philippines / Thailand
(Location: IWMI-HQ Call no: P 5992 Record No: H029864)
13 Hanberger, A. 2003. Public policy and legitimacy: A historical policy analysis of the interplay of public policy and legitimacy. Policy Sciences, 36:257-278.
Local government ; Public policy ; Time series ; Analysis ; Models ; Public health ; Legal aspects / Sweden
(Location: IWMI-HQ Call no: P 6781 Record No: H034287)
14 Gupta, I.; Mitra, A. 2004. Economic growth, health and poverty: An exploratory study for India. Development Policy Review, 22(2):193-206.
(Location: IWMI-HQ Call no: PER Record No: H034306)
15 Gehrels, H.; Gieske, A. S. M. 2003. Aquifer dynamics. In Simmers, I. (Ed.), Understanding water in a dry environment: Hydrological processes in arid and semi-arid zones. Rotterdam, Netherlands: A. A. Balkema. pp.211-250.
(Location: IWMI-HQ Call no: 551.48 G000 SIM Record No: H034353)
16 Sen, Z.; Boken, V. K. 2005. Techniques to predict agricultural droughts. In Boken, V. K.; Cracknell, A. P.; Heathcote, R. L. (Eds.), Monitoring and predicting agricultural drought: A global study. New York, NY, USA: OUP. pp.40-54.
Drought ; Monitoring ; Forecasting ; Climate change ; Time series ; Analysis ; Stochastic process ; Models
(Location: IWMI-HQ Call no: 632.12 G000 BOK Record No: H036761)
17 Gottschalk, L. 2004. Time series modelling. In Tallaksen, L. M.; van Lanen, H. A. J. (Eds.). Hydrological drought: Processes and estimation methods for streamflow and groundwater. Amsterdam, Netherlands: Elsevier. pp.273-306.
(Location: IWMI-HQ Call no: 551.57 G000 TAL Record No: H036948)
18 Bjornlund, H.; Rossini, P. 2005. Fundamentals determining prices and activities in the market for water allocations. International Journal of Water Resources Development, 21(2):355-369.
Water allocation ; Water market ; Prices ; Water scarcity ; Regression analysis ; Time series ; Analysis
(Location: IWMI-HQ Call no: PER Record No: H037242)
19 Reed, B. C.; Brown, J. F.; VanderZee, D.; Loveland, T. R.; Merchant, J. W.; Ohlen, D. O. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5:703-714.
(Location: IWMI-HQ Call no: P 6778 Record No: H034284)
20 Huh, S.; Dickey, D. .; Meador, M. R.; Ruhl, K. E. 2005. Temporal analysis of the frequency and duration of low and high streamflow: Years of record needed to characterize streamflow variability. Journal of Hydrology, 310:78-94.
(Location: IWMI-HQ Call no: P 7359 Record No: H037123)
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