Mawada Abdellatif

Long term effect of climate change on rainfall in northwest Iraq

Middle East, like North Africa, is considered as arid to semi-arid region. Water shortages in this region, represents an extremely important factor in stability of the region and an integral element in its economic development and prosperity. Iraq was an exception due to presence of Tigris and Euphrates Rivers. After the 1970s the situation began to deteriorate due to continuous decrease in discharges of these rivers, are expected to dry by 2040 with the current climate change. In the present paper, long rainfall trends up to the year 2099 were studied in Sinjar area, northwest of Iraq, to give an idea about its future prospects. Two emission scenarios, used by the Intergovernmental Panel on Climate Change (A2 and B2), were employed to study the long term rainfall trends in northwestern Iraq. All seasons consistently project a drop in daily rainfall for all future periods with the summer season is expected to have more reduction compared to other seasons. Generally the average rainfall trend shows a continuous decrease. The overall average annual rainfall is slightly above 210 mm. In view of these results, prudent water management strategies have to be adopted to overcome or mitigate consequences of future severe water crisis.

Flood risk assessment for urban water system in a changing climate using artificial neural network

Changes in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding.

Climate Change and Future Long-Term Trends of Rainfall at North-East of Iraq

Iraq is facing water shortage problem despite the presence of the Tigris and Euphrates Rivers. In this research, long rainfall trends up to the year 2099 were studied in Sulaimani city northeast Iraq to give an idea about future prospects. The medium high (A2) and medium low B2 scenarios have been used for purpose of this study as they are more likely than others scenarios, that beside the fact that no climate modeling canter has performed GCM (global climate model) simulations for more than a few emissions scenarios (HadCM3 has only these two scenarios) otherwise pattern scaling can be used for generating different scenarios which entail a huge uncertainty. The results indicate that the average annual rainfall shows a significant downward trend for both A2 and B2 scenarios. In addition, winter projects increase/decrease in the daily rainfall statistics of wet days, the spring season show very slight drop and no change for both scenarios. However, both summer and autumn shows a significant reduction in maximum rainfall value especially in 2080s while the other statistics remain nearly the same. The extremes events are to decrease slightly in 2080s with highest decrease associated with A2 scenario. This is due to the fact that rainfall under scenario A2 is more significant than under scenario B2. The return period of a certain rainfall will increase in the future when a present storm of 20 year could occur once every 43 year in the 2080s. An increase in the frequency of extreme rainfall depends on several factors such as the return period, season of the year, the period considered as well as the emission scenario used.

Quantitative assessment of sewer overflow performance with climate change in northwest England

Abstract 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 could also introduce hazardous substances into the receiving waters, which, in turn, would have an impact on the 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 emissions (B1) scenarios, simulated by three global climate models (GCMs), for a study catchment in northwest England. The future rainfall is downscaled, using climatic variables from HadCM3, CSIRO and CGCM2 GCMs, with the use of a hybrid generalized linear–artificial neural network model. The 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. These results were obtained, under the high emissions scenario, as projected by the HadCM3 as maximum. 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 in the worst case could reach up to 50% in spill volume, 39% in spill duration and 25% in spill frequency. 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. Editor Z.W. Kundzewicz; Associate editor L. See

Future Prospects for Macro Rainwater Harvesting (RWH) Technique in North East Iraq

Countries in Middle East and North Africa (MENA region) are considered as arid and semi-arid areas that are suffering from water scarcity. They are expected to have more water shortages problem due to climatic change. Iraq is located in the Middle East covering an area of 433,970 square kilometers populated by 31 million inhabitants. One of the solutions suggested to overcome water scarcity is Rainwater Harvesting (RWH). In this study Macro rainwater harvesting technique had been tested for future rainfall data that were predicted by two emission scenarios of climatic change (A2 and B2) for the period 2020-2099 at Sulaimaniyah Governorate north east of Iraq. Future volumes of total runoff that might be harvested for different conditions of maximum, average, and minimum future rainfall seasons under both scenarios (A2 and B2) were calculated. The results indicate that the volumes of average harvested runoff will be reduced when average rainfall seasons are considered due to the effect of climatic change on future rainfall. The reduction reached 10.82 % and 43.0% when scenarios A2 and B2 are considered respectively.

