Rainfall accumulation during wet seasons in Northern South America can be enhanced during La Niña ENSO phases.  Leptospirosis is a zoonotic waterborne disease that affects humans, domestic animals, and wildlife associated with occupational and recreational water activities, natural disasters, and socioeconomic conditions for which rainfall plays a key role in its transmission. We analyzed the incidence of leptospirosis, and relative risk of changes on the incidence of the disease due to rainfall accumulation in Northern Colombia during the period 2007-2021. The rainfall accumulation analysis was done for 7, 14 and 21 days based on the periods of incubation of the disease, biology of transmission, and thresholds of rainfall accumulation above the mean values. We found a statistically significant association between excess rainfall and leptospirosis at different lags for cities in Northern Colombia (Barranquilla, Santa Marta, Cartagena) and the levels of accumulated rainfall exceedance associated with leptospirosis were specific for each city. Our findings give insight into the association between leptospirosis and excess accumulated rainfall and provide climate services and local health authorities with tools to act on and prevent this important zoonotic disease.

How to cite: Builes-Jaramillo, A., Arias-Monsalve, C. S., Valencia, J., Florian, C., and Salas, H. D.: Rainfall accumulation as a driver of higher Leptospirosis risk in northern South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-697, https://doi.org/10.5194/egusphere-egu24-697, 2024.

Environmental protection is of global interest to earth’s inhabitants with increasing concerns related to climate change. Solid wastes constitute an undeniable source of environmental degradation and a possible disaster to human health. In Jos Metropolis, a number of arable lands double as waste dumpsites and are at risk of heavy metal pollution. Shallow groundwater used for domestic purposes and plants cultivated near these dumpsites are prone to contamination and the prolonged consumption of unsafe concentrations of heavy metals through edibles and/or water may trigger numerous biochemical alterations in the human body. Subsurface geophysical investigation using 2D electrical survey and the assessment of soil and water quality has been carried out in the arable land and at close geological proximity to a solid waste dumpsite located at Utan, Jos, Plateau State, Nigeria. This study focus on delineating the lateral extent and depth of leachate migration into the subsurface from the waste dumpsite. 2D resistivity survey was carried out along three traverse (A, B and C) using Wenner–Schlumberger configuration. Qualitative interpretation of the inverse resistivity models revealed low resistivity zones of < 44 Ωm to be regions of leachate accumulation. The extent of downward migration through the vertical stratigraphic interval exceeds 15.6 m trending laterally in the eastern direction of traverse A. The analysis of heavy metal determination for water samples was aided by the use of Atomic Absorption Spectrophotometer while the soil samples were analyzed using X-ray fluorescence (XRF) analytical method. The concentrations of Pb and Ni in the analyzed water samples were above the permissible limit for drinking water and concentration of heavy metals in soil samples varies significantly. This study revealed the concentration of heavy metals in soil and water samples in close geographical proximity to the waste dumpsite and the uncontrolled disposal of waste over time poses great threat to the environment and its inhabitants. Waste management practices have to be improved upon to mitigate pollution.

How to cite: Obasuyi, F. O., Oladimeji, A. M., and Yusuf, T. A.: Investigation of the lateral extent and depth of contamination using 2D electrical resistivity and the assessment of soil and water quality in the vicinity of a Waste Dumpsite in Utan Jos, Plateau State. Nigeria., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1287, https://doi.org/10.5194/egusphere-egu24-1287, 2024.

