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Agricultural Drought in Chile: from the Assessment Toward Prediction Using Satellite Data

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Agricultural Drought in Chile: from the Assessment Toward Prediction Using Satellite Data Universidad de Concepción Dirección de Postgrado Facultad de Ingeniería Agrícola Programa de Doctorado en Ingeniería Agrícola con mención en Recursos Hídricos en la Agricultura Agricultural drought in Chile: From the assessment toward prediction using satellite data Sequía Agrícola en Chile: De la evaluación hacía la predicción usando datos satelitales Tesis para optar al grado de Doctor en Ingeniería Agrícola con mención en Recursos Hídricos en la Agricultura FRANCISCO JAVIER ZAMBRANO BIGIARINI CHILLÁN-CHILE 2017 Profesor Guía: Mario Lillo Saavedra Dpto. de Mecanización y Energía, Facultad de Ingeniería Agrícola Universidad de Concepción ii Agricultural drought in Chile: From the assessment toward prediction using satellite data. Thesis approved by Mario Lillo Saavedra Ing. Civil Eléctrico, Doctor Profesor Guia Octavio Lagos Ing. Civil Agrícola, Ph.D. Evaluador Interno Aldo Montecinos Oceanógrafo, Doctor Evaluador Interno Francisco Meza Ing. Agrónomo, Ph.D. Evaluador Externo Gabriel Merino Licenciado en Física, Ph.D. Director (S) de Programa Contents List of Tables v List of Figures vi Preface ix Dedication x Acknowledgement xi Abstract xiii 1 Introduction 1 1.1 Hypothesis .............................................. 4 1.2 Research objectives .......................................... 4 1.2.1 General objective ....................................... 4 1.2.2 Specific objectives ...................................... 4 1.3 Thesis outline ............................................. 4 2 Agricultural drought in the BioBío Region of Chile 6 Abstract ................................................... 6 2.1 Introduction .............................................. 6 2.2 Study Area .............................................. 8 2.3 Data .................................................. 10 2.4 Methods ................................................ 12 2.4.1 Procedure for calculating VCI in cropland areas. ..................... 12 2.4.2 Cropland mask ........................................ 13 2.4.3 Vegetation condition index (VCI) .............................. 13 2.4.4 Standardized Precipitation Index (SPI) .......................... 13 2.4.5 Correlation between VCI and SPI ............................. 14 2.5 Results and discussion ........................................ 14 2.5.1 Spatio-temporal variation of VCI and comparison with drought declaration ...... 14 2.5.2 Correlation VCI vs SPI ................................... 17 2.6 Conclusions .............................................. 20 3 Satellite monthly precipitation products for use in Chile 22 Abstract ................................................... 22 3.1 Introduction .............................................. 23 3.2 Study area ............................................... 24 3.3 Methods ................................................ 25 3.3.1 Data .............................................. 25 3.3.2 Preparation and data analysis ................................ 26 3.4 Results ................................................. 27 iii CONTENTS iv 3.4.1 Satellite and rain gauge precipitation ............................ 27 3.4.2 Time series, annual difference and seasonal variation of rainfall ............. 29 3.4.3 Statistics of comparison between satellite-derived and in-situ measurements ...... 32 3.4.4 Spatial variation and comparison of products with long data-record ........... 35 3.4.5 Application for agricultural drought analysis ....................... 37 3.5 Conclusions .............................................. 41 Appendix .................................................. 44 3.5.1 Data processing and data analysis ............................. 44 3.5.2 Statistics ........................................... 44 4 Agricultural drought prediction for Chile 46 Abstract ................................................... 46 4.1 Introduction .............................................. 46 4.2 Study area ............................................... 48 4.3 Data .................................................. 49 4.4 Methods ................................................ 50 4.4.1 Selection of census units ................................... 50 4.4.2 Defining the growing season per census unit ........................ 50 4.4.3 Deriving a proxy for seasonal crop biomass production .................. 51 4.4.4 Predictor variables ...................................... 52 4.4.5 Prediction by OLR ...................................... 53 4.4.6 Prediction by DL ....................................... 53 4.4.7 Model evaluation ....................................... 54 4.5 Results ................................................. 