A5023805749- Hydrology and Watershed Management Studies
- Climate variability and models
- Hydrology and Drought Analysis
- Flood Risk Assessment and Management
- Water resources management and optimization
- Meteorological Phenomena and Simulations
- Hydrological Forecasting Using AI
- Tropical and Extratropical Cyclones Research
- Water-Energy-Food Nexus Studies
- Tree-ring climate responses
- Reservoir Engineering and Simulation Methods
- Plant Water Relations and Carbon Dynamics
- Energy Load and Power Forecasting
- Hydrology and Sediment Transport Processes
- Soil and Water Nutrient Dynamics
- Soil erosion and sediment transport
- Climate change impacts on agriculture
- Electric Power System Optimization
- Computational Physics and Python Applications
- Integrated Energy Systems Optimization
- Fish Ecology and Management Studies
- Oceanographic and Atmospheric Processes
- Atmospheric and Environmental Gas Dynamics
- Coastal and Marine Dynamics
- Climate Change Policy and Economics
Cornell University
2016-2025
ORCID
2024
Hollister (United States)
2021
Kongju National University
2019
Columbia University
2015-2016
University of Massachusetts Amherst
2010-2015
Amherst College
2014
New Orleans Public Library
2010-2011
Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim identify policies mapping future observations actions—in other words, framing climate as an optimal control problem. This paper uses paradigm review classify dynamic according their approaches characterization, policy structure, solution methods. We propose a set research gaps opportunities in this...
Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between numerous ecosystem services provided by Earth's largest and most biodiverse river basin. These are spatially variable, hence collective impacts newly built depend strongly on their configuration. We use multiobjective optimization to identify portfolios that simultaneously minimize flow, connectivity, sediment transport, fish diversity, greenhouse gas emissions while...
Abstract This study examines whether deep learning models can produce reliable future projections of streamflow under warming. We train a regional long short‐term memory network (LSTM) to daily in 15 watersheds California and develop three process (HYMOD, SAC‐SMA, VIC) as benchmarks. force all with scenarios warming assess their hydrologic response, including shifts the hydrograph total runoff ratio. All show shift more winter runoff, reduced summer decline ratio due increased...
A multivariate, multisite daily weather generator is presented for use in decision‐centric vulnerability assessments under climate change. The tool envisioned to be useful a wide range of socioeconomic and biophysical systems sensitive different aspects variability proposed stochastic model has several components, including (1) wavelet decomposition coupled an autoregressive account structured, low‐frequency oscillations, (2) Markov chain k‐nearest‐neighbor (KNN) resampling scheme simulate...
Abstract This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether uncertainties a significant source uncertainty relative to other sources such as variability change (2) statistical characterization from lumped, conceptual model is sufficient account for in process. To investigate these questions, an ensemble simulations propagated through models then reservoir simulation delimit...
Abstract. Deep learning (DL) rainfall–runoff models outperform conceptual, process-based in a range of applications. However, it remains unclear whether DL can produce physically plausible projections streamflow under climate change. We investigate this question through sensitivity analysis modeled responses to increases temperature and potential evapotranspiration (PET), with other meteorological variables left unchanged. Previous research has shown that temperature-based PET methods...
Abstract Many water planning and operation decisions are affected by climate uncertainty. Given concerns about the effects of uncertainty on outcomes long‐term decisions, many planners seek adaptation alternatives that robust given a wide range possible futures. However, there is no standardized paradigm for quantifying robustness in sector. This study uses new framework assessing impact future change supply systems defines demonstrates metric robustness. The based space over which an...
The implications of climate change and the potential nonstationarity hydrologic record necessitate innovative approaches to water management. This study presents a novel adaptation strategy for reservoir management under nonstationary conditions. Seasonal forecasts real‐option instrument allow operations that dynamically adapt an evolving record. System operating policies are conditioned on seasonal account year‐to‐year variability real option is established hedge against risk associated...
