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Hazard Risk Managment in Agriculture and Agroecosystems

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/)
• Research Center for the Management of Agriculutral and Environmental Risks (CEIGRAM, http://www.ceigram.upm.es/ingles/)

Convener: Margarita Ruiz-Ramos | Co-conveners: Alfredo Rodríguez, Ana Maria Tarquis, Anne Gobin, David Rivas-TabaresECSECS
| Tue, 24 May, 13:20–14:50 (CEST)
Room C

Tue, 24 May, 13:20–14:50

Chairperson: Ernesto Sanz Sancho


Bernhard Schauberger et al.

Recent adverse weather events in Europe have questioned the stability of crop production systems. We assessed the vulnerability of eleven major crops in France between 1959 and 2018 as a function of climate, crafting a novel hazard framework that combines exposure and sensitivity to weather-related hazards. Exposure was defined as the frequency of hazardous climate conditions, while sensitivity of crops was estimated by the yield response to single and compound hazards. We used reported yields available at departement (county) level. Vulnerability was computed as the exposure-weighted average of crop sensitivities. Our results do not reveal any historical evidence for an increased vulnerability of French crop production. Rather, the sensitivity to adverse weather events, and thus the overall vulnerability, has significantly decreased for six of the eleven crops between 1959 and 2018, and shown no significant decline or remained stable for the other five. Yet compound hazards can induce yield losses of 30% or more for several crops. Moreover, as heat-related hazards are projected to become more frequent with climate change, crop vulnerability may rise again in the future. Our results may support insurance design by identifying single and compound hazards that can severely affect yields.

How to cite: Schauberger, B., Makowski, D., Ben-Ari, T., Boé, J., and Ciais, P.: The decreasing vulnerability of French crop production to climatic hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1498, https://doi.org/10.5194/egusphere-egu22-1498, 2022.

Hanna Sjulgård et al.


Diverse cropping systems are associated with multiple ecosystem services and are suggested to alleviate the effects of drought and heat stress. The magnitude and frequency of extreme weather events are projected to increase in the future due to climate change, and diverse cropping systems might therefore become key for food security. However, there is still limited information on the spatiotemporal variation of crop species diversity and how it relates to differences in climate or soil type. We aimed at quantifying how crop diversity developed over time at the national and regional scale in Sweden between 1965 and 2019, and how crop species diversity is related to climatic factors and soil texture.


Sweden is an interesting case study due to the large range in latitude from north to south. The analyses were conducted using national databases containing historical records of crop production and climate data, as well information on soil texture. To quantify crop species diversity, species richness and a crop diversity index that reflects the area-weighted equivalent number of crops were used.


Crop species richness and crop diversity index increased from north to south in Sweden, and our results showed a positive relationship between mean annual temperature and latitude to crop diversity at national level. The positive relationship shows how mean annual temperature and length of the vegetation period control crop diversity across the country. There were no significant relationships between crop diversity and mean annual precipitation and soil texture, respectively. Crop species richness did not change over time at national level while crop diversity index experienced a temporal decrease. At the county level, different temporal trends were observed among counties: in some counties an increase in crop diversity and species richness occurred, while other counties had no change or a temporal decrease.


The differences in the results between national and county level show the importance to include different scales in the examination of temporal developments of crop diversity. Although crop diversity index decreased over time at national level, the temporal increase observed in almost half of the counties suggest that it is possible to increase crop diversity in Sweden. The different temporal changes between counties imply that crop diversity is affected by an interplay between natural and socioeconomic factors. Natural factors constrain which crops can be grown, but to promote diversification of agricultural crops in the future, socioeconomic factors need to be considered.

How to cite: Sjulgård, H., Colombi, T., and Keller, T.: Spatiotemporal patterns of crop diversity reveal potential for diversification in Swedish agriculture , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2330, https://doi.org/10.5194/egusphere-egu22-2330, 2022.

Antonio Saa-Requejo et al.

Frosts have a significant impact on agriculture in Spain, as in many other temperate countries. Their changing behaviour in the context of climate change is of great interest to public institutions, agricultural unions, and the agricultural insurance sector. For this reason, this study is focused on the evolution of days with temperatures below specific ranges in different projections (specifically RCP4.5 and RCP8.5) of climate change.

The wine grape crop was selected for this study, which is of great importance in the agriculture of our country and for which we have series (2005-2019) of frost event dates linked to crop damages. The values of local temperatures on those dates where not available, so this data were obtained from the observational EOBS gridded dataset. A range of relevant temperature was extracted for these data, and then the analysis of the temperatures within this range in the immediate future (2020-2050) with respect to 1990-2019 was performed.

