Space-time susceptibility modeling of hydro-morphological processes at the Chinese national scale

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Hydro-morphological processes (HMP; any process in the spectrum between debris flows and flash floods) threaten human lives and infrastructure; and their effects are only expected to worsen under the influence of climate change. Limiting the potential damage of HMPs by taking preventive or remedial actions requires the probabilistic expectation of where and how frequently these processes may occur. The information on where and how frequently a given earth surface process may manifest can be expressed via susceptibility modeling. For the whole Chinese territory, a susceptibility model for HMP is currently not available. To address this issue, we propose a yearly space-time model built on the basis of a binomial Generalized Linear Model. The target variable of such model is the annual presences/absences of HMP per catchment across China, from 1985 to 2015. This information has been accessed via the Chinese catalogue of HMP, a data repository the Chinese Government has activated in 1950 and which is still currently in use. This binary spatio-temporal information is regressed against a set of time-invariant (catchment shape indices and geomorphic attributes) and time-variant (urban coverage, rainfall, vegetation density and land use) covariates. Furthermore, we include a regression constant for each of the 31 years under consideration and also a three-years aggregated information on previously occurred (and not-occurred) HMP. We consider two versions of our modeling approach, an explanatory benchmark where we fit the whole space-time HMP data, including a multiple intercept per year. Furthermore, we also extend this explanatory model into a predictive one, by considering four temporal cross-validation schemes. As a result, we portrayed the annual susceptibility models into 30 maps, where the south-east of China is shown to exhibit the largest variation in the spatio-temporal probability of HMP occurrence. Also, we compressed the whole spatio-temporal prediction into three summary maps. These report the mean, maximum and 95% confidence interval of the spatio-temporal susceptibility distribution per catchment, per year. The information we present has a dual value. On the one hand, we provide a platform to interpret environmental effects controlling the occurrence of HMP over a very large spatial (the whole Chinese country) and temporal (31 years of records) domain. On the other hand, we provide information on which catchments are more prone to experience a HMP-driven hazard. Hence, a step further would be to select the most susceptible catchments for detailed analysis where physically-based models could be tested to estimate the potentially impacted areas. For transparency, the results generated in this work are shared in the supplementary material as GIS (geopackage) files.