Uncertainty in land cover datasets for global land‐surface models derived from 1‐km global land cover datasets

Nakaegawa, Tosiyuki

doi:10.1002/hyp.8011

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…We did not specify the future land cover dataset according to the SRES A1B scenario, but used the same dataset as for the present climate. Note that there are large uncertainties in current land classification in available satellite-based 1-km global land cover datasets (Nakaegawa, 2011;Nakaegawa, 2012). Furthermore, we did not simulate any irrigation in our projections; irrigation is known to influence global climate (Boucher et al, 2004;Puma and Cook, 2010) and river discharges (Kustu et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Discharge of major global rivers in the late 21st century climate projected with the high horizontal resolution MRI‐AGCMs

Nakaegawa¹,

Hosaka²

2013

Hydrological Processes

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Abstract:This study used an atmospheric general circulation model (AGCM) with a 20-km grid spacing and a global river routing model with 0.5 grid resolution to project climatological mean river discharges in the late 21st century under a changing climate. We also performed an ensemble simulation with the same AGCM except with a resolution of 60 km and forced with different lower boundary conditions to quantify the level of confidence in the projections. By the late 21st century, projected annual mean river discharges increased in the high latitudes and in India and the southeastern United States but decreased in broad regions of Europe, western Asia, the western United States, Central America, and the southern half of the Amazon River basin. Projected future changes in monthly river discharges include unique seasonal details not present in the annual discharge projections: in the Amazon River, for example, river discharges were projected to increase in the high-water season but decrease in the low-water season. These seasonal changes suggest that monthly river discharge projections should be useful for assessing the human impacts and risks in future planning of water resources and flood control. We devised a way to measure the consistency between the various projections made by our 60-km ensemble simulations and our standard 20-km high-resolution model. Wherever the projected changes were consistent, the future changes also tended to be statistically significant in our high-resolution model. These results encourage a future projection of discharge for a specific river basin in the world.

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Section: Discussionmentioning

confidence: 99%

Discharge of major global rivers in the late 21st century climate projected with the high horizontal resolution MRI‐AGCMs

Nakaegawa¹,

Hosaka²

2013

Hydrological Processes

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“…Various assessments of the global land cover data sets were carried out in early studies [27,29,30,[32][33][34][35][36][37][38][39][40][41]. However, the assessments were not evenly distributed among the global, and China has not been given enough attention regarding accuracy assessment of land cover dataset.…”

Section: Introductionmentioning

confidence: 99%

Assessing Consistency of Five Global Land Cover Data Sets in China

et al. 2014

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Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), MODIS Land Cover product (MODIS LC), and GLOBCOVER land cover product (GlobCover), have been widely used in many researches. However, these data sets were produced using different data sources and class definitions, which led to high uncertainty and inconsistency when using them. This study looked into the consistencies and discrepancies among the five data sets in China. All of the compared data sets were aggregated to consistent spatial resolution and extent, along with a 12-class thematic classification schema; intercomparisons among five datasets and each with reference data GLCD-2005 were performed. Results show reasonable agreement across the five data sets over China in terms of the dominating land cover types like Grassland and Cropland; while discrepancies of Forest classes, particularly Shrubland and Wetland among them are great. Additionally, GLC2000 has the highest agreement with GLCD-2005; MODIS LC gets the highest map-specific consistency compared with others; whereas UMd has the lowest agreement with GLCD-2005, but also has the lowest map-specific consistency.

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“…Nine CGCMs captured well the spatial distribution of climatological annual mean surface soil moisture (correlation coefficients greater than 0.70), and another nine CGCMs could not capture it (correlation coefficients less than 0.50). Low performance in capturing the spatial distribution of surface soil moisture might be due to differences in soil column depths and soil geographical distributions (Entin et al, 1999), as mentioned above, as well as to different land-cover types (e.g., Nakaegawa, 2011) used by the different CGCMs. Soil moisture is determined as the residual of the land-surface water balance after complicated land-atmosphere coupling processes (Seneviratne et al, 2010).…”

Section: Validation Of Historical Simulationsmentioning

confidence: 99%

Statistical evaluation of future soil moisture changes in East Asia projected in a CMIP5 multi-model ensemble

Nakaegawa¹

2017

Hydrological Research Letters

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Abstract:Simple explanations for changes in surface soil moisture in the late 21st century under global warming were explored, based on statistical significance and without consideration of complicated mechanisms. The results of a multi-model ensemble (MME) analysis showed significant increases in surface soil moisture in one northwestern inland area, and significant decreases were projected in two inland areas in southern and northern China. Among three water flux variables, precipitation (P), evaporation (E), and total runoff (R), significant changes in E explained only 10% of the total area showing significant changes in surface soil moisture. Among three combinations of two water flux variables, (P -E), (E + R), and (P -R), significant changes in (P -E) were dominant in coastal northeastern China, but this area did not overlap with areas with significant changes in surface soil moisture. Individual analyses revealed that significant increases in E, (P -R), and (E + R) explained 26%, 13%, and 9%, respectively, of the total area showing a significant decrease in the MME mean surface soil moisture. This result indicates that reliance on the MME mean may hinder understanding of the geophysical mechanism linking water flux variables with surface soil moisture.

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Uncertainty in land cover datasets for global land‐surface models derived from 1‐km global land cover datasets

Cited by 15 publications

References 40 publications

Discharge of major global rivers in the late 21st century climate projected with the high horizontal resolution MRI‐AGCMs

Discharge of major global rivers in the late 21st century climate projected with the high horizontal resolution MRI‐AGCMs

Assessing Consistency of Five Global Land Cover Data Sets in China

Statistical evaluation of future soil moisture changes in East Asia projected in a CMIP5 multi-model ensemble

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