About

Dennis Lettenmaier is a Distinguished Professor in the Department of Geography at UCLA Samueli School of Engineering. His research interests include hydrologic modeling and prediction, hydrology-climate interactions, and hydrologic change.

Research topics

• Environmental science
• Geology
• Computer Science
• Climatology
• Physical geography
• Water resource management
• Ecology
• Geomorphology
• Geography
• Soil science
• Environmental engineering
• Biology
• Remote sensing

Selected publications

• Evaluating Fire Indices for Fire Ignition and Spread Risk in the Western United States

  2025-11-12

  articleOpen access

  Wildfires are complex events that are challenging to model. Researchers and land managers often rely on fire indices as a proxy of fire risk in operational forecasts and future projections. However, in the scientific literature, the choice of which fire index to use is often arbitrary and not tied to specific fire behaviors. Here, we provide a comprehensive evaluation of 15 hydroclimate variables and fire indices based on their ability to represent daily ignition probability and spread rate in the western United States. Hydroclimate variables like saturation vapor pressure (E_S) and vapor pressure deficit (VPD) perform the best and most consistently at representing fire ignition probability across regions and vegetation types. Water-budget-based indices like 1000-hour dead fuel moisture (FM1000) and the Keetch-Byram Drought Index (KBDI) are best at representing ignition probability in forests. In contrast, indices like the Hot-Dry-Windy index (HDW) best represent fire spread rates, especially for non-forest fires, while indices from the National Fire Danger Rating System are best for forest fire spread. Partitioning the skill of variables/indices into contributions from capturing seasonality and anomalies, ignitions are distributed based on the seasonal cycles, while seasonality and anomalies are comparably important for spread rate. We demonstrate that computational complexity does not always result in better fire variables/indices, and that distinct sets of variables/indices are needed to represent different aspects of fire behavior, emphasizing the importance of making careful and justifiable selections of fire variables/indices.

  PublisherOA PDFDOI

• Decelerating Response of Western US Runoff to Shrinking Snowpacks

  Geophysical Research Letters · 2025-05-10 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Climate warming threatens snowmelt‐derived water supplies in the western US (WUS) by reducing snowfall and snowmelt runoff, yet future rates of these declines remain highly uncertain in an evolving climate. Here, we analyze historical data, land surface model warming experiments, and climate projections across three major WUS river basins. We find that runoff loss become less sensitive to warming as snowpack shrinks, stemming from reduced snowmelt‐radiation feedback, a consequence of smaller snow‐cover changes and shifts in snowmelt timing to lower‐energy periods. Near‐linear projected warming with time (IPCC SSP245) exhibit a stable, possibly decelerating decline in runoff ratios. Although decelerating runoff declines do not eliminate broader water‐management challenges under continued warming, our findings complement the view that snowmelt‐radiation feedback drives runoff decline by highlighting the negative feedback from a shrinking snowpack on runoff warming sensitivity. Our findings should facilitate more comprehensive future water supply assessments in snow‐affected regions.

  PublisherOA PDFDOI

• Monitoring reservoir storage using SWOT satellite observations and reservoir operation models

  2025-06-26

  preprintOpen accessSenior author

  Reservoirs play a critical role in water management, yet comprehensive and real-time observations of reservoir storage change remain limited, especially outside the U.S. Observations of two reservoir-related attributes, Water Surface Elevation (WSE) and Surface Area (SA), which can be used to calculate reservoir storage change, are often desynchronized, hindering precise estimation. The recently launched (December 2022) Surface Water and Ocean Topography (SWOT) satellite mission (science observations began August 2023) uses a cutting-edge interferometer to provide global, simultaneous WSE and SA maps of Earth’s water bodies, which can be leveraged to estimate reservoir storage change. We evaluate the accuracy of SWOT-based estimates of reservoir storage change in comparison with in-situ-based observations for 12 reservoirs in the Western U.S., of which four are in California, four are in the Upper Colorado River Basin (UCRB), and four are in the Columbia River Basin (CRB). Our results show that SWOT produces WSE measurements with less than 20 cm MAE (taken across all 12 reservoirs and 19 months of observations) and storage estimates with MAE less than 10%. Model-based reservoir storage estimates (constrained by SWOT observations) can fill temporal gaps accurately and efficiently, even if fewer than one-quarter of SWOT observations are valid. Our results motivate further study of the potential for estimation of reservoir storage change where few or no in-situ observations are available via assimilation of SWOT observations into a reservoir simulation model.

