Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation. Remote Sens. 2019, 11, 2977; doi:10.3390/rs11242977 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 2977 2 of 19

[1]  B. Shiferaw,et al.  Saharan Africa : An essential first step in adapting to future climate change ? , 2008 .

[2]  Hannah Jacobson,et al.  A novel approach to mapping land conversion using Google Earth with an application to East Africa , 2015, Environ. Model. Softw..

[3]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[4]  C. Milesi,et al.  Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help? , 2012 .

[5]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[6]  Dominic Kniveton,et al.  Understanding the Large Scale Driving Mechanisms of Rainfall Variability over Central Africa , 2011 .

[7]  Decadal trends of the annual amplitude of global precipitation , 2016 .

[8]  Jens Christian Refsgaard,et al.  Spatial-scale characteristics of precipitation simulated by regional climate models and the implications for hydrological modeling , 2012 .

[9]  Denis A. Hughes,et al.  Comparison of satellite rainfall data with observations from gauging station networks , 2006 .

[10]  J. Janowiak,et al.  CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution , 2004 .

[11]  R. Kripalani,et al.  Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran , 2014, Journal of Earth System Science.

[12]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[13]  Liwang Ma,et al.  Water resources and water use efficiency in the North China Plain: Current status and agronomic management options , 2010 .

[14]  Samuel S. P. Shen,et al.  A new analysis of variability and predictability of seasonal rainfall of central southern Africa for 1950–94 , 2004 .

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

[16]  S. Wijffels,et al.  Ocean Salinities Reveal Strong Global Water Cycle Intensification During 1950 to 2000 , 2012, Science.

[17]  S. Yue,et al.  LONG TERM TRENDS OF ANNUAL AND MONTHLY PRECIPITATION IN JAPAN 1 , 2003 .

[18]  Lu Liu,et al.  Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals , 2018 .

[19]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[20]  P. Srivastava,et al.  Precipitation trend analysis of Sindh River basin, India, from 102‐year record (1901–2002) , 2016 .

[21]  Barry Smit,et al.  Climate change, food security, and livelihoods in sub-Saharan Africa , 2016, Regional Environmental Change.

[22]  A. Hou,et al.  The Global Precipitation Measurement Mission , 2014 .

[23]  J. P. Matos,et al.  Evaluation of precipitation products over the Zambezi Basin , 2011 .

[24]  R. Vose,et al.  An Overview of the Global Historical Climatology Network-Daily Database , 2012 .

[25]  M. Rosegrant,et al.  Global Food Security: Challenges and Policies , 2003, Science.

[26]  C. Reason,et al.  Dry spell frequencies and their variability over southern Africa , 2004 .

[27]  M. Wallner,et al.  Rainfall characteristics and their implications for rain-fed agriculture: a case study in the Upper Zambezi River Basin , 2016 .

[28]  C. Funk,et al.  Rainfall over the African continent from the 19th through the 21st century , 2017, Global and Planetary Change.

[29]  Julie A. Silva,et al.  Relating Rainfall Patterns to Agricultural Income: Implications for Rural Development in Mozambique , 2014 .

[30]  S. Nicholson,et al.  THE RELATIONSHIP OF THE EL NIÑO–SOUTHERN OSCILLATION TO AFRICAN RAINFALL , 1997 .

[31]  Robert F. Adler,et al.  Global Precipitation: Means, Variations and Trends During the Satellite Era (1979–2014) , 2017, Surveys in Geophysics.

[32]  Miao Zhang,et al.  Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images , 2018, Remote. Sens..

[33]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[34]  M. Clark,et al.  Effects of different regional climate model resolution and forcing scales on projected hydrologic changes , 2016 .

[35]  E. Wood,et al.  Little change in global drought over the past 60 years , 2012, Nature.

[36]  R. Adler,et al.  Interdecadal variability/long-term changes in global precipitation patterns during the past three decades: global warming and/or pacific decadal variability? , 2013, Climate Dynamics.

[37]  W. Landman,et al.  Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe , 2016, Theoretical and Applied Climatology.

[38]  M. Jha,et al.  Seasonal and annual precipitation time series trend analysis in North Carolina, United States , 2014 .

[39]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[40]  J. Michaelsen,et al.  The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes , 2015, Scientific Data.

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

[42]  Peter J. Webster,et al.  Recent change of the global monsoon precipitation (1979–2008) , 2012, Climate Dynamics.

[43]  Shamsuddin Shahid,et al.  Spatial distribution of secular trends in annual and seasonal precipitation over Pakistan , 2017 .

[44]  Petra Döll,et al.  A global data set of the extent of irrigated land from 1900 to 2005 , 2014 .

[45]  Yong-Sang Choi,et al.  Rainfall variability over Zimbabwe and its relation to large‐scale atmosphere–ocean processes , 2017 .

[46]  Julie A. Silva,et al.  Extreme weather and economic well-being in rural Mozambique , 2013, Natural Hazards.

[47]  Forrest R. Stevens,et al.  Multitemporal settlement and population mapping from Landsat using Google Earth Engine , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[48]  Yuanjie Li,et al.  A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[49]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[50]  V. Levizzani,et al.  Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins , 2012 .

[51]  Enli Wang,et al.  Climate, agricultural production and hydrological balance in the North China Plain , 2008 .

[52]  S. Sorooshian,et al.  A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons , 2018 .

[53]  S. Seneviratne,et al.  Global assessment of trends in wetting and drying over land , 2014 .

[54]  Austin Troy,et al.  An Operational Before-After-Control-Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale , 2018, Remote. Sens..

[55]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[56]  Kuolin Hsu,et al.  Global Precipitation Trends across Spatial Scales Using Satellite Observations , 2017 .

[57]  Qiming Zhou,et al.  “Dry gets drier, wet gets wetter”: A case study over the arid regions of central Asia , 2018, International Journal of Climatology.