Expected Future Precipitation in Central Iraq Using LARS-WG Stochastic Weather Generator

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 periods of 2011-2030, 2046-2065, and 2080-2099. The results indicated that December-February and September-November periods, based on the ensemble mean of seven GCMs, showed an increasing trend in the periods considered; however, a decreasing trend can be found in March, April, and May in the future.

section_content: change in the climate mean state within a certain time period is referred to as climate variability which can be more detrimental than the climate change. Both climate variability and change can lead to severe impacts on different major sectors of the world such as water resources, agriculture, energy and tourism. 

Iraq was considered rich in its water resources compared with other countries where the annual allocation per capita reached 6029 m 3 in 1995 and expected to be 2100 m 3 in 2015 [1]. However, construction of dams on the Tigris and Euphrates and their tributaries outside the border of Iraq, the effect of global climate change and mismanagement of water resources are the main factors in the water shortage problems in Iraq [2] [3]. Restoring the marshes [3] - [5] and the growing demand for water in Turkey and Syria will lead to drying up the Tigris and Euphrates Rivers in 2040 [4]. One of the solutions to overcome the water shortage crises is to use non-conventional water resources (e. g. water harvesting) and to apply such techniques the spatial distribution and intensity of rainfall events are required [3] [6]- [12]. 

To develop strategies and make informed decisions about the future water allocation for different sectors and management of available water resources, they need climate change information (usually in terms of watershed scale precipitation and temperature) that can directly be used by the hydrologic impact models. Atmosphereocean coupled Global Climate Models (GCMs) are the main source to simulate the present and project the future climate of the earth under different climate change scenarios (e. g. Special Report on Emission Scenarios SRES 2000). The computational grid of the GCMs is very coarse (a grid box covers more than 40,000 km 2 ), and thus they are unable to skillfully model the sub-grid scale climate features like topography or clouds of the area in question [13]. Consequently, GCMs to date are unable to provide reliable information of rainfall for hydrological modelling. Thus, there is a need for downscaling, from coarse resolution of the GCM to a very fine resolution or even at a station scale. The downscaling methodologies developed to date can be broadly categorized as statistical and dynamical. Among the statistical downscaling methods, the use of stochastic weather generators is very popular. They are not computationally demanding, simple to apply and provide station scale climate change information [14] [15]. 

Weather generators are classified as statistical models used to generate a long synthetic series of data, fill in missing data and produce different realizations of the same data [16]. They employ random number generators and use the observed time series of a station/site as input. Stochastic weather simulation is not new and has a history starting from 1950s, as reported by Racsko et al. [17]. Among some researchers who contributed to its evolution are [18] - [23]. Wilby [16] has presented a comprehensive review of its theory and evolution over time. Weather generators have been employed to get long time series of hydro-meteorological variables which can be used, for example, by crop growth model to forecast agricultural production (e. g. [24] [25] ) and assessment of risk associated with climate variability [26]. Further details on the use of weather generator in crop production studies can be found in Semenov [27]. When the climate changes research community started looking for low cost, computationally less expensive and quick methods for impact assessment, the weather generator emerged as a most suitable solution (e. g. [28] [29] ). Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator specially designed for climate change impact studies [30]. It has been tested for diverse climates and found better than some other generators [31]. A recent study by Semenov [32] has tested LARS-WG for different sites across the world and has shown its ability to model rainfall extremes with reasonable skill. 

It has been reported in the Climate Change IPCC AR4 (2007) [33] that climate change is likely to affect the mean as well as variability of precipitation across the world. Change in variability and precipitation extremes can seriously affect the sustainable management of urban water infrastructure in big cities, such as Baghdad. If a weather generator is adequately skilful in simulating the mean as well as extreme properties of precipitation, such as wet/dry spell length and annual maximum (AM) precipitation, it can be adopted as a simplified, computationally inexpensive global solution for incorporating climate change information into decision making for planning sustainable infrastructure of a big city. 

