Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

An Assessment of Concurrency in Evapotranspiration Trends Across Multiple Global Datasets

While broad consensus exists that temperatures are increasing, there is uncertainty surrounding the direction of change manifested in actual evapotranspiration (ET) worldwide. This study assessed trends in ET across the land surface using eleven widely used global datasets for a 32-year study period. To demonstrate the agreement and disagreement of trends, the spatial distribution, concurrence, correlation and similitude were estimated. The results showed that while the global average trend in ET is -0.072 mm/month/year, the trends from individual datasets show a wide range of differences in magnitudes and directions. The considerable differences in the trends in each dataset were found to be weakly correlated to each other and highly divergent in their distribution and direction. No single dataset was sufficiently similar to another to offer a fair representation of trends. In a dynamic trend analysis using a 10-year moving window over the study period, high concurrence in the significant trends throughout the datasets was found to be rare for each time period. In general, the global data concurrence became negative by 1997 but rebounded to positive towards the end of the study period. In terms of spatial tendency, some regions were more prone to change the direction of their significant trends within the study period. This shows a high inconsistency in the location and direction of significant ET trends, implying selection of an ET dataset should consider its spatiotemporal uncertainty before use for any water balance study aiming to infer hydrological change over time.