The study of the present climate confirms that spring frosts appear in the three areas studied, with intrazonal differences being observed. In none of these zones do the most extreme winter frosts occur below -15ºC, but in all of them frosts below -10ºC do occur.

For a future with moderate warming (RCP4.5), the day of the last frost is expected to be up to 4 days earlier in most of the studied areas. The absolute minimum temperature in March, April and May is also expected to increase between 0.4 and 0.8 ºC with smaller increases as spring progress (minor increase in May than in March).

For a future with severe warming (RCP8.5), the date of the last frost is expected to be up to 8-11 days earlier. Other changes under this scenario are increasing in the absolute minimum temperature in March, April and May between 0.6 and 1.5ºC, with smaller increases in April.

Consequences for the wine grape management and varietal selection pursuing adaptation to reduce crop damages are discussed.



We are grateful for funding from the Entidad Estatal de Seguros Agrarios (www.mapa.gob.es/es/enesa/) under proyect P200220C321 titled “Accident rate in winemaking vineyards: retrospective evaluation taking into account the restructuring of the sector since 1995”. (Siniestralidad en viñedo de vinificación: evaluación retrospectiva teniendo en cuenta la reestructuración del sector desde 1995) 

How to cite: Saa-Requejo, A., Rodriguez, A., Ruiz-Ramos, M., Valencia, J. L., Tarquis, A. M., and Baeza, P.: Analysis of frost in vineyards in Spain in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11843, https://doi.org/10.5194/egusphere-egu22-11843, 2022.

Alfredo Rodríguez et al.

Permanent grasslands are a very relevant cropping system in the North of Spain and support the main dairy farms in the country. Adaptation to climate change will be required given the projected changes of regional precipitation. To support such adaptation, modelling of these systems to generate high quality projections of the system performance is required. In the region to be simulated, grasslands are managed with a mixture of cuts and grazing. Several issues hinder the modelling of this type of systems: 1) the available data of grazing intensity presents large uncertainties; 2) there are few grassland models that allows flexibility to define a variable combination of cuts and grazing; 3) soil heterogeneity. It follows, and expands to grazing, the exercise performed by Gómara et al. (2020), who used the Pasture Simulation model (PaSim) to simulate a mown permanent grassland in the French Massif Central.
The model was calibrated using data from Villaviciosa (Asturias, Spain, 5º 26' 27" W, 43° 28' 50" N, 10 m a.s.l.), located at northern Spain with a temperate climate. This calibration was used to simulate several grassland locations distributed along the Cantabrian Sea. The soil information was obtained from Trueba et al. (2000). The model was configured for the optimum management for mowing and nitrogen fertilization. The 1976-2005 period and the 2030-2059 period were selected. For the future period two representative concentration pathway emission scenarios (RCP, van Vuuren et al., 2011) were selected (i.e. RCP4.5 and RCP8.5). An ensemble of climate models will be used from the Coordinated Regional Climate Downscaling Experiment (CORDEX, Giorgi and Gutowski, 2015) bias-adjusted by using the European observational database EOBS (Haylock et al., 2008) with the empirical quantile mapping method included in the climate4R R package (Iturbide et al., 2019). 
Modelling was challenging due to a combination of complexity (many processes involved) and uncertainty (observed data are difficult to generate). The results of the simulation exercise allow for assessing PaSim skill to reproduce the performance of these complex systems, as well as to determine the main weaknesses of the model and the observational/experimental required to improve the modelling work.

Giorgi, F. and Gutowski, W.J., 2015. Annual Review of Environment and Resources, 40(1): 467-490.
Gómara I, Bellocchi G, Martin R, Rodríguez-Fonseca B, Ruiz-Ramos M, 2020. Agricultural and Forest Meteorology, 280, 107768.
Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P. and New, M., 2008.  J. Geophys. Res., 113: D20119.
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M.D., Manzanas, R., San-Martín, D., Cimadevilla, E., Cofiño, A.S. and Gutiérrez, J.M., 2019. Environ. Modell. Softw., 111: 42-54.
Trueba, C., Millán, R., Schimd, T, Lago, (2000). CIEMAT. ISBN: 84-7834-370-9. Madrid.
van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K., 2011. Clim. Change, 109: 5–31.

How to cite: Rodríguez, A., Gómara, I., Bellocchi, G., Martin, R., Martínez-Fernández, A., Carballal, A., Doltra, J., del Prado, A., and Ruiz-Ramos, M.: The challenges of modelling mixed management grasslands in North Spain under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12103, https://doi.org/10.5194/egusphere-egu22-12103, 2022.

Ernesto Sanz Sancho et al.

