This study develops a general method to evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. The method is based on the understanding that long-term annual ULST is controlled by three factors: (1) localized urbanization, (2) global climate change, and (3) interannual climate variation. Then the method removes the interannual climate fluctuations on long-term observed LST time series via linear regression and separates the contributions of urbanization and climate change to the impacts on long-term ULST via urban–rural comparison. The method is applied to Lagos, a fast-growing metropolis in the tropical West Africa, as an example for reference. Combined time-series daily daytime and nighttime MODIS Land Surface Temperature (LST) data over the years of 2003–2021 are used as the representation of land surface temperature. To avoid the potentioal interannual data biase due to uneven availability of data in the rainy seasons over years, only MODIS LST data from dry seasons are used in the study. The results are summarized as follows for Lagos: (1) long-term annual ULST is confirmed to be controlled by the three factors; (2) the proposed method can separate the contribution of the three factors to the ULST; (2) both localized urbanization and global warming are verified to contribute to the ULST increase with positive trends; (3) daytime ULST increased the most in the afternoon time at a mean rate of 1.429 °C per decade, with 0.985 °C (10 year)−1 contributed by urbanization and 0.444 °C (10 year)−1 contributed by climate warming; (4) nighttime ULST in Lagos increased the most after midnight at a rate of 0.563 °C (10 year)−1, with 0.56 °C (10 year)−1 contributed by urbanization and 0.003 °C (10 year)−1 contributed by climate warming; and (5) urbanization is generally responsible for around 60.97% of the urban warming in Lagos. Therefore, the increasing urbaniztion-induced urban heat island effect is the major cause for more heat-related health risks and climate extremes that many urban residents are suffering. The results of this study are of useful reference for both urbanization and climate change related issues in the geo-science field.

Lagos, Nigeria, is considered a rapidly growing urban hub. This study focuses on an urban development characterization with remote sensing-based variables for Lagos as well as understanding spatio-temporal precipitation responses to the changing intensity of urban development. Initially, a harmonic analysis showed an increase in yearly precipitation of about 3 mm from 1992 to 2018 for the lower bound of the fitted curve and about 2 mm for the upper bound. The yearly total precipitation revealed no significant trend based on the Mann–Kendall trend test. Subsequent analyses first involved characterizing urbanization based on nighttime light and population density data and then combined them together for the final analysis. Each time, the study area was subdivided into four zones: Zone 0, Zone 1, Zone 2, and Zone 3, which refer to non-urbanized, low-urbanized, mid-urbanized, and highly urbanized regions, respectively. The results from the Google Earth Engine-based analysis uncovered that only Zone 1 has a statistical monotonic increasing precipitation trend (Tau 0.29) with a 0.03 significance level when the combined criteria were applied. There is about a 200 mm precipitation increase in Zone 1. Insignificant patterns for the other three zones (Zone 2, Zone 3, and Zone 4) indicate that these trends are not consistent, they might change over time, and fluctuate heavily.

This study aims to develop a general method and evaluate the contributions of localized urbanization and global climate change to long-term urban land surface temperature (ULST) change. Combined daytime and nighttime daily MODIS products were used to fill data biases from satellite-observed data and applied to tropical regions during dry season from 2003 to 2021. Lagos, a fast-growing metropolis in West Africa, is selected as an example for reference. The results are summarized as follows. 1) Long-term annual ULST is confirmed to be controlled by urbanization, global climate change, and interannual climate variation. 2) Linear regression is used to remove the interannual climate fluctuations on long-term observed LST time series. 3) Urban-rural comparison method has been developed to separate the contributions of urbanization and global climate change to long-term ULST changes. Both urbanization and global climate change are verified to contribute to the ULST increase with positive trends. 4) Daytime ULST increased the most in the afternoon time at a mean rate of 1.429ºC per decade, with 0.985 ºC (10yr)-1 contributed by urbanization and 0.444 ºC (10yr)-1 contributed by global climate change. Nighttime ULST increased the most after midnight at a rate of 0.563ºC (10yr) -1, with 0.56 ºC (10yr) -1 contributed by urbanization and 0.003 ºC (10yr) -1 contributed by global climate change. 5) Urbanization is generally to be responsible for around 60.97% of the urban warming. The increasing urban-induced hot has been causing urban residents to suffer more heat-based health risks and increasing the extreme events. Therefore, the results are of useful reference for both urbanization and climate change related issues in the geo-science field.

Stormwater management needs attention as it causes surface flooding and pollution of nearby waterbodies. Parkerson Mill Creek in Auburn University, which gets polluted through surface runoff, is an example of this. In this study, a Personal Computer Stormwater Management Model (PCSWMM) was used to determine the susceptibility of the existing stormwater network to flooding on the Auburn University campus. Maximum water velocity mapping was used to identify areas associated with 3 categories of velocity (high, medium, and low) to find areas of potential erosion. Among the various sustainable stormwater management initiatives, it was found through a literature review that bioretention cells had the greatest potential to improve stormwater quality by screening pollutants from runoff water as well as minimizing erosion by reducing surface water velocity. Suitability analysis for bioretention cells identified 8 areas on the campus where bioretention cell could be installed for the most effective stormwater management. This study highlights the usability of PCSWMM models and techniques in increasing the efficiency of the stormwater system in any locality.