Assessment of uncertainties in global land cover products for hydro‐climate modeling in India

Earth's land cover (LC) has significant influence on land-atmospheric processes and affects the climate at multiple scales. There are multiple global LC (GLC) data sets which are yet to be evaluated for uncertainties and their propagation into the simulation of land surface fluxes (LSFs) in land surface/climate modeling. The present study assesses the uncertainties in seven GLC products with reference to a regional data set for the simulation of LSFs in India using a macro-scale land surface model. There is considerable overestimation of the extent of croplands in most of the GLCs. The uncertainties in LCs exert significant bias in the simulation of the LSFs of actual evapotranspiration (ETa), latent heat (LE), and sensible heat (H) fluxes. Uncertainty propagation in LSFs is proportional to the bias in cropping intensity under rainfed condition. The high underrepresentation of cropland area in the UMd data set results in highest bias in LSFs whereas the least cropland bias in Globland30 leads to least bias. Irrigation has higher potential to alter the LSFs than uncertainties related to LC especially in regions with large area under irrigation like India. The changes in LSFs are higher in arid/semiarid regions with medium irrigation intensity than in subhumid regions with high irrigation intensity. This has significant implications for the country's future irrigation expansion plans in the arid/semiarid regions. The study also emphasizes the need for focused efforts to quantify the uncertainties from varying irrigation intensities in the next generation CMIP6 experiments.

[1]  Harsh L. Shah,et al.  Hydrologic Changes in Indian Subcontinental River Basins (1901–2012) , 2016 .

[2]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[3]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[4]  G. Destouni,et al.  Local flow regulation and irrigation raise global human water consumption and footprint , 2015, Science.

[5]  A. Robock,et al.  Impacts of land cover data quality on regional climate simulations , 2010 .

[6]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[7]  Thibault Mathevet,et al.  All that glitters is not gold: the case of calibrating hydrological models , 2012 .

[8]  Keith E. Schilling,et al.  Increasing streamflow and baseflow in Mississippi River since the 1940 s: Effect of land use change , 2006 .

[9]  Arturo E. Hernandez,et al.  Generation and analysis of the 2005 land cover map for Mexico using 250m MODIS data , 2012 .

[10]  G. Destouni,et al.  Irrigation Effects on Hydro-Climatic Change: Basin-Wise Water Balance-Constrained Quantification and Cross-Regional Comparison , 2014, Surveys in Geophysics.

[11]  Laurence C. Smith,et al.  How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground‐truth database for West Siberia , 2007 .

[12]  M. Hansen,et al.  A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products , 2000 .

[13]  G. Destouni,et al.  Hydrological responses to climate change conditioned by historic alterations of land-use and water-use , 2011 .

[14]  W. Rawls,et al.  Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions , 2006 .

[15]  Xubin Zeng,et al.  Global Vegetation Root Distribution for Land Modeling , 2001 .

[16]  Weiqi Zhou,et al.  Evaluation of the National Land Cover Database for Hydrologic Applications in Urban and Suburban Baltimore, Maryland 1 , 2010 .

[17]  Roger A. Pielke,et al.  Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt , 2006 .

[18]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[19]  J. Foley,et al.  Simulated impacts of irrigation on the atmospheric circulation over Asia , 2009 .

[20]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[21]  Steffen Fritz,et al.  Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications , 2008 .

[22]  S. Fritz,et al.  Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .

[23]  Victor Barrena Arroyo,et al.  A land cover map of Latin America and the Caribbean in the framework of the SERENA project , 2013 .

[24]  A. K. Gosain,et al.  Climate change impact assessment on hydrology of Indian river basins , 2006 .

[25]  D. Lettenmaier,et al.  Hydrologic effects of land and water management in North America and Asia: 1700–1992 , 2006 .

[26]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[27]  P. Döll,et al.  Groundwater use for irrigation - a global inventory , 2010 .

[28]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[29]  D. Raje,et al.  Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins , 2014 .

[30]  Balaji Rajagopalan,et al.  Effects of irrigation and vegetation activity on early Indian summer monsoon variability , 2009 .

[31]  Maoyi Huang,et al.  Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters , 2013 .

[32]  I. Rodríguez‐Iturbe Ecohydrology: A hydrologic perspective of climate‐soil‐vegetation dynamies , 2000 .

[33]  Hannes Isaak Reuter,et al.  An evaluation of void‐filling interpolation methods for SRTM data , 2007, Int. J. Geogr. Inf. Sci..

[34]  Michael J. Puma,et al.  Irrigation as an historical climate forcing , 2014, Climate Dynamics.

[35]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[36]  C. Madhusoodhanan,et al.  Climate change impact assessments on the water resources of India under extensive human interventions , 2016, Ambio.

[37]  Y. Xue,et al.  Hydrological Land Surface Response in a Tropical Regime and a Midlatitudinal Regime , 2002 .

[38]  A. Ducharne,et al.  Comprehensive data set of global land cover change for land surface model applications , 2008 .

[39]  W. Steffen,et al.  Human modification of global water vapor flows from the land surface. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Jungho Im,et al.  Downscaling of MODIS One Kilometer Evapotranspiration Using Landsat-8 Data and Machine Learning Approaches , 2016, Remote. Sens..

[41]  A. Ruane,et al.  Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation , 2015 .