Rangelands ecosystems contain more than a third of the global land surface, sustaining key ecosystem services and livelihoods. Unfortunately, they suffer from severe degradation on a global scale. Normalized Differenced Vegetation Index (NDVI) has been used to monitor low ground cover vegetation, especially relevant for arid and semiarid regions.

MODIS data are commonly used to calculate NDVI to monitor rangelands. In this study, we used time series metrics and Hurst Exponent from multifractal detrended fluctuation analysis to cluster different rangeland types to monitor and classify temporally and spatially diverse rangelands.

The Northwest (Noroeste) agricultural region in the province of Murcia was selected from the Southeast of Spain. We selected approximately 20.000 pixels to cover different areas that include land uses that are utilized for grazing. The selection aimed to collect pixels where other land uses were kept to a minimum, given the great spatial variability in Spain. We collected the time series using satellite data of MODIS (MOD09Q1.006) from 2000 to 2020. The pixels have a spatial resolution of 250 x 250 m2 and a temporal resolution of 8 days. This selected area represents a mix of cereal croplands, tree croplands, grasslands, scrublands, and forested areas; all of them with an arid climate.

We used unsupervised random forest and compared the produced clusters with the classification from the Spanish parcels classification systems to test our model. Our goal is to study the ability of unsupervised clustering using NDVI time series and their multifractal character to categorize and monitor their vegetation status, key information for farmers and managers to adapt to a changing situation due to climate change. This information can be used in other arid areas with similar geophysical conditions.

Acknowledgments: The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.


Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1-4), 87-114.

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J. C., & Tarquis, A. M.  (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Almeida-Ñauñay, A.F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488.

How to cite: Sanz Sancho, E., Almeida-Ñauñay, A., Díaz-Ambrona, C. G., Saa-Requejo, A., Ruiz-Ramos, M., Rodríguez, A., and Tarquis, A. M.: Clustering arid rangeland pixels using NDVI series and fractal analysis to classify land uses. Case in Southeastern Spain., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-287, https://doi.org/10.5194/egusphere-egu22-287, 2022.

Andrés Felipe Almeida Ñauñay et al.

Mediterranean agriculture faces drought as one of the most challenging obstacles to overcome. Especially, in semiarid grasslands, where every year the biomass production suffers severe damage due to several factors, being one of them the lack of precipitation. For this reason, semiarid vegetation monitoring allows us to improve the management and conservation of these essential ecosystems. Meteorological drought is commonly monitored using indices such as the Standard Precipitation Index (SPI) and the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agricultural drought is measured by the Vegetation Health Index (VHI). In this work, we present different methodologies to optimize the correlation between both droughts by standardizing the vegetation index and selecting the best time scale throughout the year.

First, we selected drought-vulnerable Mediterranean grasslands zones in the centre of Spain. By doing this, we pretend to evaluate the performance and the sensibility of the drought indices. MODIS data (MOD09Q1) was used to calculate the Normalized Difference Vegetation Index (NDVI), then it is standardised to define a standardized vegetation index (SVI). The meteorological indices SPI and SPEI were calculated using data collected from nearby weather stations. Overall, our results revealed that SPEI was better correlated with SVI and obtained better results in the critical seasons, in comparison to SPI. The quarterly scale was the most suitable, showing a higher relationship than the monthly scale. This fact suggest that vegetation growth phases should be considered in agricultural drought detection. The most sensitive time frame throughout the year was spring and autumn, implying that drought indices (SPI and SPEI) along with vegetation index (SVI) could offer an improvement in the monitoring during these periods.



Escribano Rodríguez, J.A., Díaz-Ambrona, C. H. y Tarquis Alfonso, A.M. (2014). Selección de índices de vegetación para la estimación de la producción herbácea en dehesas. Pastos, 44(2), 6-18.

Martín-Sotoca, J. J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., and Tarquis, A. M. (2019). Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds, Nat. Hazards Earth Syst. Sci., 19, 1685–1702, https://doi.org/10.5194/nhess-19-1685-2019

Sanz, Ernesto Antonio Saa-Requejo, Carlos H. Díaz-Ambrona, Margarita Ruiz-Ramos, Alfredo Rodríguez, Eva Iglesias, Paloma Esteve, Bárbara Soriano and Ana M. Tarquis (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Andrés F. Almeida-Ñauñay, Rosa María Benito, Miguel Quemada, Juan Carlos Losada and Ana M. Tarquis (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

How to cite: Almeida Ñauñay, A. F., Sanz, E., Villeta, M., Quemada, M., and Tarquis, A. M.: Meteorological and agricultural drought indices in semiarid grasslands monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-360, https://doi.org/10.5194/egusphere-egu22-360, 2022.

Peter K. Musyimi et al.

