Geostatistical evaluation of spatial variability of land subsidence rates in Lagos, Nigeria

Accurate land subsidence prediction is fundamental for effective management of land subsidence and other associated geohazards. Lagos, the most densely populated city in Nigeria, has been adversely affected by widespread land subsidence. Previous studies have largely been restricted to analyzing space geodetic technique data. Unfortunately, this technique imposes spatial limitations resulting in incomplete coverage of the study areas due to atmospheric effects, orbital satellite characteristics, or surface characteristics. The application of geostatistics to georeferenced data obtained from space-based measurements can reliably provide complete information on spatial variability. The objective of this study is to provide an overview of current subsidence rates and to assess the spatial variability of land subsidence in the study area by using geostatistics that integrate semivariogram and ordinary kriging. The study area was partitioned into eight subregions; for each subregion, the experimental variogram was determined from the observed data and fitted to the optimal models. Seven subregions were fitted to exponential models and one was fitted to the spherical model. The model semivariograms were used in the kriging analysis to estimate the spatial variability of subsidence rates. The results showed that the nugget-to-sill ratio lies between 44 and 70%, indicating that the spatial variation of subsidence rates at the scale under study is moderately distributed and the mechanism of deformation is similar. The nugget effect of the subsidence rate is between 0.25 and 0.75, indicating that subsidence rates are influenced by various contributions. The predicted spatial variability of subsidence rates by Ordinary Kriging presents reliable values of R-squared (0.32–0.42) and RMSE (0.21–0.30). Critical subsidence rates characterised the areas around the Atlantic Ocean coastal alluvium deposit, the Lagos lagoon, and the Ogun River flood alluvium. This study successfully demonstrates the suitability of the geostatistical tools to evaluate spatial variability of subsidence rates.

Abstract licence: CC BY-NC-ND

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