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In this study, the influences of El Niño-southern oscillation (ENSO) and the Indian Ocean sea surface temperature (SST) on rainfall variability in the central and the southern regions of Sudan are investigated. The investigation is conducted using 49 years of data from 12 rainfall stations. The results show that the driest years are associated with warm ENSO and Indian Ocean SST conditions. In the two regions, the correlation of the annual rainfall with the Indian Ocean SST is found to be relatively higher than that with the ENSO SST, with the Indian Ocean SST having stronger influences in the central region. The multiple correlation results indicate that both of the ENSO and Indian Ocean SSTs explain about 34% of the annual rainfall variability in the central region and 21% in the southern region. Two types of qualitative rainfall prediction model are developed for each region. One type uses the ENSO SST as input and the other uses the Indian Ocean SST. The verification results show that the Indian Ocean SST model is better than the ENSO SST model.

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The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab

Changes in rainfall patterns associated with climate change can affect the operation of a combined sewer system, with the potential increase in rainfall amount. This could lead to excessive spill frequencies and would also likely introduce hazardous substances into the receiving waters, which in turn, would have an impact on quality of shellfish and bathing waters. This paper quantifies the spilling volume, duration and frequency of 19 Combined Sewer Overflows (CSOs) to receiving waters under two climate change scenarios, the high (A1FI) and the low (B1) scenarios, simulated by three GCMs, for a studied catchment in north-western England. The future rainfall is downscaled, using climatic variables from HadCM3, CSIRO and CGCM2 GCMs, with use of a hybrid Generalised Linear -Artificial Neural Network model. Results from the model simulation for the future in 2080 showed an annual increase of 37% in total spill volume, 32% in total spill duration, and 12% in spill frequency for the Shellfish water limiting requirements, under the high scenario, as projected by the HadCM3 as maximum while the other GCMs projected different changes. Nevertheless the catchment drainage system is projected to cope with the future conditions in 2080 by all three GCMs. The results also indicate that under scenario B1 a significant drop was projected by CSIRO, which could reach up to 50% in spill volume, 39% in spill duration and 25% in spill frequency in the worst case. The results further show that during the bathing season, a substantial drop is expected in the CSO spill drivers as predicted by all GCMs under both scenarios.Key words artificial neural network, bathing waters, combined sewer overflows, climate change, generalised linear model, pollution, shellfish waters INTRODUCTIONThe quality of coastal waters may be adversely affected by increased stormwater overflows and hence will result in the lowering of shellfish harvesting water classifications (Andrew 2008) and also affect the quality of bathing and drinking waters. Thus, it is important to limit pollution from Combined Sewer Overflows (CSOs) and improve unsatisfactory intermittent discharges. This requirement is going to be particularly challenging to comply with once we start to consider the impacts of climate change on the safe operation of sewerage systems (Keirle and Hayes 2007). This is likely to have a greater impact on watercourses in dry summers and possibly aesthetic problems leading to more complaints by the public. Furthermore, during prolonged dry summer periods there will be an increase in pollutant levels, both on the surface and in sewer silts, which will have a greater pollutant impact on receiving watercourses at a time when their flows are low (Hurcombe 2001). In the 2011 Return from the UK water companies to OFWAT, the total number of unsatisfactory intermittent discharges (UIDs) from CSOs in England and Wales was estimated as 24,812 of which approximately 25% are believed to be monitored (OFWAT 2011). In addition, there were 6,039 CSOs in Scotland ...

The paper describes downscaling of extreme precipitation in Ireland using a probabilistic method.The method described uses a combined peak-over-threshold (POT) -generalised Pareto distribution (GPD) approach in which the scale parameter of the GPD is allowed to vary with a dominant climate forcing at the location of interest. The dominant climatic forcing is represented by predictors selected from large-scale climatic variables provided by the NCEP/NCAR data. Data from six rainfall stations are used in the study to build the models for each station. The extRemes software is used to build the models as it allows parameters of the fitted distribution to vary as a function of covariate(s).The developed models were tested for goodness-of-fit with the observed data, and model fit was found to be much improved when the scale parameter was assumed to vary with the selected covariates. Return level -return period relations are developed based on the models developed and four future time periods are simulated to investigate the effects of climate change on both precipitation magnitude and frequency. Based on the findings of this research, significant changes in precipitation extremes are projected for Ireland, which includes wetter winters and drier summers, especially in inland areas.

The Middle East (ME) is characterized by its water shortage problem. This region with its arid climate is expected to be the most vulnerable in the world to the potential impacts of climate change. Iraq (located in ME) is seriously experiencing water shortage problem. To overcome this problem rain water harvesting can be used. In this study the applicability of the long-term weather generator model in downscaling daily precipitation Central Iraq is used to project future changes of precipitation based on scenario of seven General Circulation Models (GCMs) outputs for the pe