Evapotranspiration across the rain–snow transition in a semi‐arid watershed

Kraft, Maggi; McNamara, J. P.

doi:10.1002/hyp.14519

Cited by 5 publications

(5 citation statements)

References 52 publications

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“…During wet seasons (e.g., from March to May), the actual ET (ET a ) is assumed to be equal to potential ET (ET p ), and we use ET a measured from MODIS at a nearby site (Kraft & McNamara, 2022; see data points in Figure 2e) to determine the crop factor K c by fitting the calculated ET p during the wet season to ET a . The calculated daily potential ET (ET p ) at the catchment is plotted in Figure 2e.…”

Section: Hydrometeorological Datamentioning

confidence: 99%

“…The reference ET 0 in the Penman‐Monteith equation is expressed as

\mathrm{E}{\mathrm{T}}_{0}=\frac{0.408{\Delta }\left({R}_{\mathrm{n}}-G\right)+\gamma \frac{900}{T+273}{u}_{2}\left({e}_{\mathrm{s}}-{e}_{\mathrm{a}}\right)}{{\Delta }+\gamma \left(1+0.34{u}_{2}\right)},

where R n is the net radiation at the crop surface, G is the soil heat flux density, T is the mean daily air temperature 2 m above the ground, u 2 is the wind speed 2 m above the ground, e s is the saturation vapor pressure, e a is the actual vapor pressure, Δ is the slope of the vapor pressure curve, and γ is psychrometric constant. The reference ET (ET 0 ) can be linked to potential ET (ET p ) by a crop coefficient K c , expressed as

{\text{ET}}_{\mathrm{p}}={K}_{\mathrm{c}}{\text{ET}}_{0}.

During wet seasons (e.g., from March to May), the actual ET (ET a ) is assumed to be equal to potential ET (ET p ), and we use ET a measured from MODIS at a nearby site (Kraft & McNamara, 2022; see data points in Figure 2e) to determine the crop factor K c by fitting the calculated ET p during the wet season to ET a . The calculated daily potential ET (ET p ) at the catchment is plotted in Figure 2e.…”

Section: Study Site and Field Datamentioning

confidence: 99%

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Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

Chen,

Niu,

Mendieta

et al. 2023

Water Resources Research

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Hydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models.

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Section: Hydrometeorological Datamentioning

confidence: 99%

“…The reference ET 0 in the Penman‐Monteith equation is expressed as

\mathrm{E}{\mathrm{T}}_{0}=\frac{0.408{\Delta }\left({R}_{\mathrm{n}}-G\right)+\gamma \frac{900}{T+273}{u}_{2}\left({e}_{\mathrm{s}}-{e}_{\mathrm{a}}\right)}{{\Delta }+\gamma \left(1+0.34{u}_{2}\right)},

{\text{ET}}_{\mathrm{p}}={K}_{\mathrm{c}}{\text{ET}}_{0}.

Section: Study Site and Field Datamentioning

confidence: 99%

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

Chen,

Niu,

Mendieta

et al. 2023

Water Resources Research

Self Cite

View full text Add to dashboard Cite

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“…As ET is further limited by the amount of available moisture in the soil, and hence related to precipitation input as well as to seasonal snow, we compared differences in ET to differences in precipitation input itself, as well as to differences in total snow water. We were able to link differences in simulated ET between model configurations with regards to meteorological forcing data to differences in total snow water, highlighting the importance of rain-snow transitions for ET calculations and further underlining results from Kraft and McNamara (2022). We were, however, not able to directly link differences between model configurations to differences in precipitation input.…”

Section: Discussionmentioning

confidence: 73%

Regionally optimized high resolution input datasets enhance the representation of snow cover and ecophysiological processes in CLM5

Malle,

Mazzotti,

Karger

et al. 2023

Preprint

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Abstract. Land surface processes, crucial for exchanging carbon, nitrogen, water, and energy between the atmosphere and terrestrial Earth, significantly impact the climate system. Many of these processes vary considerably at small spatial and temporal scales, in particular in mountainous terrain and complex topography. To examine the impact of spatial resolution and quality of input data on modeled land surface processes, we conducted simulations using the Community Land Model 5 (CLM5) at different resolutions and based on a range of input datasets over the spatial extent of Switzerland. Using high-resolution meteorological forcing and land-use data, we found that increased resolution not only improved the representation of snow cover in CLM5 (up to 52 % enhancement) but also propagated through the model, directly affecting gross primary productivity and evapotranspiration. These findings highlight the significance of high spatial resolution and high-confidence input datasets in land surface models, enabling better quantification and constraint of process uncertainties. They have profound implications for climate impact studies. As improvements were observed across the cascade of dependencies in the land surface model, high spatial resolution as well as high-quality forcing data becomes necessary for accurately capturing the impacts of recent climate change. This study further highlights the utility of multi-resolution modeling experiments when aiming to improve process-based representation of variables in land surface models. By embracing high-resolution modeling, we can enhance our understanding of Earth's systems and their responses to climate change.

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“…With changing melt rates, it is unclear how changes in the timing and rate of snowmelt will affect the amount of melt that goes to ET. Previous work suggests that lower snow accumulation and earlier melt may lead to earlier increases in ET and the start of the growing season (Cooper et al, 2020; Hamlet et al, 2007; Kraft & McNamara, 2022). If this is the case, more snowmelt may go to ET and less to groundwater recharge and annual runoff (Barnhart et al, 2016; Christensen et al, 2021; Goulden & Bales, 2014).…”

Section: Introductionmentioning

confidence: 97%

Alignment between water inputs and vegetation green‐up reduces next year's runoff efficiency

Newcomb,

Van Kirk,

Godsey

et al. 2024

Hydrological Processes

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In the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow‐vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow‐covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre‐drought relationships over‐predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre‐drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation‐Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring.

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Evapotranspiration across the rain–snow transition in a semi‐arid watershed

Cited by 5 publications

References 52 publications

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

Regionally optimized high resolution input datasets enhance the representation of snow cover and ecophysiological processes in CLM5

Alignment between water inputs and vegetation green‐up reduces next year's runoff efficiency

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