This paper presents a decision-scaling based framework to determine whether one or more preselected planning alternatives for multiobjective water-resources system are robust variety of nonstationary hydroclimatic conditions and modeling uncertainties. The methodology is advanced beyond previous applications with an efficient procedure select realizations climate variability Bayesian methods assess the effects hydrologic uncertainty. Monte Carlo simulations used identify long-term that...
Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve simulation accuracy understand prediction at interior ungaged sites sparsely gaged watershed. The is conducted using version HYMOD (HYMOD_DS) applied Kabul River basin. Several experiments are benefits costs associated with different choices, including (1) whether multisite data should be used simultaneously or stepwise manner during fitting,...
Abstract Approaches for probability density function (pdf) development of future climate often assume that different models provide independent information, despite model similarities stem from a common genealogy (models with shared code or developed at the same institution). Here we use an ensemble projections Coupled Model Intercomparison Project Phase 5 to develop probabilistic and without accounting intermodel correlations, seven regions across United States. We then pdfs estimate...
Abstract Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for vulnerability assessments that simulate based on patterns regional atmospheric flow (i.e., regimes) conditioned global‐scale features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain regime simulation; (2) block bootstrapping Gaussian copula multivariate, multisite...
Abstract Short‐term weather forecasts have the potential to improve reservoir operations for both flood control and water supply objectives, especially in regions currently relying on fixed seasonal pools mitigate risk. The successful development of forecast‐based policies should integrate uncertainty from modern forecast products create unambiguous rules that can be tested out‐of‐sample periods. This study investigates such operating efficiency while maintaining protection, combining...
The cascade of uncertainty that underscores climate impact assessments regional hydrology undermines their value for long‐term water resources planning and management. This study presents a statistical framework quantifies propagates the uncertainties hydrologic model response through projections future streamflow under change. Different sources are accounted using Bayesian modeling. distribution residuals is formally characterized to quantify predictive skill, Markov chain Monte Carlo...
Abstract Warm, moist, and longitudinally confined tropical air masses are being linked to some of the most extreme precipitation flooding events in midlatitudes. The interannual frequency intensity such atmospheric rivers (ARs), or moisture exports (TMEs), connected risk areas where convergence occurs. This study presents a nonstationary, regional analysis extremes Northern California that is conditioned on variability TMEs entering region. Parameters multisite peaks‐over‐threshold model...
Abstract We develop multioutput neural network models to predict flow‐duration curves (FDCs) in 9,203 ungaged locations the Southeastern United States for six decades between 1950 and 2009. The model architecture contains multiple response variables output layer that correspond individual quantiles along FDC. During training, predictions are made each quantile, a combined loss function is used back propagation parameter updating. accounts covariance generates physically consistent outputs...
Abstract There is a chronic disconnection among purely probabilistic flood frequency analysis of hazards, risks, and hydrological mechanisms, which hamper our ability to assess future impacts. We present vulnerability‐based approach estimating riverine risk that accommodates more direct linkage between decision‐relevant metrics the dominant mechanisms cause flooding. adapt conventional peaks‐over‐threshold (POT) framework be used with extreme precipitation from different climate processes...
Abstract Policy search methods provide a heuristic mapping between observations and decisions have been widely used in reservoir control studies. However, recent studies observed tendency for policy to overfit the hydrologic data training, particularly sequence of flood drought events. This technical note develops an extension bootstrap aggregation (bagging) cross‐validation techniques, inspired by machine learning literature, improve performance on out‐of‐sample hydrological sequences. We...
Abstract Deterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for risk (HRM). The stochastic component of DWMs is often ignored practice, leading to a systematic bias extreme events. Since traditional HRM struggle account anthropogenic change, there need convert into (SWMs) use HRM. A DWM can be converted an SWM using post‐processing (pp) approach add error the predictions....
This study is the first of a two-part series presenting novel weather regime-based stochastic generator to support bottom-up climate vulnerability assessments water systems in California. In Part 1 this series, we present details model development and validation. The based on identification simulation regimes, or large-scale patterns atmospheric flow, which are then used condition local, daily at 6 km resolution across state. We conduct thorough validation baseline, 1000-year evaluate its...