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In many parts of the world, weather represents one of the major uncertainties affecting performance and management of agricultural systems. Due to global climate changes the climatic variability and the occurrence of extreme weather events is likely to increase leading to substantial increase in agricultural risk and destabilisation of farm incomes. This issue is not only important for farm managers but also for policy makers, since income stabilisation in agriculture is frequently considered as a governmental task.

The aim of this session is to discuss the state of the art research in the area of analysis and management of weather-related risks in agriculture. Both structural and non-structural measures can be used to reduce the impact of climate variability including extreme weather on crop production. While the structural measures include strategies such as irrigation, water harvesting, windbreaks etc., the non-structural measures include the use of the medium-range weather forecast and crop insurance.

The topic is at the borderline of different disciplines, in particular agricultural and financial economics, meteorology, modelling and agronomy. Thus, the session offers a platform to exchange ideas and views on weather-related risks across these disciplines with the focus on quantifying the impact of extreme weather on agricultural production including impacts of climate change, analysis of financial instruments that allow reducing or sharing weather-related risks, evaluation of risk management strategies on the farm level, development of the theory of risk management and to exchange practical experiences with the different types of weather insurance.

This session has been promoted by:
• Natural hazard Early career scientists Team (NhET, https://blogs.egu.eu/divisions/nh/tag/early-career-scientists/)
• Boosting Agricultural Insurance based on Earth Observation data (BEACON, https://beacon-h2020.com/)
• Research Center for the Management of Agriculutral and Environmental Risks (CEIGRAM, http://www.ceigram.upm.es/ingles/)

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Co-organized by SSS9
Convener: Ana Maria Tarquis | Co-conveners: Anne Gobin, Stefanos Mystakidis, Jonathan RizziECSECS, Wenwu Zhao, Luigi LombardoECSECS
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| Attendance Thu, 07 May, 08:30–10:15 (CEST)

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Chat time: Thursday, 7 May 2020, 08:30–10:15

Chairperson: Ana Maria Tarquis/Jonathan Rizzi
D1787 |
EGU2020-709<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Han Wang, Wenwu Zhao, and Yanxu Liu

Soil water erosion is a severe environmental issue which seriously damaging the sustainability of agriculture. Regional climate change could aggravate the threat of erosion, whereas vegetation greening in China (an increasing trend in vegetation cover) could act as a mitigation to the threat. On the basis of the Revised Universal Soil Loss Equation, we proposed a framework for performing an assessment of water erosion risk in China during 1998-2018. A contribution index was constructed to describe the influences of rainfall erosivity and cover management on water erosion risk changes in China during 1998-2018. The research objective was to explore the spatial pattern of water erosion risk change in China in recent decades and to identify the factor that has the largest contribution to the risk change. Results showed that: (a) The area with decreasing water erosion risk in China accounted for 34.97%, and the area with significant decreasing trends accounted for 20.04% of the middle and highly risky state areas. (b) The region that rainfall erosivity contributed more than cover management for absolute value accounted for 76.54%, whereas the contribution of cover management was increasing. (c) Vegetation greening can partly offset the stress caused by climate change. Water erosion risk in China decreased more than increased in risky state area. The pixels with cover management contribute more than rainfall erosivity was concentrated within the area where risk is decreasing, indicating a great contribution of vegetation greening to the risk mitigation. Consequently, enhancing the vegetation growth in the highly risky state water erosion region could reduce the erosion threat in China.

How to cite: Wang, H., Zhao, W., and Liu, Y.: Does vegetation greening partly offset increasing rainfall pressure? Risk assessment of the water erosion tendency in China over the past 20 years., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-709, https://doi.org/10.5194/egusphere-egu2020-709, 2019

D1788 |
EGU2020-883<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Oumayma Bounouh, Houcine Essid, and Imed Riadh Farah

