No abstract

This study was aimed to recognize the spatial and temporal features of urban development and its impact on the climate of Baghdad City. The analytical method of the study relies on changes in Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Land Surface Temperature(LST); GIS technology was used to measure these statistics. Landsat (5,8) and Sentinel2A imagery were used to detect the change of urbanization growth, vegetation change, and land surface temperature during the study period from 2001 to 2020, whereas used the supervised classification technique for determination for LULC variations. The results showed significant changes among the LULC classes in the studied periods, as most of the LULC changes were caused by human activities. The most prominent changes in LULC were the urban expansion on agricultural land, continuously in all years, which led to the decline of vegetation resulting from land degradation. The Building area increased from around 863 km2 in 2001 to 1469 km2 in 2020 concentrated mainly in the center, the northeastern and southeastern part of the city. Moderate plants decrease from around 88.5 km2 in 2001 to 59 km2 in 2020 and dense plants decreased from 0.0252 km2 in 2001 to 0.0135 km2 in 2020. This has led to negative effects on the climate where temperature rates increased from (26-47) degrees Celsius in 2001 to (32-56) degrees Celsius in the last year of the study, the highest temperatures were recorded in urban growth areas and areas without vegetation.

Extreme rainfall is one of the environmental hazards with disastrous effects on the human environment. Water resources management is very vulnerable to any changes in rainfall intensities. A spatiotemporal analysis is essential for study the impact of climate change and variability on extreme rainfall. In this study, daily rainfall data for 36 meteorological stations in Iraq during 1981–2017 were used to investigate the spatiotemporal pattern of 10 extreme rainfall indices using RClimDex package. These indices were classified into two categories: rainfall total (PRCPTOT, SDII, R95p, R99p, RX1day, and RX5day) and rainfall days (CDD, CWD, R10, and R20). Depending on the mean annual precipitation data, the study area was divided into three climatic zones to examine the time series features of those 10 indices. Results showed a tendency to increase in precipitation toward the northwestern part of Iraq, and more than 70% of stations achieved a positive trend for most indices. The most frequent negative trend appeared in eight stations distributed in the western and southern parts of Iraq, namely (Heet, Haditha, Anah, Rutba, Qaim, Nukheb, Najaf, and Fao). A significant positive trend appeared obviously in PRCPTOT and R95p with a rate of 0.1–4.6 and 0.5–2.7 mm per year, respectively. Additionally, the least trend increasing appeared in all precipitation days indices specifically in R10 and R20. Time series analyses revealed a positive trend in all regions under study, except SDII in the southern region. The most significant rate of change was noticed in regions one and two (northern and middle parts of Iraq), particularly for PRCPTOT and R95p 3.26 and 2.45 mm per day, respectively. Only the northern and eastern regions of Iraq experienced a high probability of significant extreme rainfall.

Predicting weather by numerical models have been used extensively in research works for Middle East, mostly for dust storms, rain showers, and flash floods with a less deal of interest on snow precipitation. In this study, the Global/Regional Integrated Model System (GRIMs) that was developed in South Korea was used to predict a rare snowfall event occurred in three countries in Middle East (Syria, Jordan and Iraq) located between (25-65 oE; 12-42 oN) in year 2008. The main aim of this study was to test GRIMs efficiency, which would be used for the first time in Middle East, to make predictions of weather parameters such as pressure, temperature, and relative humidity especially in the selected area. In addition, the study would investigate the conditions that caused the snowfall event. GRIMs model was installed, compiled, and run on a Linux platform by using NCEP-NCAR reanalysis dataset as initial conditions on 0.5 × 0.5 grid resolution to make simulations for three days at intervals of three hours. The output of the model was evaluated by making comparisons with actual data obtained from the GFS Agency dataset and the model showed its efficiency. The snowfall event was synoptically discussed in details. It was found that the snowfall event was a result of fast succession systems of a strong cold high pressure and then a deep warm low pressure. The high instability in the region had led to form large cumuliform clouds with snow precipitation as a rare event in very long period.

Aridity is one of the main factors which distinguish the climate of a region and has significant influence on human activities. This study investigated the spatial distribution of the aridity indices to determine the climate conditions in Iraq over the period (1981-2015), depending on the data of the air temperature and rainfall which obtained from 28 stations distributed through Iraq. The used aridity indices are: Lang, Erinc, Emberger, UNEP, De Martonne and Thornthwaite. The spatial distribution was determined using inverse distance weighting (IDW) interpolated method. The results of aridity indices analysis shows that the hyper-arid, arid, and semi-arid categories are predominant with almost (91%) to (100%) of the country’s area. Dry sub-humid, moist sub-humid and humid categories occupies less than (10%) with most of indices at stations of (Arbil, Sulaymaniyah, and Salahaddin). To evaluate the seasonal spatial distributions, De Martonne was utilized. During winter, the climate types ranged from semi-arid to very-humid, while at spring season from arid to humid. Autumn season dominated by arid at (97%) of study area. The summer season was the driest compared with the other seasons. The change point for aridity indices was detected by using the cumulative sum charts (CUSUMs), it is found for the most stations in (1997). Consequently, the spatial distribution for the aridity indices were analyzed through two periods (1981-1997 and 1998-2015), this analysis showed that the arid and hyper-arid areas were increased in the second period compared with the first period with obvious extension toward the north of Iraq.