Satellite soil moisture for yield prediction in water limited regions

<div> <p>Accurate and reliable early warning systems can support anticipatory disaster risk financing which can be more cost effective than post-disaster emergency response. One of the challenges in anticipatory disaster risk financing is basis risk, as a result of data and model uncertainty. The increasing availability of Earth Observation (EO) data provides the opportunity to develop shadow models or include different variables in early warning systems and weather index insurance. Especially of interest is the early indication of climate impacts on agricultural production. Traditionally, crop and yield prediction models use meteorological data such as precipitation and temperature, or optical based indicators such as Normalized Difference Vegetation Index (NDVI), for yield prediction.  In recent years, soil moisture has gained popularity for yield prediction as it controls the water availability for plants.  </p> </div><div> <p>Here, we will present the use of different satellite-based rainfall and soil moisture products, in combination with NDVI, to develop a yield deficiency indicator over two water limited regions. An analysis for Senegal and Morocco is performed at the national level using yield data of four major crops from the Food and Agriculture Organization of the United Nations. Freely available EO datasets for rainfall, soil moisture, root zone soil moisture and NDVI were used. All datasets were spatially resampled to a 0.1° grid, temporally aggregated to monthly anomalies and finally detrended and standardized. First, regression analysis with yearly yield was performed per EO dataset for single months. For this, EO datasets where aggregated over areas where the specific crop was grown. Secondly, based on these results multiple linear regression was performed using the months and variables with the highest explanatory power. The multiple linear regression was used to provide spatially varying yield predictions by trading time for space. The spatial predictions were validated using sub-national yield data from Senegal.  </p> </div><div> <p>The analysis demonstrates the added-value of satellite soil moisture for early yield prediction. Both in Senegal and Morocco rainfall and soil moisture showed a high predictive skill early in the growing season: negative early season soil moisture anomalies often lead to low yield. NDVI showed more predictive power later in the growing season. For example, in Morocco soil moisture at the start of the season can already explain 56% of the variability in yield. NDVI can explain 80% of the yield, however this is at the end of the growing season. Combining anomalies of the optimal months based on the different variables in multiple linear regression improved yield prediction. Again, including NDVI led to higher predictive power, at the cost of early warning.  This analysis shows very clearly that soil moisture can be a valuable tool for anticipatory drought risk financing and early warning systems. </p> </div>

Landslide Hazard Information System for Landslide Disaster Risk Financing: Earth Observation and Modelling Products for Near-Real-Time Assessment

<p>The frequency and impact of disasters rise at the global scale, calling for effective disaster risk management and innovative risk financing solutions. Disaster Risk Financing (DRF) can increase the ability of national and local governments, homeowners, businesses, agricultural producers, and low-income populations to respond more quickly and resiliently to disasters by strengthening public financial management and promoting market-based disaster risk financing. For landslide events, the usage of DRF products is not yet extensive, mainly due to challenges in capturing the appropriate destabilization factors and triggers, as well as forecasting the physical properties of a landslide event (such as its type, location, size, number of people affected, and/or exposed infrastructure). The availability and quality of satellite EO derived data on rainfall that triggers landslides (Global Precipitation Measurement mission / GPM) and observations of the landslides themselves (Copernicus Sentinel radar and multispectral sensors, very high resolution -VHR- optical sensors) greatly improved in recent years. In the same time, effective models are refined and support near-real time landslide hazard assessment (e.g. Landslide Hazard Assessment for Situational Awareness / LHASA; Flow path assessment of gravitational hazards at a Regional scale / FLOW-R).</p><p>The objective of this work is to present the prototype platform LANDSLIDE HAZARD INFORMATION SYSTEM (LHIS) which aims to support landslide DRF priorities using Earth Observation data and models. The functions of the platform are to be able to anticipate, forecast and respond to incipient landslide events (in Near-Real Time, NRT) by providing estimates of parameters suitable for parametric insurance calculations, including landslide inventories, susceptibility and hazard maps, potential damages and costs analyses. The LHIS prototype is accessible on the GEP / Geohazards Exploitation Platform allowing easy access, processing and visualization of EO-derived products. The prototype consists of three modular components with respectively: 1) a Landslide Detection component to create Landslide Inventories, 2) a Landslide Hazard Assessment component using global and national geospatial datasets leading to Landslide Susceptibility Maps, Scenario-based Hazard Maps and NRT Rainfall-based Hazard Maps, and 3) Landslide Impact Assessment component combining landslide hazard maps with population and infrastructure datasets to derive Landslide Exposure Maps and Landslide Impact Index. The landslide detection module is based on the analysis of time series of optical and SAR data; the landslide hazard and impact assessment modules are based on the LHASA, FLOW-R and PDI models.</p><p>The information system is being developed and tested in Morocco in collaboration with the solidarity fund against catastrophic events (FSEC) and the World Bank for two contrasting use cases in the Rif area (North Morocco) and the Safi area (Central Morocco) exposed to various landslide situations occurring in different environmental and climatic contexts.</p>

Multi-hazard parametric catastrophe bond trigger design for subduction earthquakes and tsunamis

This study presents trigger design methods and performance evaluations of multi-hazard parametric catastrophe bonds for mega-thrust subduction earthquakes and tsunamis. The catastrophe bonds serve as alternative disaster risk financing tools for insurers and reinsurers as well as municipalities and governments. Two types of parametric catastrophe bond trigger are investigated. A scenario-based method utilizes available earthquake source-based information, such as magnitude and location, whereas a station-intensity-based method can be implemented when seismic and tsunami hazard monitoring systems are in place in a region. The case study results, focusing on wooden buildings in Miyagi Prefecture, indicate that the station-intensity-based trigger methods outperform the scenario-based trigger methods significantly. Incorporating seismic and tsunami hazard information from multiple recording stations results in smaller trigger errors. The station-intensity-based methods are applicable to building portfolios at both municipality levels and regional levels.