Normalized Difference Vegetation Index (NDVI) serves as a significant reference for crop health monitoring. NDVI time series forecasting is a critical issue because of the importance of the involving fields, e.g., food scarcity, climate changes and biodiversity. Therefore, several forecasting models have been suggested and implemented in the literature. Herein, we propose a combination of forecasts using seasonally fitted probability functions changing weights. Contrary to commonly suggested combination models, this one does not rely on overall error measures and/or features, but on time slots similarities between probability density function (PDF) of real observations and forecasts. It is validated with 18 years MOD13Q1 NDVI time series describing a cereal canopy area that belongs to the northwestern of Tunisia. Additionally, the chosen forecasting models are Box Jenkins and Neural Network model. The forecasting accuracy was assessed using the root mean square error (RMSE). According to the results, each season had a different best-fit probability distribution function. Overall, these latter are: Gamma, Beta, Weillbul, and Extreme Generalised Value (EGV). Moreover, the suggested model has shown better forecasting accuracy than individual models, hybrid models and commonly used combining tool (RMSE respectively, 0.003, 0.45, 0.35, 0.38). Interestingly, another seasonally varying weights were determined based on the normal distribution. But, our suggested model showed better forecasting accuracy than this latter (RMSE of normally distributed changing weights= 0.30).

 
 

 

 

How to cite: Bounouh, O., Essid, H., and Farah, I. R.: Seasonally fitted probability functions changing weights for combining vegetation indices forecasting models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-883, https://doi.org/10.5194/egusphere-egu2020-883, 2019

D1789 |
EGU2020-911<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Yiming An and Wenwu Zhao

Soil conservation service is an important regulating ecosystem service. We estimated the soil conservation rate of the top five largest basins in the world from 2000 to 2018, classified the trend of conservation rate for each basin and each location as four types (i.e., significant decrease, decrease, increase and significant increase), and analyzed the relationships between soil conservation rate and driving factors. Results show that the Yangtze River basin produces the highest average soil conservation rate (with the value of 1429.68 t ha-1 yr-1). The Yangtze, Mississippi and Yellow River basins show a generally increasing conservation trend. Partial principal component analysis between soil conservation rate and driving factors show that slope gradient has the greatest impact on soil conservation rate, followed by rainfall and NDVI. Vegetation greening (increasing NDVI) could partly offset the effect of increasing rainfall on soil conservation rate in the Mississippi and Yellow River basins. More direct and quantitative variables should be used to represent human activities to analyze the impact on soil conservation change.

How to cite: An, Y. and Zhao, W.: Changes in soil conservation service and its driving factors: case studies in global top five largest basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-911, https://doi.org/10.5194/egusphere-egu2020-911, 2019

D1790 |
EGU2020-2640<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"></span>
| Highlight
Mikhail Semenov and Nimai Senapati

Improving yield potential and closing the yield gap are important to achieve global food security. Europe is the largest wheat producer, delivering about 35% of wheat globally, but European wheat's yield potential from genetic improvements is as yet unknown. We estimated wheat ‘genetic yield potential’, i.e. the yield of optimal or ideal genotypes in a target environment, across major wheat growing regions in Europe by designing in silico ideotypes. These ideotypes were optimised for current climatic conditions and based on optimal physiology, constrained by available genetic variation in target traits. A ‘genetic yield gap’ in a location was estimated as the difference between the yield potential of the optimal ideotype compared with a current, well-adapted cultivar. A large mean genetic yield potential (11–13 t ha−1) and genetic yield gap (3.5–5.2 t ha−1) were estimated under rainfed conditions in Europe. In other words, despite intensive wheat breeding efforts, current local cultivars were found to be far from their optimum, meaning that a large genetic yield gap still exists in European wheat. Heat and drought tolerance around flowering, optimal canopy structure and phenology, improved root water uptake and reduced leaf senescence under drought were identified as key traits for improvement. Closing this unexploited genetic yield gap in Europe through crop improvements and genetic adaptations could contribute towards global food security.

How to cite: Semenov, M. and Senapati, N.: Substantial genetic yield gap estimated for wheat in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2640, https://doi.org/10.5194/egusphere-egu2020-2640, 2020

D1791 |
EGU2020-4591<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Yongxiu Sun, Shiliang Liu, Yuhong Dong, Shikui Dong, and Fangning Shi