Rainfed agriculture in Kenya is approximately 98% and highly susceptible to climate variability. Humid climatic regions of Kenya are key to sustainability of agricultural sector. This study focused on influence of coffee on real evapotranspiration in Nyeri and Embu Counties of humid Mount Kenya Region. This is because the economy of Kenya relies mostly on Coffee as the fourth largest export earner. Quality controlled 9-year long dataset was sought from Nyeri and Embu synoptic stations. Site specific soil parameters and coffee coefficient were used in computations of estimates. Penman-Monteith standard equation was used to estimate daily values of reference evapotranspiration. Average daily, monthly ET0 and annual total estimates were computed. The ET0 estimates were modelled using 1D Palmer-type soil model to estimate real evapotranspiration using soil parameters for the station at 1 m arable depth. Results showed a very slight variation among the average annual estimates of ET0 between the two humid regions. For instance, in Nyeri the average annual estimate ET0 was 1488±52 mm/year while in Embu it was 1488±48 mm/year. Average annual ET depicted slightly higher variation with estimates of 813±216 mm/year in Nyeri and 830±166 mm/year in Embu. Average monthly estimates of ET0 and ET were almost the same with estimates of 124±21 mm/month and 68±30 mm/month in Nyeri and 124±23 mm/month and 71±37 mm/month in Embu respectively. Results also indicated that daily average,  ET0 , ET and ET estimates with application of Kc  varied insignificantly with  4.1±1 mm/day, 2.2±1 mm/day and 2.2±1 mm/day in Nyeri respectively while the estimates were nearly the same in Embu. Coffee coefficient (Kc) had slight influence on real evapotranspiration in humid climatic regions under study. This is because the Kc values were almost 1 with a range of between 0.9 to 0.95. In addition, the study area receives adequate precipitation hence no soil water stress. Further, the slight differences among the ET with and without application of Kc were due to the linear function of available soil moisture used in the computation of ET from reference evapotranspiration (ET0). The study is important in investigating the role of 1D Palmer type soil model on ET0 and coffee coefficient influence on real evapotranspiration in Kenya in these regimes of climate extremes.

How to cite: Musyimi, P. K., Székely, B., and Weidinger, T.: Reference evapotranspiration estimation and influence of coffee on real evapotranspiration in humid climatic regions of Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-388, https://doi.org/10.5194/egusphere-egu22-388, 2022.

Anne Gobin and Astrid Vannoppen

Managing weather related risks in cropping systems includes strategies to share the risk such as insurance schemes. Advances in satellite sensor technology and interpolated regional weather data enable insights in the relationship between extreme weather events and yields losses which in turn offers possibilities for area-yield insurance schemes.

Extreme weather events  during sensitive phenological stages of the growing season differed significantly (p < 0.05) between low and high crop yields (Gobin, 2012; Gobin, 2018). Spatial return levels confirmed the exceptionality of 2016 and 2018 as extreme wet and dry years with high return periods and yield impacts (Gobin and Van de Vyver, 2021). We investigated the importance of weather data, satellite sensor-derived vegetation indices and water balance simulations in estimating crop yields of winter wheat, potato and sugar beet for the period 2016-2018. The water balance simulations were performed with the Aquacrop model, while the yield simulations were realised with the machine learning technique random forest regression. Results for winter wheat showed that NDVI series did not respond to crop yield affecting weather conditions (Vannoppen and Gobin, 2021). Weather and/or soil water depletion during sensitive phenological stages in combination with the NDVI integral during the growing season explained up to 57 of late potato, 66% of winter wheat, 68% of early potato and 84% of sugar beet yield variability.

Machine learning techniques proved valuable in estimating crop yields thereby elucidating the importance of weather conditions during sensitive crop stages. The crop yield models developed make use of commonly available remote sensing indicators and weather data, and are commensurate with regional scale decision making.


Gobin, A., 2012. Impact of heat and drought stress on arable crop production in Belgium. Natural Hazards and Earth System Sciences 12: 1911–1922. https://doi.org/10.5194/nhess-12-1911-2012

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

Gobin, A., Van de Vyver, H., 2021. Spatio-temporal variability of dry and wet spells and their influence on crop yields. Agricultural And Forest Meteorology, 308-309, Art.No. 108565. https://doi.org/10.1016/j.agrformet.2021.108565

Vannoppen, A., Gobin, A., 2021. Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy-Basel, 11 (5), Art.No. 946. https://doi.org/10.3390/agronomy11050946

How to cite: Gobin, A. and Vannoppen, A.: Modelling the impacts of extreme weather events on crop yields using water balance and satellite sensor data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10664, https://doi.org/10.5194/egusphere-egu22-10664, 2022.

David Rivas-Tabares et al.

