scholarly journals A Budyko framework for estimating how spatial heterogeneity and lateral moisture redistribution affect average evapotranspiration rates as seen from the atmosphere

2017 ◽  
Vol 21 (1) ◽  
pp. 217-233 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
James W. Kirchner
Keyword(s):  
Spatial Heterogeneity ◽  
Land Surface ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Grid Cell ◽  
Earth System ◽  
Grid Cells ◽  
Closure Relation ◽  
System Models ◽  
Earth System Models

Abstract. Most Earth system models are based on grid-averaged soil columns that do not communicate with one another, and that average over considerable sub-grid heterogeneity in land surface properties, precipitation (P), and potential evapotranspiration (PET). These models also typically ignore topographically driven lateral redistribution of water (either as groundwater or surface flows), both within and between model grid cells. Here, we present a first attempt to quantify the effects of spatial heterogeneity and lateral redistribution on grid-cell-averaged evapotranspiration (ET) as seen from the atmosphere over heterogeneous landscapes. Our approach uses Budyko curves, as a simple model of ET as a function of atmospheric forcing by P and PET. From these Budyko curves, we derive a simple sub-grid closure relation that quantifies how spatial heterogeneity affects average ET as seen from the atmosphere. We show that averaging over sub-grid heterogeneity in P and PET, as typical Earth system models do, leads to overestimations of average ET. For a sample high-relief grid cell in the Himalayas, this overestimation bias is shown to be roughly 12 %; for adjacent lower-relief grid cells, it is substantially smaller. We use a similar approach to derive sub-grid closure relations that quantify how lateral redistribution of water could alter average ET as seen from the atmosphere. We derive expressions for the maximum possible effect of lateral redistribution on average ET, and the amount of lateral redistribution required to achieve this effect, using only estimates of P and PET in possible source and recipient locations as inputs. We show that where the aridity index P/PET increases with altitude, gravitationally driven lateral redistribution will increase average ET (and models that overlook lateral redistribution will underestimate average ET). Conversely, where the aridity index P/PET decreases with altitude, gravitationally driven lateral redistribution will decrease average ET. The effects of both sub-grid heterogeneity and lateral redistribution will be most pronounced where P is inversely correlated with PET across the landscape. Our analysis provides first-order estimates of the magnitudes of these sub-grid effects, as a guide for more detailed modeling and analysis.

scholarly journals A Budyko framework for estimating how spatial heterogeneity and lateral moisture redistribution affect average evapotranspiration rates as seen from the atmosphere

2016 ◽  
Author(s):  
Elham Rouholahnejad ◽  
James W. Kirchner
Keyword(s):  
Spatial Heterogeneity ◽  
Land Surface ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Grid Cell ◽  
Earth System ◽  
Grid Cells ◽  
Closure Relation ◽  
System Models ◽  
Earth System Models

Abstract. Most earth system models are based on grid-averaged soil columns that do not communicate with one another, and that average over considerable sub-grid heterogeneity in land surface properties, precipitation (P), and potential evapotranspiration (PET). These models also typically ignore topographically driven lateral redistribution of water (either as groundwater or surface flows), both within and between model grid cells. Here we present a first attempt to quantify the effects of spatial heterogeneity and lateral redistribution on grid-cell-averaged evapotranspiration (ET) as seen from the atmosphere over heterogeneous landscapes. Our approach uses Budyko curves, as a simple model of ET as a function of atmospheric forcing by P and PET. From these Budyko curves, we derive a simple sub-grid closure relation that quantifies how spatial heterogeneity affects average ET as seen from the atmosphere. We show that averaging over sub-grid heterogeneity in P and PET, as typical earth system models do, leads to overestimates of average ET. For a sample high-relief grid cell in the Himalaya, this overestimation bias is shown to be roughly 12 %; for adjacent lower-relief grid cells it is substantially smaller. We use a similar approach to derive sub-grid closure relations that quantify how lateral redistribution of water could alter average ET as seen from the atmosphere. We derive expressions for the maximum possible effect of lateral redistribution on average ET, and the amount of lateral redistribution required to achieve this effect, using only estimates of P and PET in possible source and recipient locations as inputs. We show that where the aridity index P/PET increases with altitude, gravitationally driven lateral redistribution will increase average ET (and models that overlook lateral redistribution will underestimate average ET). Conversely, where the aridity index P/PET decreases with altitude, gravitationally driven lateral redistribution will decrease average ET. The effects of both sub-grid heterogeneity and lateral redistribution will be most pronounced where P is inversely correlated with PET across the landscape. Our analysis provides first-order estimates of the magnitudes of these sub-grid effects, as a guide for more detailed modeling and analysis.

