Climate change will affect the water resources system, on global and regional levels. Over the past thirty years, the High Atlas Mountains in Morocco have experienced severe droughts, which causes a decrease in water supply that affects both agriculture and the urban water system. In this paper, we assess the impact of climate change and socio-economic activities on water supply and demand in the Ourika watershed (High Atlas of Morocco), then we evaluate the efficiency and sustainability of regional adaptation strategies for water supply management. For this, we simulate and analyze the future water situation using the statistical downscaling model (SDSM) and the water assessment and planning tool (WEAP). After the model’s calibration and validation, the precipitation, minimum (Tmin) and maximum (Tmax) temperatures, water demand and unmet water demand were projected for 2100 horizon, using different climate change scenarios. The results revealed that the model’s performance, calibration and validation were found to be satisfactory. The analysis shows that the mean precipitation will decrease by 49.25% and 34.61% by 2100, under A2 and B2 emission scenarios of the Intergovernmental Panel on Climate Change (IPCC). The projected mean Tmax and Tmin will be warmer than the baseline period, with Tmax increasing by 4.2 °C (A2) and 3.6 °C (B2), and Tmin by 3.5 °C (A2) and 2.9 °C (B2) by 2100. The results also show that water demand and the unmet water demand will increase in all scenarios, the pressure on water resources will increase, leading to water scarcity. The results reveal that, under the influence of climate change, future unmet water demand is expected to reach 64 million cubic meters (MCM) by 2100. The results demonstrate that the assessments of the proposed adaptation strategies are effective, but not sufficient to ensure water sustainability for the Ourika watershed.

We combine Landsat and MODIS data in the simple biosphere model to assess the impact of urbanization on surface climate in Marrakech, circa 2010. We find the growing season surface temperature differences between urban and other cover types to vary between 1.6°C and 6.0°C at 1:00 p.m., and between 0.7°C and 1.1°C at 5:00 a.m., local time. Annually however, the built-up area warmed the city 0.3°C during daytime and 0.1°C at night compared to a simulated preurban situation in which the area was fully vegetated. A complete urbanization of the area would decrease its carbon uptake by 0.13 tons and increase its daytime surface temperature by 1.3°C, with 5.72% increase in energy consumption. However, in a “smart” urban growth scenario, we assume the build-up to cover 50% of the area, allowing it to occur first on bare lands, and then we convert all remaining bare lands to orchards; this would counterbalance 60% of daytime warming and sequester 31% more carbon, compared to the actual situation. We find irrigated golf courses to be the most water-consuming covers types, requiring 15 times the region's summer rainfall, and urban areas to distribute 43.8% of incoming precipitation as surface runoff versus only 16.74% for all other cover types combined. This can be a predictor for flash floods.

In this paper, we assessed and compared land surface temperature (LST) in urban centers using data from Landsat, MODIS, and the Simple Biosphere model (SiB2). We also evaluated the sensitivity of the model’s LST to different land cover types, fractions (percentages), and emissivities compared to reference points derived from Landsat thermal data. This was demonstrated in three climatologically- and morphologically-different cities of Atlanta, GA, New York, NY, and Washington, DC. Our results showed that in these cities SiB2 was sensitive to both the emissivity and the land cover type and fraction, but much more sensitive to the latter. The practical implications of these results are rather significant since they imply that the SiB2 model can be used to run different scenarios for evaluating urban heat island (UHI) mitigation strategies. This study also showed that using detailed emissivities per land cover type and fractions from Landsat-derived data caused a convergence of the model results towards the Landsat-derived LST for most of the studied cases. This study also showed that SiB2 LSTs are closer in magnitude to Landsat-derived LSTs than MODIS-derived LSTs. It is important, however, to emphasize that both Landsat and MODIS LSTs are not direct observations and, as such, do not represent a ground truth. More studies will be needed to compare these results to in situ LST data and provide further validation.

Cities are poised to absorb additional people. Their sustainability, or ability to accommodate a population increase without depleting resources or compromising future growth, depends on whether they harness the efficiency gains from urban land management. Population is often projected as a bulk national number without details about spatial distribution. We use Landsat and population data in a methodology to project and map U.S. urbanization for the year 2020 and document its spatial pattern. This methodology is important to spatially disaggregate projected population and assist land managers to monitor land use, assess infrastructure and distribute resources. We found the U.S. west coast urban areas to have the fastest population growth with relatively small land consumption resulting in future decrease in per capita land use. Except for Miami (FL), most other U.S. large urban areas, especially in the Midwest, are growing spatially faster than their population and inadvertently consuming land needed for ecosystem services. In large cities, such as New York, Chicago, Houston and Miami, land development is expected more in suburban zones than urban cores. In contrast, in Los Angeles land development within the city core is greater than in its suburbs.