Probabilistic and Scenario Based Projections of Mean And Extreme Sea Levels Under Climate Change

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Practitioner Interview

Phone interview with Jayantha Obeysekera from South Florida Water Management District by David Watkins, Jr., and Ali Mirchi. Interview questions asked inquired about (i) practitioner’s professional background, (ii) practitioner’s personal experience with systems analysis techniques and software in their job, (iii) role, benefits, and challenges in using systems analysis concepts in the water resources engineering profession, and (iv) recommendations for improving education of environmental and water resources systems analysis in universities

Implications of variability and trends in coastal extreme water levels

Coastal communities are flooding more often due to sea level rise (SLR), but some years are worse than others. We use a statistical model to show how the probabilities of coastal high waters, often referred to as extreme water levels—a combination of above average tides and storm surge—have shifted higher or lower every year with SLR and from changes in the tides and climatic (persistent weather and ocean) patterns. There are many U.S. and Pacific coastal regions where year‐to‐year variability is 15 cm or more, which is as large as the last 30 years of SLR and this pattern is projected to continue over the next 30 years. Considering additional SLR over the next 30 years could help compensate for year‐to‐year variabilitySupport was provided from the US Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) under Project RC‐2644. Jayantha Obeysekera had support from the Institute of Environment, FIU

Deep Learning Models for Water Stage Predictions in South Florida

Simulating and predicting water levels in river systems is essential for flood warnings, hydraulic operations, and flood mitigations. In the engineering field, tools such as HEC-RAS, MIKE, and SWMM are used to build detailed physics-based hydrological and hydraulic computational models to simulate the entire watershed, thereby predicting the water stage at any point in the system. However, these physics-based models are computationally intensive, especially for large watersheds and for longer simulations. To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage. The downstream stage of the Miami River in South Florida is chosen as a case study for this paper. The dataset is from January 1, 2010, to December 31, 2020, downloaded from the DBHYDRO database of the South Florida Water Management District (SFWMD). Extensive experiments show that the performance of the DL models is comparable to that of the physics-based models, even during extreme precipitation conditions (i.e., tropical storms). Furthermore, we study the decline in prediction accuracy of the DL models with an increase in prediction lengths. In order to predict the water stage in the future, our DL models use measured variables of the river system from the recent past as well as covariates that can be reliably predicted in the near future. In summary, the deep learning models achieve comparable or better error rates with at least 1000x speedup in comparison to the physics-based models

Global and Regional Sea Level Rise Scenarios for the United States

The Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force, jointly convened by the U.S. Global Change Research Program (USGCRP) and the National Ocean Council (NOC), began its work in August 2015. The Task Force has focused its efforts on three primary tasks: 1) updating scenarios of global mean sea level (GMSL) rise, 2) integrating the global scenarios with regional factors contributing to sea level change for the entire U.S. coastline, and 3) incorporating these regionally appropriate scenarios within coastal risk management tools and capabilities deployed by individual agencies in support of the needs of specific stakeholder groups and user communities. This technical report focuses on the first two of these tasks and reports on the production of gridded relative sea level (RSL, which includes both ocean-level change and vertical land motion) projections for the United States associated with an updated set of GMSL scenarios. In addition to supporting the longer-term Task Force effort, this new product will be an important input into the USGCRP Sustained Assessment process and upcoming Fourth National Climate Assessment (NCA4) due in 2018. This report also serves as a key technical input into the in-progress USGCRP Climate Science Special Report (CSSR)

Impacts of the 2004 tsunami on groundwater resources in Sri Lanka, Water Resour

[1] The 26 December 2004 tsunami caused widespread destruction and contamination of coastal aquifers across southern Asia. Seawater filled domestic open dug wells and also entered the aquifers via direct infiltration during the first flooding waves and later as ponded seawater infiltrated through the permeable sands that are typical of coastal aquifers. In Sri Lanka alone, it is estimated that over 40,000 drinking water wells were either destroyed or contaminated. From February through September 2005, a team of United States, Sri Lankan, and Danish water resource scientists and engineers surveyed the coastal groundwater resources of Sri Lanka to develop an understanding of the impacts of the tsunami and to provide recommendations for the future of coastal water resources in south Asia. In the tsunami-affected areas, seawater was found to have infiltrated and mixed with fresh groundwater lenses as indicated by the elevated groundwater salinity levels. Seawater infiltrated through the shallow vadose zone as well as entered aquifers directly through flooded open wells. Our preliminary transport analysis demonstrates that the intruded seawater has vertically mixed in the aquifers because of both forced and free convection. Widespread pumping of wells to remove seawater was effective in some areas, but overpumping has led to upconing of the saltwater interface and rising salinity. We estimate that groundwater recharge from several monsoon seasons will reduce salinity of many sandy Sri Lankan coastal aquifers. However, the continued sustainability of these small and fragile aquifers for potable water will be difficult because of the rapid growth of human activities that results in more intensive groundwater pumping and increased pollution. Long-term sustainability of coastal aquifers is also impacted by the decrease in sand replenishment of the beaches due to sand mining and erosion

Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida.

Abstract Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low-and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May-June-July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study