Role of Climate Variability and Human Activity on Poopó Lake Droughts between 1990 and 2015 Assessed Using Remote Sensing Data

In 2015, an emergency state was declared in Bolivia when Poopo Lake dried up. Climate variability and the increasing need for water are potential factors responsible for this situation. Because field data are missing over the region, no statements are possible about the influence of mentioned factors. This study is a preliminary step toward the understanding of Poopo Lake drought using remote sensing data. First, atmospheric corrections for Landsat (FLAASH and L8SR), seven satellite derived indexes for extracting water bodies, MOD16 evapotranspiration, PERSIANN-CDR and MSWEP rainfall products potentiality were assessed. Then, the fluctuations of Poopo Lake extent over the last 26 years are presented for the first time jointly, with the mean regional annual rainfall. Three main droughts are highlighted between 1990 and 2015: two are associated with negative annual rainfall anomalies in 1994 and 1995 and one associated with positive annual rainfall anomaly in 2015. This suggests that other factors than rainfall influenced the recent disappearance of the lake. The regional evapotranspiration increased by 12.8% between 2000 and 2014. Evapotranspiration increase is not homogeneous over the watershed but limited over the main agriculture regions. Agriculture activity is one of the major factors contributing to the regional desertification and recent disappearance of Poopo Lake.

[1]  A. Fisher,et al.  Comparing Landsat water index methods for automated water classification in eastern Australia , 2016 .

[2]  S. Jacobsen,et al.  The Situation for Quinoa and Its Production in Southern Bolivia: From Economic Success to Environmental Disaster , 2011 .

[3]  Nemati Amirreza,et al.  DAILY PRECIPITATION CLIMATE DATA RECORD FROM MULTISATELLITE OBSERVATIONS FOR HYDROLOGICAL AND CLIMATE STUDIES , 2016 .

[4]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[5]  C. Mobley,et al.  Estimation of the remote-sensing reflectance from above-surface measurements. , 1999, Applied optics.

[6]  Ali Selamat,et al.  Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery , 2014, Remote. Sens..

[7]  Kurtis J. Thome,et al.  Data continuity of Landsat-4 TM, Landsat-5 TM, Landsat-7 ETM+, and Advanced Land Imager (ALI) sensors , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[8]  Beck Hylke,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2014) by merging gauge, satellite, and reanalysis data , 2017 .

[9]  Lifu Zhang,et al.  Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types , 2015, Remote. Sens..

[10]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[11]  Michael J. Choate,et al.  Effects of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper plus radiometric and geometric calibrations and corrections on landscape characterization , 2001 .

[12]  Gail P. Anderson,et al.  Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.

[13]  S. Jacobsen,et al.  What is Wrong With the Sustainability of Quinoa Production in Southern Bolivia – A Reply to Winkel et al. (2012) , 2012 .

[14]  L. Bengtsson,et al.  Long-term and extreme water level variations of the shallow Lake Poopó, Bolivia , 2006 .

[15]  C. Woodcock,et al.  Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation , 2003 .

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

[17]  Xiaoqing Wu,et al.  Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations , 2015, Geo spatial Inf. Sci..

[18]  Marie-Paule Bonnet,et al.  Assessment of satellite rainfall products over the Andean plateau , 2016 .

[19]  Diofantos G. Hadjimitsis,et al.  The Importance of Accounting for Atmospheric Effects in the Application of NDVI and Interpretation of Satellite Imagery Supporting Archaeological Research: The Case Studies of Palaepaphos and Nea Paphos Sites in Cyprus , 2011, Remote. Sens..

[20]  Dirk Raes,et al.  Economic assessment at farm level of the implementation of deficit irrigation for quinoa production in the Southern Bolivian Altiplano. , 2013 .

[21]  Jean-François Crétaux,et al.  Remote Sensing-Derived Bathymetry of Lake Poopó , 2013, Remote. Sens..

[22]  Georg Kaser,et al.  Modelling observed and future runoff from a glacierized tropical catchment (Cordillera Blanca, Perú) , 2007 .

[23]  Pavel Kabat,et al.  Climate Variability and Trends in Bolivia , 2012 .

[24]  Brian L. Markham,et al.  Radiometric Cross Calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) , 2014, Remote. Sens..

[25]  S. Calmant,et al.  Accuracy assessment of SRTM v4 and ASTER GDEM v2 over the Altiplano watershed using ICESat/GLAS data , 2015 .

[26]  M. Morana,et al.  A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM + , 2001 .

[27]  M. S. Moran,et al.  Temporal and spatial changes in grassland transpiration detected using Landsat TM and ETM+ imagery , 2003 .

[28]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[29]  K. Moffett,et al.  Remote Sens , 2015 .

[30]  Manuel Collet,et al.  Current state of glaciers in the tropical Andes: a multi-century perspective on glacier evolution and climate change , 2013 .

[31]  J. Michaelsen,et al.  A global satellite-assisted precipitation climatology , 2015 .

[32]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[33]  Zhiming Feng,et al.  Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors , 2013, Remote. Sens..

[34]  H. B. Mann Nonparametric Tests Against Trend , 1945 .

[35]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[36]  H. Diaz,et al.  Threats to Water Supplies in the Tropical Andes , 2006, Science.

[37]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[38]  S. Sorooshian,et al.  PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies , 2015 .

[39]  D. Hadjimitsis,et al.  Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices , 2010 .

[40]  Sergio M. Vicente-Serrano,et al.  Recent temperature variability and change in the Altiplano of Bolivia and Peru , 2016 .

[41]  Richard G. Allen,et al.  Dynamics of reference evapotranspiration in the Bolivian highlands (Altiplano) , 2004 .

[42]  D. Burn,et al.  Detection of hydrologic trends and variability , 2002 .

[43]  Jaap Schellekens,et al.  MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data , 2016 .