Quantifying drought variations at multi-time scales is important to assess the potential impacts of climate change on terrestrial ecosystems, especially vulnerable desert grassland. Based on the Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI), we assessed the influences of different time-scales drought (SPEI-3, SPEI-6, SPEI-12, SPEI-24, and SPEI-48 with 3, 6, 12, 24 and 48 months, respectively) on vegetation dynamics in the Qaidam River Basin, Qinghai-Tibet Plateau. Results showed that: (1) Temporally, annual and summer NDVI increased, while spring and autumn NDVI decreased from 1998 to 2015. Annual, spring and summer SPEI increased and autumn SPEI decreased. (2) Spatially, annual, spring, summer, and autumn NDVI increased in the periphery of the Basin, with 45.98%, 22.68%, 43.90%  and 30.80% of the study area, respectively. SPEI showed a reverse variation pattern with NDVI, with an obvious decreasing trend from southeast to northwest. (3) Annual vegetation growth in most areas (69.53%, 77.33%, 86.36%, 90.19% and 85.44%) was correlated with drought at all time-scales during 1998-2015. However, high spatial and seasonal differences occurred among different time-scales, with the maximum influence in summer under SPEI24. (4) From month to annual scales, NDVI of all land cover types showed higher correlation to long-term drought of SPEI24 or SPEI48. Vegetation condition index (VCI) and SPEI were positively correlated at all time-scales and had a more obvious response in summer. The highest correlation was VCI of grassland (June-July) or forest (April-May, August-October) and SPEI48. This study contributes to exploring the effect of drought on vegetation dynamics at different time scales, further providing credible guidance for regional water resources management.

How to cite: Sun, Y., Liu, S., Dong, Y., Dong, S., and Shi, F.: Effects of multi-time scales drought on vegetation dynamics in Qaidam River Basin, Qinghai-Tibet Plateau from 1998 to 2015, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4591, https://doi.org/10.5194/egusphere-egu2020-4591, 2020

D1792 |
EGU2020-7282<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"></span>
Roopam Shukla, Amsalu Woldie Yalew, Stephanie Gleixner, Bernhard Schauberger, and Christoph Gornott

Vulnerability to climate change differs spatially within the country owing to regional differences in exposure, sensitivity, and adaptive capacity. The paper aims to assess the vulnerability of smallholder farming systems in Ethiopia to observed climate change, to gain insight into factors that may shape vulnerability in the future. Spatial dynamics in vulnerability is assessed at subnational level (zone-level) and temporal dynamics is studied across three time periods i.e. historical (1985-2005), current (2005-2015), and future (2035-2045). The study uses an index-based approach, which is suitable for assessing vulnerability as it includes both biophysical and socio-economic dimensions. This approach combines the environmental and socio-economic data from different sources (agricultural surveys, climate, and remote sensing data) to capture the multi-dimensional attributes of vulnerability. This research contributes to evidence-based adaptation planning in Ethiopia by identifying areas and patterns of high vulnerability and its components.

How to cite: Shukla, R., Yalew, A. W., Gleixner, S., Schauberger, B., and Gornott, C.: Mapping the spatial and temporal dynamics in vulnerability of smallholder farming systems in Ethiopia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7282, https://doi.org/10.5194/egusphere-egu2020-7282, 2020

D1793 |
EGU2020-8057<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Matias Heino, Weston Anderson, Michael Puma, and Matti Kummu

It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.

How to cite: Heino, M., Anderson, W., Puma, M., and Kummu, M.: The relationship between extreme weather and low crop yields, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8057, https://doi.org/10.5194/egusphere-egu2020-8057, 2020

D1794 |
EGU2020-9937<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Andres Almeida-Ñauñay, Rosa M. Benito, Miguel Quemada, Juan Carlos Losada, and Ana Maria Tarquis

Grassland ecosystems are extremely complex and set up intricate structures, whose characteristics and dynamic properties are greatly influenced by climate and meteorological patterns. Climate change and global warming are factors that could impact negatively in the quality and productivity of these ecosystems.

Remote sensing techniques have been demonstrated as a powerful tool for monitoring extensive areas. In this study, two semi-arid grassland plots were selected in the centre of Spain. This region is characterized by low precipitation and moderate productivity per unit. Through scientific research, spectral vegetation indices (VIs) have been developed to characterize vegetation cover. The most common VI is the Normalized Difference Vegetation Index (NDVI). However, in vegetation scarcity conditions, bare soil reflectance is increased, and the feasibility of NDVI is reduced. This study aims to perform a method to compare soil and agro-climatic variables effect on vegetation time-series indices.

The construction of the time series was based on multispectral images of MODIS TERRA (MOD09A1.006) product acquired from 2002 till 2018. Three pixels with a temporal resolution of 8 days and a spatial resolution of 500 x 500 m were chosen in each area. To estimate and analyse VIs series, Red (620-670 nm) and Near Infrared (841-876 nm) channels were extracted and filtered by the quality of pixel. All spectral bands showed statistically significant differences confirming that both areas presented different soil properties. Moreover, average annual precipitation was different in each area of study.