  PublisherOA PDFDOI

• California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

  Proceedings of the National Academy of Sciences · 2024-04-29 · 18 citations

  articleOpen access

  The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges-which we define as the 20 y April 1 SWE event-are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

  PublisherOA PDFDOI

• Understanding Terrestrial Water and Carbon Cycles and Their Interactions Using Integrated SMAP Soil Moisture and OCO‐2 SIF Observations and Land Surface Models

  Journal of Geophysical Research Atmospheres · 2024-09-12 · 1 citations

  article

  Abstract Recently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (K s ) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (V max ) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

  PublisherDOI

• Increasing atmospheric evaporative demand across the Tibetan plateau and implications for surface water resources

  iScience · 2024-12-18 · 5 citations

  articleOpen access

  would decrease streamflow by 5%-9%. Our findings provide a deeper understanding of the accelerating hydroclimatic changes and their impact on surface water resources in the Tibetan Plateau.

  PublisherDOI

• Improving runoff simulation in the Western United States with Noah-MP and VIC models

  Hydrology and earth system sciences · 2024-07-16 · 9 citations

  articleOpen accessCorresponding

  Abstract. Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts.

  PublisherDOI

• The Role of Antecedent Winter Soil Moisture Carryover on Spring Runoff Predictability in Snow-Influenced Western U.S. Catchments

  Journal of Hydrometeorology · 2024-08-20 · 3 citations

  articleSenior author

  Abstract Spring runoff is critical to agricultural, industrial, and municipal water supply as well as environmental uses in the western United States. Although spring runoff in this region has long been predicted based on late winter and early spring montane snow water storage, the role of soil moisture carryover from the previous winter is less understood and utilized in forecasts. We quantify the relationship between antecedent winter soil moisture and spring runoff for 85 unmanaged catchments in the western United States. Using a regression-based approach, we estimate the proportional reduction in error in seasonal runoff forecasts associated with soil moisture. We classify the catchments into two regimes: interior (cold and relatively dry) and maritime (warmer and wetter), based on seasonal variations in soil moisture. Soil moisture generally is relatively unchanged through the winter period in interior catchments, whereas it increases (mostly gradually, but occasionally rapidly) in maritime catchments. We find that winter temperature dominates soil moisture variability in both types of catchments. We find two patterns in the predictability of spring runoff. First, including antecedent winter soil moisture as a predictor enhances forecast accuracy and variance explanation in spring runoff for both interior and maritime catchments, particularly and with greater significance in interior catchments. Second, spring runoff prediction skill from antecedent winter soil moisture increases with elevation. Overall, we find strong evidence that the use of antecedent soil moisture can improve spring runoff forecast skill for catchments in the western United States. Significance Statement Spring runoff forecasts are crucial for water supply in the western United States, but typically exclude information about soil moisture despite its key role in the water balance. Our study explores how and why incorporating information about winter’s soil moisture levels affects spring runoff forecasts across 85 catchments in the western United States. We find that doing so can produce modest improvements in the prediction skill of spring runoff for both interior and maritime catchments but is particularly noticeable in interior catchments and at higher elevations where temperatures are lower. Overall, our research strongly supports the idea that using information about soil moisture from the previous winter can improve accuracy in spring runoff forecasts for the western United States.

