Impact of land uses, drought, flood, wildfire, and cascading events on water quality and microbial communities: A review and analysis

Global river water quality under climate change and hydroclimatic extremes

Stream water quality prediction using boosted regression tree and random forest models

This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability. 

An Overview of Flood Concepts, Challenges, and Future Directions

Over the past few years, Deep Learning (DL) methods have garnered substantial recognition within the field of hydrology and water resources applications. Beginning with a discussion on fundamental concepts of DL, we discussed the state-of-the-art DL architectures such as Long-Short-Term-Memory (LSTM), Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Encoder-Decoder models that have gained much attention over the recent years. The recent advancements in the DL model, such as the Attention model and Transformer Neural Network, that are designed to handle sequential time series data, are also discussed. An overview of integrating physics-based hydrological models with state-of-the-art DL models, known as Physics-Guided Deep Learning (PGDL), and its potential for improving the accuracy and interpretability of hydrological predictions are discussed. We emphasized that PGDL has the potential to enhance the physical consistency and robustness of the hydrologic predictions. We further delve into Explainable Artificial Intelligence (XAI), examining various techniques for constructing interpretable models. The objective is to empower users to comprehend and confidently trust machine learning algorithms' results (model outputs). Furthermore, we delved into the diverse applications of Deep Learning (DL) in hydrology and water resources sectors, encompassing areas such as drought and flood forecasting, remote sensing applications, water quality assessments, subsurface flow inversion problems, groundwater level prediction, and hydro-climate variable downscaling. 

Deep Learning in Hydrology and Water Resources Disciplines: Concepts, Methods, Applications, and Research Directions

A numerical model was developed to investigate the behavior of round buoyancy-driven particle clouds in a quiescent ambient. The model was validated by comparing model simulations with prior experimental and numerical results and then applied the model to examine the difference between releases of positively and negatively buoyant particles. The particle cloud model used the entrainment assumption while approximating the flow field induced by the cloud as a Hill’s spherical vortex. The motion of individual particles was resolved using a particle tracking equation that considered the forces acting on them and the induced velocity field. The simulation results showed that clouds with the same initial buoyancy magnitude and particle Reynolds number behaved differently depending on whether the particles were more dense or less dense than the ambient fluid. This was found even for very low initial buoyancy releases, suggesting that the sign of the buoyancy is always important and that, therefore, the Boussinesq assumption is never fully appropriate for such flows.

Understanding the Influence of the Buoyancy Sign on Buoyancy-Driven Particle Clouds

Drought is an insidious, natural, and recurrent phenomenon initiated mainly by intense precipitation shortage and exacerbated by additional factors such as abnormal rise in temperature, variation in wind speed, and background aridity. As a result, precipitation is used as the most common variable to develop indicators or proxies to quantify drought. The precipitation information plays an important role in deriving commonly used drought indicators, such as Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI). This chapter systematically highlights the importance of rainfall in drought assessment. An overview of the role of rainfall in drought assessment and the most common precipitation-based drought indices is provided. The various challenges and limitations associated with quantifying the evolution of drought using these rainfall-based drought indices are emphasized. Finally, a case study is provided presenting a comparative analysis between SPI, SPEI, and PDSI (across different spatial scales) for two climate divisions located in Texas for a long-term historical (1960–2015) period. 

Rainfall and droughts

Rising temperature and shifting precipitation patterns due to climate change are likely to intensify droughts throughout the world. Understanding the drought characteristics of possible future scenarios under climate change requires verification of past drought events using appropriate drought indices. Consequently, this study investigates the application of two widely used drought indices, the standardized precipitation index (SPI) and standardized streamflow index (SSI), to characterize historical droughts, drought trends, and their impact on water quality and stream integrity for a selected study basin in New Jersey. Results indicated that both SPI and SSI were able to identify historical drought events, including three drought emergency periods and the most recent drought-watch periods. A significant positive meteorological drought was observed at the western side of the basin, whilst a significant positive hydrological drought was found in the eastern side. The average pollutant concentration of drought periods were lesser than non-drought periods due to reduction of different processes, such as erosion and transport of sediment and nutrients into rivers and streams, during drought periods as opposed to non-drought periods. The findings from this study will serve to bolster the ongoing efforts to formulate better drought management strategies for future climate change in the Raritan Basin.

https://www.mdpi.com/2071-1050/13/15/8175/pdf

Meteorological and Hydrological Drought Analysis and Its Impact on Water Quality and Stream Integrity

Evaluation of high-resolution satellite products for streamflow and water quality assessment in a Southeastern US watershed

Evaluation of Various Land Use Metrics for Enhancing Stream Water Quality Predictions

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