scholarly journals Evaluation of Drydown Processes in Global Land Surface and Hydrological Models Using Flux Tower Evapotranspiration

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 356 ◽  
Author(s):  
Alberto Martínez-de la Torre ◽  
Eleanor Blyth ◽  
Emma Robinson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Vegetation Type ◽  
Specific Model ◽  
Rainfall Event ◽  
Rate Of Change ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Flux Tower ◽  
Surface Models

A key aspect of the land surface response to the atmosphere is how quickly it dries after a rainfall event. It is key because it will determine the intensity and speed of the propagation of drought and also affects the atmospheric state through changes in the surface heat exchanges. Here, we test the theory that this response can be studied as an inherent property of the land surface that is unchanging over time unless the above- and below-ground structures change. This is important as a drydown metric can be used to evaluate a landscape and its response to atmospheric drivers in models used in coupled land–atmosphere mode when the forcing is often not commensurate with the actual atmosphere. We explore whether the speed of drying of a land unit can be quantified and how this can be used to evaluate models. We use the most direct observation of drying: the rate of change of evapotranspiration after a rainfall event using eddy-covariance observations, or commonly referred to as flux tower data. We analyse the data and find that the drydown timescale is characteristic of different land cover types, then we use that to evaluate a suite of global hydrological and land surface models. We show that, at the site level, the data suggest that evapotranspiration decay timescales are longer for trees than for grasslands. The studied model’s accuracy to capture the site drydown timescales depends on the specific model, the site, and the vegetation cover representation. A more robust metric is obtained by grouping the modeled data by vegetation type and, using this, we find that land surface models capture the characteristic timescale difference between trees and grasslands, found using flux data, better than large-scale hydrological models. We thus conclude that the drydown metric has value in understanding land–atmosphere interactions and model evaluation.

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

2015 ◽  
Vol 12 (24) ◽  
pp. 7503-7518 ◽  
Author(s):  
M. G. De Kauwe ◽  
S.-X. Zhou ◽  
B. E. Medlyn ◽  
A. J. Pitman ◽  
Y.-P. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Soil Water Potential ◽  
Abrupt Onset ◽  
Future Climate Change ◽  
Land Surface Models ◽  
Drought Sensitivity ◽  
Coupled Climate Models ◽  
Surface Models

Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.

scholarly journals Comparison of different evaporation estimates over the African continent

2014 ◽  
Vol 18 (1) ◽  
pp. 193-212 ◽  
Author(s):  
P. Trambauer ◽  
E. Dutra ◽  
S. Maskey ◽  
M. Werner ◽  
F. Pappenberger ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Model ◽  
Hydrological Models ◽  
African Continent ◽  
Land Surface Models ◽  
Continental Scale ◽  
Actual Evaporation ◽  
Balance Approach ◽  
Surface Models ◽  
Energy Balance Approach

Abstract. Evaporation is a key process in the water cycle with implications ranging, inter alia, from water management to weather forecast and climate change assessments. The estimation of continental evaporation fluxes is complex and typically relies on continental-scale hydrological models or land-surface models. However, it appears that most global or continental-scale hydrological models underestimate evaporative fluxes in some regions of Africa, and as a result overestimate stream flow. Other studies suggest that land-surface models may overestimate evaporative fluxes. In this study, we computed actual evaporation for the African continent using a continental version of the global hydrological model PCR-GLOBWB, which is based on a water balance approach. Results are compared with other independently computed evaporation products: the evaporation results from the ECMWF reanalysis ERA-Interim and ERA-Land (both based on the energy balance approach), the MOD16 evaporation product, and the GLEAM product. Three other alternative versions of the PCR-GLOBWB hydrological model were also considered. This resulted in eight products of actual evaporation, which were compared in distinct regions of the African continent spanning different climatic regimes. Annual totals, spatial patterns and seasonality were studied and compared through visual inspection and statistical methods. The comparison shows that the representation of irrigation areas has an insignificant contribution to the actual evaporation at a continental scale with a 0.5° spatial resolution when averaged over the defined regions. The choice of meteorological forcing data has a larger effect on the evaporation results, especially in the case of the precipitation input as different precipitation input resulted in significantly different evaporation in some of the studied regions. ERA-Interim evaporation is generally the highest of the selected products followed by ERA-Land evaporation. In some regions, the satellite-based products (GLEAM and MOD16) show a different seasonal behaviour compared to the other products. The results from this study contribute to a better understanding of the suitability and the differences between products in each climatic region. Through an improved understanding of the causes of differences between these products and their uncertainty, this study provides information to improve the quality of evaporation products for the African continent and, consequently, leads to improved water resources assessments at regional scale.