In this field pot study, effect of irrigation practice (continuous flooding (CF) and intermittent drainage (ID) treatment) on greenhouse gas (GHGs, CO₂ and CH₄) emission was determined from three Korean paddies (BG, MG, and JS series), varying soil properties such as soil texture, labile carbon, and mineral types.  Emission of GHGs was evidently influenced by irrigation practices, to a different extent, depending on paddy’s redox response to flooding events.  The Eh decline upon flooding was slower in JS pot, where pore-water concentration of ferric and sulfate ions is the highest (~ up to 3-fold) among three paddies.  MG pot was 2- to 3-fold percolative than others and the Eh drop during flooding period was the smallest (remaining above -50 mV) among three pots.  By adopting ID, CH₄ emission (t CO₂-eq ha-1 yr-1) was reduced in a wide range by 5.6 for JS pot, 2.08 for BG pot, and 0.29 for MG pot relative to CF, whereas CO₂ emissions (t CO₂-eq ha-1 yr-1) was increased by 1.25 for JS pot, 1.07 for BG pot, and 0.48 for MG pot due to the enhanced carbon oxidation upon drainage.  Grain yield and aboveground biomass production from ID were no less than those from CF (p < 0.05).  Consequently, benefit of global warming potential (S GWP) by ID varied as in order of JS (37%) > BG (14%) > MG (~0 %) pots, and negligible effect observed for MG pot was due to the equivalent trade-off between CO₂ and CH₄. Our findings imply that that the efficacy of drainage on GHG mitigation depends on the redox response of paddies.

Keyword

Climate Change, Greenhouse gas, Paddy, Intermittent drainage

Acknowledgement

This research was in part supported by the Korea Environment Industry & Technology Institute (KEITI), funded by Korea Ministry of Environment (MOE) (No. 2022002450002 (RS-2022-KE002074)) and in part supported by Korea University Grant.

How to cite: Hyun, S., Hwang, W., Park, M., An, Y.-J., Hong, S., and Jeong, S.-W.: Effect of intermittent drainage on the emission of two greenhouse gases (CO2 and CH4) from three paddies in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2496, https://doi.org/10.5194/egusphere-egu24-2496, 2024.

Agricultural produce’s yield can be heavily impacted by changes in the weather patterns. With the current global warming scenario, the extremes temperature anomalies are expected to occur more frequently, posing a significant threat to the crop yields. To better plan the agricultural practices and crop rotation, it would be highly beneficial to understand the impact of temperature anomalies on crop yields. Here in this study, we investigated the impact of changing air temperature extremes on the yields of strawberries in farms in California's Central Valley. By using a copula modeling framework, the study has identified the risks of crop yield loss associated with temperature extremes. Based on this analysis, various scenarios of crop yield loss have been identified, and the likelihood of encountering those scenarios based on changes in temperature extremes has been estimated. The results of this study can be immensely helpful in planning agricultural practices and implementing appropriate measures to mitigate the risks. With air temperature forecasts readily available from various sources, nature-based solutions can be effectively implemented to combat the negative effects of temperature extremes on crop yields.

How to cite: Unnikrishnan, P., Ponnambalam, K., and Hipel, K.: Estimating the risk of crop yield loss due to changing regional air temperatures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3235, https://doi.org/10.5194/egusphere-egu24-3235, 2024.

Vegetation restoration such as human-induced and natural growth has seen a significant increase over the past two decades. However, this surge has raised concerns regarding its potential impact on water resources and its consequential hindrance to local social and economic development. Policymakers are particularly focused on mitigating the negative hydrological effects of vegetation restoration. Nevertheless, the implications for water yields in the context of forest management types, such as planted and natural forests, remain unclear. In this study, we explored hydrological responses to forest expansion in both planted and natural forest watersheds, utilizing evapotranspiration data synthesized from 12 data products, forest management maps, and climate datasets. Our analysis, based on the Budyko framework, revealed that water yield reduction in arid watersheds with planted forests (PFs) exceeded that in watersheds with expanding natural forests (NFs). Interestingly, vegetation restoration, whether in PFs or NFs watersheds, could even lead to an increase in water yield. Attribution analysis highlighted ecological restoration, rather than climate conditions, as the primary contributor to the observed water yield decrease. In NFs watersheds, the decrease was primarily linked to underlying characteristics, while in PFs watersheds, changes in water yield sensitivity to the land surface played a crucial role. It is noteworthy that vegetation restoration in humid zones exhibited a negligible impact on water yield. Even in NFs watersheds where water yield decreased due to tree cover expansion in drylands, natural growth emerged as a viable option to mitigate local hydrological effects in arid zones.

