This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.

Nitrogen (N) and phosphorus (P) eutrophication in marine ecosystems is a global problem. Marine eutrophication has a negative impact on food security, ecosystem health and economy through disruptions in tourism, fisheries and health industries. Both N and P have known point and non-point sources. Control of point sources has been easier than non-point sources particularly agricultural sources for both N and P as well as fossil fuel combustion for N, which remains a major challenge. Implementing mitigation strategies for N has been reported to be effective for P mitigation; however, the converse is not true due to mobility and volatility of N. Excessive N and P cause algae blooms, anoxic conditions, and ocean acidification with these conditions leading to dead zones, fish kill, toxin production, altered plant species diversity, food web disruption, tourism disruption and health issues. Management of N and P pollution includes reduction of leaching from farms through crop selection, timely and precise application of fertilizer and building artificial wetlands, proper management of animal waste, reduction of fossil fuel N emission, mitigating N and P from urban sources and restoration of aquatic ecosystem. Mitigation measures need to focus on dual nutrient strategy for successful N and P reduction.