  PublisherDOI

• Did Historical Storms Used in Probable Maximum Precipitation (PMP) Estimation Reach Maximum Efficiency? A Large Model Ensemble Approach

  Journal of Geophysical Research Atmospheres · 2024-12-16

  articleOpen accessSenior author

  Abstract The flood that would result from the greatest depth of precipitation “meteorologically possible”, or Probable Maximum Precipitation (PMP) is used in the design of dam spillways and other high‐risk structures. Historically, PMP has been estimated by scaling depth‐area‐duration relationships obtained from severe historical storms. Over the last decade, numerical weather prediction models have been used to instead simulate precipitation resulting from the addition of atmospheric moisture (called relative humidity maximization, or RHM). Despite the major improvement this represents, model‐based PMP relies on a key assumption, which this paper re‐evaluates in Oroville dam's Feather River watershed (California). Model‐based as well as earlier procedures assume that severe historical storms achieved maximum efficiency (moisture conversion to precipitation) and only maximize moisture. We examine the most severe storms found in the CESM2‐LE global climate model ensemble, which constitutes a very large artificial record (∼1,150 years) in comparison with the historical record, to understand the upper bounds of storm efficiency and precipitation. We downscale the 10 most severe CESM2‐LE storms (by precipitation totals), and identify key storm attributes (vertical motion, convection and convergence) that control precipitation efficiency. In comparison with historical storms, we find that CESM‐LE storms can have 30% higher efficiency and 32% higher precipitation, but produce only 8% higher PMP estimates, suggesting some convergence of model ensemble and historical storms in terms of PMP. The understanding of the controls on storm efficiency that our work provides leverages past work focused on moisture and supports the development of more reliable PMP storm amplification guidance.

  PublisherOA PDFDOI

• A Physically Constrained Model-Based Moisture Amplification Approach for Probable Maximum Precipitation (PMP) Estimation

  Journal of Hydrometeorology · 2024-09-01 · 6 citations

  articleSenior author

  Abstract The flood that would result from the greatest depth of precipitation “meteorologically possible” or probable maximum precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling precipitation totals obtained from severe historical storms, assuming more moisture could have been available. Over the last decade, numerical weather prediction models have been used to instead predict precipitation resulting from the addition of moisture in the simulations [called relative humidity maximization (RHM)]. Despite the major improvement they represent, two important barriers limit the applicability of model-based methods: first, the existence of different moisture amplification approaches that produce different estimates, and second, the need for a regional implementation of those techniques that were developed for individual basins. Taking Oregon’s mountainous coastal watersheds affected by atmospheric river storms as a case study, we develop a moisture amplification approach, which we call relative humidity perturbation (RHP) ratio that is physically constrained by historical maximum moisture. We find that both the magnitude and location of moisture increase matter and that RHP ratio produces lower amplified precipitation totals but storms that are more consistent with observed events than other methods such as RHM. We additionally find that it is possible to position a storm near-optimally over several basins in a homogeneous area, enabling the production of regional PMP estimates. The understanding we develop of the control moisture exerts on PMP-magnitude precipitation totals allows us to develop a more physically based methodology for the development of reliable storm amplification guidance.

  PublisherDOI

Recent grants

• Collaborative Research: Understanding Change in the Climate and Hydrology of the Arctic Land Region: Synthesizing the Results of the ARCSS Fresh Water Initiative Projects

  NSF · $300k · 2006–2010

• Collaborative Research: A Land surface Model Hind- Cast for the Terrestrial Arctic Drainage System

  NSF · $455k · 2003–2008

Frequent coauthors

• Eric F. Wood

  107 shared

• Bart Nijssen

  94 shared

• David M. Mocko

  Goddard Space Flight Center

  80 shared

• Lu Su

  University of California, San Diego

  76 shared

• Andrew W. Wood

  U.S. National Science Foundation

  73 shared

• Michael Barlage

  NSF National Center for Atmospheric Research

  73 shared

• C. D. Peters-Lidard

  70 shared

• Pierre Gentine

  66 shared

Education

• Ph.D., Hydrology

  University of California, Los Angeles

  1983

• M.S., Hydrology

  University of California, Los Angeles

  1978

• B.S., Hydrology

  University of California, Los Angeles

  1976