Agricultural catchments are prone to high crop yield variability because of extreme weather events, and their impact destabilises agricultural income at different territorial scales. This study aims to use optimisation algorithms coupled with the Soil Water and Assessment Tool – SWAT to allocate specific agri-environmental measures to mitigate the impact of climate change in water fluxes availability at subbasin scale.

The SWAT tool as a semi-distributed model uses the hydrological response unit – HRU concept to split catchments into several territorial management units with similar soil properties, slopes, and land use. The HRUs were used as optimisation unit to change land use and crop management. The work was performed in a catchment located in the southern part of Cuenca city in Ecuador; the area delineates the Tarqui river. The primary land use of the area is grassland-livestock systems and seasonal cropping. Two steps were performed: first, a model run with calibration and validation was set as baseline model. A second step include an optimisation set of modeled scenarios derived from future stakeholder alternatives defined in a previous study as sustainable practices in the area.

Several SWAT model alternatives were optimised, changing crop sequences, fertilisation rates, and crop scheduling dates. As a result, stakeholders' perception majorly matches with scenarios results in optimising water availability during low flow periods increasing streamflow and soil water availability. However, several unexpected alternatives, coming from optimisation, hint at farmers and ranchers. These new options explore other uses and crop sequences that increase income and reduce fertilisation costs.


The authors acknowledge support from European Union NextGenerationEU and RD 289/2021 and the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain


  • David Rivas-Tabares, Ana M. Tarquis, Ángel de Miguel, Anne Gobin, Bárbara Willaarts. Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions. Sci. Total Environ., 803, 149906, 2022. https://doi.org/10.1016/j.scitotenv.2021.149906
  • Rivas-Tabares, A. de Miguel, B. Willarts and A.M. Tarquis. Self-organising map of soil properties in the context of hydrological modeling. Applied Mathematical Modelling, 88,175-189, 2020. https://doi.org/10.1016/j.apm.2020.06.044

How to cite: Rivas-Tabares, D., Tarquis Alfonso, A. M., and Célleri, R.: Optimising agri-environmental measures at catchment scale through specific allocation with the SWAT model – A case study in southern Andes of Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10790, https://doi.org/10.5194/egusphere-egu22-10790, 2022.

Miguel Angel Valenzuela Mahecha et al.

Water scarcity is increasingly recurring in irrigated agriculture in Mediterranean climate regions, and it is, therefore, necessary to establish alternatives to enable irrigators to deal with such problems, in a planned manner and in accordance with the technical and economic implications.  Although insurance schemes for droughts has long been standardized for rainfed crops, their application to irrigated crops is still under discussion. This study presents a new index-based drought insurance scheme, totally aligned with the river basin drought management procedures.

When considering a highly regulated water system, where natural water availability is altered by the operation of water infrastructure, traditional drought indicators (e.g., SPI, SPEI, SRI) lose significance, and ad-hoc index formulations tailored to the basin characteristics are required to reflect both regulation effects and natural fluctuations in the basin. Spain provides a paradigmatic example of a practical and systematic policy for the identification and mitigation of operational droughts: the river basin authorities are bound by law to design basin-specific state indexes. The state indexes are monthly monitored and used to trigger water demand and supply measures when entering a drought period, according to the specifications of the drought management plan. The study was carried out in an irrigation district (90% citrus fruits) in the Jucar river basin in Spain, a highly regulated water system.

Three insurance scheme options were evaluated: 1, a variable premium and/or variable franchise based on the forecast of water availability for the insured irrigation campaign, 2, a multi-annual insurance contract, and 3, an advance contract with a constant premium. In each of them, the values of the fair risk premiums, the maximum compensation, and the deductible franchise were established for different state indexes based on different combinations of system state variables (such as reservoir storages and inflows) and precipitation. The design of the insurances was done under the preexisting drought system operating rules to reduce the issue of the moral hazard, which is one of the main problems for this kind of insurance index. The selection of the insurance scheme is based on the gross margin of citrus crops with and without insurance contracts, including the value of additional premium loads, in addition to a basis risk analysis.

To evaluate the performance of the insurance, synthetic hydrological time series were generated using an ARMA model and implemented in the basin-wide water resource simulation management developed in DSS Shell AQUATOOL. The premium-claim ratio was used to assess the performance of the insurance company, finding stable values that can generate a balance of the long-term insurance scheme.

How to cite: Valenzuela Mahecha, M. A., Pulido-Velazquez, M., and Macian-Sorribes, H.: Hydrological drought index insurance in irrigated agriculture in a highly regulated system: an economic instrument for risk mitigation for the Jucar River Basin , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11989, https://doi.org/10.5194/egusphere-egu22-11989, 2022.