NDVI calculation is only based on NIR and RED bands. To improve the estimation of vegetation in semi-arid areas, several indices have been developed to minimize the soil effect. Each one of them incorporates soil influence in a different way, i.e., Soil Adjusted Vegetation Index (SAVI) adds a constant soil adjustment factor (L), whereas, MSAVI, incorporate an L variable and dependant on soil characteristics.

Recurrence plots (RP) and recurrence quantification analysis (RQA) were computed to characterize the influence of agro-climatic variables in vegetation index dynamics. Characterization was based on various RQA measures, such as Determinism (DET), average diagonal length (LT) or entropy (ENT).

Our results showed different RPs depending on the area, VI utilized and precipitation. MSAVI patterns were further distinct, meanwhile, NDVI showed a noisy pattern. LT values in MSAVI were higher than in SAVI implying that MSAVI recurrent events are much longer than SAVI. Simultaneously, LT and DET values in ZSO, with a higher rain, were above ZEA values in MSAVI.

This indicates that incorporating more detailed information of soil and precipitation reinforce vegetation index estimation and allow to obtain a more distinct pattern of the time series. Therefore, in arid-semiarid grasslands, they should be considered.

ACKNOWLEDGEMENTS

The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain) and Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330, are highly appreciated.

How to cite: Almeida-Ñauñay, A., Benito, R. M., Quemada, M., Losada, J. C., and Tarquis, A. M.: Recurrence Quantification Techniques of vegetation time-series indices in semiarid grasslands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9937, https://doi.org/10.5194/egusphere-egu2020-9937, 2020

D1795 |
EGU2020-10669<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"></span>
Ana Maria Tarquis, David Rivas-Tabares, Juan J. Martín-Sotoca, and Antonio Saa-Requejo

In most Mediterranean climate regions drought events are of great importance and their effects on rainfed crops are evident. Crop yields of rainfed cereal are highly dependent of the soil-plant-atmosphere system, especially referred to the weather conditions and soil properties. However, very few studies are found on the importance of both factors on crop condition.

Several plots were localized in the midlands of Eresma-Adaja watershed. Combining remote sensing data and agricultural survey work those with monocrop cereal sequences were identify. These plots were clustered based on which soil class were allocated based on a Self-Organizing Map and precipitation regimen elaborated in the area (Rivas-Tabares et al., 2019). Within this area, two contrasting soil properties sites were selected to assess plots with at least 20 years of rainfed monocropping sequences but under similar weather regime. This allows us to analyze the effect and relationships of soil type and rainfall with Normalized Difference Vegetation Index (NDVI) in time.

The NDVI average from both areas are statistically different in the growing season suggesting that soils and weather conditions are motivating the spectral variability of sites. The influence of soil texture and rainfall regimen related to NDVI values and interannual variability during the crop growth are discussed.

References

Rivas-Tabares, D., AM Tarquis, B Willaarts, Á De Miguel. 2019. An accurate evaluation of water availability in sub-arid Mediterranean watersheds through SWAT: Cega-Eresma-Adaja. Agricultural Water Management 212, 211-225.

 

ACKNOWLEDGEMENTS

Finding for this work was partially provided by Boosting agricultural Insurance based 465 on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020. The authors also acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain. The data provided by ITACyL and AEMET is greatly appreciated.

How to cite: Tarquis, A. M., Rivas-Tabares, D., Martín-Sotoca, J. J., and Saa-Requejo, A.: Monitoring rainfed cereals under different soils and rainfall pattern, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10669, https://doi.org/10.5194/egusphere-egu2020-10669, 2020

D1796 |
EGU2020-10675<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Mehdi H. Afshar, Timothy Foster, Ben Parkes, Koen Hufkens, Francisco Ceballos, and Berber Kramer

Extreme weather events pose significant risks to the livelihoods of smallholder farmers across Asia and Africa. Weather index-based insurance provides a potential solution to mitigate risks caused by crop failures, providing farmers with a payout in the event of a poor harvest. It also reduces costs relative to traditional indemnity insurance by eliminating the need for resource-intensive, in-situ assessment of losses. However, one challenge associated with weather index-based insurance is basis risk – where the payouts triggered by the index do not match actual crop losses. High levels of basis risk are observed across many existing weather index-based insurance products, and represent a key constraint to successful upscaling.  