How to cite: Yan, Y., Liu, Z., and Jaramillo, F.: The distinct hydrological responses to vegetation restoration between planted and natural forests watersheds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3708, https://doi.org/10.5194/egusphere-egu24-3708, 2024.

The study focuses on assessing the impact of climate change on water balance components in the Upper Ghatprabha River Basin in India. The Soil Water Assessment Tool (SWAT) is utilized to simulate streamflow in the basin. Calibration and validation of SWAT are performed across multiple sites using the Sequential Uncertainty Fitting Algorithm (SUFI 2). Performance assessment relied on metrics such as the Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R²). Future climate projections are based on an ensemble mean of 13 bias-corrected GCM models for the Shared Socioeconomic Pathways (SSP) scenarios SSP245 and SSP585. The simulation of future basin water balance components involves segmenting the entire timeframe into S1 (2015-2040), S2 (2041-2070), and S3 (2071-2100). Projections indicate an increase in maximum and minimum temperatures, with precipitation potentially rising by up to 47% in the basin under the SSP245 scenario by the end of the century. Hydrological simulations reveal increased surface runoff and evapotranspiration under the SSP245 scenario compared to historical data. The percentage change in blue water components under both SSP scenarios shows an increase of more than 50% compared to the historical data. In comparison, that of green water components only increases to a maximum of 8% in all the timeframes (S1, S2 and S3). Notably, the impact of climate change is more pronounced under the SSP585 scenario compared to SSP245. These changes significantly impact the water resources of the Upper Ghatprabha River Basin; necessitating focused attention on future planning and management strategies for water resources.

How to cite: Jain, S. and Jain, M. K.: Assessment of Blue and Green Water Availability in the Upper Ghatprabha River Basin under Climate Change Impacts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4988, https://doi.org/10.5194/egusphere-egu24-4988, 2024.

Durum wheat is a critical staple crop in arid and semi-arid regions worldwide, that plays a significant role in local food security. Providing essential nutrients and a high protein content, it is widely used for the production of pasta and couscous. Various constraints and drivers affect durum wheat productivity, including biotic and abiotic stressors, agronomic practices, and CO₂ concentrations. Their influence varies based on duration and intensity of the stressor, as well as the durum wheat growth phase in which they occur. Drought and heat were shown to act as primary yield limiting factors. Furthermore, the water footprint, a comprehensive measure for the volume of water associated with crop production, helps to analyse durum wheat cultivation from a water-food nexus perspective. Given that climate change is affecting the main influencing factors of durum wheat’s productivity and of its water footprint, such as precipitation, temperature, and atmospheric CO₂ levels, its cultivation is expected to undergo alterations as well. In this context, we explore the present state of durum wheat productivity and the potential influence of changing climatic conditions on its future cultivation worldwide. The current state of research on future durum wheat production is characterised by contradictory results, compromising projections of significant declines due to heat and drought stress as well as strong increases in productivity as a consequence of the CO₂-fertilisation effect, for the same or nearby locations. Understanding the complex interactions between climate change, durum wheat productivity and the associated water footprint is of great importance to derive sustainable adaptation strategies and move one step closer into ensuring future food and water security.

How to cite: Grosse-Heilmann, M., Cristiano, E., Viola, F., and Deidda, R.: Influencing factors of durum wheat productivity under current and future climatic conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5462, https://doi.org/10.5194/egusphere-egu24-5462, 2024.

