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Summary Evaluating the climate anomalies impacts on the Upper Blue Nile Basin (UBNB), Ethiopia, a large basin with scarce hydroclimatic data, through hydrologic modeling is a challenge. A fully distributed, physically-based model, a modified version of the Interactions Soil–Biosphere Atmosphere model of Météo France (MISBA), and a lumped, conceptual rainfall–runoff Sacramento model, SAC-SMA of the US National Weather Service, were used to simulate the streamflow of UBNB. To study the potential hydrologic effect of climate anomalies on the UBNB, rainfall and temperature data observed when climate anomalies were active, were resampled and used to drive MISBA and SAC-SMA. To obtain representative, distributed precipitation data in mountainous basins, it was found that a 3% adjustment factor for every 25 m rise in elevation was needed to orographically correct the rainfall over UBNB. The performance of MISBA applied to UBNB improved after MISBA was modified so that it could simulate evaporation loss from the canopy, providing coefficient of determination ( R 2 ) = 0.58, and root mean square error (RMSE) = 0.34 m 3 /s in comparison with the observed streamflow. In contrast, the performance of SAC-SMA at the calibration run and the validation run is better than that of MISBA, such that R 2 is 0.79 for calibration and 0.82 for validation even though it models the hydrology of UBNB in a lumped, conceptual framework as against the physically-based, fully distributed framework of MISBA. El Niño tends to decrease the June–September rainfall but increase the February–May rainfall, while La Niña has opposite effect on the rainfall of UBNB. Based on the simulations of MISBA and SAC-SMA for UBNB, La Niña and Indian Ocean Dipole (IOD) tend to have a wetting effect while El Niño has a drying effect on the streamflow of the UBNB. In addition, El Niño Southern Oscillation (ENSO) and IOD increase the streamflow variability more than changing the magnitude of streamflow. The results provide useful information on the effects of global oceanic anomalies on the hydrology of UBNB. [ABSTRACT FROM AUTHOR]

Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February-May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June-September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB ( r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen-Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin. [ABSTRACT FROM AUTHOR]

Albeit weather surveillance radar (WSR)-88D stage III radar rainfall (RR) data can generally capture the spatial variability of precipitation fields, its rainfall depth for cold seasons dominated by stratiform storms tends to be underestimated. This study proposed merging WSR-88D stage III data with rain gauge data using the Haar wavelet scheme and compared its with that merged by the statistical objective analysis (SOA) scheme. The idea is to exploit the strength of radar that better captures the spatial variability of rainfall and that of rain gauges that measure the rainfall depth more accurately. A Haar wavelet was used because of its simplicity and the appealing physical interpretation of its coefficients as directional gradients of rainfall, whose spatial correlation structure was accounted for through a polynomial function. From analysing 89 storms over the Blue River Basin (BRB), Oklahoma, during 1994–2000, the results show that the underestimation problem of WSR-88D RR was generally more pronounced during the cold season dominated by stratiform storms than warm season dominated by convective storms. The wavelet scheme was better than SOA in reducing the radar's underestimation of rainfall depths while maintaining the spatial variability of the original radar data, as shown by its merged rainfall patterns and the more accurate streamflow hydrographs simulated by a semi-distributed, physics-based rainfall-runoff model – semi-distributed physics-based hydrologic model using remote sensing (DPHM-RS) – driven by the merged data. [ABSTRACT FROM AUTHOR]

Summary: A combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN–GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall–runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN–GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season’s lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan. [Copyright &y& Elsevier]

