ABSTRACT:

This repository contains the Monte Carlo simulation ensemble dataset supporting the manuscript "Probabilistic Assessment of Compounding Dam Breach and Flood Hazards in Ungauged, Data-Scarce Watersheds" submitted to the Journal of Hydrology.
The dataset includes Monte Carlo realizations and ensemble hydrographs generated for the Wadi Derna watershed (560 km², northeastern Libya) across three rainfall tiers, produced using the probabilistic flood hazard framework developed in the associated manuscript. The framework propagates uncertainty through the full flood modeling chain, from rainfall–runoff generation through dam breach formation, using HEC-HMS with over 200 uncertain parameters sampled across 10,000 Monte Carlo realizations per rainfall tier.

Contents
Monte Carlo Realizations:
Tier 1 (50–200 mm): 10,000 parameter sets and corresponding model outputs
Tier 2 (200–350 mm): 10,000 parameter sets and corresponding model outputs
Tier 3 (350–500 mm): 10,000 parameter sets and corresponding model outputs

Ensemble Hydrographs:
Discharge hydrograph ensembles downstream of Al-Bilad Dam for each rainfall category
Summary statistics including the 5th, 10th, 50th, 90th, and 95th quantile traces for each category

Associated Publication:
Nemnem, A. M., Kurter, E. C., and Imran, J. (under review). Probabilistic Assessment of Compounding Dam Breach and Flood Hazards in Ungauged Watersheds.

ABSTRACT:

We present a homogeneous and spatially explicit dataset at county and tract levels that supports climate change risk assessment across the contiguous United States, designed to enable user-defined construction of hazard, exposure, vulnerability, resilience, and composite risk indices through application-specific weighting of variables and selection of hazards or future hazard projections. The dataset follows the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) risk framework, which defines risk through four key components: hazard, exposure, vulnerability, and resilience. It includes 130 variables compiled from multiple national sources for 3,198 counties and 83,481 tracts. Major data sources include the Baseline Resilience Indicators for Communities (BRIC) from the University of South Carolina, the National Risk Index (NRI) from the Federal Emergency Management Agency (FEMA), and the Social Vulnerability Index (SVI) from the Centers for Disease Control and Prevention (CDC). Additional datasets were integrated to provide a more comprehensive set of exposure variables. To facilitate integration of variables with different units and scales, all data were winsorized at the 5th and 95th percentiles and normalized using fuzzy membership functions. The resulting normalized values represent relative positions within the distribution of each indicator and provide a consistent numerical framework for visualization, spatial analysis, and user-defined risk modeling. This process reduces the influence of outliers while preserving data variability. The resulting dataset provides a consistent foundation for constructing composite risk indices, replicating or extending IPCC AR6-aligned analyses, and supporting climate risk and adaptation research. It is particularly suited for applications in systemic risk assessment, spatial modeling, and policy evaluation.

ABSTRACT:

Overtopping failures of earthen dams are a growing concern under intensifying extreme rainfall. This study introduces a probabilistic fragility framework that combines hydrologic modeling, hydraulic analysis, and soil erodibility to assess erosion risk under uncertain load and resistance. Fragility curves derived from Monte Carlo simulations quantify erosion initiation and severity as functions of rainfall depth and excess shear stress. Application to multiple dams affected during the 2015 South Carolina floods reproduces observed outcomes ranging from complete breach to no damage. The framework also captures cascading effects, where failure of an upstream structure increases downstream loading and erosion potential. Compared with deterministic approaches, the method better represents outcome variability and provides a more reliable basis for dam safety assessment and flood risk mitigation.