Mobile phones, televisions, computers and other liquid crystal devices have become the electronic products widely used by humans in modern society. Liquid crystal monomers (LCMs) are the key material of liquid crystal display and considered as potential persistent, bioaccumulative, and toxic (PBT) substances in recent years, but there is a limited of information regarding their occurrence in human body. We used EPI suite software from USEPA to evaluate its physical and chemical properties, analyzed its concentration in serum and urine by GC-MS, and finally assessed its health risk to humans through the calculation of daily intake. In this paper, 15 LCMs were detected in serum and urine samples of the general population, with median concentrations ranging from 9.7 to 124.8 and 2.68 to 36.98 µg/L, respectively. The correlation of LCM in serum and urine suggests that they have potential common applications and similar sources. The results showed that the CLrenal of LCMs in the Northwest China population was 0.61, 7.79, 6.04, 4.81, 9.37, 4.85, 19.94, 10.64, 3.80, 7.44, 8.26, 15.39, 7.52, 10.17, 13.54 mL/kg/day for EBCN, BCBP, PBIPHCN, DFPrB, FPrCB, BEEB, BMBC, DFPCB, DFEEB, EPrCPB, EEPrTP, EDFPB, DFPrPrCB, EFPeT, TeFPrT, respectively. The daily intake for ∑LCMs in the adult of northwest China was 22.35 ng/kg bw/day, indicating a potential exposure risk to the general population. This study provides the first evidence for the presence of LCM in serum and urine in the daily population and finds a correlation between LCMs, but the differences in B/U ratio and renal clearance indicate the need for further investigation of its metabolism and clearance in the human body.

How to cite: Yang, K., Cheng, H., Quan, W., Gong, Y., and Ai, Y.: Human health risks estimations from Liquid crystal monomers（LCMs）in Northwest China : partitioning, clearance and exposure in paired human serum and urine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7771, https://doi.org/10.5194/egusphere-egu24-7771, 2024.

Abstract : Irrigated olive trees constitute the main arboricultural component of orchards in semi-arid regions, and the optimization of irrigation practices is crucial to sustain the production, increase agricultural water productivity and reallocate water savings to other higher-value uses. Numerous technical strategies have been implemented in the last two decades, to promote water conservation in irrigated agriculture, namely the adoption of subsurface drip irrigation system. This study delves into a comprehensive comparative analysis between subsurface (SDI) and surface (DI) drip irrigation systems over an olive orchard, with an emphasis on the evolution of evaporative fraction (EF) and the ratio of transpiration (T) to evapotranspiration (ET), soil moisture distribution patterns, as well as water use efficiency and water productivity. The experiment was carried out over two irrigated olive plots located in the Tensift basin (Morocco), from May to October 2022. Each plot is subjected to a specific irrigation pattern, and equipped with an Eddy-Covariance system to quantify the energy balance components, along with Time-Domain-Reflectometry (TDR) sensors installed at various depths, to monitor the soil water content. Besides, the partitioning of ET into T and evaporation (E) over the two irrigation systems was performed using the Conditional Eddy-Covariance (CEC) scheme and validated using sap flow measurements collected over SDI plot during April 2023. The ET of the DI system was higher than that of the SDI one, with diurnal ET values ranging between 0.58-3.02 (mm/day) and 0.48-2.74 (mm/day) for DI and SDI systems, respectively. Our findings suggest that although a smaller irrigation water amount was applied in SDI (194 mm) compared to DI (320 mm), crop yield revealed no significant differences. This thorough assessment intends to add substantial knowledge to the lasting debate about sustainable irrigation practices over olive orchards and assist policymakers in making informed decisions to enhance water use efficiency while sustaining overall agricultural production.

Keywords: subsurface and surface drip irrigation; evapotranspiration; water productivity; water use efficiency; olive trees; semi-arid areas.

How to cite: Ourrai, S., Aithssaine, B., Amazirh, A., Er-raki, S., Bouchaou, L., Jacob, F., Kharrou, M. H., and Chehbouni, A.: Optimizing irrigation practices for sustainable olive production in semi-arid areas: A comparative analysis of the efficiency of Subsurface and Surface drip irrigation systems , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8378, https://doi.org/10.5194/egusphere-egu24-8378, 2024.