By applying wavelet analysis and wavelet principal component analysis (WPCA) to individual wavelet-scale power and scale-averaged wavelet power, homogeneous zones of rainfall variability and predictability were objectively identified for September–November (SON) rainfall in East Africa (EA). Teleconnections between the SON rainfall and the Indian Ocean and South Atlantic Ocean sea surface temperatures (SST) were also established for the period 1950–97. Excluding the Great Rift Valley, located along the western boundaries of Tanzania and Uganda, and Mount Kilimanjaro in northeastern Tanzania, EA was found to exhibit a single leading mode of spatial and temporal variability. WPCA revealed that EA suffered a consistent decrease in the SON rainfall from 1962 to 1997, resulting in 12 droughts between 1965 and 1997. Using SST predictors identified in the April–June season from the Indian and South Atlantic Oceans, the prediction skill achieved for the SON (one-season lead time) season by the nonlinear model known as artificial neural network calibrated by a genetic algorithm (ANN-GA) was high [Pearson correlation ρ ranged between 0.65 and 0.9, Hansen–Kuipers (HK) scores ranged between 0.2 and 0.8, and root-mean-square errors (rmse) ranged between 0.4 and 0.75 of the standardized precipitation], but that achieved by the linear canonical correlation analysis model was relatively modest (ρ between 0.25 and 0.55, HK score between -0.05 and 0.3, and rmse between 0.4 and 1.2 of the standardized precipitation). [ABSTRACT FROM AUTHOR]

By applying harmonic analysis to El Niño–Southern Oscillation (ENSO) composites of the 6-month Standardized Precipitation Index (SPI) and rainfall anomalies for the 1900–1996 period, and based on the 90% confidence limits established from bootstrap resampling, it was found that ENSO responses in East African rainfall are region and season dependent, and the influence of El Niño is stronger and opposite that of La Niña. Among five regions of unique ENSO responses identified, northeastern (R4) and southern Tanzania (R5) seem to have the most consistent (in terms of vector coherence, percentage of variance extracted by the first harmonic, and SPI magnitude) ENSO responses. R5 experiences positive (negative) response under La Niña (El Niño) influence during January and June of the post-ENSO year. Southern Uganda and much of the Lake Victoria basin show some significant positive ENSO response for November, December, and January. The temporal and regional patterns of ENSO response periods were also analyzed using the index time series and boxplots on the 6-month SPI. Boxplots confirm a shift in the distribution of 6-month SPI between ENSO and non-ENSO affected seasons. [ABSTRACT FROM AUTHOR]

A linear statistical model, canonical correlation analysis (CCA), was driven by the Nelder-Mead simplex optimization algorithm (called CCA-NMS) to predict the standardized seasonal rainfall totals of East Africa at 3-month lead time using SLP and SST anomaly fields of the Indian and Atlantic Oceans combined together by 24 simplex optimized weights, and then "reduced' by the principal component analysis. Applying the optimized weights to the predictor fields produced better March-April-May (MAM) and September-October-November (SON) seasonal rain forecasts than a direct application of the same, unweighted predictor fields to CCA at both calibration and validation stages. Northeastern Tanzania and south-central Kenya had the best SON prediction results with both validation correlation and Hanssen-Kuipers skill scores exceeding 10.3. The MAM season was better predicted in the western parts of East Africa. The CCA correlation maps showed that low SON rainfall in East Africa is associated with cold SSTs off the Somali coast and the Benguela (Angola) coast, and low MAM rainfall is associated with a buildup of low SSTs in the Indian Ocean adjacent to East Africa and the Gulf of Guinea. [ABSTRACT FROM AUTHOR]

This study presents a storm-event based bivariate frequency analysis approach to determine design rainfalls in which, the number, intensity and duration of actual rainstorm events were considered. To derive more realistic design storms, the occurrence probability of an individual rainstorm event was determined from the joint distribution of storm intensity and duration through a copula model. Hourly rainfall data were used at three climate stations respectively located in Singapore, South Korea and Canada. It was found that the proposed approach could give a more realistic description of rainfall characteristics of rainstorm events and design rainfalls. As results, the design rainfall quantities from actual rainstorm events at the three studied sites are consistently lower than those obtained from the conventional rainfall depth-duration-frequency (DDF) method, especially for short-duration storms (such as 1-h). It results from occurrence probabilities of each rainstorm event and a different angle for rainfall frequency analysis, and could offer an alternative way of describing extreme rainfall properties and potentially help improve the hydrologic design of stormwater management facilities in urban areas. [ABSTRACT FROM AUTHOR]