The main objective of the present study is to project future precipitation in the Central Iraq region city of Baghdad which can be used for impact assessments that require local-scale climate scenarios. The approach is based on the LARS-WG weather generator (Semenov 2007 (Semenov, 2008)). A new version of the software is described, which incorporates predictions from the IPCC AR4 multi-model ensemble (Table 1 ). Given site parameters derived from observed daily weather, WG can generate local-scale daily climate scenarios for the future at any location in the world consistent with the AR4 climate perditions. By treating each GCM prediction from the AR4 ensemble as an equally possible evolution of climate, we can explore the uncertainty in impact assessment resulting from the uncertainty in climate predictions. The results will also help decision makers and researchers in future planning for the water resources in Iraq and help in finding ways and means to minimize the effect of climate change on the inhabitants and the environment. ) is selected for analysis as required for the calibration of the weather generator. In climate change studies, this period is used to represent the current or baseline climate (cf. [13] ). The rainfall data used in this study was obtained from the Iraqi National Meteorological Organisation. The annual rainfall in Iraq varies where it reaches 150 mm within the western desert, more than 1000 mm within the mountains at the north to about 200 mm at the eastern part of the country [3] [6] [7] [34]. The overall average annual rainfall is of the order of 213 mm per year. The rainy season begins in October and ends in May. The central region of Iraq is characterised by a rainy season that roughly starts in October and continues all the way until May of the following year. During the rainy season, rainfall can be heavier during October and January and lighter in between. However, the summer period (June, July and August) are described as a dry period in which rain has rarely recorded. Figure 2 shows a typical rainy season in Bagdad for the rainy year 1965/1966.

section_content: In order to predict the local precipitation data, large scale predictors simulated by GCMs are needed. In the new version of the LARS-WG, predictions based on various emission scenarios from 15 GCMs used in the IPCC AR4 have been incorporated. Considering the civil situation of Iraq, SRA2 emission scenario that pays more attention to local tradition and population growth was chosen in this study. Among the 15 GCMs, seven of them had SRA2 scenario and were used to predict the future change of local-scale precipitation in three periods: 2011-2030, 2046-2065, and 2080-2099 (as listed in Table 1 ).
section_name: Study Area and Data

section_name: Methodology

section_content: LARS-WG is a stochastic weather generator and used for simulating weather data at a single site under both current and future conditions [17] [30] [31] [35]. LARS-WG uses observed daily weather data for a given site to compute a set of parameters for probability distributions of weather variables as well as well as correlation between them, which are used to generate synthetic weather time series of arbitrary length by randomly selecting values from the appropriate distributions. To approximate probability distributions of dry and wet series of daily precipitation, Tmax and Tmin, LARS-WG uses a semiemprical distribution (SED) that is defined as the cumulative probability distribution function (CDF). The number of intervals (n) used in SED is 23 in the new version (Version 5. 0), which offers more accurate representation of the observed distribution compared with the ten used in the previous version. For each climatic variable v, a value of climatic variable vi corresponding to the probability pi is calculated as: ( ) { } min : 0,. .,

where P() denotes probability based on observed data {v obs }. For each climatic variable, two values, p 0 and p n are fixed as p 0 = 0 and p n = 1, with corresponding values of v 0 = min{v obs } and v n = max{v obs }. To approximate the extreme values of a climatic variable accurately, some pi are assigned close to 0 for extremely low values of the variable and close to 1 for extremely high values; the remaining values of pi are distributed evenly on the probability scale. 

Because the probability of very low daily precipitation (<1 mm) is typically relatively high and such low precipitation has very little effect on the output of a process-based impact model, we used only two values, v 1 = 0. 5 mm and v 2 = 1 mm to approximate precipitation within the interval [0, 1] with the corresponding probabilities calculated as pi = P(v obs ≤ vi) i = 1, 2. To account for extremely high long dry and wet series, two values close to 1 are used in SEDs for wet and dry series, p n-1 = 0. 99 and p n-2 = 0. 98. 

For maximum and minimum temperatures, two values close to 0 and two close to 1 are used to account for extremely low and high temperatures, i. e., p 2 = 0. 01, p 3 = 0. 02, p n-1 = 0. 99 and p n-2 = 0. 98. All pi values (0 < I < n). In the new version of LARS-WG (5. 5), the maximum and minimum temperature for dry and wet days are
section_name: Description of LARS-WG Model

section_content: In LARS-WG, the process of generating synthetic weather data can be divided into three distinct steps, which are briefly described as follows. More detailed description of the modelling procedure can be referred to [37]. 