Leveraging unsupervised learning for optimizing the number of sub-grid tiles for land surface modeling over the Contiguous United States

2021 ◽  
Author(s):  
Laura Torres-Rojas ◽  
Noemi Vergopolan ◽  
Jonathan D. Herman ◽  
Nathaniel W. Chaney
Keyword(s):  
United States ◽  
Land Surface ◽  
Grid Cell ◽  
Earth System ◽  
Environmental Characteristics ◽  
System Models ◽  
Parameter Combination ◽  
Stable Conditions ◽  
Tile Complexity ◽  
Earth System Models

<p>The representation of land surface’s sub-grid heterogeneity in Earth System models remains a persistent challenge. The evolution of grid-cell partitioning techniques has evolved from user-defined equally sized tiles (Chen et al., 1997) to structural partition techniques based on vegetation or soil spatial distribution (Melton & Arora, 2014), and finally, to advanced clustering techniques, based on the concept of Hydrological Response Units (HRU) (Chaney et al., 2018). These sub-grid tiling schemes for Land Surface Models (LSM) have emerged as efficient and effective options to represent sub-grid heterogeneity. However, such approaches rely on an arbitrarily-defined number of tiles per macroscale grid cell with no assurance of a robust representation of heterogeneity. To address this challenge, we introduce a physically coherent approach that uses a Random Forest Model (RFM) to precompute the optimal tile configuration per macro-grid cell. An RFM is trained on a set of environmental covariates, their spatial organization features over the modeling domain (i.e., correlation lengths), and hydrological target-variables errors of several model outputs.</p><p>We assemble and run the HydroBlocks LSM for 100 tiles’ configurations for 100 domains of 0.5x0.5-degree resolution in the Contiguous United States (CONUS). The tiles’ configuration is defined by two clustering algorithm parameters and one height discretization one. From this parameter combination, 10,000 simulations emerged. For each simulation, we compiled the spatial standard deviation of specific hydrological target-variables and evaluated the tiles’ configuration convergence by comparing various multi-objective optimization methodologies to determine the optimal compromise solutions on each study domain. Preliminary results show that as the number of tiles increases, the hydrological fluxes and states converge toward stable conditions. With the optimal parameter combination set for each domain and information on the environmental characteristics, an RFM is trained to predict the optimal cluster configuration. Using this approach, we demonstrate how a reduced-order model can effectively compute a priori the appropriate tile complexity based solely on environmental characteristics.</p><p><strong>References</strong></p><p>Chaney, N. W. el al. (2018). Harnessing big data to rethink land heterogeneity in Earth system models. Hydrology and Earth System Sciences, 22(6), 3311–3330. https://doi.org/10.5194/hess-22-3311-2018</p><p>Chen, T. H. et al. (1997). Cabauw experimental results from the Project for Intercomparison of Land-Surface Parameterization Schemes. Journal of Climate, 10(6), 1194–1215. https://doi.org/10.1175/1520-0442(1997)010<1194:CERFTP>2.0.CO;2</p><p>Melton, J. R., & Arora, V. K. (2014). Sub-grid scale representation of vegetation in global land surface schemes: implications for estimation of the terrestrial carbon sink. Biogeosciences, 11, 1021–1036. https://doi.org/10.5194/bg-11-1021-2014</p>

scholarly journals Mapping local and global variability in plant trait distributions