Ana Maria Tarquis et al.

Pastures are one of the most crucial land covers from the ecological and agricultural point of view. In Spain, pasture areas are being especially more vulnerable to the effects of climate change, which highlights the need to have tools for assessing and characterizing pastures, to understand the vegetation behaviour better, and anticipate potential risks such as events of drought or frosts mitigating the negative impacts that take place both in the crop and at an economic level for the farmers.

This work aims to establish an early warning system in pastures and evaluate the combined drought index by studying the behaviour of the NDVI (vegetation index) with the temporal dynamics of temperature and precipitation in two areas in north-central Spain (Ávila and Segovia). For this, the grass areas were selected, the behaviour of the climatic patterns and the vegetation index were studied, pastograms were analyzed to characterize and evaluate the amount of grass produced, and correlations were made to assess the behaviour of precipitation and the NDVI between development phases over 20 years.

The data analyzed and the methodologies followed for the study areas determine two highlighting points in the growth of the grasses, autumn and spring. There is also a linear relationship between cumulative precipitation and cumulative NDVI in both zones, which, together with the pastures model, allow obtaining the production estimate to characterize them. With this information obtained from the analysis for both Ávila Zone – ZAV and Segovia Zone – ZSE, a combined drought index and alarm system is proposed based on the values ​​of the standard curves and scores of the meteorological and physiological index for each zone during the period 2000-2020.


The second author acknowledges the Center for Studies and Research for the Management of Agricultural and Environmental Risks (CEIGRAM) funding through its 2020 call for grants to young researchers. The support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020 is highly appreciated.


  • Ernesto Sanz, A. Saa-Requejo, C.H. Díaz-Ambrona, M. Ruiz-Ramos, A. Rodríguez, E. Iglesias, P. Esteve, B. Soriano and Ana M. Tarquis. Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland. Entropy, 2021, 23(5), 576. https://doi.org/10.3390/e23050576
  • Andres Almeida-Ñauñay, Rosa M. Benito, Miguel Quemada, Juan C. Losada and Ana M. Tarquis. The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 2021, 23(5), 559; https://doi.org/10.3390/e23050559
  • Ernesto Sanz, A. Saa-Requejo, C.H. Díaz-Ambrona, M. Ruiz-Ramos, A. Rodríguez, E. Iglesias, P. Esteve, B. Soriano and Ana M. Tarquis. Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 2021, 13(5), 840; https://doi.org/10.3390/rs13050840
  • Eva Iglesias, K. Báez & C.H. Diaz-Ambrona. Assessing drought risk in Mediterranean Dehesa grazing lands. Agricultural Systems, 2026, 149, 65-74. https://doi.org/10.1016/j.agsy.2016.07.017

How to cite: Tarquis, A. M., Vargas, L., Rivas-Tabares, D., and Diaz-Ambrona, C. G. H.: Design of a Combined Drought Index for the Creation of an Early Warning System in Grasslands. Case Study in the Sierra de Guadarrama, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12213, https://doi.org/10.5194/egusphere-egu22-12213, 2022.

Oumayma Bounouh et al.

Olive trees play a vital role in Tunisia, a North African Mediterranean country. The Mediterranean basin region is experiencing severe climate change conditions. Indeed, traditional olive trees have the distinguishable ability to resist climatic conditions. However, warming trends and unusual both raining, and drought periods are threatening this crop by causing drying phenomenon and decreasing yield’s quality and quantity. Therefore, this culture is attracting great attention for effectively analyzing, and monitoring their changes to cope with the projected changes accurately and put the necessary adaptation strategies. Such work remains a challenge via field measurement approaches. Meanwhile, satellite imagery provides a wide range of data. In this work, we took advantage of MOD13Q1 products to analyze the relationships between vegetation indices and their hidden components and the land surface temperature (LST) for various reasons: Firstly, to assess the relationship between the LST and vegetation indices and their components. Secondly, to determine which temporal profiles are more closely related to each other. Thirdly, to quantify the impact of climate change on olive sites. To this aim, the wavelet transform is used to decompose the time series. Moreover, various similarity and statistical measures are calculated to better quantify these relationships. On one hand, no significant correlation is measured for the trend components. Moreover, the olive trend has shown a positive slope. In contrast, LST depicted negative dynamics. On the other hand, interestingly, the temporal profiles seemed similar. And the wavelet coherence showed a consistent relationship between them. Based on our findings, we remark the limitation of classical correlation measures in depicting the relationship between the discussed variables. Therefore, we conclude that a good causality study must rely on time point relation detection and not on the overall similarity between the environmental variables and the vegetation indices.