Climate change and water scarcity has pushed more countries with direct ocean access to seek desalination solutions to face part of their need for domestic water networks or industrial usage, while conserving coastal ecosystems. Seawater monitoring is crucial in implementing a desalination plant as it ensures the efficiency and sustainability of the desalination process, especially in the case of a plant powered by renewable energy sources. Seawater is the main input of desalination processes and coastal areas are the locations of the release of the salty waste. An autonomous buoy can be used to monitor the seawater parameters which are essential to sizing a desalination plant.

There have been recent developments of autonomous buoy systems for monitoring different water parameters, however lacking in certain aspects. Some of the elements of these buoys include limited range of data transmission, high-cost designs, immobility and limited number and types of sensors. Also, there has been lacking implementation of autonomous buoys used in development of desalination plants. 

The proposed low-cost autonomous buoy is designed and constructed using cost effective materials. It increases the possibility of multiplying the sensor count to have a more accurate data mapping system. The low cost provides the opportunity of having more devices where there is a higher probability of equipment loss due to possible theft or remoteness of travel. The power supply is an oversized solar array with a backup battery and solar charger. An Arduino microcontroller is connected to two probes and a GPS sensor. The data is logged on a SD memory card with data transmitted via the Iridium satellite constellation, consisting of 75 satellites. There are two parts of construction involved in the project: the construction of the outer shell of the buoy and the design of the inner circuitry and components. The project involves multiple steps of experimentation: first in a laboratory/controlled area then deployed in the Seto Inland Sea, Japan. The various steps ensure the data collected by the sensors is reliable, valid, and suitable for scientific research. After this successful implementation, the buoy will be adapted and deployed in the Caribbean Sea surrounding Jamaica.

Initial results show a promising possibility of measuring seawater parameters such as GPS location, salinity, and sea surface temperature for any body of water. Utilizing the span of the Iridium satellite communication system, this ensures that virtually all regions of the Earth can be measured. The sizing of the solar powering components allows for at least 1 year of monitoring in the worst-case scenario and 4-5 years in the best-case scenario. The integration of autonomous buoys in the desalination process enhances efficiency in the plant design stages and reduces potential costs which contributes to the optimization of the desalination system. The environmental integration and the operation of the plant will be improved as a result of the enhanced assessment of the input and waste release conditions.

Keywords: Seawater Monitoring, Remote Sensing, Desalination, Autonomous buoy, Autonomous measurements

How to cite: Williams, Z., Soto Calvo, M., and Lee, H. S.: Design of a low-cost autonomous seawater measurement buoy to scale and optimize a green-powered desalination plant, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8539, https://doi.org/10.5194/egusphere-egu24-8539, 2024.

Climate change is threatening food security. To ensure food security, we do not only have to safeguard agricultural production - crop yields also need to be optimally distributed. For that, decision-makers need reliable crop forecasts so that they can plan which regions are likely to experience crop yield losses and which regions will produce a surplus. Earth observation and machine learning are key tools to calculate such forecasts. However, extreme crop yield losses, for example caused by severe droughts, are often underestimated. To test this, we developed a machine learning-based crop yield anomaly forecasting system for the Pannonian Basin and examined its performance, with a focus on drought years. We trained the model (XGBoost) with crop yield data from 43 regions in southeastern Europe and predictors describing soil moisture, vegetation, and meteorological conditions. Maize and winter wheat yield anomalies were forecasted with different lead times (zero to three months) before the harvesting season. Our results show that the crop yield forecasts are significantly more reliable from 2 months before the harvest than before in both, drought and non-drought years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, the wheat yield losses remain underestimated, but the maize forecasts are fairly accurate. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest. 

How to cite: Bueechi, E., Fischer, M., Crocetti, L., Trnka, M., Grlj, A., Zappa, L., and Dorigo, W.: Food security in a changing climate - how can Earth observation and machine learning help? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9386, https://doi.org/10.5194/egusphere-egu24-9386, 2024.

