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The authors determine the effectiveness of the Rainfall Index Annual Forage Program (RIAFP) in offsetting yield risk of winter annual forage growers. The authors also evaluate the effectiveness in reducing risk of potential alternative weather indices.

The RIAFP is designed to compensate forage producers when yield losses occur. Prior research found weak correlation between the rainfall index and actual winter annual forage yields. The authors use long-term small-plot variety trials of rye, ryegrass, wheat, triticale and oats with rainfall recorded on site and measure the correlation of the index with actual rainfall and actual yields. The alternative indices include frequency of precipitation events and of days with temperature below freezing.

The correlation between actual rainfall and the current RMA index was strongly positive as in previous research. Correlations between forage yields and monthly intervals of the current RMA index were mostly statistically insignificant, and many had an unexpected sign. All indices had some correlations that were inconsistent across time intervals and forage variety. The inconsistent signs suggest a nonlinear relationship with weather and forage yield, indicating that rainfall can be too much or too little. The number of days below freezing has the most potential of the three measures examined.

Producers should view the winter forage RIAFP as a risk-increasing income-transfer farm program. A product to reduce the risk for forage producers may need to use a crop growth simulation model or another approach that can capture the nonlinearity.

Considerably more data were considered than in past research. Past research did not consider alternative weather indices. The program should be continued if its goal is to serve as disguised income transfer, but it should be discontinued if its goal is to reduce risk.

The purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.

Various regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.

The authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.

By including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.

The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.

A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.

The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.

This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.

This study aims to investigate aspects related to the changing trends of the rainfall extremes in the entire Mekong Delta in the period of 32 years (1984-2015) applying rainfall extreme indices. First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen’s slope estimate.

First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen’s slope estimate.

The results of R20 pointed out that an insignificant upward tendency was found in the coastal provinces, whereas an insignificant downward tendency was also recorded in the inland provinces. Regarding the number of R50, a similar trend to R20 was recorded with five stations slightly increased and five stations slightly decreased. For the number of R100, the results recorded an absence of significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend.

For the number of R100, the results recorded an absence of the significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend. Of note is the fact that the number of R100 occurred more frequently in the northern provinces, which means the northern region is facing a high risk of flooding.

Examine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.

Based on 0.5° gridded rainfall and temperature data (1960–2015) and projections of the WRF-RCM (2016–2040), the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are calculated for Central Java over different time spans. The drought indices are correlated to annual and seasonal rice production, based on which a weather index insurance structure is developed.

The six-month SPI correlates best with the wet season rice production, which generates most output in Central Java. The SPI time series reveals that drought severity increases in future years (2016–2040) and leads to higher payouts from the weather index structure compared to the historical period (1960–2015).

The developed methodology in using SPI for historical and projected periods allows the development of weather index insurance in other regions which have a clear link between rainfall deficit and agricultural production volatility.

Meteorological drought indices are a viable alternative for weather index insurance, which is usually based on rainfall amounts. RCM outputs provide valuable insights into future climate variability and drought risk and prolong the time series, which should result in more robust weather index insurance products.

In the equatorial Pacific, rainfall is affected by global climate phenomena, such as El Niño Southern Oscillation (ENSO). However, current publicly available methodologies for valuing weather derivatives do not account for the influence of ENSO. The purpose of this paper is to develop a complete framework suitable for valuing rainfall derivatives in the equatorial Pacific.

In this paper, we implement a Markov chain for the occurrence of rain and a gamma model for the conditional quantities using vector generalized linear models (VGLM). The ENSO forecast probabilities reported by the International Research Institute for Climate and Society (IRI) are included as independent variables using different alternatives. We then employ the Esscher transform to price rainfall derivatives.

The methodology is applied and calibrated using the historical rainfall data collected at the El Dorado airport weather station in Bogotá. All the estimated coefficients turn out to be significant. The results prove more accurate than those of Markovian gamma models based on purely statistical descriptions of the daily rainfall probabilities.

This procedure introduces the novelty of incorporating variables related to the climatic phenomena, which are the forecast probabilities regularly published for the occurrence of El Niño and La Niña.

The paper analyzes the hedging efficiency of weather-indexed insurance for corn production in Northeast of China. The purpose of this paper is to identify the potential weather variables that impact corn yields and to analyze the efficiency of weather-indexed insurance under varying thresholds for payouts (strike values).

Statistical relationships between climate variables and crop yields are used to construct weather-indexed insurance that enable a farmer to hedge against adverse precipitation outcomes. Mean root square loss is used to compare the efficiency of various weather products.

