Journal of Hydrologic Engineering

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.

Assuming time-averaged normalized velocity as a random variable, the present work developed a Renyi entropy-based model for deriving one-dimensional and two-dimensional velocity distributions in seepage-affected alluvial channels. The model requires the maximization of Renyi entropy using the principle of maximum entropy, subject to specified constraints. The derived velocity distributions can have maximum velocity on or below the free surface. The velocity distributions were evaluated with laboratory observations and were also compared with theoretical velocity distributions reported in the literature. The Renyi entropy-based 2D velocity distributions satisfactorily agreed with experimental data, and compared well with reported distributions. Also, the Renyi entropy provided more accurate 2D velocity distribution in the near-bed flow of seepage experiments than the other technique. The present paper concluded that the Renyi entropy model can be used to predict the velocity distribution in seepage flows.

The overarching goal of stream restoration projects is to foster the adaptive capacities of stream functions while maintaining their resilience to excessive stresses. This paper introduces a methodology for evaluating the impact of stream restoration design strategies on multiple stream functions. The proposed method implements a functions-based, conceptual model of stream restoration based on the theory of complex adaptive system and agent-based modeling. The model illustrates the variable responses and stream functions recovery under different sets of initial conditions and stresses imposed on the system. For purposes of illustration, a systematic procedure is outlined to demonstrate the model application for quantitative risk assessment of stream functions. The results showed that the stream functions respond and adapt based on the removal of stressors and externalities and type of management intervention. However, some of these functions undergo these changes at slower rate and may take decades to recover. A balanced investment in watershed management with reach-scale restoration is recommended to reduce, to the maximum extent practicable, stressors impacting stream health and achieve desired goals and outcomes.

Spatial and temporal variability of streamflow due to climate change affects hydrological processes and irrigation demands at a basin scale. This study investigated the impacts of climate change on the Kurau River in Malaysia using metalearning, an ensemble machine learning technique using support vector regression (SVR) and random forest (RF) coupled with the Coupled Model Intercomparison Project CMIP6 multi-Global Climate Model (GCM). Five global climate models and three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used. The climate sequences generated by the delta change factor method were applied as input to the metalearning model to predict the streamflow changes in the Kurau River from 2021 to 2080. The model fitted reasonably well, with Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), percent bias (PBias), and RMS Error (RMSE) of 0.79, 0.83, 2.52, and 4.51, respectively, for the training period (1976–1995) and 0.72, 0.72, 5.85, and 6.90, respectively, for the testing period (1995–2005). Future projections of multi-GCM over the 2021–2080 period under three SSPs predicted an increase in rainfall for all months except April–June during the dry period (off-season), with a higher increase occurring during the wet period (main season). Temperature projections indicated an increase in maximum and minimum temperatures under all SSP scenarios, with a higher increase of approximately 2.0°C under SSP5-8.5 predicted during the 2051–2080 period relative to the baseline period of 1976–2005. The model predicted that the seasonal changes in streamflow of two planting periods range between $-7.5\%$ and 7.1% and between 1.2% and 5.9% during the off-season and the main season, respectively. A significant streamflow decrease was predicted in April and May for all SSP scenarios due to high temperatures during the off-season, with SSP5-8.5 being the worst. The impact assessment of climate variabilities on the availability of water resources is vital to identify appropriate adaptation strategies to deal with an expected increase in irrigation demand due to global warming in the future. The predicted future streamflow under the potential climate change impacts is crucial for the Bukit Merah Reservoir to establish suitable operational policies for irrigation release.