The demand for farmland products is increasing worldwide, causing unprecedented stress on the global agricultural system and, consequently, on water resources. Here we analyse the impact of drought events on rainfed agriculture, a topical issue given the prolonged and severe drought events currently occurring around the world and thus including highly productive areas. We investigate the agricultural yields of key crops that represent 61% of the world’s production of proteins for human consumption (i.e. corn, wheat, rice, and soybeans). Our analysis spans from the early 1900s to 2022, allowing us to assess the total agricultural area under drought stress per year and the most vulnerable types of crops. We identify significant trends in the extent of agricultural land under stress, considering both historical and recent periods. This comprehensive analysis enables us to estimate the frequency of occurrences of crop-specific cultivated areas under stress over time, unravelling the pattern of drought impact on global agriculture.

How to cite: Rinaldo, T., Ridolfi, E., Moccia, B., Marconi, F., D'Odorico, P., Russo, F., and Napolitano, F.: The impact of drought on the water-food nexus at the global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13506, https://doi.org/10.5194/egusphere-egu24-13506, 2024.

Continued population growth, changing climate and increased pressure on water resources will dramatically increase the pressure on Chinese agriculture in the coming decades. Although there have been some reports of yield stagnation in the world’s major cereal crops, including maize, rice and wheat, the reasons for stagnation have not been quantified thoroughly. Here, we use statistical data to examine the trends in crop yields for two key Chinese crops: maize and wheat and their drivers in China’s drylands. Results showed that although yields continue to increase in many areas, we found that across 70.2% of maize- and 51.9% of wheat- growing prefectures or provinces, yields either never improved, stagnated or collapsed. The reasons for the decline and stagnation of crop yield were mainly caused by the change of growing season precipitation and irrigation fraction. New investments such as increased irrigation fraction in underperforming regions, as well as strategies to continue increasing yields in the high-performing areas, are required.

How to cite: Zi, S.: Recent patterns of crop yield growth, stagnation and their drivers in China’s dryland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13868, https://doi.org/10.5194/egusphere-egu24-13868, 2024.

Agriculture is highly dependent on climate because rainfall, temperature and sunlight are the primary determinants of crop development. Climate change driven effects such as variation in precipitation and changes in temperatures are likely to affect agricultural yields. Systematic planning of agricultural activities considering these effects is essential. As a first step towards this longer term objective, this work quantifies the effect of climate change on crop in short and long term in India. Wheat is chosen as the crop of interest. Madhya Pradesh, one of the leading wheat producing states in India, is the region under focus, and Betul district is selected for a initial studies. The CERES-wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) tool is used to estimate the impact of climate change on wheat yield. The CERES-wheat model has been calibrated and validated, and the calibrated parameters have been used to simulate wheat yield in the future. The base period for calculating base wheat yield is 2009-2019. Future wheat yields are calculated for two periods (2025-2055 and 2056-2085). The projected changes in precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) in future compared to the base period are calculated using four different General Circulation Models (GCMs) and four Shared Socioeconomic Pathways (SSPs). To increase the study's robustness, 1000 samples are systematically generated using Latin Hypercube Sampling (LHS). A stochastic weather generator (WG), WeaGETS, is used to create a synthetic time series of climate variables. Using the 1000 different combinations of changes in climate variables, 1000 climate scenarios are generated using WeaGETS. The climate variables used to determine the relationship between climate and wheat yield were mean rainfall, rainfall variance, Tmax, and Tmin. Wheat yield ranged from 2065 to 3207 kg/ha during the baseline period, and it is expected to vary from 1629 to 3638 kg/ha between 2025 and 2055. Looking ahead to 2056-2085, wheat yields are estimated to range from 1363 to 3555 kg/ha. The sensitivity analysis results between climate variables and wheat yield for both periods suggest that wheat yield is positively correlated with mean rainfall and rainfall variance and negatively correlated with Tmax and Tmin. Maximum temperature has a significant negative correlation with wheat yield in both periods after excluding the effect of other climate variables. However, in the last stage of wheat yield development, the grain filling stage, Tmin is more critical than Tmax. These results highlight the need for systematic planning to manage negative impacts of climate change on wheat cultivation in India. These results will used as a basis for suggesting adaptation strategies to manage the impact of climate change on wheat yield.