54 4.5.1 Census-specific definition of growing season ........................ 54 4.5.2 OLR predictions ....................................... 54 4.5.3 DL predictions ........................................ 55 4.5.4 Model evaluation and comparison .............................. 56 4.6 Discussion ............................................... 57 4.7 Conclusions .............................................. 61 5 Conclusions 62 A Other useful satellite data 64 Abstract ................................................... 64 A.1 Introduction .............................................. 64 A.1.1 Vegetation and precipitation indices ............................ 64 A.1.2 Recent satellite data and drought indices ......................... 65 A.2 Conclusion .............................................. 65 Bibliography 66 List of Tables 2.1 Percentage of cropland area (%) of 15 administrative units of the BioBío Region, Chile, with cropland area ≥ 10% from 2001 to 2013 and the 13-year mean. ................. 9 2.2 Drought classification scheme for SPI and VCI (Bhuiyan et al., 2006; Du et al., 2013) .... 14 2.3 Pearson correlation value (r) between time scale 1 to 6 and standardized VCI for 15 admin- istrative units in the BioBío Region, Chile, with cropland area > 10% and considering the mean values between November and April. ............................. 16 3.1 Summary statistics for precipitation data from weather stations (in-situ) and PERSIANN- CDR, TMPA 3B43 v7, CHIRPS 2.0 satellite products, in five zones of Chile. Total number of observations (n), weather stations by zone (Stations), missing observations, mean (X¯), first quartile (X25%), median (X50%), third quartile (X75%) and maximum (Xmax). The time- period used was 1981-2015, 1983-2015 and 1998-2015 for in-situ, CHIRPS 2.0, PERSIANN- CDR and TMPA 3B43 v7, respectively. .............................. 29 3.2 Summary of statistics aggregate for zones North, North-Central, Central, South-Central and South; for products TMPA 3B43 v7, PERSIANN-CDR and CHIRPS 2.0. CC, ME, MAE, bias, Eff , FBS, POD, FAR and HSS ................................ 32 3.3 Packages from R environment used for the processing of the remote sensing data and the data analysis. ............................................. 44 3.4 Contingency table for comparing rain gauge measurements and satellite rainfall estimates. The threshold correspond to the value above which rainfall is considered detected. In this case a value of 1mm was used. ................................... 45 4.1 Variable relative importance derived from deep learning models, mean (X¯) for 16 models (2000-2001 and 2015-2016 growing seasons) for 1, 2, 3 and 4 month before EOS (mb). .... 59 v List of Figures 1.1 (a) Scientific agreement vs expertise in climate based in the research of Cook et al. (2013),(b) Global mean surface temperature since 1880 (Hansen et al., 2010) ............... 1 1.2 Drought propagation in the Anthropocene. The propagation from meteorological drought to soil moisture and hydrological drought (black arrows) is initiated by climatic (left; yellow) and human (right; red-brown) drivers. Drought is modified by hydrological catchment processes (dotted lines) that are altered by human activities (white arrows). The resulting ecological and socioeconomic impacts initiate responses, which in turn result in changes to the human influence on drought and the climate variability (grey arrows). Source: Loon et al.(2016) .. 2 1.3 Hypothetical schematic of the distinction between climate-induced drought, human-induced drought and human-modified drought. Observed water levels (solid black line) that arein- fluenced by both natural and anthropogenic factors are compared to simulated water levels from virtual model experiments (dashed line) that consider only natural drivers. Note that human-modified drought can be aggravated or alleviated with respect to the natural situation. Source: Loon et al. (2016) ...................................... 3 2.1 (a) BioBio Region administrative units in a digital terrain model with 26 weather stations. (b) Location of the BioBio Region, Chile. ............................. 9 2.2 Bioclimatic precipitation variables of the BioBío Region, Chile ................. 10 2.3 Bioclimatic temperature variables of the BioBío Region, Chile .................. 10 2.4 Land cover classes in the BioBío Region, Chile, based on the IGBP land cover scheme for the MODIS MCD12Q1 version 5.1 product. .............................. 11 2.5 Time-series comparison between raw NDVI (points) and smoothed NDVI by Lowess (lines) for five points on five administrative units in cropland areas of the BioBío Region. ...... 12 2.6 Variation of the (a) global VCI percentage (%) of cropland surface with different VCI classes and (b) boxplot of global VCI intensity (%) for the growing seasons between 2000/2001 and 2014/2015
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