About

Daniel Wright is the Arno Lenz Memorial Associate Professor in Civil and Environmental Engineering at the University of Wisconsin-Madison. His research, teaching, and outreach focus on extreme rainfall, floods, and how both are influenced by meteorology, urbanization, and climate change. His work includes satellite and ground-based rainfall remote sensing, rainfall and flood processes at urban and regional scales, computationally-intensive simulation for probabilistic natural hazards risk assessment, statistical, stochastic, and physical hydrology, and modernization of stormwater and flood management theory and practice. He has been supported through numerous research grants and has received several awards, including the NSF CAREER Award, the American Geophysical Union Early Career Award, and the Arno Lenz Memorial Professorship. He is a co-founder of the Infrastructure Working Group within the Wisconsin Initiative on Climate Change Impacts and is a co-author on the 5th National Climate Assessment. Additionally, he is the founding site director of the Center for Interdisciplinary Research on Convective Storms, a partnership involving multiple institutions and organizations.

Research topics

• Geography
• Computer Science
• Environmental science
• Artificial Intelligence
• Operations research
• Geology
• Atmospheric sciences
• Risk analysis (engineering)
• Physics
• Engineering
• Meteorology
• Remote sensing

Selected publications

• Designing an inundation monitoring and real-time urban flood forecasting system: a synthetic study

  Journal of Hydrology · 2026-04-18

  articleOpen access

  • A unified framework integrates flood modeling, ML, and sparse sensors for urban flood forecasting. • Stochastic rainfall model generates diverse precipitation scenarios for design. • Novel method optimizes sensor placement for accurate inundation map reconstruction. • ML model combines rainfall & observations, outperforming traditional models by 20–50% Urban flooding due to extreme precipitation poses an increasing threat to cities, necessitating accurate, timely inundation predictions. This study presents a novel machine learning-based framework for urban inundation forecasting, addressing the challenges in computational efficiency and predictive accuracy. The approach integrates an ensemble of space–time varying rainfall scenarios, a high-fidelity flood model, and a machine learning surrogate model. Stochastically generated synthetic rainfall scenarios include extreme precipitation with various return periods. They are inputs to an urban flood model tRIBS-Urban that produces an ensemble of synthetic inundation fields that are synthetic “observations”. We combine Principal Component Analysis and Karhunen-Loève Expansion to optimize flood sensor placement and accurately reconstruct inundation maps from “observations” at a few locations. Application of a spatio-temporal vision transformer (ViT) as a surrogate model for tRIBS-Urban demonstrates excellent performance in capturing spatiotemporal patterns of inundation depth, with real-time forecasts produced in a few seconds for 1–12 h lead times, showing high accuracy (with Root Mean Square Error approximately 0.15 m and Kling-Gupta Efficiency greater than 0.75) and minimal cumulative error. By incorporating both rainfall and inundation observations, the inundation forecasts improved by 20–50% compared to those generated by the model without considering inundation data. This framework therefore shows significant potential to enhance flood prediction capabilities, offering a promising solution for improving urban flood resilience. Key points: ● A novel machine learning framework integrates flood physical modeling and sparse sensor data for forecasting urban flood inundation. ● A stochastic rainfall model offers diverse spatiotemporal precipitation scenarios, addressing limitations in infrastructure design standards. ● A novel method optimizes flood sensor placement to accurately reconstruct inundation maps from (synthetic) sparse observations. ● A machine learning model combines rainfall and observations to ensure high accuracy in flood prediction, outperforming traditional models by 20–50%.

  PublisherDOI

• Impacts of rainfall spatiotemporal variability on pressurized flow conditions in urban drainage systems

  Journal of Hydrology · 2025-07-26 · 1 citations

  article
  PublisherDOI

• Convergent latitudinal erosion of circadian systems in a rapidly diversifying order of fishes

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-31

  preprintOpen access1st author

  Abstract Biological clocks enable organisms to anticipate cyclical environmental changes. Some habitats, such as those at high latitudes or deep sea, experience seasonally diminished or absent diel cues upon which species entrain their circadian rhythms. Fishes of the order Perciformes have rapidly diversified and adapted to these arrhythmic ecosystems, raising the possibility that evolutionary modifications to their circadian biology contributes to their success as one of the most species-rich orders of vertebrates. Here, we used a comparative genomic approach to investigate patterns of biological clock gene loss and circadian rhythms across 33 perciform and six outgroup species. We found both widespread and lineage-specific loss and relaxed selection in core clock genes, particularly in the convergently evolving polar and deep-sea Notothenioidei and Cottioidei suborders. This trend of circadian gene loss was significantly correlated with latitude, with higher-latitude species showing greater loss. Whether these losses and relaxed selection lead to changes in circadian rhythms is unknown for most perciforms. To address this, we performed metabolic phenotyping on three notothenioid species and found no circadian metabolic oscillations during the late austral fall, including in the sub-Antarctic Eleginops maclovinus , sister to the Antarctic adaptive radiation. We propose that diminished reliance on endogenous biological clocks may be an adaptive feature that facilitates the survival and diversification of perciform fishes in polar and arrhythmic environments.