Based on efficiency comparisons, it turns out that in some, but not all circumstances, cumulative rainfall (CR) insurance can be used to hedge weather risk. When CR explains one-third or more of the variation in corn yields, a hedge can offset the revenue loss caused by the corresponding weather risk; but when it explains much less of the yield variation, it is inefficient for hedgers to buy weather insurance. If CR explains variation in crop yields, it is increasingly efficient to employ CR-indexed insurance as strike values decline for put options or increase for call options.

The paper provides a method for calculating the premium for an insurance product that provides a payout if CR in a growing season is too low.

This research is important because it illustrates the potential benefits of using weather insurance as an agricultural risk management strategy in China.

Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts to ensure the safe operation of intercity railways in metropolitan areas.

An approach coupled with the Euler multiphase model as well as the standard k-ɛ turbulence model is used to investigate the coupled flow feature surrounding trains and viaducts, including airflow and raindrops, and the numerical results are validated with those of the wind tunnel test. Additionally, the train’s dynamic response and the operating safety region in different crosswind speeds and rainfall is investigated based on train’s aerodynamic loads and the train wheel–rail dynamics simulation.

The aerodynamic loads of trains at varying running speeds exhibit an increasing trend as the increase of wind speed and rainfall intensity. The motion of raindrop particles demonstrates a significant similarity with the airflow in wind and rain environments, as a result of the dominance of airflow and the supplementary impacts of droplets. As the train’s operating speed ranged between 120 and 200 km/h and within a rainfall range of 20–100 mm/h, the safe operating region of trains decreased by 0.56%–7.03%, compared with the no-rain condition (0 mm/h).

The impact of crosswind speeds and rainfall on the train’s aerodynamic safety is studied, including the flow feature of crosswind and different particle-sized raindrops around the train and viaduct, aerodynamic loads coefficients suffered by the intercity train as well as the operating safety region of intercity trains on the viaduct.

Novel micro‐insurance schemes are emerging to help the poor better deal with droughts and other disasters. Climate change is projected to increase the intensity and frequency of disasters and is already adding stress to actual and potential clients of these schemes. As well, insurers and reinsurers are increasingly getting worried about increasing claim burdens and the robustness of their pricing given changing risks. The purpose of this paper is to review and suggest ways to methodologically tackle the challenges regarding the assessment of drought risk and the viability of index‐based insurance arrangements in the light of changing risks and climate change.

Based on novel modeling approaches, the authors take supply as well as demand side perspectives by combining risk analysis with regional climate projections and linking this to household livelihood modeling and insurance pricing. Two important examples in Malawi and India are discussed, where such schemes have been or are about to be implemented.

The authors find that indeed micro‐insurance instruments may help low‐income farming households better manage drought risk by smoothing livelihoods and reducing debt, thus avoiding poverty traps. Yet, also many obstacles to optimal design, viability and affordability of these schemes, are encountered. One of those is climate change and the authors find that changing drought risk under climate change would pose a threat to the viability of micro‐insurance, as well as the livelihoods of people requesting such contracts.

The findings and suggestions may corroborate the case for donor support for existing or emerging insurance arrangements helping the poor better cope with climate variability and change. Furthermore, a closer linkage between climate and global change models with insurance and risk management models should be established in the future, which could be beneficial for both sides.

The purpose of this paper is to investigate the patterns and trends of drought incidence in north east highlands of Ethiopia using monthly rainfall record for the period 1984-2014.

Standard precipitation index and Mann – Kendal test were used to analyze drought incident and trends of drought occurrences, respectively. The spatial extent of droughts in the study area has been interpolated by inverse distance weighted method using the spatial analyst tool of ArcGIS.

Most of the studied stations experienced drought episodes in 1984, 1987/1988, 1992/1993, 1999, 2003/2004 and 2007/2008 which were among the worst drought years in the history of Ethiopia. The year 1984 was the most drastic and distinct-wide extreme drought episode in all studied stations. The Mann–Kendal test shows an increasing tendencies of drought at three-month (spring) timescale at all stations though significant (p < 0.05) only at Mekaneselam and decreasing tendencies at three-month (summer) and 12-month timescales at all stations. The frequency of total drought was the highest in central and north parts of the region in all study seasons.

This detail drought characterization can be used as bench mark to take comprehensive drought management measures such as early warning system, preparation and contingency planning, climate change adaptation programs.