A review: urban heat island and its impact on building energy consumption

Civilization and urbanization are the two key demands of humans in the 20th century. Over the last few decades, a considerable number of the human population have moved to urban areas. This phenomenon has led to an explosion of the population in some of the major cities around the globe, including in Saudi Arabia. Urban Heat Island (UHI) is a climatic condition in which urban settlements experience increased air temperature than their neighboring rural counterparts. The UHI is attributed to the anthropogenic modification of land surfaces, population growth, urban development, and its consequential production of waste heat, which is endangering human health and the environment as well as the quality of living. Series of factors have been responsible for UHI, including building orientation, material albedo, land use, high-rise constructions, and human activities. The present study investigates the significance of the UHI features and their relation to building energy consumption. A list of contributing factors to UHI was identified and analyzed. The study suggests that there is a positive relationship between urban greening and urban material concerning energy consumption. Thus, this is a potential study gap that needs to be addressed to analyze the impact of UHI, particularly in the context of Saudi Arabia

SWAT model application to estimate runoff for ungauged arid catchments experiencing rapid urbanisation: Riyadh case study

The built-up area of Riyadh city increased from approximately 4.5 km² in 1950 to reach approximately 1,600 km² by 2022 spreading over vast areas of the Wadi Hanifah and Wadi As Silayy catchments. The rapid growth of the city has led to repeated urban flooding. There is an urgent need to study surface runoff and how it is affected by land-use/land-cover (LULC) change in the ungauged catchments of the city. This study addressed that knowledge gap and was the first attempt to calibrate, validate, and run a semi-distributed model to simulate runoff depths and discharge rates for Riyadh's main catchments and sub-basins using five historical and five future scenarios. The Soil Water Assessment Tool (SWAT) was used for the modelling. TerraClimate evapotranspiration (ET) data was used to calibrate the SWAT model owing to a dearth of observed runoff data across Riyadh city. The literature review revealed that the use of Terraclimate ET to calibrate SWAT models is still very limited so far. The only previous study found is Herman et al. (2020). Therefore, this study is fairly unique in that it uses Terraclimate ET to successfully calibrate and validate a SWAT model. A one-by-one sensitivity analysis was performed to evaluate the impact of changing parameter values on the runoff simulations. The results indicated that simulated runoff sensitivity to selected parameter values in the calibrated SWAT models was minimal in the study area, where the relationships between simulated annual runoff and max and min runoff resulted in a very strong R2 (0.9998). The calibrated and validated SWAT models were run monthly and daily to simulate runoff and to assess the impact of several LULC change scenarios on surface runoff for both historical and future periods. The results of SWAT models of the main catchments and sub-basins located within the built-up areas demonstrated the positive effect of Riyadh’s development on runoff and discharge values for historical LULC scenarios and LULC 2030 probabilities scenarios. But the increasing rates of simulated runoff were not the same for all sub-basins due to the different proportions of urbanisation in each sub-basin. On the contrary, simulation results showed that runoff depths and discharge rates in sub-basins outside the boundaries of the built-up areas of Riyadh did not have significant changes when using historical LULC scenarios or LULC 2030 probabilities scenarios. The increase in runoff depths and discharge rates in the sub-basins reflected the direct influence of the urbanisation process on surface runoff. The increase in simulated surface runoff and discharge can be attributed mainly to the potential decrease of relatively permeable barren lands and the increase of impervious urban surfaces. Limitations faced during the SWAT model development suggest further research should aim to get detailed and accurate runoff estimates in Riyadh city to sufficiently assist decision-makers and city officials to adopt runoff and flood hazard management schemes in the city

SWAT model application to estimate runoff for ungauged arid catchments experiencing rapid urbanisation: Riyadh case study