  PublisherOA PDFDOI

• Advancing Ensemble Streamflow Prediction Through Satellite‐Based Precipitation Product and Model Parameter Uncertainty Quantification

  Journal of Advances in Modeling Earth Systems · 2025-10-01

  articleOpen access

  Abstract Satellite‐based quantitative precipitation estimates (QPE), such as NASA's Integrated Multi‐satellitE Retrievals for GPM (IMERG), provide easily accessible continental‐to‐global precipitation forcings for flood prediction and other hydrologic applications. Nevertheless, when used in hydrologic prediction, uncertainty in satellite‐based QPE often leads to significant bias. This forcing uncertainty is further blended with other error sources, including process representation, parameter values, and their interactions. The identification and decoupling of these uncertainties can enhance our understanding of their respective impacts, thereby improving hydrologic prediction. Addressing this issue worldwide is challenging, however, largely due to the scarcity of precipitation ground truth and complex uncertainty interactions. Therefore, we propose an efficient uncertainty quantification framework for ensemble streamflow prediction, which keeps different uncertainty sources separable through hierarchical Bayesian inference. Satellite‐based QPE uncertainty is characterized by a novel near‐realtime quasi‐global satellite‐only ensemble precipitation data set (STREAM‐Sat), which is completely independent of ground‐based precipitation measurements. Model parameter uncertainty in a distributed physics‐based hydrologic model is inferred by an Iterative Ensemble Smoother (IES). To illustrate the impact and limitations of precipitation uncertainty, we compared ensemble streamflow predictions driven by both model parameter and satellite precipitation uncertainties and ensemble streamflow predictions driven by model parameter uncertainty and deterministic QPE. We demonstrate that the quantification of satellite‐based QPE uncertainty notably improves the accuracy and reliability of near‐realtime streamflow predictions in data scarce regions. This study also lays a foundation for satellite‐based streamflow prediction in ungauged regions.

  PublisherOA PDFDOI

• STREAM‐Sat: A Novel Near‐Realtime Quasi‐Global Satellite‐Only Ensemble Precipitation Dataset

  Water Resources Research · 2025-03-01 · 1 citations

  articleOpen access

  Abstract Satellite‐based precipitation observations can provide near‐global coverage with high spatiotemporal resolution in near‐realtime. Their utility, however, is hindered by oftentimes large uncertainties that vary substantially in space and time. This problem is particularly pronounced in regions which lack dense ground‐based measurements to quantify or reduce such uncertainty. Since this uncertainty is, by definition, a random process, probabilistic representations are needed to advance their operational application. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in numerical weather and climate prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of observational uncertainties and the scarcity of “ground truth” to characterize them. In this study, we attempt to resolve these two challenges and propose the first quasi‐global (covering all continental land masses within 50°N‐50°S) satellite‐only ensemble precipitation dataset (STREAM‐Sat), derived entirely from NASA's Integrated Multi‐SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM's radar‐radiometer combined precipitation product (2B‐CMB). No ground‐based measurements are used to generate STREAM‐Sat, and it is suitable for near‐realtime use without extending the 4‐hr latency and 0.1°, 30‐min spatiotemporal resolution of IMERG Early. We compare STREAM‐Sat against several precipitation datasets, including global satellite‐based, rain gage‐based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the comparison datasets in all respects, it does hold relative advantages due to its unique combination of accuracy, resolution, rainfall spatiotemporal structure, latency, and utility in hydrologic and hazard applications.

  PublisherOA PDFDOI

• The Needs, Challenges, and Priorities for Advancing Global Flood Research

  Wiley Interdisciplinary Reviews Water · 2025-05-01 · 11 citations

  articleOpen access

  ABSTRACT In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization‐Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co‐production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer‐term adaptation to alleviate flood risks in society.

  PublisherDOI

• Ensemble-Based Uncertainty Quantification Can Improve Satellite Precipitation for Hydrologic Prediction

  2025-09-08

  preprintOpen access1st authorCorresponding

  Large uncertainties have hindered the uptake of satellite precipitation data (SPD) in hydrologic applications. While this problem could be addressed by quantifying precipitation uncertainty and propagating it through hydrologic models, there has been little consensus on what form of uncertainty information is needed. In this commentary, we define precipitation error and uncertainty in the context of hydrologic prediction. We also describe the hydrologic conditions in which this uncertainty matters. We argue that the path forward is the development of ensemble representations of satellite precipitation uncertainty. Unlike other approaches, ensemble-based depictions can be readily integrated into existing hydrologic modeling frameworks.