-Model calibration Model calibration is done to use the function "SITE ANALYSIS" in LARS-WG, which analyses observed weather data (e. g., precipitation and maximum and minimum temperature) to determine their statistical characteristics and store this information in two parameter files. 

-Model validation The parameter files derived from observed weather data during the model calibration process are used to generate synthetic weather data having the same statistical characteristics as the original observed data. Model validation is to analyse and compare the statistical characteristics of the observed and synthetic weather data to assess the ability of LARS-WG to simulate the precipitation at the chosen sites in order to determine whether or not it is suitable for use.
section_name: Outline of the Stochastic Weather Generator Process LARS-WG

section_content: The parameter files derived from the observed weather data during the model calibration process can also be used to generate synthetic data corresponding to a particular climate change scenario simulated by GCMs.
section_name: Generation of Synthetic Weather Data

section_content: By perturbing parameters of distributions for a site with the predicted changes of climate derived from global or regional climate models, a daily climate scenario for this site could be generated and used in conjunction with a process-based impact model for assessment of impacts. To generate climate scenarios at a site for a certain future period and an emission scenario, the LARS-WG baseline parameters, which are calculated from observed weather for this site for a baseline period, for instance 1961-2000, are adjusted by the Δ-changes for the future period and the emissions predicted by a GCM for each climatic variable for the grid covering the site. In this study, the local-scale climate scenarios based on the SRA2 scenario simulated by the selected seven GCMs are generated by using LARS-WG (5. 5) for the time periods of 2011-2030 (T1), 2046-2065 (T2), and 2080-2099 (T3) to predict the future change of precipitation in Baghdad. Semenov and Stratonovich (2010) introduced and used the procedure to generate the local-scale climate scenarios based on the IPCC AR4 multi-model ensemble to assess the changes in probability of heat stress around flowering for wheat at several locations in Europe.
section_name: Generation of Climate Scenario

section_name: Results and Discussion

section_content: The daily precipitation data for Baghdad for the period 1961-2000 (40 years) was used to calibrate and validate the model for the region. To assess the ability of LARS-WG, in addition to the graphic comparison, some statistical tests are also performed. The Kolmogorov-Smirnov (K-S) test is performed on testing equality of the seasonal distributions of wet and dry series (WDSeries) and distributions of daily rainfall (RainD) calculated from observed data and downscaled data. The test calculates a p-value, which is used to accept or reject the hypotheses that the two sets of data could have come from the same distribution (i. e., when there is no difference between the observed and simulated climate for that variable). A very low p-value, and a corresponding high K-S value means the simulated climate is unlikely to be the same as the observed climate; hence must be rejected. Although a p-value of 0. 05 is the common significance level used in most statistics, the authors Semenov and Barrow [30] of the model suggests a p-value of 0. 01 be used as the acceptable significance limit of the model results. Significant differences between the observed and simulated data may arise from the model smoothing the observed data, errors in the observed data, random variation in the observed data, and unusual climate phenomenon at a climate station making a particular year's climate very different. Table 2 shows the statistical analyses results of the model's performance in simulating the seasonal observed data and Table 3 shows the model performance for simulating the daily rain in each month. In both tables, the letter "N" represents the number of test carried. 

Assessment of the LARS-WG performance in simulating the seasonal precipitation in Baghdad is inserted in the "Assessment" column in Table 2. It can be clearly observed from this assessment that the model performs very well in fitting the winter "DJF" and autumn "SON" precipitation as fitting of the wet and dry spells series distributions in these two seasons are either very good or perfect. The model performance in fitting of the wet and dry spells series distributions in the spring "MAM" season is also assessed as good to perfect for the distribution of the wet and dry spells, respectively. However, the model performance in fitting the distributions of wet/dry spells series in the summer "JJA" season is somewhat difference. While the model performs very well in fitting the dry spell distribution, it performs poorly in fitting the wet spell one. The reason for the poor performance here is attributed to the summer season being a dry one. As there is no or rare rain recorded in this season (cf. Section 2 and Figure 2 ), the weather generator would not be able to fit any wet spell and thus it would perform poorly. 