How to cite: Bounouh, O., Tarquis, A. M., and Riadh Farah, I.: Investigation of climate change impact on olive trees in Tunisia via MODIS LST and NDVI products and correlation measures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13255, https://doi.org/10.5194/egusphere-egu22-13255, 2022.

Jacopo Furlanetto et al.

Extreme weather events such as hailstorms represent a threat to crops, causing both economic and food supply losses. Hailstorm intensity is likely to increase in the future pushing more farmers to purchase crop insurances to prevent related economic losses. Currently, insurers mostly rely on field inspectors for crop damage assessments, which can build up limitations such as: (i) partial subjectivity in damage estimations; (ii) inaccuracies in wide-area assessments; (iii) difficulties in accounting for damage spatial variability. Sensors mounted on UAVs (Unmanned Aerial Vehicles) and satellites can fulfill these requirements when coupled with advanced spectral analysis techniques, such as spectral mixture analysis (SMA). In this experiment we applied SMA on UAV hyperspectral images to quantify dead-and-alive organs during the growth of winter wheat (Triticum aestivum L.) and estimate yield loss due to hail damage. The experiment was conducted on a 17-ha field located in the surroundings of Venice (NE Italy). The experiment involved four simulated hail treatments (null, low, medium and high damage) at three plant growing stages (flowering, milky and over-ripe). Treatments were in triplicate for a total of 30 plots, nine sized 60x60 m and 18, 20x20 m. Damages were inflicted using a prototype specifically designed at the University of Padova, consisting of a rotating pole with whips attached and positioned on the back of a tractor. Damage intensity was adjusted with the aid of insurance field inspectors. A UAV M600 Pro (DJI, Shenzhen, China) was equipped with a nanohyperspec (400-1000 nm) camera (Headwall, Boston, USA). Pixel ground resolution was about 0.04 m. UAV surveys were performed after each damage, leaving a period of 7-10 days to the crop for developing a detectable morphologic and physiologic response (e.g., leaf drying, development of necrosis). At each flight, crop samples were collected, and pure spectral signatures of dead and alive stems, leaves and spikes were analyzed using an ASD Fieldspec 4 (Malvern Panalytical Ltd, Malvern, UK) in proximal sensing configuration. SMA algorithm was run on UAV imagery by selecting endmembers composed of intact green plant organs, bare soil and dead spikes, thus allowing for differentiation between damaged and undamaged vegetation. Results showed that increasing yield loss due to hail damage intensity was associated with an increasing number of dead spikes. Proximal-sensed hyperspectral signatures highly differentiated between undamaged and damaged vegetation, especially in the red-edge and chlorophyll absorption (~ 680 nm) regions. In this context, the SMA technique was promising for disentangling dead spikes from alive organs, aiding the area-damaged classification and allowing hyperspectral imagery for a direct estimate of yield losses.

How to cite: Furlanetto, J., Longo, M., Nicoli, L., Caceffo, D., Persichetti, A., Morari, F., and Dal Ferro, N.: Spectral mixture analysis to quantity winter wheat (Triticum aestivum L.) damage caused by hailstorms , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4868, https://doi.org/10.5194/egusphere-egu22-4868, 2022.

Matteo Longo et al.

Hailstorm damage in agriculture often results in considerable loss in harvestable product. Currently, crop damage is quantified by insurance companies through field inspectors’ assessment, a time-consuming activity that is potentially affected by estimation errors over large areas. Coupling remote sensing and crop modeling represents a promising solution for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. With the general aim of developing an automated platform that can identify crop yield failure at field level, the Ceres-Maize (DSSAT v 4.7.5) model was integrated with remote sensing data to reproduce maize growth and production dynamics. A two-year experiment was established at the Ca’ Tron farm on the low lying of Veneto Plain (NE Italy). The crop damage was performed using a custom-made machine at three intensities (low, medium, and high) depending on crop defoliation and four plant growing stages (early vegetative, flowering, early-milky, and dough stages). Each treatment was replicated three times on plots of 20x20 or 60x60m. Additional four subplots with no damage were used as control. Leaf area index (LAI) and biomass were measured after each damage event. LAI was estimated from both drone-borne multispectral sensors and satellites imageries (Sentinel-2), and ground-validated using a ceptometer. The Ceres-Maize model was used to predict obtainable and potential crop yields: 1. by embedding the estimated LAI reduction at the time of damage into the “PEST” sub-model; 2. by calibrating the model seasonal LAI dynamics using the drone- and Sentinel-based LAI observations over the cropping season. The first year of the experiment was used to calibrate DSSAT, the second one to validate its performance.

Results showed a satisfactory agreement between measured and simulated Ceres-Maize LAI dynamics. The final yields were also well reproduced among treatments. On the other hand, the model did not fully capture the residue biomass and the harvest index. Assimilating remote-sensing-based parameters in crop models appears to have promising benefits for the insurance market, providing more robust and less time-consuming methodologies.