Assessing combined sewer overflows with long lead time for better surface water management

During high-intensity rainfall events, the capacity of combined sewer overflows (CSOs) can exceed resulting in discharge of untreated stormwater and wastewater directly into receiving rivers. These discharges can result in high concentrations of microbial pathogens, biochemical oxygen demand, suspended solids, and other pollutants in the receiving waters. The frequency and severity of the CSO discharge are strongly influenced by climatic factors governing the occurrence of urban stormwater runoff, particularly the amount and intensity of the rainfall. This study attempts to assess the impact of climate change (change in rainfall amount and frequency) on CSO under the high (A1FI) and low (B1) Special Report on Emissions Scenarios of the greenhouse concentration derived from three global circulation models in the north west of England at the end of the twenty-first century.

Improving accuracy of downscaling rainfall by combining predictions of different statistical downscale models

Abstract A flexible framework of multi-model of three statistical downscaling approaches was established in which predictions from these models were used as inputs to Artificial Neural Network (ANN). Traditional ANN, Simple Average Method (SAM), and combining models (SDSM, Multiple Linear Regressions (MLR), Generalized Linear Model (GLM)) were applied to a studied site in North-western England. Model performance criteria of each of the primary and combining models were evaluated. The obtained results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various rainfall characteristics under different circumstances. The combining ANN model showed more adaptability by acquiring better overall performance, while GLM, MLR and showed comparable results and the SDSM reveals relatively less accurate results in modelling most of the rainfall amount. Furthermore traditional ANN has been tested and showed poor performance in reproducing the observed rainfall compared with above methods. The results also show that the superiority of the combining approach model over the single models is promising to be implemented to improve downscaling rainfall at a single site.

Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan

ABSTRACT The Blue Nile River is utilized in Sudan as the main source of irrigation water. However, the river has a long, dry, low-flow season (October–May), which necessitates the use of regulations and rules to manage its water use during this period. This depends on the use of accurate lead time forecasts of inflows to the reservoirs built along the river. Thus a reliable and tested forecasting tool is needed to provide inflow forecast, with sufficient lead time. In the present study, artificial neural network (ANN) is used to model the recession curve of the flow hydrograph at El-Deim gauging station, which subsequently is used as inflows to the Roseires Reservoir on the Blue Nile River. Different scenarios of ANN have been tested to forecast 23 10-day mean discharges during the recession period and their performances were assessed. Results from the optimal ANN model were compared to those simulated with an autoregressive (AR1) model to check their accuracy. Modelling results showed that the ANN model developed is capable of accurately forecasting the inflows to the Roseires Reservoir and outperforms the AR1 model. It has then proposed for use in operation of the reservoir for purposes of predicting irrigation water supply.

Application of the UKCP09 WG Outputs to Assess Performance of Combined Sewers System in a Changing Climate

AbstractIn many parts of the world, old sewer systems have been designed without consideration for change in climate, so probabilities and risks of sewer surcharge and flooding are elevated due to increase in extreme rainfall events as a consequence of global warming. The current paper is aiming to assess how the climate change on interannual to multidecadal timescale (2020s; 2050s; 2080s) will affect design standards of wastewater networks due to the presumed increase in rainfall intensity and frequency in the Northwest of England area (selected site). Design storms have been analyzed for future rainfall obtained from the UK Climate Projection version 2009 (UKCP09) weather generator (WG), which was applied to the existing urban drainage system to check the level of service in winter and summer seasons. Two emission scenarios (SRES) have been adopted to simulate the greenhouse gas concentration; high scenario (A1FI) and low scenario (B1). Results indicate that the impact of increase in the design storm of...

Short-Term Urban Water Demand Prediction Considering Weather Factors

Accurate and reliable forecasting plays a key role in the planning and designing of municipal water supply infrastructures. Recent studies related to water demand prediction have shown that water demand is driven by weather variables, but the results do not clearly show to what extent. The principal aim of this research was to better understand the effects of weather variables on water demand. Additionally, it aimed to offer an appropriate and reliable technique to predict municipal water demand by using the Gravitational Search Algorithm (GSA) and Backtracking Search Algorithm (BSA) with Artificial Neural Network (ANN). Moreover, eight weather factors were adopted to evaluate their impact on the water demand. The principal findings of this research are that the hybrid GSA-ANN (Agent = 40) model is superior in terms of fitness function (based on RMSE) for yearly and seasonal phases. In addition, it is evidently clear from the findings that the GSA-ANN model has the ability to simulate both seasonal and yearly patterns for daily data water consumption.