Sand and gravel are amongst the most mined and consumed resources in the world, especially in the Global South where the demand for sand increases due to urbanization. Large parts of this extraction occur in rivers, with adverse environmental consequences. Mitigation of riverine sand and gravel mining (RSM) impacts on freshwater systems requires a robust understanding of the scale and controlling factors of RSM. However, very limited data exist on the occurrence of this process. This is especially true over larger spatial scales. Here we aim to bridge this gap and gain more insight into the occurrence of RSM at a subcontinental scale. More specifically, we (1) develop a systematic mapping procedure of RSM to collect the first large-scale dataset of RSM occurrence focusing on India as a case study. Using this dataset, we then (2) statistically analyze the factors potentially controlling spatial patterns of RSM across India. Factors were included that represent both the demand and supply of sand. Based on these results, we (3) develop a logistic regression model to estimate the probability of RSM occurrence. Overall, our work shows the enormous scale of RSM in India: of the 808 randomly selected and investigated river reaches (with lengths of ca. 10 km), 61.6 % showed clear evidence of RSM. Statistical analyses revealed that the presence of RSM is mainly linked to variables describing the demand for sand (e.g. distance to city, percentage of built-up area around the river reach), while variables relating to supply (e.g. soil texture, expected sediment discharge) showed much weaker correlations. Only rainfall variability was a clearly significant factor, which may relate to river reach accessibility. Based on these findings, we present a first model and map that predicts the susceptibility to RSM in India.
Keywords: Google Earth mapping; Large scale model; Logistic regression; River; Urbanization.
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