Kristen M. Whitney

Hydroclimatic extremes are intensifying, escalating risks to ecosystems, infrastructure, and vulnerable communities. Rapid technological innovation and scientific discovery offer powerful tools to address these challenges, yet a gap remains between hydroclimate knowledge production and its use in decision‐making. Bridging this divide requires democratizing hydroclimate science through expanded participation in research design and implementation, as well as in the distribution of recognition, resources, and decision power. Democratizing science can foster outcomes that are (a) more credible, through transparency and integration of decision‐maker perspectives; (b) more just, through fair distribution of research opportunities, resources, recognition, and decision power; and (c) more resilient, by strengthening the capacity of communities and institutions to adapt, learn, and recover from climate pressures. Drawing on insights from the 2024 Global Energy and Water EXchanges (GEWEX) Early Career Researcher Workshop, which gathered participants from 19 countries across disciplines, we outline pathways toward more democratic hydroclimate science grounded in diverse participation and shared leadership. We highlight three priorities: building trust through co‐production, improving the usability of hydroclimate information, and promoting responsible research and innovation in emerging technologies. Case studies illustrate how early‐ and mid‐career researchers and practitioners are advancing these priorities and modeling practices that connect innovation with equity. Achieving lasting and effective change requires research and funding institutions to recognize and support collaborative leadership, and to value relevance and accountability alongside technical excellence. Strengthening such democratic frameworks can help ensure that hydroclimate science advances more just research practices and supports the resilience of the communities it serves.

Hydroclimatic extremes are intensifying, escalating risks to ecosystems, infrastructure, and vulnerable communities. Rapid technological innovation and scientific discovery offer powerful tools to address these challenges, yet a gap remains between hydroclimate knowledge production and its use in decision-making. Bridging this divide requires democratizing hydroclimate science through expanded participation in research design and implementation, as well as in the distribution of recognition, resources, and decision power. Democratizing science can foster outcomes that are (1) more credible, through transparency and integration of decision-maker perspectives; (2) more just, through fair distribution of research opportunities, resources, recognition, and decision power; and (3) more resilient, by strengthening the capacity of communities and institutions to adapt, learn, and recover from climate pressures. Drawing on insights from the 2024 Global Energy and Water EXchanges (GEWEX) Early Career Researcher Workshop, which gathered participants from 19 countries and across disciplines, we outline pathways toward more democratic hydroclimate science grounded in diverse participation and shared leadership. We highlight three priorities: building trust through co-production, improving the usability of hydroclimate information, and promoting ethical use of emerging technologies. Case studies illustrate how early- and mid-career researchers and practitioners are advancing these priorities and modeling practices that connect innovation with equity. Achieving lasting and effective change requires research and funding institutions to recognize and support collaborative leadership, and to value relevance and accountability alongside technical excellence. Strengthening such democratic frameworks can help ensure that hydroclimate science advances more just research practices and supports the resilience of the communities it serves.

Snow reanalyses that combine process based models and remote sensing observations of snow provide estimates of seasonal snow water equivalent (SWE) evolution that surpass the accuracies of traditional modeling approaches. However, snow reanalyses are only available over smaller subregions and sometimes use computationally expensive modeling approaches. We investigate whether 1 km‐resolution and daily SWE from a popular reanalysis could be learned by connecting only trusted meteorological fields (multidecadal precipitation patterns and daily air temperature) and remotely sensed snow cover using a deep learning model. Relative to point observations of SWE evolution in the western United States, the deep learning model was able to reproduce the spatial and temporal evolution estimated by the snow reanalysis. Further, we found that the deep learning model was efficient and could be expanded geographically to estimate SWE evolution in the European Alps, demonstrating a high average coefficient of correlation (0.81) and low peak‐SWE bias (<1%) versus point estimates of SWE in seasonally snowy locations, with no statistical difference between regions at different elevations and with different forest cover. This study demonstrates how deep learning approaches could be used to mine connections between daily SWE evolution, snow cover remote sensing, and limited meteorological information to generate and expand the geographical extent of fine‐resolution historical snow estimates in complex terrains.

