A harmonized global land evaporation dataset from model-based products covering 1980–2017

Abstract. Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25∘. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the Amazon Plain in South America and Congo Basin in central Africa and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to demonstrating a good performance, the REA method also successfully converged the models based on the reliability of the inputs. The resulting REA data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

Download Full-text

A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980–2017

10.5194/essd-2021-61 ◽

2021 ◽

Author(s):

Jiao Lu ◽

Guojie Wang ◽

Tiexi Chen ◽

Shijie Li ◽

Daniel Fiifi Tawia Hagan ◽

...

Keyword(s):

Central Africa ◽

Model Performance ◽

Congo Basin ◽

Data Sets ◽

Observation Data ◽

Ensemble Averaging ◽

Data Merging ◽

Multiple Data ◽

Multiple Data Sets ◽

High Consistency

Abstract. Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).

Download Full-text

Assessing the Potential of Satellite-Retrieved and Global Land Data Assimilation System-Simulated Soil Moisture Datasets for Soil Moisture Mapping in Bangladesh

10.1002/essoar.10509937.1 ◽

2022 ◽

Author(s):

Siam Maksud ◽

Alvee Bin Hannan ◽

Nasreen Jahan

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Global Land ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

Download Full-text

Simulation of river discharge in ungauged catchments by forcing GLDAS products to a hydrological model (a case study: Polroud basin, Iran)

Water Science & Technology Water Supply ◽

10.2166/ws.2019.160 ◽

2019 ◽

Vol 20 (1) ◽

pp. 277-286

Author(s):

Hadis Pakdel Khasmakhi ◽

Majid Vazifedoust ◽

Safar Marofi ◽

Abdollah Taheri Tizro

Keyword(s):

River Discharge ◽

Good Alternative ◽

Observation Data ◽

Ungauged Catchments ◽

Discharge Data ◽

The North ◽

Temperature And Precipitation ◽

North Of Iran ◽

Land Data Assimilation ◽

Land Data Assimilation System

Abstract Due to unavailability of sufficient discharge data for many rivers, an appropriate approach is required to provide accurate data for estimating discharge in ungauged watersheds. In this study, Global Land Data Assimilation System (GLDAS) datasets were integrated with Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to simulate the outlet river discharge in Polroud watershed, located in the North of Iran. Temperature and precipitation products generated by GLDAS were calibrated using regression analysis based on observation data for the period of 2004–2006. Then, river discharge was simulated by using HEC-HMS based on two different datasets (GLDAS meteorological product and gauged data) on the scale of the basin for the same period. The results clearly indicated that the forcing of GLDAS data into HEC-HMS model leads to promising results with acceptable correlation with observed data. Although, in comparison with direct GLDAS runoff products, the proposed approach improved the accuracy of river discharge, the problem of underestimation still reduces the expected accuracy. Because of global accessibility, GLDAS datasets would be a good alternative in ungauged or poorly gauged watersheds.

Download Full-text

Wind Speed Seasonality in a Brazilian Amazon-Savanna Region from the Global Land Data Assimilation System

Ciência e Natura ◽

10.5902/2179460x41092 ◽

2020 ◽

Vol 42 ◽

pp. e12

Author(s):

Leonardo Henrique De Sá Rodrigues ◽

Marcos Aurélio Alves Freitas ◽

Luan Victor Soares Pereira ◽

Brunna Caroline Correia Dias ◽

Vicente Marques Silvino ◽

...

Keyword(s):

Wind Speed ◽

Data Assimilation ◽

Wind Energy ◽

Linear Regression Analysis ◽

The State ◽

Global Land ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

The objective of this study was to develop a methodology for the use of remote sensing data for the planning of wind energy projects in Maranhão. Monthly wind speed and precipitation data from 2000 to 2016 were used. Initially, wind velocity data were processed using the principal component analysis (PCA) technique. Next, the grouping technique known as k-means was used. Finally, a linear regression analysis was performed with the objective of identifying the parameters to be used in the validation of the data estimated by the Global Land Data Assimilation System (GLDAS) base against the data measured by the meteorological stations. Four homogeneous zones were identified; the zone with the highest values of monthly average wind speeds is in the northern region of the state on the coast. The period of greatest intensity of the winds was identified to be in the months of October and November. The lowest values of precipitation were observed during these months. The analyses carried out by this study show a favorable scenario for the production of wind energy in the state of Maranhão.

Download Full-text

Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

Sensors ◽

10.3390/s19194144 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4144 ◽

Cited By ~ 1

Author(s):

Li ◽

Wang ◽

Zhang ◽

Wen ◽

Zhong ◽

...

Keyword(s):

Water Storage ◽

Terrestrial Water Storage ◽

Global Land ◽

Terrestrial Water ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System ◽

Better Than

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

Download Full-text

From Monitoring to Forecasting the Land Surface Condition Using a Land Data Assimilation System: Application over the Contiguous United States

10.5194/egusphere-egu2020-4460 ◽

2020 ◽

Author(s):

Anthony Mucia ◽

Clément Albergel ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Jean-Christophe Calvet

Keyword(s):

United States ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Initial Conditions ◽

Global Land ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

Data Assimilation System ◽

Assimilation System

LDAS-Monde is a global Land Data Assimilation System developed in the research department of M&#233;t&#233;o-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2o x 0.2o spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.

Download Full-text