scholarly journals Multimodel Estimate of the Global Terrestrial Water Balance: Setup and First Results

2011 ◽  
Vol 12 (5) ◽  
pp. 869-884 ◽  
Author(s):  
Ingjerd Haddeland ◽  
Douglas B. Clark ◽  
Wietse Franssen ◽  
Fulco Ludwig ◽  
Frank Voß ◽  
...  
Keyword(s):  
Water Balance ◽  
Land Surface ◽  
Water Model ◽  
Hydrological Models ◽  
Model Intercomparison ◽  
Land Surface Models ◽  
Surface Models ◽  
Terrestrial Water ◽  
Intercomparison Project ◽  
Energy Balance Approach

Abstract Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

scholarly journals Do land surface models need to include differential plant species responses to drought? Examining model predictions across a latitudinal gradient in Europe

2015 ◽  
Vol 12 (15) ◽  
pp. 12349-12393 ◽  
Author(s):  
M. G. De Kauwe ◽  
S.-X. Zhou ◽  
B. E. Medlyn ◽  
A. J. Pitman ◽  
Y.-P. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Latitudinal Gradient ◽  
Climate Models ◽  
Soil Water Potential ◽  
Abrupt Onset ◽  
Future Climate Change ◽  
Land Surface Models ◽  
Drought Sensitivity ◽  
Surface Models

Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models, realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the northernmost sites, and low drought sensitivity at the southernmost sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.

scholarly journals Representation of Water Management in Hydrological and Land Surface Models

2019 ◽  
Author(s):  
Fuad Yassin ◽  
Saman Razavi ◽  
Mohamed Elshamy ◽  
Bruce Davison ◽  
Gonzalo Sapriza-Azuri ◽  
...  
Keyword(s):  
Case Studies ◽  
Land Surface ◽  
Reservoir Operation ◽  
Large Scale ◽  
Model Parameters ◽  
Land Surface Models ◽  
Operation Model ◽  
Reservoir Model ◽  
Simulation Accuracy ◽  
Surface Models

Abstract. Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise linear relationships between reservoir storage, inflow, and release, to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a generalized parameterization that requires a relatively limited amount of data; and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH modelling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both of the parameter identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operation on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.

scholarly journals The effect of climate on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socio-economic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought, and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices of soil moisture, runoff, and streamflow from an ensemble of global hydrological models forced by a consistent meteorological dataset. Drought propagation is strongly related to climate, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 297 in-situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 20 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

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.

scholarly journals Inferring past land-use induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

2012 ◽  
Vol 9 (9) ◽  
pp. 12505-12542
Author(s):  
J. P. Boisier ◽  
N. de Noblet-Ducoudré ◽  
P. Ciais
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Type ◽  
Satellite Observations ◽  
Land Surface Models ◽  
Historical Changes ◽  
Surface Models ◽  
Past Land Use

Abstract. Cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land-use induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the magnitude of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We have derived monthly albedo climatologies for croplands and four other land-cover types from MODIS satellite observations. We have then estimated the changes in surface albedo since preindustrial times by combining these climatologies with the land-cover maps of 1870 and 1992 used by modelers in the context of the LUCID intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter and 2% in summer between 1870 and 1992 over areas that have experienced intense deforestation in the northern temperate regions. The MODIS-based reconstructions of historical changes in surface albedo were then compared to those simulated by the various models participating to LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the reconstructions, that is larger increases in winter than in summer driven by the presence of snow. However, individual models show significant differences with the satellite-based reconstructions, despite the fact that land-cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how land-surface models parameterize albedo. Another reason, of secondary importance, results from differences in the simulated snowpack. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point to major deficiencies within the models; we therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate global land-surface models.

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