A common feature of existing weather index-based insurance contracts is that payouts are triggered based on weather indices defined over fixed calendar periods, specified to capture the typical duration of the crop growing season or key phenological stages in a given agricultural system. In reality, however, the timing of a crop’s sensitivity to weather often varies significantly between individual plots or farmers due to differences in management practices (e.g., sowing date, variety choice) and meteorological conditions (e.g., temperature and precipitation) that affect rates of crop development. Failure to consider this heterogeneity is potentially a significant driver of basis risk, and suggests that opportunities may exist to improve the quality of index insurance by designing phenology-specific insurance contracts. 

In this study, we evaluate the impacts of improved monitoring of crop phenology on the performance of index-based crop yield models through a range of synthetic model-based simulated experiments for wheat and rice production in Haryana and Odisha states in India. We use a calibrated process-based crop simulation model (APSIM) to evaluate yields for a range of potential weather realizations and agricultural management practices typically observed in our case study regions. Subsequently, we develop non-linear statistical (i.e. index-based) models using non-parametric regression techniques (Multivariate adaptive regression splines; MARS) to reproduce APSIM-simulated yields as a function of rainfall and temperature conditions during key sensitive crop growth stages. 

Our results show that by considering field-level heterogeneity in crop phenology and development, it is possible to reliably estimate (>0.8 r-squared) wheat and rice yields. In contrast, model performance deteriorates significantly when variability in growth stage between individual simulated fields is not considered or when weather predictors are aggregated over the entire growing season as opposed to specific growth stages. These findings show that considering crop phenology can dramatically improve the performance of statistical yield models and, in turn, the accuracy of an index-based insurance product. Nevertheless, reductions in basis risk must also be balanced against the increasing complexity and implementation costs of these potential products in smallholder environments.

How to cite: H. Afshar, M., Foster, T., Parkes, B., Hufkens, K., Ceballos, F., and Kramer, B.: Improving performance of index insurance using crop models and phenological monitoring, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10675, https://doi.org/10.5194/egusphere-egu2020-10675, 2020

D1797 |
EGU2020-11283<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
Beatrice Monteleone, Mario Martina, and Brunella Bonaccorso

Agricultural production is highly sensitive to extreme weather events such as droughts, floods and storms. According to the Food and Agriculture Organization, between 2005 and 2015 natural disasters cost the agricultural sectors of developing country economies a staggering $96 billion in damaged or lost crop and livestock production. Drought was one of the leading culprits. Eighty-three percent of all drought-caused economic losses documented by FAO's study were absorbed by agriculture, with a price tag of $29 billion. Since extreme droughts are expected to increase worldwide both in number and severity, the development of appropriate strategies to reduce and mitigate drought impacts on agricultural production will be essential to enable farmers to quickly recover from the disaster. There is growing interest in insurance as an instrument for managing drought risk in agriculture. Insurance is a self-reliant mitigation measure that increases society's resilience, particularly in the financial sector. There are two main options of crop risk transfer solutions: indemnity-based programs, in which the basis for compensation is the actual loss; and weather index-based (or parametric) programs. Parametric programs are based on variables called indices, often retrieved from remote-sensing observations. Indices should be highly correlated with agricultural losses. A parametric policy for drought pays out if a specific value of the index is achieved in a specific period. Index-based insurance shows various attractive features: the value of the index cannot be influenced by farmers, indemnities are based on observable variables (the indices), on-farm inspections to assess the damages are no more necessary and finally funds to recover from the disaster are provided quickly.

The aim of this work is the design of a parametric insurance framework against drought to be applied in the Caribbean region as well as in other regions with similar conditions. Initially a new drought index, the Probabilistic Precipitation and Vegetation Index (PPVI) was developed to identify drought. PPVI was computed combining two consolidated drought indices, the Standardized Precipitation Index (SPI) and the Vegetation Health Index (VHI). SPI was calculated from precipitation retrieved from satellite (the Climate Hazard Group Infrared Precipitation dataset was used) and VHI is already a remote-sensing product. Then a framework allowing an objective identification of drought weeks was implemented. The framework was used in combination with PPVI and the model was calibrated in order to reproduce past drought events at specific locations. A relationship between drought and negative crop yield anomalies was established. Significant crop growth periods were taken into consideration: establishment, vegetative, flowering and yield formation. The probability of having a negative crop yield anomaly when a significant growth period was in drought was computed. The sensitivity to drought of each crop growth period was evaluated based on this probability. In the end a loss index to relate drought with yield reduction suffered by farmers was developed. The entire framework was tested in the Dominican Republic and cereals losses (maize and sorghum) were evaluated. Results were promising.

