Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establishing connections across multiple physical processes, such as rainfall and runoff. These data, inherently connected both spatially and temporally, possess intrinsic causal relations that can be leveraged for robust and accurate forecasting. Recently, spatio-temporal graph neural networks (STGNNs) have been adopted, excelling in various domains, such as urban traffic management, weather forecasting, and pandemic control, and they also promise advances in streamflow management. However, learning causal relationships directly from vast observational data is theoretically and computationally challenging. In this study, we employ a river flow graph as prior knowledge to facilitate the learning of the causal structure and then use the learned causal graph to predict streamflow at targeted sites. The proposed model, Causal Streamflow Forecasting (CSF) is tested in a real-world study in the Brazos River basin in Texas. Our results demonstrate that our method outperforms regular spatio-temporal graph neural networks and achieves higher computational efficiency compared to traditional simulation methods. By effectively integrating river flow graphs with STGNNs, this research offers a novel approach to streamflow prediction, showcasing the potential of combining advanced neural network techniques with domain-specific knowledge for enhanced performance in hydrologic modeling.

Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-driven solutions are feasible, this is complicated by significant data sparsity due to the low proportion of wetlands (3-6%) in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas. To solve this limitation, we propose two strategies: (1) knowledge transfer from regions with rich wetlands (such as the Eastern US) to regions with sparser wetlands (such as the Southwestern area). , and (2) spatial data enrichment strategy that relies on an adaptive propagation mechanism. This mechanism differentiates between node pairs that have positive and negative impacts on each other for Graph Neural Networks (GNNs). We conduct rigorous experiments to substantiate our proposed method's effectiveness, robustness, and scalability compared to state-of-the-art baselines. Additionally, an ablation study demonstrates that each module is essential in prioritizing potential wetlands.

The capacity to anticipate streamflow is critical to the efficient functioning of reservoir systems as it gives vital information to reservoir operators about water release quantities as well as help quantify the impact of environmental factors on downstream water quality. Yet, streamflow modelling is difficult owing to the intricate interactions between different watershed outlets. In this paper, we argue that one possible solution to this problem is to identify the causal structure of these outlets, which would allow for the identification of crucial watershed outlets while capturing the spatiotemporally informed complex relationships leading to improved hydrological resource management. However, due to the inherent complexity of spatiotemporal causal learning problems, extending existing causal discovery methods to a whole basin is a major hurdle. To address these issues, we offer STREAMS, a new framework that uses Reinforcement Learning (RL) to optimize the search space for causal discovery and an LSTM-GCN based autoencoder to infer spatiotemporal causal features for streamflow rate prediction. We conduct extensive experiments on the Brazos river basin carried out within the scope of a US Army Corps of Engineers, Engineering With Nature Initiative project, including empirical studies of generalization performance to verify the nature of the inferred relationships.

Physical process-based hydrological models are widely adopted to simulate the water quantity or quality. One of the most commonly used hydrological models is Soil and Water Assessment Tool (SWAT). SWAT models for a large watershed can have over tens of thousands of Hydrological Resource Units (HRUs) which necessitates considerable computational resources. One way to speed up applications of the SWAT model could be to leverage machine learning techniques to identify the crucial features for the prediction task – feature selection. However, majority of the feature selection techniques rely on correlations or some form of a score metric (e.g. mutual information). Furthermore, since correlation does not imply causation, it is important to identify the causal features to improve the prediction accuracy while enhancing the interpretability of machine learning models. However, the SWAT model uses multiple data inputs and features that typically vary by space/HRUs, but may or may not vary over time. This makes it difficult to directly utilize causal discovery models to infer the causal relations. Furthermore, due to the lack of the ground truth causal graph for the SWAT model it is difficult to comment on the validity of the learned causal relations. To overcome these problems, we propose a novel framework that first infers the causal relations for the daily scale of the SWAT data using causal discovery algorithms. Then, it utilizes a community detection module to group similar features together for better interpretability. Finally, it identifies the stable causal relations that appear most often across all the timesteps and leverage them for the prediction of the water quantity. By utilizing only the causal features for the prediction of the target variable can lead to high accuracy as it removes the reliance on spurious correlations. Furthermore, we conduct extensive experiments to validate the effectiveness of the proposed framework along with a real-world case study to evaluate whether the selected features are interpretable or not.

Causal learning has become an essential attribute in majority of the machine learning models. One of the widely studied fields in causal learning is causal discovery which aims to identify potential cause-effect relationships from observational data. Temporal causal discovery models are specifically curated to enforece the temporal constraints while discovering the causal relationships. However, in physical systems such as hydrological systems, there are additional constraints such as spatial constraints that play a crucial role in deciding whether a node is a causal parent for another node or not. Failing to enforce these additional constraints may mislead the model to classify an irrelevant relationship as a causal relationship. Furthermore, causal discovery models are evaluated against a ground truth causal graph. However, the hydrological systems contain a huge number of features making it challenging to obtain a ground-truth causal graph. To deal with the aforementioned problems, in this study we propose a new Spatio-Temporal Causal Discovery Framework named, STCD. By enforcing temporal and spatial constraints STCD aims at identifying meaningful causal relationships. Furthermore, to evaluate the causal relations inferred by STCD in the absence of the ground-truth causal graph, we utilize only the causal parents of a target variable for prediction across different years. We demonstrate that utilizing only the causal features identified by STCD to predict the flow-rate for a target location attains superior performance.