Accurate characterization of gridded meteorological distributions in complex terrain—especially relationships between meteorological fields and altitude—is essential for simulating snowpack dynamics. This is challenging due to sparse long-term observations and strong spatial variability in near-surface meteorology. This study evaluated elevation-based dynamic lapse rate corrections—which account for local, hourly variations in temperature, pressure, humidity, and longwave radiation—against the commonly assumed static lapse rate of −6.5°C km ⁻¹ in a 1-km resolution land surface model. We examined snow water equivalent (SWE), snow depth (SD), snow cover (SC), and 2-m air temperature (T2) across western U.S. coastal mountains. Both correction methods improved T2 and snow simulations relative to those without correction, with similarly broad improvements over the simulation period (June 2006–December 2020). Dynamic correction led to marginally broader snow improvements in some regions relative to no correction—for example, in high-elevation Upper Cascade leeward regions, SWE root-mean-square error improved across 91% of the area with dynamic correction, compared to 89% with static correction. While overall improvement differences between methods were small for the full simulation period, dynamic correction demonstrated clearer advantages during an extreme snow drought year. SC improvements were less pronounced, but dynamic correction better detected snow during the drought year, while static correction performed slightly better during a surplus year. Although future climate scenarios were not modeled, the increasing prevalence of snow droughts highlights the value of this approach for improving snowpack simulations under a warming climate. Significance Statement: Accurate snowpack predictions are essential for managing water resources in snow-dependent regions like the western United States, where snowmelt supports year-round supply. This study showed that applying elevation-based dynamic lapse rate corrections to downscaled meteorological data can significantly improve simulations of air temperature, snow water equivalent, snow depth, and snow cover. The dynamic lapse rate approach captured local, hourly variability through a computationally efficient estimation from gridded air temperature data. Notably, simulations using dynamic corrections outperformed those using a static lapse rate during an extreme snow drought year. As snow drought risks increase, these findings suggest that incorporating dynamic lapse rate corrections into land surface models can improve snowpack predictions and better inform water resource planning.

In a warming climate, wildfires are becoming increasingly common, especially in semi‐arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post‐fire recovery of hydrological processes to return to pre‐fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post‐fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire‐induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post‐fire.

Since 1980, the U.S. has experienced 391 climate disaster events that each caused over $1 billion in losses. Droughts account for 13% of these disasters, with total losses over $360 billion. Accelerated climate change and development in areas vulnerable to climate impacts are escalating these water security risks, requiring urgent shifts in scientific research towards more actionable information for decision-making and climate resilience. A key challenge to developing science-based solutions for these events is creating robust data and tools that provide relevant policy and water management information that are easy to access and interpret, transparent in their development and use, and tailored for stakeholder needs. We present three of our ongoing interdisciplinary research efforts leveraging advancements in land surface modeling (LSM) and Earth observations (EOs) to address water security challenges across North America. First, NASA’s Land Information System team is developing phase three of the North American Land Data Assimilation System (NLDAS-3). NLDAS is a crucial LSM environment supporting water resource management, drought monitoring, and other uses. Building on stakeholder feedback and progress in data assimilation (DA), NLDAS-3 will provide more timely, robust, and accessible information for water resource operations and research. Next, we highlight a collaborative effort between university researchers and water resource managers for long-term planning in the Colorado River Basin. This multi-phase work resulted in future hydrologic scenarios that were incorporated into drought shortage negotiations, a user-centered web tool with interactive analyses of future hydrology scenarios under forest disturbance and climate uncertainties, and ongoing development of a hydrologic monitoring and early warning forecast system. Finally, we detail efforts to disentangle human and climate influences on agricultural drought in the Western U.S. This work integrates LSM, DA, and data-driven analyses to provide metrics for enhanced drought monitoring and resilience, extendable to other regions. Through these examples, we will discuss lessons learned, challenges, and opportunities to inform environmental research seeking similar actionable solutions relevant across sectors and globally.

