Rocky Talchabhadel

We present a 42-year (1984–2025) Landsat consistent satellite vegetation trajectory for coastal wetlands in the Shell Beach area in the Breton Sound estuary, Louisiana. We applied the Controlled Interrupted Time Series (CITS) analysis to the satellite record to quantify the causal effect of the 2009 Mississippi River Gulf Outlet (MRGO) closure on the coastal wetland vegetation. The analysis used NDVI, kNDVI, and NDII across 88 vegetation transect plots located within five Coastal Reference and Monitoring Systems (CRMS) stations in the Shell Beach wetlands. Vegetation communities identified included Saline, Brackish, Freshwater, and Intermediate marsh. Sentinel-2 data from 2015 to 2025 were retained as an independent parallel record for NDRE analysis only. Quarterly median composites were decomposed using the Seasonal-Trend decomposition using LOESS (STL) to isolate de-seasonalized vegetation anomalies. The CITS design used segmented Ordinary Least Squares (OLS) regression with Newey–West HAC standard errors (lag = 3) at the study area. Northern Barataria Bay was used as an untreated regional control site to remove concurrent climate and sea level rise confounders. Whilst Hurricane Katrina and subsequent years (2005–2008) were excluded from the models, the single group ITS identified significant negative post-closure slope change across three indices. These were NDVI (β3 = −0.0034 yr⁻¹, p = 0.000), NDII (β3 = −0.0032 yr⁻¹), and kNDVI (β3 = −0.0016 yr⁻¹). These values indicated continued site-level decline relative to the pre-closure trend. Community-stratified ITS analysis showed a distinct divergent pattern with Freshwater marshes demonstrating significant recovery, with NDVI β3 = +0.0190 yr⁻¹, p = 0.000, whilst Saline, Brackish, and Intermediate communities continued to decline. CITS Difference-in-Differences (DiD) confirmed that site-level NDII and kNDVI declines were MRGO-specific. The DiD findings were that NDII = −0.00313 yr⁻¹, p < 0.001; kNDVI = −0.00123 yr⁻¹, p = 0.008. These findings isolated that physiological water stress and the non-linear biomass losses were a result of the MRGO-closure. The Freshwater DiD for NDVI (+0.02071 yr⁻¹, p = 0.000) was the strongest evidence of MRGO-specific recovery. Barataria Freshwater declined, whilst the Shell Beach Freshwater recovered. The results demonstrated that multi-index decadal Landsat monitoring with seasonal decomposition and full inter-sensor harmonization is essential for restoration trajectory assessment in managed coastal wetlands.

Mental models guide how people understand and respond to socio-environmental systems, but recovering their causal structure from empirical data remains difficult. Fuzzy Cognitive Maps (FCMs) represent mental models as directed graphs, yet their edge weights are typically assigned subjectively. We introduce causal inference cognitive mapping, a causal inference framework that estimates FCM edge weights directly from time-series data using Double Machine Learning (DML). Each directed edge is treated as a treatment–outcome relationship, and DML estimates causal effects, adjusting for confounding via orthogonalization and cross-fitting. Effects are scaled into signed edge weights, yielding causally interpretable adjacency matrices compatible with standard FCM analysis. We implement the framework in a Python package, causal-mm, and we validate it using synthetic datasets, achieving low bias and high sign accuracy, and then apply it to a real-world socio-environmental system with multidecadal observations, identifying dominant causal pathways across mental models.

Suspended sediment plays a critical role in river ecosystem health, nutrient transport, and water quality, while also affecting navigation infrastructure and reservoir sedimentation in regulated rivers. A sound understanding of sediment dynamics in complex river systems consisting of natural lakes and engineered navigation structures remains a critical challenge for river management and water quality assessment. This study investigates the longitudinal patterns of suspended sediment concentration (SSC) along a ~500-km reach of the Upper Mississippi River containing Lake Pepin and multiple lock-and-dam structures. In this study, we analyze remotely sensed SSC estimates from the RivSED database (2001–2019). The SSC datasets were then integrated with in situ streamflow measurements and potential soil erosion to characterize sediment supply and transport dynamics and relate with upstream contributing watershed’s attributes. Results reveal distinct sediment behavior patterns: (1) Lake Pepin functions as a significant sediment trap, creating a clear discontinuity in SSC with mean concentrations decreasing from ~25 mg/L upstream to ~13 mg/L within the lake; (2) longitudinal SSC profiles show re-establishment patterns downstream of the lake, reaching ~23 mg/L approximately 100 km below the outlet; (3) strong positive correlation (r = 0.80, R² = 0.64, p < 0.001) exists between watershed sediment export and river-reach-scale sediment fluxes. Temporal analysis across these upstream monitoring stations shows sediment export rates ranging from 10,000 to 200,000 tons/year, with notable inter-annual variability driven by discharge patterns. This research demonstrates the utility of combining a spectrum of datasets for exploring sediment dynamics in complex riverine systems. Though the current study is a case study, the study results provide crucial insights for navigation management, ecosystem health assessment, and watershed management strategies in similar settings.

