Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models

Abstract

Across the world, the assessment of environmental impacts attributable to infrastructure and development projects often require a comparison between observed post-impact outcomes with what "would have happened" in the absence of the impact (i.e., the counterfactual). Environmental impact assessment (EIA) methods traditionally determine the counterfactual based on strong assumptions of stationarity (e.g., using before and after comparisons) and can be particularly challenging to use in the context of substantial data gaps, a vexing problem when combining several time-series data from different sources. Here we propose and test a widely applicable statistical approach for quantifying environmental impacts that avoids the stationarity assumption and circumvents issues associated with data gaps. Specifically, we used a Gaussian Copula (GC) model to assess the hydrological impacts of the Tucuruí dam on the Tocantins River in the Brazilian Amazon. Using multi-source water level and climate data, GC predictions of pre-dam hydrology for the validation period were excellent (Nash-Sutcliffe coefficients of 0.83 to 0.98 and 93-96% of observations within the 95% predictive intervals). In the post-dam period, the river had higher dry-season water levels both upstream and downstream relative to the predicted counterfactual, and the timing and duration of wet-season drawdown was delayed and extended, substantially altering the flood pulse. These impacts were evident as far as 176 km away from the dam, highlighting widespread hydrological impacts. The GC model outperformed standard multiple regression models in representing predictive uncertainty while also avoiding the stationarity assumption and circumventing the issue of sparse and incomplete data. We thus believe the GC approach has wide utility for integrating disparate time-series data to quantify the impacts of dams and other anthropogenic phenomena on riverine hydrology globally.

Keywords: Amazon; Counterfactual; Dams; Data gap; Hydrologic alteration; Impact assessment; Sparse data; Stationarity.