How to cite: Monteleone, B., Martina, M., and Bonaccorso, B.: A parametric insurance framework based on remote-sensing observations to mitigate drought impacts through risk financing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11283, https://doi.org/10.5194/egusphere-egu2020-11283, 2020

D1798 |
EGU2020-11467<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"></span>
| Highlight
Anne Gobin

Agricultural yield is largely determined by weather conditions during the crop growing season. A comparison of meteorological indicators between low and high arable yields revealed significant (p > 0.05) differences in meteorological indicators (Gobin, 2018), and these change with crop. Further analysis revealed differences in climate resilience (Kahiluoto et al., 2019).

An important aspect of crop yield assessment concerns crop growth development and subsequent yield prediction (Durgun et al., 2016). Current models have predominantly concentrated on the relation between meteorological data and crop yield (Gobin et al., 2017). A lot of data are available on the input side to include soil and weather, but very few on crop development and yield at the field scale.

A new era of satellite remote sensing and sensor technology has already offered a paradigm shift to data rich environments with unprecedented possibilities to monitor crop development at higher spatial, temporal and spectral resolutions. Combining modelling and statistical analysis with monitoring from remote sensing presents new opportunities to understand crop growth as a basis for crop yield assessment (Durgun et al., 2020) and further developments in the agriculture, insurance and bio-economy sector.

Examples of common arable crop growth assessment will be drawn from different grants and projects.

References:

  • Durgun, Ö, Gobin, A., Duveillier, G., Tychon, B., 2020. A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. International Journal of Applied Earth Observations and Geoinformation, 86. https://doi.org/10.1016/j.jag.2019.101988
  • Durgun, Y.Ö., Gobin, A., Vandekerchove, R., Tychon, B., 2016. Crop Area Mapping using 100m PROBA-V time series. Remote Sensing 8(7), 585; www.doi.org/10.3390/rs8070585.
  • Gobin, A., Kersebaum K.C., Eitzinger J., Trnka M., Hlavinka P., Takáč J., Kroes J., Ventrella D., Dalla Marta A., Deelstra J., Lalić B., Nejedlik P., Orlandini S., Peltonen-Sainio P., Rajala A., Saue T., Şaylan L., Stričevic R., Vučetić V., Zoumides C., 2017. Variability in the water footprint of arable crop production across European regions. Water 2017, 9(2), 93; https://doi.org/10.3390/w9020093
  • Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009
  • Kahiluoto H., Kaseva, J., Balek, J., Olesen, J.E., Ruiz-Ramos, M., Gobin, A., Kersebaum, K.C., Takáč, J., Ruget, F., Ferrise, R., Bezak, P., Capellades, G., Dibari, C., Mäkinen, H., Nendel, C., Ventrella, D., Rodríguez, A., Bindi, M., Trnka M., 2019. Decline in climate resilience of European wheat. Proceedings of the National Academy of Sciences of the USA 116: 123-128. https://doi.org/10.1073/pnas.1804387115

How to cite: Gobin, A.: Crop yield evaluation using sentinel satellite imagery and modelling methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11467, https://doi.org/10.5194/egusphere-egu2020-11467, 2020

D1799 |
EGU2020-12764<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
yijia Wang, bojie Fu, and yanxu Liu

Abstract: The Tibetan Plateau, as an ecologically fragile area with typical alpine meadow ecosystems, is sensitive to climate change, especially drought. However, spatial heterogeneity of the vegetation dynamics plays an important role in response to climate change, while there is relatively lacked evidence on their spatial control factors. Here, multivariate remote sensing data were used to construct vegetation index and multi-scale drought index to understand the vegetation dynamics and drought trend of the Tibetan plateau from 2000 to 2015, for revealing their differences or spatial response through correlation analysis. Elevation, land surface temperature, land cover and snow cover were selected as spatial control factors and the results showed that the vegetation was greening in the east while browning in the west. The vegetation indices including EVI, LAI and GPP were all closely related to drought index, while the magnitudes of response were spatially different. The contributions of control factors for the responses were not inconsistency because of the differential ecological meaning of the vegetation indices. Our results provide a spatial basis for the ecosystem management in the Tibetan Plateau by clarifying the spatial heterogeneity of control factors on the response of vegetation dynamics to drought.