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for simulating snowpack dynamics. This is challenging at spatial resolutions smaller than common gridded meteorological datasets (e.g., resolutions smaller than 5 to 250 km) due to sparse long-term temperature measurements at those resolutions and local factors like cool air pooling and inversions. Near-surface air temperatures ( T _a ) are often assumed to decrease with elevation at a constant rate of 6.5°C km ^-1 , leading to significant model errors in snow evolution and other key processes. This study evaluated the impact of local dynamical adjustments to downscaled T _a on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization land surface model. Forcings were derived from remote sensing and reanalysis precipitation products and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric products at the downscaled 1-km resolution. Hourly lapse rates at each grid cell were calculated by applying linear regression to T _a and elevation from neighbor grids (one grid length in the x or y direction) at the native MERRA-2 resolution and applied to the downscaled 1-km T _a product. We assessed the impact on simulated snow cover and depth across simulations forced with the downscaled T _a (1) without lapse rate correction, (2) corrected with a static lapse rate (6.5°C km ^-1 ), and (3) corrected with the dynamic hourly lapse rate. We evaluated model skill improvement with dynamic or static lapse rate correction, and no correction against satellite-derived products. Both lapse rate correction methods led to similar improvements on average, relative to no correction. However, dynamic lapse rate correction showed more pronounced improvements in simulating perennial snowpacks at mid-elevations and in deficit years, indicating the method can better resolve heterogeneous snowpack conditions that are key for water resource management.

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for the accurate simulation of snowpack dynamics. This becomes increasingly difficult at spatial resolutions smaller than common gridded meteorological forcing datasets due to the sparsity of long-term temperature measurements and the influence of local factors like cool air pooling and inversions. Near-surface air temperatures (Ta) are often assumed to decrease with elevation at a constant rate of 6.5oC km-1, which could lead to large model errors in snow evolution and other processes key to snow hydrology, water resource management, and other applications. This study evaluates the impact of local dynamical adjustments to downscaled Ta on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization (NoahMP) land surface model. Forcings are derived from remote sensing and reanalysis precipitation products and the (Modern-Era Retrospective Analysis for Research and Applications, version 2) MERRA-2 atmospheric products (including Ta) at the downscaled 1-km resolution. Hourly lapse rates at each grid cell are calculated by applying linear regression to Ta and elevation from surrounding grid cells (within one grid lengths in the x or y direction) at the Ta native MERRA-2 resolution and applied to the downscaled 1-km Ta product. We will present the impact on simulated snow water equivalent, snow cover, and snow depth across simulations forced with the downscaled Ta (1) without lapse rate correction, (2) corrected with a constant lapse rate (6.5oC km-1), and (3) corrected with the dynamic hourly lapse rate. Results will be compared against remote sensing-based products.

Snow reanalyses that combine process-based models and remote sensing observations of snow provide estimates of seasonal snow water equivalent (SWE) evolution that surpass the accuracies of traditional modeling approaches. However, snow reanalyses are only available over smaller subregions, and sometimes use computationally expensive modeling approaches. We investigate whether 1 km-resolution and daily SWE from a popular reanalysis could be learned by connecting only trusted meteorological fields (multidecadal precipitation patterns and daily air temperature) and remotely sensed snow cover using a deep learning model. Relative to point observations of SWE evolution in the western United States, the lightweight deep learning model was able to reproduce the spatial and temporal evolution estimated by the snow reanalysis. Further, we found that the deep learning model could be trained in the western United States and then reused to estimate SWE evolution in the European Alps, demonstrating a high average coefficient of correlation (0.81) and low peak-SWE bias (< 1%) versus point estimates of SWE. SWE from the deep learning model also outperformed SWE estimates from physically based land surface simulations, capturing elevation-driven impacts on SWE spatial heterogeneity and interannual differences in seasonal SWE magnitudes important for water resources, climate regulation, and local ecology. This study demonstrates how deep learning approaches could be used to mine connections between daily SWE evolution, snow cover remote sensing, and limited meteorological information to generate and expand the geographical extent of fine-resolution historical snow estimates in complex terrains.

Vegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.