scholarly journals HESS Opinions: A perspective on different approaches to determine the contribution of transpiration to the surface moisture fluxes

2014 ◽  
Vol 11 (3) ◽  
pp. 2583-2612 ◽  
Author(s):  
S. J. Sutanto ◽  
B. van den Hurk ◽  
G. Hoffmann ◽  
J. Wenninger ◽  
P. A. Dirmeyer ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Cycle ◽  
Climate Modeling ◽  
Global Climate ◽  
Climatic Conditions ◽  
Data Sets ◽  
Land Surface Models ◽  
Surface Models ◽  
Moisture Fluxes ◽  
Evaporative Flux

Abstract. Current techniques to disentangle the total evaporative flux from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total moisture fluxes, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based vs. non isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the analysis of partitioning are discussed including their limitations, and explanations of possible discrepancies between the methods are briefly discussed. We conclude that every method has its own uncertainties and these may lead to a high bias in the results, e.g. instruments inaccuracy and error, some assumptions used in analyses, parameters calibration. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors, however, models tend to produce lower transpiration fractions. The relatively low transpiration fractions in current state of the art land surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope enabled land-surface and global climate modeling studies allow the evaluation of the parameterization of land surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

scholarly journals HESS Opinions "A perspective on isotope versus non-isotope approaches to determine the contribution of transpiration to total evaporation"

2014 ◽  
Vol 18 (8) ◽  
pp. 2815-2827 ◽  
Author(s):  
S. J. Sutanto ◽  
B. van den Hurk ◽  
P. A. Dirmeyer ◽  
S. I. Seneviratne ◽  
T. Röckmann ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Cycle ◽  
Climate Modeling ◽  
Global Climate ◽  
Climatic Conditions ◽  
Data Sets ◽  
Land Surface Models ◽  
Total Evaporation ◽  
Surface Models ◽  
Methodological Aspects

Abstract. Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

scholarly journals Climate Impacts of Making Evapotranspiration in the Community Land Model (CLM3) Consistent with the Simple Biosphere Model (SiB)

2009 ◽  
Vol 10 (2) ◽  
pp. 374-394 ◽  
Author(s):  
Peter J. Lawrence ◽  
Thomas N. Chase
Keyword(s):  
Climate Sensitivity ◽  
Land Surface ◽  
Climate Modeling ◽  
Land Surface Models ◽  
Surface Hydrology ◽  
Community Land Model ◽  
Wide Range ◽  
Surface Models ◽  
Biosphere Model ◽  
Surface Parameterizations

Abstract In recent climate sensitivity experiments with the Community Climate System Model, version 3 (CCSM3), a wide range of studies have found that the Community Land Model, version 3 (CLM3), simulates mean global evapotranspiration with low contributions from transpiration (15%), and high contributions from soil and canopy evaporation (47% and 38%, respectively). This evapotranspiration partitioning is inconsistent with the consensus of other land surface models used in GCMs. To understand the high soil and canopy evaporation and the low transpiration observed in the CLM3, select individual components of the land surface parameterizations that control transpiration, canopy and soil evaporation, and soil hydrology are compared against the equivalent parameterizations used in the Simple Biosphere Model, versions 2 and 3 (SiB2 and SiB3), and against more recent developments with CLM. The findings of these investigations are used to develop new parameterizations for CLM3 that would reproduce the functional dynamics of land surface processes found in SiB and other alternative land surface parameterizations. Global climate sensitivity experiments are performed with the new land surface parameterizations to assess how the new SiB, consistent CLM land surface parameterizations, influence the surface energy balance, hydrology, and atmospheric fluxes in CLM3, and through that the larger-scale climate modeled in CCSM3. It is found that the new parameterizations enable CLM to simulate evapotranspiration partitioning consistently with the multimodel average of other land surface models used in GCMs, as evaluated by Dirmeyer et al. (2005). The changes in surface fluxes also resulted in a number of improvements in the simulation of precipitation and near-surface air temperature in CCSM3. The new model is fully coupled in the CCSM3 framework, allowing a wide range of climate modeling investigations without the surface hydrology issues found in the current CLM3 model. This provides a substantially more robust framework for performing climate modeling experiments investigating the influence of land cover change and surface hydrology in CLM and CCSM than the existing CLM3 parameterizations. The study also shows that changes in land surface hydrology have global scale impacts on model climatology.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

scholarly journals Towards a public, standardized, diagnostic benchmarking system for land surface models

2012 ◽  
Vol 5 (3) ◽  
pp. 819-827 ◽  
Author(s):  
G. Abramowitz
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Land Surface Models ◽  
Surface Models ◽  
Definition Of ◽  
Benchmarking System

Abstract. This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS) is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

scholarly journals Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

2021 ◽  
Author(s):  
Evan Baker ◽  
Anna Harper ◽  
Daniel Williamson ◽  
Peter Challenor
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Global Climate ◽  
Computational Cost ◽  
Land Surface Model ◽  
Parameter Tuning ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.

scholarly journals Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

2020 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Massimiliano Zappa ◽  
James W. Kirchner
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Aggregation Bias ◽  
Surface Model ◽  
Land Surface Models ◽  
Evaporation Model ◽  
Surface Models ◽  
Large Scale Land

Abstract. Evapotranspiration (ET) influences land-climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedbacks to large-scale hydrological processes and its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called aggregation bias that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale-dependence of this aggregation bias and show that it can lead to overestimation of daily ET fluxes by as much as 21 % averaged over the whole country. We show how our approach can be used to identify the dominant drivers of aggregation bias, and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

2007 ◽  
Vol 164 (8-9) ◽  
pp. 1789-1809 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Margaret A. LeMone ◽  
Fei Chen ◽  
Souleymane Fall
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Land Surface Models ◽  
Land Cover Data ◽  
Surface Models

scholarly journals Reconciling Land–Ocean Moisture Transport Variability in Reanalyses with P − ET in Observationally Driven Land Surface Models

2016 ◽  
Vol 29 (23) ◽  
pp. 8625-8646 ◽  
Author(s):  
Franklin R. Robertson ◽  
Michael G. Bosilovich ◽  
Jason B. Roberts
Keyword(s):  
Land Surface ◽  
Moisture Transport ◽  
Global Climate ◽  
Principal Component ◽  
Rain Gauge ◽  
Atmospheric Moisture ◽  
Land Surface Models ◽  
Dynamic Component ◽  
Near Surface ◽  
Surface Models

Abstract Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC = P − ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern. Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P − ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979–2012 ranging from 0.07 to −0.03 mm day−1 decade−1 are reduced by the adjustments to 0.016 mm day−1 decade−1, much closer to the LSM P − ET estimate (0.007 mm day−1 decade−1). Neither is significant at the 90% level. ENSO-related modulation of VMFC and P − ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.

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