Meteorological forcings under different climate scenarios exert substantial control over hydrologic-hydrological processes in watersheds and river systems. This study presents a comprehensive assessment of uncertainty in hydrologic projections by integrating a wide range of climate forcings, multiple bias-correction approaches, and several Shared Socioeconomic Pathways (SSPs). Specifically, we (i) quantify the total uncertainty in projected hydrologic responses, (ii) attribute uncertainty to individual sources, and (iii) examine how uncertainty propagates along the hydroclimatic modeling chain. The analysis is demonstrated for a range of watersheds using a fully calibrated Soil and Water Assessment Tool (SWAT) model. The hydrologic simulations are forced by outputs from thirty global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset at a spatial resolution of 0.25° (~25 km) under two SSPs. To further refine the climate inputs, a linear bias-correction method is applied to daily temperature and precipitation time series to align long-term mean monthly values during the reference period (1985–2014) with PRISM observations. A total of four bias-correction scenarios are considered: (1) original NEX-GDDP-CMIP6 data, (2) precipitation-corrected data, (3) temperature-corrected data, and (4) jointly corrected temperature and precipitation data. This framework yields four forcing scenarios for each GCM–SSP combination, resulting in a total of 240 simulations (4 × 30 GCMs × 2 SSPs) for each watershed. Streamflow changes are evaluated for the near-future period (2031-2060) and far future period (2061-2090), relative to the historical baseline (1985-2014). Changes in probability distributions and cumulative distribution functions are analyzed across climate models, bias-correction methods, and SSPs. In addition, the relative contributions of individual uncertainty sources are quantified at monthly, seasonal, and annual time scales. By systematically accounting for uncertainties arising from climate forcings, bias-correction techniques, and socioeconomic pathways, this study provides a robust characterization of the range of plausible hydrologic futures. Such uncertainty-informed streamflow projections are essential for water-resources planning, flood and drought risk management, and the development of effective long-term water-management strategies.

Intensifying hydrologic and environmental extremes are making aging dams around the world increasingly vulnerable as many of them have surpassed their design service life and are classified as high-hazard potential dams. Traditionally used deterministic dam breach analysis methods can underestimate flood risk since they do not necessarily capture an entire spectrum of possible outcomes and their associated uncertainties. This study presents an alternative probabilistic approach for dam breach analyses and flood risk assessments accommodating various modeling approaches and uncertainty quantification techniques. We integrate stochastic simulation methods with a computationally efficient flood modeling tool to enable large-ensemble analysis. We explicitly consider uncertainties in three key components: (1) breach parameters - breach geometry and timing derived from empirical prediction methods, (2) forcings - rainfall characteristics, including depth, temporal patterns, and antecedent soil moisture, and (3) downstream hydraulic conditions and hydrodynamic responses. Advanced sampling strategies, such as copula methods, are adopted to propagate uncertainties from different sources through the modeling chain while maintaining computational efficiency.The framework is demonstrated through the application to a recent Sanford Dam failure case, where we compare different breach prediction methods and evaluate their impacts on downstream flood characteristics. The deterministic breach model is first validated against observed failure characteristics and downstream flood impacts before extending to probabilistic analysis. Multivariate analysis reveals that breach formation timing characteristics exert stronger influence on peak outflow and flood wave arrival time than geometric breach parameters. Generated probabilistic flood inundation maps showing overall probability, flood depth, and timing provide critical information for emergency response and resilience planning. Initial results that probabilistic approach provides refined confidence bounds for flood risk estimates, informing decision-making with quantified uncertainty. Incorporating future projections highlights that extreme rainfall intensification could increase dam overtopping probabilities, with the magnitude depending on projection scenarios. Our study infuses evidence-based comparison of modeling approaches and supports a more realistic dam-break flood risk assessment for aging dams under non-stationary environmental conditions, informing emergency planning, and infrastructure management strategies.