The built-up area of Riyadh city increased from approximately 4.5 km² in 1950 to reach approximately 1,600 km² by 2022 spreading over vast areas of the Wadi Hanifah and Wadi As Silayy catchments. The rapid growth of the city has led to repeated urban flooding. There is an urgent need to study surface runoff and how it is affected by land-use/land-cover (LULC) change in the ungauged catchments of the city. This study addressed that knowledge gap and was the first attempt to calibrate, validate, and run a semi-distributed model to simulate runoff depths and discharge rates for Riyadh's main catchments and sub-basins using five historical and five future scenarios. The Soil Water Assessment Tool (SWAT) was used for the modelling. TerraClimate evapotranspiration (ET) data was used to calibrate the SWAT model owing to a dearth of observed runoff data across Riyadh city. The literature review revealed that the use of Terraclimate ET to calibrate SWAT models is still very limited so far. The only previous study found is Herman et al. (2020). Therefore, this study is fairly unique in that it uses Terraclimate ET to successfully calibrate and validate a SWAT model. A one-by-one sensitivity analysis was performed to evaluate the impact of changing parameter values on the runoff simulations. The results indicated that simulated runoff sensitivity to selected parameter values in the calibrated SWAT models was minimal in the study area, where the relationships between simulated annual runoff and max and min runoff resulted in a very strong R2 (0.9998). The calibrated and validated SWAT models were run monthly and daily to simulate runoff and to assess the impact of several LULC change scenarios on surface runoff for both historical and future periods. The results of SWAT models of the main catchments and sub-basins located within the built-up areas demonstrated the positive effect of Riyadh’s development on runoff and discharge values for historical LULC scenarios and LULC 2030 probabilities scenarios. But the increasing rates of simulated runoff were not the same for all sub-basins due to the different proportions of urbanisation in each sub-basin. On the contrary, simulation results showed that runoff depths and discharge rates in sub-basins outside the boundaries of the built-up areas of Riyadh did not have significant changes when using historical LULC scenarios or LULC 2030 probabilities scenarios. The increase in runoff depths and discharge rates in the sub-basins reflected the direct influence of the urbanisation process on surface runoff. The increase in simulated surface runoff and discharge can be attributed mainly to the potential decrease of relatively permeable barren lands and the increase of impervious urban surfaces. Limitations faced during the SWAT model development suggest further research should aim to get detailed and accurate runoff estimates in Riyadh city to sufficiently assist decision-makers and city officials to adopt runoff and flood hazard management schemes in the city

Spatial and temporal analysis of dust storms in Saudi Arabia and associated impacts, using Geographic Information Systems and remote sensing

Dust storm events occur in arid and semi-arid areas around the world. These result from strong surface winds and blow dust and sand from loose, dry soil surfaces into the atmosphere. Such events can have damaging effects on human health, environment, infrastructure and transport. In the first section of this PhD dissertation, focus on the suitability of the existing of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula are examined. These are the: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (Band 31–32); (c) BTD (Band 20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). This work also develops dust detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analysing associated temporal trends. The results suggest the most suitable indices for identifying dust storms over different land cover types across the Arabian Peninsula are BTD31–32 and the RSB index. Methods such as NDDI and BTD20 – 31 have limitations in detecting dust over multiple land-cover types. In addition, MEDI was found to be an unsuccessful index for detecting dust storms over all types of land cover in the study area. Furthermore, this thesis explores the spatial and temporal variations of dust storms by using monthly meteorological data from 27 observation stations across Saudi Arabia during the period (2000–2016), considering the associations between dust storm frequency and temperature, precipitation and wind variables. In terms of the frequency of dust in Saudi Arabia, the results show significant spatial, seasonal and inter-annual. In the eastern part of the study area, for example, dust storm events have increased over time, especially in Al-Ahsa. There are evident relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction and precipitation. This thesis also describes the impact of dust on health, and specifically on respiratory admissions to King Fahad Medical City (KFMC) for the period (February 2015 – January 2016).This study uses dust data from the World Meteorological Or-ganization (WMO) for comparing and analysing the daily weather conditions and hospital admissions. The findings indicate that the total number of emergency respiratory admissions during dust events was higher than background levels by 36% per day on average. Numbers of admissions during ‘widespread dust’ events were 19.62% per day higher than during periods of ‘blowing dust’ activity. The average number of hospital admissions for lower respiratory tract infections (LRTI) was 11.62 per day during widespread dust events and 10.36 per day during blowing dust. The average number of hospital admissions for upper respiratory tract infections (URTI) was 10.25 per day during widespread dust events and 7.87 per day during blowing dust ones. I found clear seasonal variability with a peak in the number of emergency admissions during the months of February to April. Furthermore, qualitative evidence suggests that there is a significant impact on hospital operations due to the increase in patients and pressure on staffing and hospital consumables in this period. Taken together, these findings suggest the (BTD 31–32) and (RSB) are the most suitable indices of the five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula and over different land cover. There are important spatial and temporal pattern variations, as well as seasonal and inter-annual variability, in the occurrence of dust storms in Saudi Arabia. There is also a seasonal pat-tern to the number of hospital admissions during dust events. This is research in-tended to fill the knowledge gap in the dust detection filed. Here I address the knowledge gap by evaluating the identified dust methods over the whole Arabian Peninsula and by considering different land cover. To my knowledge, this is the first study analysed the temporal trends in indices values considering dust and dust-free conditions. Previous work has only focused on 13 stations for analysing dust storms over Saudi Arabia. Therefore, this study has analysed the seasonal and inter-annual and spatial variation by using data from 27 observations in Saudi Arabia. This study addresses the relationship between dust storm frequency and the three meteorological factors (i.e. temperature, precipitation and wind variables) which have not yet been clarified in previous studies. In addition, this research fills the gap in the literature by investigating the correlation between different types of dust events such as (wide-spread dust and blowing dust) and their effects on the hospital admissions for upper and lower respiratory tract issues for pediatric in Riyadh city