Keywords: vegetation dynamics; drought response; grassland ecosystem; spatial heterogeneity; remote sensing; Tibetan Plateau

How to cite: Wang, Y., Fu, B., and Liu, Y.: Remote sensed evidence on the control factors of grassland ecosystem response to drought in the Tibetan Plateau, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12764, https://doi.org/10.5194/egusphere-egu2020-12764, 2020

D1800 |
EGU2020-13741<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"><span title="Early career scientist: an ECS is an undergraduate or postgraduate (Masters/PhD) student or a scientist who has received their highest degree (BSc, MSc, or PhD) within the past seven years. Provided parental leave fell into that period, up to one year of parental leave time may be added per child, where appropriate.">ECS</span></span>
María-Elena Rodrigo-Clavero, Claudia-Patricia Romero-Hernández, and Javier Rodrigo-Ilarri

In this work a new environmental indicator for the analysis of land use change over time (ENV-IND) is presented. The ENV-IND indicator has been defined and assigned to every land use included on the SIOSE, the official Information System on Land Occupation of Spain. The methodology is based on assigning an ENV-IND value for every polygon considered by the SIOSE as a function of the areal percentage occupied by every land use inside each polygon.

SIOSE is integrated into the National Land Observation Plan (PNOT) whose objective is to generate a database of Land Occupation for all Spain, integrating all the information available from the regional and central Administration of Spain. The ENV-IND indicator has been defined for 80 different land use categories and its value depend in the joint consideration of the following factors: anthropization nature, water consumption, environmental sustainability and landscape value.

The evolution of the ENV-IND indicator over time has been obtained for the whole Valencia Region for three different dates (2005-2009-2015) and shows that the environmental value is decreasing with time in terms of the ENV-IND indicator. The ENV-IND indicator is therefore applicable as a tool to quantify and analyze trends of the environmental quality related with land use change.

 

How to cite: Rodrigo-Clavero, M.-E., Romero-Hernández, C.-P., and Rodrigo-Ilarri, J.: Land use evolution over time using public data and a new environmental indicator. Application to the Valencia region (Spain), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13741, https://doi.org/10.5194/egusphere-egu2020-13741, 2020

D1801 |
EGU2020-13925<span style="font-size: .8em!important; font-weight: bold; vertical-align: super; color: green!important;"></span>
Ruja Mansorian, Mohammad Zare, and Guy Schumann

In this study, long-term time series of precipitation data were used for determining the drought condition using the standard precipitation index (SPI) for 3, 6 and 12 month time scales. The indicators were calculated with two methods: a) using a gamma distribution and transforming the probability of occurrence to standard normal distribution, b) using the non-parametric plotting position method. Then, the SPI values for two consequent years 2013-14 and 2014-15 were extracted from data to study on meteorological drought. The SPI index calculations showed that the first year had near normal, whereas the second year had extreme drought condition. In parallel, 34 Landsat 8 satellite images were downloaded during the indicated time period to determine normalized difference vegetation index (NDVI) and vegetation condition index (VCI) as agricultural drought indices. The mean values of VCI for each month were considered as representative value for drought condition of the area. When the agricultural and meteorological drought indices were determined, the correlation coefficient (r) were calculated for finding the relation between these types of droughts. the results show that the highest correlation between SPI-3,6 and 12-month time scales and VCI occurred in 4, 2 and 4 months lag time respectively, with corresponding r value of 0.67, 0.65 and 0.69. The best agreement between these indices with calculated lag time proves the hypothesis that agricultural drought occurs after meteorological drought. Therefore, the results could be applied by farmers to plan an appropriate irrigation scheduling for upcoming droughts, specially, in arid and semi-arid areas. It could be concluded that for having suitable planning in water scarcity condition, understanding the situation helps water planners have better insight about management polices to minimize the effects of this natural hazard on human. To sum up, finding a relation between different types of droughts is helpful for monitoring, predicting and detecting droughts to better prepare for drought phenomena and to minimize losses

How to cite: Mansorian, R., Zare, M., and Schumann, G.: Study on the correlation between meteorological and agricultural drought, based on remotely sensed indices, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13925, https://doi.org/10.5194/egusphere-egu2020-13925, 2020