Similarly, assessment of LARS-WG performance in simulating the daily rain in each month is inserted in the "Assessment" column in Table 3. The assessment results show that LARS-WG performance in simulating distributions of the daily rain in all months is perfect except in the months of the summer season. The reason for poor performance here is as given previously in the case of poor performance in modelling the seasonal distribution of wet/dry days. 

From the results Table 2 and Table 3, it can be noted that LARS-WG is more capable in simulating the seasonal distributions of the wet/dry spells and the daily precipitation distributions in each month. These two properties are very important when using the model results in impact studies. 

To increase confidence in LARS-WG capability for predicting future precipitation, comparisons between statistics calculated from simulated precipitation with the corresponding ones calculated from the observed data are carried here. Figure 3 shows comparison between the monthly mean and standard deviation yielded by the two series. Figure 3 reveals a very good performance of LARS-WG in fitting both statistics. Overall, the mean monthly totals are very well modelled by LARS-WG. In terms of standard deviation, LARS-WG shows an excellent performance, except for March and April where LARS-WG slightly underestimates the standard deviation. 

The simulation of wet/dry spell lengths is very important, as it can be used for the assessment of drought risk or drainage network efficiency of a big city. The simulation results of LARS-WG are shown in Figure 4(a As explained earlier, LARS-WG generates random data which is comparable to the observed data in its statistical properties only. Examination of Figure 5 shows that both the observed and simulated values are of the same order which indicates good performance of LARS-WG simulation. 

Based on the above analysis and comparisons, it can be concluded that the LARS-WG model has very good performance in generating daily and extreme precipitation in Baghdad and can reasonably be used to predict daily precipitation for near, medium and far future for purposes of impact studies.
section_name: Results of Calibration and Validation of LARS-WG

section_content: The calibrated LARS-WG for Baghdad found above was then used to predict daily precipitation is Central Iraq for the periods of 2011-2030 (near future), 2046-2065 (medium future), and 2080-2099 (far future) based on the SRA2 scenarios generated from seven GCMs (cf. Table 1 ). 

The results of the precipitation prediction by using LARS-WG were plotted on Figure 6 and Figure 7. 

In Figure 6, the box-whisker plots showed the distribution of precipitation downscaled from seven GCMs by using LARS-WG in the periods of 2011-2030, 2046-2065, and 2080-2099 comparing with the current observation. The plot elements and the statistics are as follows: the length of the box represents the inter- For the prediction of precipitation in the far future shown in Figure 6 (c), there are consistent trends among the seven GCM models to predict precipitation less than the value of baseline period except those from CNCM3 which showed great differences of predictions from the other six. Therefore, the likely prediction of future precipitation in Central Iraq in the far future is a pronounced reduction in precipitation compared to the baseline period. 

The ensemble means of precipitation predictions from seven GCMs were calculated to further illustrate the future change in the period of 2011-2030, 2046-2065, and 2080-2099, and the differences between the ensemble means and the baseline value for the seasonal precipitation were plotted in Figure 7 for the periods of 2011-2030, 2046-2065, and 2080-2099. In Figure 7, it can be seen that in Central Iraq, the precipitation in DJF (December, January, and February) shows an increasing trend in the periods of 2011-2030 and 2046-2065, and a decreasing trend in period 2080-2099. This result is consistent with other early studies in Iraq [8] [34]. For the precipitation in MAM (March, April, and May) the plot shows a reduction trend in all considered future periods with the reduction projected to be very significant towards the far future. The future is also going to witness no or rare rain during the JJA (June, July, and August). An obvious increasing trend of precipitation in SON (September, October, and November) is projected in Central Iraq in the future. From the above discussions, it can be inferred that Central Iraq has different change trends of precipitation in the rainy seasons in the future.
section_name: Generation of Future Precipitation Scenarios

section_content: In the present study, we first tested the applicability of the LARS-WG model in downscaling daily precipitation
section_name: Conclusions

section_name: Acknowledgements

section_content: The research presented has been financially supported by Luleå University of Technology, Sweden and by " Swedish Hydropower Centre-SVC " established by the Swedish Energy Agency, Elforsk and Svenska Kraftnät together with Luleå University of Technology, The Royal Institute of Technology, Chalmers University of Technology and Uppsala University. Their support is highly appreciated. Thanks to the Iraqi Metrological Office for providing the data.
section_name: Acknowledgements