The impact of neighbourhood geometries on outdoor thermal comfort and energy consumption from urban dwellings: a case study of the Riyadh city, the kingdom of Saudi Arabia

The thesis addresses the contributions of urban geometry under hot-arid summer condition in the Riyadh City, Saudi Arabia, toward both; the development of comfortable microclimate conditions in outdoor spaces at pedestrian level, and energy consumption from urban dwellers. The urban geometry is described in this thesis by three variables, including building height to street width aspect ratios (H/w), sky view factor (SVF) at pedestrian level, and the solar orientations of the canyons. These three variables are used in this study to investigate their influence on the microclimate conditions and the associated outdoor thermal comfort and energy consumption from urban dwellers. The work intends to shed light on the existing geometries of different urban locations in Riyadh City and the associated thermal conditions, as well as the thermal perceptions and preferences of outdoor users. Therefore, integrated empirical studies that are composed of three original surveys are carried out, including a study of land surface temperature of Riyadh City, outdoor thermal comfort survey, and in situ microclimate measurements in different urban locations at neighbourhood scale and within urban canyons. Following that, microclimate and energy modelling are carried out on a number of hypothetical urban geometries that proposed according to the current buildings and planning regulations in the Riyadh City, i.e. building materials, opening ratios on building facades, buildings and streets layouts and minimum width of local streets. Yet, since the study measures the impact of scenarios modifications of urban geometry on the issues under investigation, thus, additional buildings heights and different setback aspect ratios have been added. The proposed hypothetical urban geometries investigated include various street aspect ratio (H/st.) equal to 0.5, 1, 1.5 and 2, and setback aspect ratio (H/sb.) equal to 0, 2, 4 and 8. The proposed urban settings that resulted from the combination of the various streets and setbacks aspect ratios are modelled on four different orientations, including EW, NS, NE-SW and NW-SE, and a total of 64 different urban geometries were evaluated

