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 Comparison of different evaporation estimates over the African continent

2013 ◽  
Vol 10 (7) ◽  
pp. 8421-8465 ◽  
Author(s):  
P. Trambauer ◽  
E. Dutra ◽  
S. Maskey ◽  
M. Werner ◽  
F. Pappenberger ◽  
...  
Keyword(s):  
Land Surface ◽  
Hydrological Model ◽  
Regional Scale ◽  
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 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 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. 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. The satellite based products (GLEAM and MOD16) do not show regular behaviour when compared to the other products, though this depends on the region and the season considered. The results from this study allow for a better understanding of 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 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 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 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 MPR 1.0: A stand-alone Multiscale Parameter Regionalization Tool for Improved Parameter Estimation of Land Surface Models

2021 ◽  
Author(s):  
Robert Schweppe ◽  
Stephan Thober ◽  
Matthias Kelbling ◽  
Rohini Kumar ◽  
Sabine Attinger ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Parameter Space ◽  
Land Surface ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Land Surface Models ◽  
Continental Scale ◽  
Environmental Models ◽  
Surface Models

Abstract. Distributed environmental models such as land surface models (LSM) require model parameters in each spatial modelling unit (e.g. grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimen- sionality of parameter space in these models is to use regularization techniques. One such highly efficient technique is the Multiscale Parameter Regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of netCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model mHM (www.ufz.de/mhm). By using this tool for the generation of continental-scale soil hydraulic param- eters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 % in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.

scholarly journals Predicting Surface Runoff from Catchment to Large Region

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hongxia Li ◽  
Yongqiang Zhang ◽  
Xinyao Zhou
Keyword(s):  
Surface Runoff ◽  
Land Surface ◽  
Hydrological Model ◽  
Large Region ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Research Fields ◽  
Surface Models ◽  
Distributed Hydrological Models

Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1) modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2) parameterizing hydrological models in ungauged catchments, (3) improving hydrological model structure, and (4) using new remote sensing precipitation data.

scholarly journals Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

2014 ◽  
Vol 7 (1) ◽  
pp. 1197-1244
Author(s):  
A. Valade ◽  
P. Ciais ◽  
N. Vuichard ◽  
N. Viovy ◽  
N. Huth ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Sugar Cane ◽  
Land Surface ◽  
Cane Yield ◽  
Land Surface Models ◽  
Continental Scale ◽  
Surface Models ◽  
Input Parameters ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Agro-Land Surface Models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, a particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of Agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS' phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte-Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugar cane cultivation in Australia and Brazil. Ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting climate-mediated different sensitivities of modeled sugar cane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

scholarly journals Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

2014 ◽  
Vol 7 (3) ◽  
pp. 1225-1245 ◽  
Author(s):  
A. Valade ◽  
P. Ciais ◽  
N. Vuichard ◽  
N. Viovy ◽  
A. Caubel ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Land Surface ◽  
Surface Model ◽  
Land Surface Models ◽  
Continental Scale ◽  
Surface Models ◽  
Input Parameters ◽  
Sugarcane Yield ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil–vegetation–atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE–STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE–STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE–STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models.

scholarly journals Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite

2011 ◽  
Vol 8 (5) ◽  
pp. 9113-9171 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Land Surface Models ◽  
Area Index ◽  
Continental Scale ◽  
Surface Models ◽  
Vegetation Parameters ◽  
The Impact

Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.

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