[42]  C. Justice,et al.  Global land cover classification by remote sensing: present capabilities and future possibilities , 1991 .

[43]  Christopher A. Scott,et al.  Impacts of irrigation and anthropogenic aerosols on the water balance, heat fluxes, and surface temperature in a river basin , 2008 .

[44]  B. Poulter,et al.  Use of various remote sensing land cover products for plant functional type mapping over Siberia , 2013 .

[45]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[46]  Y. Qian,et al.  A Modeling Study of Irrigation Effects on Surface Fluxes and Land–Air–Cloud Interactions in the Southern Great Plains , 2013 .

[47]  Dennis P. Lettenmaier,et al.  Effects of irrigation on the water and energy balances of the Colorado and Mekong river basins , 2006 .

[48]  O. Boucher,et al.  Direct human influence of irrigation on atmospheric water vapour and climate , 2004 .

[49]  Qiuhong Tang,et al.  A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate , 2015 .

[50]  D. Toll,et al.  Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data , 2010 .

[51]  蒋子堃 A Jurassic wood providing insights into the earliest step in Ginkgo wood evolution. , 2016 .

[52]  Arjen Ysbert Hoekstra,et al.  Going against the flow: A critical analysis of inter-state virtual water trade in the context of India’s National River Linking Program , 2009, Physics and Chemistry of the Earth, Parts A/B/C.

[53]  Georgia Destouni,et al.  Developing water change spectra and distinguishing change drivers worldwide , 2014 .

[54]  Martha C. Anderson,et al.  Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin , 2014 .

[55]  Damien Sulla-Menashe,et al.  A Global Land Cover Climatology Using MODIS Data , 2014 .

[56]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[57]  Jean-François Mas,et al.  A Suite of Tools for Assessing Thematic Map Accuracy , 2014 .

[58]  David Lobell,et al.  Empirical evidence for a recent slowdown in irrigation-induced cooling , 2007, Proceedings of the National Academy of Sciences.

[59]  B. Cook,et al.  Irrigation induced surface cooling in the context of modern and increased greenhouse gas forcing , 2011 .

[60]  Trent W. Biggs,et al.  Mapping daily and seasonal evapotranspiration from irrigated crops using global climate grids and satellite imagery: Automation and methods comparison , 2016 .

[61]  N. Coops,et al.  Satellites: Make Earth observations open access , 2014, Nature.

[62]  Hao Jiang,et al.  Assessing Consistency of Five Global Land Cover Data Sets in China , 2014, Remote. Sens..

[63]  J. Polcher,et al.  Global effect of irrigation and its impact on the onset of the Indian summer monsoon , 2012, Climate Dynamics.

[64]  Roger A. Pielke,et al.  The impact of agricultural intensification and irrigation on land-atmosphere interactions and Indian monsoon precipitation — A mesoscale modeling perspective , 2009 .

[65]  R. Oglesby,et al.  Weakening of Indian Summer Monsoon Rainfall due to Changes in Land Use Land Cover , 2016, Scientific Reports.

[66]  A. Henderson‐sellers,et al.  A global archive of land cover and soils data for use in general circulation climate models , 1985 .

[67]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[68]  N. Miller,et al.  Regional simulations to quantify land use change and irrigation impacts on hydroclimate in the California Central Valley , 2011 .

[69]  Jianjun Ge,et al.  Impacts of land use/cover classification accuracy on regional climate simulations , 2007 .

[70]  Reed M. Maxwell,et al.  Human impacts on terrestrial hydrology: climate change versus pumping and irrigation , 2012 .

[71]  Nandin-Erdene Tsendbazar,et al.  Comparative assessment of thematic accuracy of GLC maps for specific applications using existing reference data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[72]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[73]  Margaret S. Torn,et al.  Vegetation controls on surface heat flux partitioning, and land‐atmosphere coupling , 2015 .

[74]  O. P. Sreejith,et al.  Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region , 2014 .

[75]  Geoffrey J. Hay,et al.  Uncertainties in land use data , 2006 .

[76]  Eric F. Wood,et al.  Predicting the Discharge of Global Rivers , 2001, Journal of Climate.

[77]  A. Ducharne,et al.  The impact of global land-cover change on the terrestrial water cycle , 2013 .

[78]  Anamika Arora,et al.  Climate change impact assessment of water resources of India , 2011 .

[79]  Andrew E. Suyker,et al.  Land management and land-cover change have impacts of similar magnitude on surface temperature , 2014 .

[80]  Renaud Mathieu,et al.  Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa , 2014, Remote. Sens..

[81]  D. Raje,et al.  Bayesian parameter uncertainty modeling in a macroscale hydrologic model and its impact on Indian river basin hydrology under climate change , 2012 .

[82]  P. Diwakar,et al.  Quantification and monitoring of deforestation in India over eight decades (1930–2013) , 2015, Biodiversity and Conservation.

[83]  Le Yu,et al.  Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution , 2015 .

[84]  Rasim Latifovic,et al.  Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data , 2004 .

[85]  Shu Peng,et al.  A web-based system for supporting global land cover data production , 2015 .

[86]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[87]  P. Dirmeyer,et al.  University of Nebraska-Lincoln DigitalCommons @ University of Nebraska-Lincoln Papers in Natural Resources Natural Resources , School of 2014 Land cover changes and their biogeophysical effects on climate , 2016 .