A Hydrologic Climate Study for an Arid Region

Water is the most precious natural resource in arid regions due to the limitation of water resources, expanding population, and increasing volumes of industrial and domestic waste. The purpose of this research was to evaluate methods to estimate water quantity in an arid region. The research consisted of three separate studies. In the first study, hydrologic models used to estimate water quantity were evaluated for suitability of use in arid regions. Most hydrologic models that have been used in arid regions were originally developed for humid regions. Rainfall events in arid regions can be characterized as short-term, high intense rainstorms causing severe runoff in arid regions. This study provides an assessment various rainfall-runoff models and a comparison of methods and/or modifications used by researchers to adapt these models to arid regions. Mike 11, Sacramento, Pitman, and the IHACRES models have been used in arid regions with mixed results. The second study evaluated the annual rainfall for the Tabuk region obtained from observed datasets for the period 1978–2013. The objective of this study was to determine Tabuk catchment climate characteristics in terms of precipitation. The Tabuk region has common aridity characteristics in terms of the small precipitation amounts and high temperature rate. There is a drop in the annual rainfall from (25-30) mm to (5-10) mm (1978-2004). The lowest annual rainfall (0-6.0 mm) occurred in the year 2004, which is the driest year in 35-year period. The mean annual rainfall is less than 33.5 mm. The third study analyzed flash floods caused by short-intense rainstorms. The objective of this study was to determine flood risk related to identified precipitation depths. The project quantized the runoff corresponding to different design storms and used hydraulics and geospatial data to determine flood elevations. The study constructed hydrologic and hydraulic models to quantify flood hazards in the adjacent area of Wadi Abu Nashayfah. Peak discharges for the wadi were computed by using observed rainfall data, and the output of this process was applied to compute water surface elevations within the flow channel. The depth of precipitation at which the channel was overtopped was determined in several locations. The predicted overtopping was compared to historic events with good agreement

Mapping and Assessment of Evapotranspiration over Different Land-Use/Land-Cover Types in Arid Ecosystem

Evapotranspiration (ET) is an essential process for defining the mass and energy relationship between soil, crop and atmosphere. This study was conducted in the Eastern Region of Saudi Arabia, to estimate the actual daily, monthly and annual evapotranspiration (ETa) for different land-use systems using Landsat-8 satellite data during the year 2017/2018. Initially, six land-use and land-cover (LULC) types were identified, namely: date palm, cropland, bare land, urban land, aquatic vegetation, and open water bodies. The Surface Energy Balance Algorithm for Land (SEBAL) supported by climate data was used to compute the ETa. The SEBAL model outputs were validated using the FAO Penman-Monteith (FAO P-M) method coupled with field observation. The results showed that the annual ETa values varied between 800 and 1400 mm.year−1 for date palm, 2000 mm.year−1 for open water and 800 mm.year−1 for croplands. The validation measure showed a significant agreement level between the SEBAL model and the FAO P-M method with RMSE of 0.84, 0.98 and 1.38 mm.day−1 for date palm, open water and cropland respectively. The study concludes that the ETa produced from the satellite data and the SEBAL model is useful for water resource management under arid ecosystem of the study area

Mapping Soil Salinity and Its Impact on Agricultural Production in Al Hassa Oasis in Saudi Arabia