2017 ◽  
Vol 114 (51) ◽  
pp. E10937-E10946 ◽  
Author(s):  
Ethan E. Butler ◽  
Abhirup Datta ◽  
Habacuc Flores-Moreno ◽  
Ming Chen ◽  
Kirk R. Wythers ◽  
...  
Keyword(s):  
Land Surface ◽  
Plant Traits ◽  
Grid Cell ◽  
Dry Mass ◽  
Global Scale ◽  
Leaf Nitrogen ◽  
Earth System ◽  
System Models ◽  
Plant Trait ◽  
Earth System Models

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

scholarly journals Global assessment of how averaging over land-surface heterogeneity affects modeled evapotranspiration rates

2019 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Ying Fan ◽  
James W. Kirchner
Keyword(s):  
Spatial Heterogeneity ◽  
Land Surface ◽  
Large Scale ◽  
Potential Evapotranspiration ◽  
Spatial Scales ◽  
Grid Cell ◽  
Heat Fluxes ◽  
Mechanistic Modeling ◽  
Grid Cells ◽  
Heterogeneity Bias

Abstract. The major goal of large-scale Earth System Models (ESMs) is to understand and predict global change. However, computational constraints require ESMs to operate on relatively large spatial grids (typically ~1 degree or ~100 km in size) with the result that the heterogeneity in land surface properties and processes at smaller spatial scales cannot be explicitly represented. Averaging over this spatial heterogeneity may lead to biased estimates of energy and water fluxes in ESMs. For example, evapotranspiration rates and the properties that regulate them are spatially heterogeneous at scales orders of magnitude smaller than typical ESM grid cells. Here we quantify the effects of spatial heterogeneity on grid-cell-averaged evapotranspiration (ET) rates, as seen from the atmosphere over heterogeneous landscapes across the globe. In an earlier study, we used a Budyko framework to functionally relate ET to precipitation (P) and potential evapotranspiration (PET), and used a sub-grid closure relation to quantify the effects of sub-grid heterogeneity on average ET at 1° by 1° grid cells- the scale of typical ESM. We showed that because the relationships driving ET are nonlinear, averaging over sub-grid heterogeneity in P and PET leads to overestimation of average ET. In this study, we extend that work to the globe and examine the global distribution of this bias, its scale dependence, and the underlying mechanisms. Our analysis shows that this heterogeneity bias is more pronounced in mountainous terrain, in landscapes where P is inversely correlated with PET, and in regions with temperate climates and dry summers. We also show that the magnitude of this heterogeneity bias grows on average, and expands over larger areas, as the size of the grid cell increases. Correcting for this overestimation of ET in ESMs is important for modeling the water cycle, as well as for future temperature predictions, since current overestimations of ET rates imply smaller sensible heat fluxes, and potential underestimation of dry and warm conditions in the context of climate change. Our work provides a basis for translating the heterogeneity bias into correction factors in large-scale ESMs, and highlights the regions where more detailed mechanistic modeling is needed.

scholarly journals A New Global Storage‐Area‐Depth Data Set for Modeling Reservoirs in Land Surface and Earth System Models

2018 ◽  
Vol 54 (12) ◽  
Author(s):  
Wondmagegn Yigzaw ◽  
Hong‐Yi Li ◽  
Yonas Demissie ◽  
Mohamad I. Hejazi ◽  
L. Ruby Leung ◽  
...  
Keyword(s):  
Land Surface ◽  
Earth System ◽  
Data Set ◽  
System Models ◽  
Depth Data ◽  
Storage Area ◽  
Earth System Models

Impact of improved land model depth and hydrology on climate change projections

2020 ◽  
Author(s):  
Norman Steinert ◽  
Fidel González-Rouco ◽  
Stefan Hagemann ◽  
Philipp de Vrese ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Earth System ◽  
Energy Propagation ◽  
Near Surface ◽  
Bottom Boundary ◽  
System Models ◽  
Model Component ◽  
And Storage ◽  
Earth System Models