  PublisherOA PDFDOI

• Uncertainty Quantification for Deep Learning-based Streamflow Prediction

  2025-12-26

  articleOpen access

  Accurate and timely flood warnings are essential for reducing flooding risks. Achieving this objective requires both accurate data and careful model parameterization. The absence of high-resolution ground-based precipitation observations in many locations leaves relatively low-accuracy satellite-based precipitation products as one of the few alternatives. At the same time, advancements in deep learning-based flood prediction have led to improvements in both accuracy and computational efficiency. Despite the progress made by integrating satellite-based precipitation data and deep learning-based hydrologic model, the quantification of uncertainty—stemming from both data and model—remains largely unexamined. This gap results in less reliable predictions, as overfitting and limited explainability are common concerns in deep learning. In this paper, uncertainties arising from satellite-based precipitation inputs, streamflow observations, limited training samples, and model parameters are jointly addressed. The basin-averaged precipitation uncertainty is represented by a parametric probabilistic distribution function, whose parameters can be inferred from the deep learning-based hydrologic model—without the need for higher-accuracy precipitation “ground truth”. Streamflow observation uncertainty is involved by a multiplicative Gaussian noise. Limited training sample uncertainty is quantified using a mixture density network, while uncertainty in neural network parameters is captured through variational inference. Our results demonstrate that this integrated approach to uncertainty quantification enhances both the prediction accuracy and the explainability of ensemble streamflow predictions. It provides a foundation for “uncertainty-aware” deep learning-based streamflow prediction.

  PublisherOA PDFDOI

• Morphing sub-daily rainfall fields based on temperature shifts to project future changes in rainfall extremes

  2025-03-14

  preprintOpen access

  Understanding how the space-time properties of extreme rainfall shifts due to climate change is essential for assessing risks in water-related hazards. However, future sub-daily rainfall fields, which are the main trigger of pluvial and flash floods, are not readily available for most locations and many climate change scenarios, challenging the assessment of future hazards and risks. An alternative solution to running computationally expensive convection-permitting climate models to obtain future short-duration rainfall fields is morphing recorded rainfall fields considering temperature as a driving factor. Here, we suggest using a Gamma-based spatial quantile mapping (GSQM) method with temperature as a covariate to project an archive of plausible future rainfall fields that can be used to assess future changes in extreme rainfall frequency. Combined with a stochastic storm transposition (SST) method, which can estimate rainfall frequency for arbitrary spatial scales based on gridded rainfall, future changes in regional rainfall extremes can be efficiently projected. Using Beijing as a case study, we employ 22 years of 1 km2 hourly rainfall and hourly air temperature data to demonstrate the validity of the GSQM-SST approach. First, the observed scalings governing changes in rainfall fields with temperature have been explored across various intensities of rainfall. Then, those scalings are used to morph the rainfall fields’ intensities, areas, and spatial coefficients of variation. Finally, future extremes of 2- to 100-year return levels under several warming scenarios are estimated by integrating the GSQM and SST methods.

  PublisherDOI

• City-scale evaluation of urban ecohydrologic processes on surface energy balances, heat, and humidity

  Urban Climate · 2025-09-01

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  PublisherDOI

Recent grants

• CAREER: A Dynamic-Stochastic Approach to Rainfall and Flood Frequency Analysis Across Scales

  NSF · $508k · 2018–2025

Frequent coauthors

• Kamala London

  University of Toledo

  28 shared

• Samantha H. Hartke

  U.S. National Science Foundation

  22 shared

• Guo Yu

  Desert Research Institute

  21 shared

• Elin M. Skagerberg

  Red Cross University College of Nursing

  18 shared

• James A. Smith

  Flinders University

  16 shared

• Amina Memon

  Brighton and Sussex Medical School

  14 shared

• Zhe Li

  Hainan Meteorology Administration

  14 shared

• Mary Lynn Baeck

  Princeton University

  13 shared

Labs

• Center for Interdisciplinary Research on Convective StormsPI

Awards & honors

• 2024 University of Wisconsin-Madison, Arno Lenz Memorial Pro…
• 2023 University of Wisconsin-Madison, Vilas Faculty Early Ca…
• 2021 Natural Hazards Section, American Geophysical Union, Ea…
• 2020 University of Wisconsin-Madison, Exceptional Service Aw…
• 2020 U.S. Bureau of Reclamation, Science and Technology Proj…