Soil salinity is considered as one of the major environmental issues globally that restricts agricultural growth and productivity, especially in arid and semi-arid regions. One such region is Al Hassa Oasis in the eastern province of Saudi Arabia, which is one of the most productive date palm (Phoenix dactylifera L.) farming regions in Saudi Arabia and is seriously threatened by soil salinity. Development of remote sensing techniques and modelling approaches that can assess and map soil salinity and the associated agricultural impacts accurately and its likely future distribution should be useful in formulating more effective, long-term management plans. The main objective of this study was to detect, assess and map soil salinity and and its impact on agricultural production in the Al Hassa Oasis. The presented research first started by reviewing the related literature that have utilized the use of remote sensing data and techniques to map and monitor soil salinity. This review started by discussing soil salinity indicators that are commonly used to detect soil salinity. Soil salinity can be detected either directly from the spectral reflectance patterns of salt features visible at the soil surface, or indirectly using the vegetation reflectance since it impacts vegetation. Also, it investigated the most commonly used remote sensors and techniques for monitoring and mapping soil salinity in previous studies. Both spectral vegetation and salinity indices that have been developed and proposed for soil salinity detection and mapping have been reviewed. Finally, issues limiting the use of remote sensing for soil salinity mapping, particularly in arid and semi-arid regions have been highlighted. In the second study, broadband vegetation and soil salinity indices derived from IKONOS images along with ground data in the form of soil samples from three sites across the Al Hassa Oasis were used to assess soil salinity in the Al-Hassa Oasis. The effectiveness of these indices to assess soil salinity over a dominant date palm region was examined statistically. The results showed that very strongly saline soils with different salinity level ranges are spread across the three sites in the study area. Among the investigated indices, the Soil Adjusted Vegetation Index (SAVI), Normalized Differential Salinity Index (NDSI) and Salinity Index (SI-T) yielded the best results for assessing the soil salinity in densely vegetated area, while NDSI and SI-T revealed the highest significant correlation with salinity for less densely vegetated lands and bare soils. In the third study, combined spectral-based statistical regression models were developed using IKONOS images to model and map the spatial variation of the soil salinity in the Al Hassa Oasis. Statistical correlation between Electrical Conductivity (EC), spectral indices and IKONOS original bands showed that the Salinity Index (SI) and red band (band 3) had the highest correlation with EC. Integrating SI and band 3 into one model produced the best fit with R2 = 0.65. The high performance of this combined model is attributed to: (i) the spatial resolution of the images; (ii) the great potential of SI in enhancing and delineating the spatial variation of soil salinity; and (iii) the superiority of band 3 in retrieving soil salinity features and patterns. Soil salinity maps generated using the selected model showed that strongly saline soils (&gt;16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors.16 dS/m) with variable spatial distribution were the dominant class over the study area. The spatial variability of this class over the investigated areas was attributed to a variety factors, including soil factors, management related factors and climate factors. In the fourth study, Landsat time series data of years 1985, 2000 and 2013 were used to detect the temporal change in soil salinity and vegetation cover in the Al Hassa Oasis and investigate whether there is any linkage of vegetation cover change to the change in soil salinity over a 28-year period. Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) differencing images were used to identify vegetation and salinity change/no-change for the two periods. The results revealed that soil salinity during 2000-2013 exhibited much higher increase compared to 1985-2000, while the vegetation cover declined for the same period. Highly significant (p In the fifth study, the effects of physical and proximity factors, including elevation, slope, soil salinity, distance to water, distance to built-up areas, distance to roads, distance to drainage and distance to irrigation factors on agricultural expansion in the Al Hassa Oasis were investigated. A logistic regression model was used for two time periods of agricultural change in 1985 and 2015. The probable agricultural expansion maps based on agricultural changes in 1985 was used to test the performance of the model to predict the probable agricultural expansion after 2015. This was achieved by comparing the probable maps of 1985 and the actual agricultural land of 2015 model. The Relative Operating Characteristic (ROC) method was also used and together these two methods were used to validate the developed model. The results showed that the prediction model of 2015 provides a reliable and consistent prediction based on the performance of 1985. The logistic regression results revealed that among the investigated factors, distance to water, distance to built-up areas and soil salinity were the major factors having a significant influence on agricultural expansion. In the last study, the potential distribution of date palm was assessed under current and future climate scenarios of 2050 and 2100. Here, CLIMEX (an ecological niche model) and two different Global Climate Models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Saudi Arabia. A sensitivity analysis was conducted to identify the CLIMEX model parameters that had the most influence on date palm distribution. The model was also run with the incorporation of six non-climatic parameters, which are soil taxonomy, soil texture, soil salinity, land use, landform and slopes, to further refine the distributions. The results from both GCMs showed a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100 due to increment of heat stress. The lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the greatest impact on sensitivity, while other parameters were moderately sensitive or insensitive to change. A more restricted distribution was projected with the inclusion of non-climatic parameters. Overall, the research demonstrated the potential of remote sensing and modeling techniques for assessing and mapping soil salinity and providing the essential information of its impacts on date palm plantation. The findings provide useful information for land managers, environmental decision makers and governments, which may help them in implementing more suitable adaptation measures, such as the use of new technologies, management practices and new varieties, to overcome the issue of soil salinity and its impact on this important economic crop so that long-term sustainable production of date palm in this region can be achieved. Additionally, the information derived from this research could be considered as a useful starting point for public policy to promote the resilience of agricultural systems, especially for smallholder farmers who might face more challenges, if not total loss, not only due to soil salinity but also due to climate change

The application of optical satellite imagery and census data for urban population estimation: A case study for Ahmedabad, India

The rapid growth of India\u27s urban population leads to the need to employ new technologies for population modelling. In this study, optical satellite images and census data are used to model the population distribution for the city of Ahmedabad (northwest India. The selected spatial scales for which the population data are generated correspond to those often used for earthquake risk modelling and loss estimation

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.Peer reviewe