How to cite: Longo, M., Furlanetto, J., Dal Ferro, N., Caceffo, D., and Morari, F.: Coupling process-based models and remote sensing data to predict yield loss by hail damage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4594, https://doi.org/10.5194/egusphere-egu22-4594, 2022.

Paloma Campos et al.

Agriculture is facing the challenge of providing food for a growing world population in a context of climate change. The Mediterranean region is characterized for a semi-arid climate. Thus, water scarcity is coupled with the development of intensive crops that require irrigation, such as olive orchards. Recently, biochar –the solid aromatic carbonaceous product of the pyrolysis of residual biomasses– has been proposed as an amendment for reducing soil water loss [1] and increasing plant productivity [2].  The main objective of this study was to compare the effects of the application of biochar and green-compost (the organic amendment traditionally used) on soil properties and crop productivity at a super-intensive plantation of arbequina olive trees under deficit irrigation located at “La Hampa” field station (Coria del Río, Seville, Spain). Thus, soils were amended with 40 t ha-1 of olive-waste biochar, green-compost or a biochar-compost mixture (50 % w/w). Un-amended plots were used as control. On a monthly basis, soil pH, water holding capacity, humidity and penetrability resistance, as well as TC and TN contents of soils were determined. Finally, the total weight of produced-olives per tree was measured.

Results showed that biochar application was the most effective amendment in increasing soil water holding capacity and moisture. All the organic amendments reduced the soil penetrability resistance. Olive production increased about 15 % at the biochar amended plots. Thus, the application of organic amendments, especially biochar, improved soil physical properties and led to a higher crop production.

Acknowledgements: The BBVA foundation is gratefully acknowledged for funding the scholarship Leonardo to “Investigadores y Creadores Culturales 2020”, what made this project possible.


[1] Campos et al., 2021. Agronomy 11, 1394. https://doi.org/10.3390/agronomy11071394

[2]De la Rosa et al., 2014. Science of the Total Environment 499, 175-184. http://dx.doi.org/10.1016/j.scitotenv.2014.08.025

How to cite: Campos, P., Sánchez-Martín, Á., Santa-Olalla, A., Miller, A. Z., and de la Rosa, J. M.: Effects of biochar addition into intensive-olive orchard soils under deficit irrigation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1887, https://doi.org/10.5194/egusphere-egu22-1887, 2022.

Andrea Urgilez-Clavijo and Ana María Tarquis Alfonso

The agricultural expansion frontier in Ecuador is mainly attributed to the deforestation process. Exacerbated rates of forest loss have been motivated more significant impacts in the territory, even negatively affecting the new uses of land, agriculture, and livestock. Climatic conditions have been changing in larger deforested areas, increasing landslides, floods, water level rise, and drought. This work aims to go deeper in understanding the deforestation process patterns by analyzing the structure of the expansion at the patch level.

We used the concept of patch skeletons and the Local connected fractal analysis (LCFA) through its temporal dynamics to identify complex hotspots inside the new agricultural areas. The K-means algorithm was used to perform LCFA segmentation and colouring to identify the complex intensity of the deforestation structure. This may indicate active expansion areas associated with high risked areas to perform agriculture and livestock systems because of high ecosystem dynamics recovery.

Hot spotting derived from the fractal analysis and k-means clustering not only serves for reforestation but will also lead to decision-makers for monitoring other associated environmental impacts. Most of the deforested areas in Ecuador after 5 to 7 years in agriculture were abandoned because of the nutrient loss and agricultural failure activities because of feeble farming systems infrastructure. LCFA and colouring communicate in a straightforward spatially explicit visualization strategy the hot spot method to geographically allocate the complex points of the deforested structure of the patches.


The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.


Andrea Urgilez-Clavijo, J. de la Riva, D. Rivas-Tabares and A.M. Tarquis. Linking deforestation patterns to soil types: A multifractal approach. European Journal of Soil Science, 2021, 72(2), 635-655. https://doi.org/10.1111/ejss.13032

Andrea Urgilez-Clavijo, D. Rivas-Tabares, J.J. Martín-Sotoca and Ana M. Tarquis. Local fractal connections to characterize the spatial processes of deforestation in Ecuadorian Amazon. Entropy, 23(6), 748, 2021, https://doi.org/10.3390/e23060748

How to cite: Urgilez-Clavijo, A. and Tarquis Alfonso, A. M.: Understanding the deforestation process may mitigate environmental risks in recent agricultural frontier expansion in Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10754, https://doi.org/10.5194/egusphere-egu22-10754, 2022.