What Rainfall Does Not Tell Us - Enhancing Financial Instruments with Satellite-Derived Soil Moisture and Evaporative Stress

Advanced parametric financial instruments, like weather index insurance (WII) and risk contingency credit (RCC), support disaster-risk management and reduction in the world’s most disaster-prone regions. Simultaneously, satellite data that are capable of cross-checking rainfall estimates, the “standard dataset” to develop such financial safety nets, are gaining importance as complementary sources of information. This study concentrates on the analysis of satellite-derived multi-sensor soil moisture (ESA CCI, Version v04.2), the evapotranspiration-based Evaporative Stress Index (ESI), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall estimates in nine East African countries. Based on spatial correlation analysis, we found matching spatial/temporal patterns between all three datasets, with the highest correlation coefficient occurring between October and March. In large parts of Kenya, Ethiopia, and Somalia, we observed a lower (partly negative) correlation coefficient between June and August, which was likely caused by issues related to cloud cover and the volume scattering of microwaves in sandy, hot soils. Based on simple linear and logit regression analysis with annual, national maize yield estimates as the dependent variable, we found that, depending on the chosen period (averages per year, growing or harvesting months), there was added value (higher R-squared) if two or all three variables were combined. The ESI and soil moisture have the potential to close sensitive knowledge gaps between atmospheric moisture supply and the response of the land surface in operational parametric insurance projects. For the development and calibration of WII and RCC, this means that better proxies for historical and potential future drought impact can strengthen “drought narratives”, resulting in a better match between calculated payouts/credit repayment levels and the actual needs of smallholder farmers.

[1]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[2]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[3]  Günter Blöschl,et al.  Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes , 2004 .

[4]  D. Lawrence,et al.  Regions of Strong Coupling Between Soil Moisture and Precipitation , 2004, Science.

[5]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[6]  Frank Veroustraete,et al.  Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation , 2008, Sensors.

[7]  Earth's Global Energy Budget , 2009 .

[8]  Wolfgang Wagner,et al.  ASCAT Soil Moisture: An Assessment of the Data Quality and Consistency with the ERS Scatterometer Heritage , 2009 .

[9]  Daniel E. Osgood,et al.  Index insurance and climate risk: prospects for development and disaster management , 2009 .

[10]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[11]  D. Lobell,et al.  Robust negative impacts of climate change on African agriculture , 2010, Environmental Research Letters.

[12]  Uang,et al.  The NCEP Climate Forecast System Reanalysis , 2010 .

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

[14]  J. M. Norman,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2011 .

[15]  Christopher B. Barrett,et al.  Covariate Catastrophic Risk Management in the Developing World: Discussion , 2011 .

[16]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[17]  Wade T. Crow,et al.  An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling , 2011 .

[18]  Martha C. Anderson,et al.  Towards an integrated soil moisture drought monitor for East Africa , 2012 .

[19]  Apurba Shee,et al.  Collateral-free lending with risk-contingent credit for agricultural development: indemnifying loans against pulse crop price risk in India , 2012 .

[20]  Wade T. Crow,et al.  An objective methodology for merging satellite‐ and model‐based soil moisture products , 2012 .

[21]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[22]  W. Wagner,et al.  Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture , 2012 .

[23]  Wade T. Crow,et al.  An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model , 2012 .

[24]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .

[25]  Martha C. Anderson,et al.  Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index , 2013 .

[26]  J. Eitzinger,et al.  The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications , 2013 .

[27]  Stephen J. Connor,et al.  Combined use of satellite estimates and rain gauge observations to generate high‐quality historical rainfall time series over Ethiopia , 2014 .

[28]  Malgosia Madajewicz,et al.  MANAGING RISKS TO AGRICULTURAL LIVELIHOODS : IMPACT EVALUATION OF THE HARITA PROGRAM IN TIGRAY , ETHIOPIA , 2009 – 2012 , 2014 .

[29]  Clement Atzberger,et al.  The Potential and Uptake of Remote Sensing in Insurance: A Review , 2014, Remote. Sens..

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

[31]  Wade T. Crow,et al.  The impact of vertical measurement depth on the information content of soil moisture times series data , 2014 .

[32]  W. Wagner,et al.  Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data , 2014 .

[33]  T. Hessels,et al.  Comparison and Validation of Several Open Access Remotely Sensed Rainfall Products for the Nile Basin , 2015 .

[34]  C. D. Bella,et al.  Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina , 2015 .

[35]  Martha C. Anderson,et al.  Using Temporal Changes in Drought Indices to Generate Probabilistic Drought Intensification Forecasts , 2015 .

[36]  Feng Gao,et al.  Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003–2013 , 2015 .

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

[38]  Matthew Brown,et al.  The Use of Remotely Sensed Rainfall for Managing Drought Risk: A Case Study of Weather Index Insurance in Zambia , 2016, Remote. Sens..

[39]  M. Svoboda,et al.  Handbook of Drought Indicators and Indices , 2016 .

[40]  Feng Gao,et al.  The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts , 2016 .

[41]  Amy McNally,et al.  Evaluating ESA CCI soil moisture in East Africa , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[42]  Adrianos Retalis,et al.  Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period , 2016 .

[43]  Wouter Dorigo,et al.  Satellite soil moisture for advancing our understanding of earth system processes and climate change , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[44]  Wenzhi Zhao,et al.  Comparison of hourly and daily Penman-Monteith grass- and alfalfa-reference evapotranspiration equations and crop coefficients for maize under arid climatic conditions , 2017 .

[45]  Yi Y. Liu,et al.  ESA CCI Soil Moisture for improved Earth system understanding : State-of-the art and future directions , 2017 .

[46]  Martha C. Anderson,et al.  Estimating morning change in land surface temperature from MODIS day/night observations: Applications for surface energy balance modeling , 2017, Geophysical research letters.

[47]  Zhongbo Su,et al.  An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa , 2017, Remote. Sens..

[48]  Jie Yang,et al.  Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal , 2017, Remote. Sens..

[49]  Jianjun Zhao,et al.  Negative soil moisture-precipitation feedback in dry and wet regions , 2018, Scientific Reports.

[50]  Martha C. Anderson,et al.  Exploiting the Convergence of Evidence in Satellite Data for Advanced Weather Index Insurance Design , 2018, Weather, Climate, and Society.

[51]  P. Peterson,et al.  Validation of the CHIRPS satellite rainfall estimates over eastern Africa , 2018, Quarterly Journal of the Royal Meteorological Society.

[52]  J. Rivera,et al.  Validation of CHIRPS precipitation dataset along the Central Andes of Argentina , 2018, Atmospheric Research.

[53]  Chris Funk,et al.  How will East African maize yields respond to climate change and can agricultural development mitigate this response? , 2018, Climatic Change.

[54]  Martha C. Anderson,et al.  Microwave implementation of two-source energy balance approach for estimating evapotranspiration. , 2017, Hydrology and earth system sciences.