<p>The representation of the thermal and hydrological state in the land model component of Earth System Models is crucial to have a realistic simulation of subsurface processes and the coupling between the atmo-, lito- and biosphere. There is evidence suggesting an inaccurate simulation of subsurface thermodynamics in current-generation Earth System Models, which have Land Surface Models that are too shallow. In simulations with a bottom boundary too close to the surface, the energy propagation and spatio-temporal variability of subsurface temperatures are affected. This potentially restrains the simulation of land-air interactions and subsurface phenomena, e.g. energy/moisture balance and storage capacity, freeze/thaw cycles and permafrost evolution. We introduce modifications for a deeper soil into the JSBACH soil model component of the MPI-ESM for climate projections of the 21st century. Subsurface layers are added progressively to increase the bottom boundary depth from 10m to 1400m. This leads to near-surface cooling of the soil and encourages regional terrestrial energy uptake by one order of magnitude and more. <br>The depth-changes in the soil also have implications for the hydrological regime, in which the moisture between the surface and the bedrock is sensitive to variations in the thermal regime. Additionally, we compare two different global soil parameter datasets that have major implications for the vertical distribution and availability of soil moisture and its exchange with the land surface. The implementation of supercool water and water phase changes in the soil creates a coupling between the soil thermal and hydrological regimes. In both cases of bottom boundary and water depth changes, we explore the sensitivity of JSBACH from the perspective of changes in the soil thermodynamics, energy balance and storage, as well as the effect of including freezing and thawing processes and their influence on the simulation of permafrost areas in the Northern Hemisphere high latitudes. The latter is of particular interest due to their vulnerability to long-term climate change.</p>

scholarly journals Harnessing big data to rethink land heterogeneity in Earth system models

2018 ◽  
Vol 22 (6) ◽  
pp. 3311-3330 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Marjolein H. J. Van Huijgevoort ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Paul C. D. Milly ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Grid Cell ◽  
Environmental Data ◽  
Distributed Model ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Carbon Cycles ◽  
System Models ◽  
Clustering Approach ◽  
Earth System Models

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4∘ (∼ 25 km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.

scholarly journals Two-way coupling between the sub-grid land surface and river networks in Earth system models

2020 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Laura Torres-Rojas ◽  
Noemi Vergopolan ◽  
Colby K. Fisher
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Environmental Data ◽  
Added Value ◽  
Earth System ◽  
Field Scale ◽  
System Models ◽  
Routing Scheme ◽  
Earth System Models ◽  
River Routing

Abstract. Over the past decade, there has been appreciable progress towards modeling the water, energy, and carbon cycles at field-scales (10–100 m) over continental to global extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid tiles, or Hydrologic Response Units (HRUs), learned via a hierarchical clustering approach from available global high-resolution environmental data. However, until now, there has yet to be a macroscale river routing approach that is able to leverage HydroBlocks' approach to sub-grid heterogeneity, thus limiting the added value of field-scale land surface modeling in Earth System Models (e.g., riparian zone dynamics, irrigation from surface water, and interactive floodplains). This paper introduces a novel dynamic river routing scheme in HydroBlocks that is intertwined with the modeled field-scale land surface heterogeneity. The primary features of the routing scheme include: 1) the fine-scale river network of each macroscale grid cell's is derived from very high resolution (

scholarly journals Use of various remote sensing land cover products for PFT mapping over Siberia

2013 ◽  
Vol 6 (1) ◽  
pp. 255-296
Author(s):  
C. Ottlé ◽  
J. Lescure ◽  
F. Maignan ◽  
B. Poulter ◽  
T. Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Tree Cover ◽  
Surface Model ◽  
Earth System ◽  
System Models ◽  
Earth System Models

Abstract. High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover datasets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing earth system models. Earth system models also require specific land cover classification systems based on plant functional types, rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover datasets against one another and with auxiliary data to identify key uncertainties that contribute to variability in Plant Functional Type (PFT) classifications that would introduce errors in earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, continuous vegetation fields (MODIS-VCF) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover dataset, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFTs maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT dataset, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground-truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to better represent the water and carbon cycles in northern latitudes. Updated land cover datasets are critical for improving and maintaining the relevance of earth system models for assessing climate and human impacts on biogeochemistry and biophysics. The new PFT map at 5 km scale is available for download from the PANGAEA website, at: doi:10.1594/PANGAEA.810709.

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