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The low and high flow characteristic of the Blue Nile River (BNR) basin is presented. The study discusses low and high flow, flow duration curve (FDC) and trend analysis of the BNR and its major tributaries. Different probability density functions were fitted to better describe the low and high flows of the BNR and major tributaries in the basin. Wavelet analysis was used in understanding the variance and frequency‐time localization and detection of dominant oscillations in rainfall and flow. FDCs were developed, and low flow (below 50% exceedance) and high flow (over 75% exceedance) of the curves were analysed and compared. The Gravity Recovery and Climate Experiment (GRACE) satellite‐based maps of monthly changes in gravity converted to water equivalents from 2003 to 2006 for February, May and September showed an increase in the moisture influx in the BNR basin for the month of September, and loss of moisture in February and May. It was also shown that 2004 and 2005 were drier with less moisture influx compared to 2003 and 2006. On the basis of the Kolmogorov‐Smirnov, Anderson‐Darling and Chi‐square tests, Gen. Pareto, Frechet 3P, Log‐normal, Log‐logistics, Fatigue Life and Phased Bi‐Weibull distributions best describe the low and high flows within the BNR basin. This will be beneficial in developing flow hydrographs for similar ungauged watersheds within the BNR basin. The below 50% and above 75% exceedance on the FDC for five major rivers in addition to the BNR showed different characteristics depending on size, land cover, topography and other factors. The low flow frequency analysis of the BNR at Bahir Dar showed 0·55 m3/s as the monthly low flow with recurrence interval of 10 years. The wavelet analysis of the rainfall (at Bahir Dar and basin‐wide) and flows at three selected stations shows inter‐ and intra‐annual variability of rainfall and flows at various scales. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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In this study, monthly and annual Upper Blue Nile Basin rainfall data were analyzed to learn the rainfall statistics and its temporal and spatial distribution. Frequency analysis and spatial characterization of rainfall in the Upper Blue Nile Basin are presented. Frequency analysis was performed on monthly basin rainfall. Monthly basin average rainfall data were computed from a network of 32 gauges with varying lengths of records. Monthly rainfall probability distribution varies from month to month fitting Gamma‐2, Normal, Weibull and Log‐Normal distributions. The January, July, October and November basin rainfall fit the Gamma‐2 probability distribution. The February, June and December ones fit Weibull distribution. The March, April, May and August rainfall fit Normal distribution. The September rainfall fits Log‐Normal distribution. Upper Blue Nile Basin is relatively wet with a mean annual rainfall of 1423 mm (1960–2002) with a standard deviation of 125 mm. The annual rainfall has a Normal probability distribution. The 100‐year‐drought basin annual rainfall is 1132 mm and the 100‐year‐wet basin annual rainfall is 1745 mm. The dry season is from November through April. The wet season runs from June through September with 74% of the annual rainfall. October and May are transition months. Monthly and annual rainfalls for return periods 2‐, 5‐, 10‐, 25‐, 50‐ and 100‐year dry and wet patterns are presented. Spatial distribution of annual rainfall over the basin is mapped and shows high variation with the southern tip receiving as high as 2049 mm and the northeastern tip as low as 794 mm annual average rainfall. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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Declining trends in pan and lake evaporation have been reported. It is important to study this trend in every region to evaluate the validity of the trend and water management implications. Data from nine pan evaporation sites in South Florida were evaluated to see if there is a trend and if the quality of the data is sufficient for such analysis. The conclusion is that pan evaporation measurements are prone to too many sources of errors to be used for trend analysis. This condition is demonstrated in South Florida and in other regions by differences in magnitude and direction between spatially related pan stations and unexplainable observations. Also, potential evapotranspiration (ETp) was estimated with the Simple (Abtew equation) and the Penman–Monteith method. Both cases indicated no decline in evapotranspiration for the period of analysis. Based on the decline in humidity and the increasing trend in vapor pressure deficit for the short period of analysis, 1992–2009, it appears that South Florida is experiencing increase in evaporation and evapotranspiration at this time assuming no systematic error in the weather stations' observations. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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