Uncovering the impact of hydrological connectivity on nitrate transport at the catchment scale using explainable AI

Nitrate contamination of water bodies is a major concern worldwide, as it poses a risk of eutrophication and biodiversity loss. Nitrate travels from agricultural land to streams through different hydrological pathways, which are abstrusely activated under different hydrological conditions. Certainly, hydrological conditions can alter the connection between different parts of the catchment and streams, in many cases independent of the discharge levels, leading to modifications in transport dynamics, retention, and nitrate removal rates in the catchment. While enhanced nitrate transport can be linked to high levels of hydrological connectivity, little is known about the effects of the spatial patterns of hydrological connectivity on the transport of nutrients at the catchment scale.

In this study, we combined daily stream nitrate concentration and discharge data at the outlet of 15 predominantly agricultural catchments in the United States (191–16,000 km2 area, 3500 km2 median area, and 77% median agriculture coverage) with soil moisture data from  SMAP-Hydroblocks (Vergopolan et al., 2021). SMAP-Hydroblocks is a hyperresolution soil moisture dataset at the top 5 cm of soil column at 30-m spatial resolution and 2-3 days revisit time (2015-2019), and it is derived through a combination of satellite data, land-surface and radiative transfer modeling, machine learning, and in-situ observations.

We configured a deep learning model for each catchment, driven by 2D soil moisture fields and 1D discharge time series, to evaluate the impact of streamflow magnitude and spatial patterns of soil moisture on streamflow nitrate concentration. The model setup comprises two parallel branches. The first branch incorporates a Long Short-term Memory (LSTM) model, the current state-of-the-art for time-series data modeling, utilizing daily discharge as input data. The second branch contains a Convolutional LSTM network (ConvLSTM) that incorporates daily soil moisture series, the fraction of agriculture of each pixel, and the height above the nearest drainage as a measurement of structural hydrological connectivity. Finally, a fully connected neural network combines the outputs of the two branches to predict the time series of nitrate concentration at the catchment outlet.

Preliminary results indicate that the model performs satisfactorily in one-third of the catchments, with Nash-Sutcliffe Efficiency (NSE) values above 0.3 for the test period, which covers the final 25% of the time series, and this is achieved without tuning the hyperparameters. The model failed to simulate nitrate concentrations (resulting in negative NSE values) typically in larger catchments. Using these simulations and explainable AI, we will quantify the importance of different inputs, in particular, we tested the relative importance of soil moisture for simulating nitrate concentrations. While the literature shows most of the predictive power for nitrate comes from streamflow rates, we show how soil moisture fields add value to the prediction and understanding of hydrologic connectivity. Finally, we will fine-tune the model for each catchment and include more predictors to enhance the reliability of model simulations.