How to cite: Lakshmanan, A., Shastri, Y., and Singh, R.: Climate change impact on wheat yield in India: Study using CERES-wheat model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18617, https://doi.org/10.5194/egusphere-egu24-18617, 2024.

The Canary Islands constitute an archipelago of Spain, also being a European outermost region composed of eight islands. Overall, these islands face a high risk of experiencing the impacts of climate change, particularly rising sea levels, floods, temperature increases, and a decrease in water resources, factors that significantly affect the daily life of the population in the islands. As the effects of climate change are linked to greenhouse gas emissions, it is crucial to measure the emissions from the main sectors of the Canary Islands to implement effective mitigation and reduction measures, as well as to increase energy production through renewable sources. For this reason, the Government of the Canary Islands has commissioned the project to determine the carbon footprint and water footprint of the main sectors of the region, including the production of drinking water and wastewater management, agriculture, and tourism. The results indicate that seawater desalination for drinking water, being a significant energy consumer with low penetration of renewable energy in the Canary Islands' electricity mix, is the facility contributing the most to greenhouse gas generation in the water cycle in the region. It is followed by wastewater treatment plants and extraction wells from the aquifer. In the case of agriculture, focusing on the consumption of tropical crops such as avocados and bananas, key export crops, it is noteworthy that avocados are major water consumers, slightly exceeding the water consumption of bananas. This poses challenges in the face of an uncertain future due to reduced natural precipitation resulting from climate change. Lastly, the analysis of tourism emissions highlights that hotel activities and rental vehicles are significant contributors to greenhouse gas emissions. Although these emissions are indirect for the archipelago, other studies have emphasized the high emissions associated with the arrival of tourists by air to the islands. This study stands as the first to analyze the emissions of the main sectors in the Canary Islands, providing an opportunity for governmental actions to reduce these emissions and mitigate climate change in the islands.

Keywords: Climate change; outermost region; vulnerability; sustainable development

Acknowledgements

This research was supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement 101037424 and Project ARSINOE (Climate Resilient Regions Through Systems Solutions and Innovations).

How to cite: Santamarta, J. C., Cruz-Pérez, N., Rodríguez-Alcántara, J. R., Rodríguez-Martín, J., García-Gil, A., Gasco-Cavero, S., and Marazuela, M. Á.: Estimating and Suggesting measures to reduce carbon emissions and water footprint linked to water collection, agriculture, and tourism in the Canary Islands (Spain), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19902, https://doi.org/10.5194/egusphere-egu24-19902, 2024.

The global food supply and food security are altered by field, soil, and weather conditions during crop production. Researching food productivity became crucial as the global population increased. In particular, crop losses bring low food supply and price instabilities at the regional and global levels. With that in mind, we reviewed ten crop models and the most simulated impacts from soil-crop-atmosphere interactions in maize, rice, and wheat production. Since 2012, modellers have mainly used APSIM to predict water availability, temperature changes and Greenhouse Gas  (GHG) concentration to predict crop phenology, growth and development, grain filling and nutrient content, and yield. Since 2013, AquaCrop has been used to simulate scenarios focused on water balance in crop production systems, water stress and irrigation planning. Interestingly, Biome-BGCMuso was developed as a biogeochemical model and was not considered good by crop modellers. However, After updates, version v6.2 can simulate different management and field conditions for fifteen crops, considering heat, nitrogen and drought stress. Since 2008, crop modellers used CropSyst to evaluate water availability, nitrogen use efficiency (UE), temperature shifts and GHG concentration in rainfed and irrigated crop systems. Since 2002,  crop modellers have used DAISY to predict crop growth, nitrogen and water UE, grain content, yield gap, and losses. Since 2011, researchers have used DSSAT-CERES for mitigation strategy planning by predicting crop growth, soil characteristics, changes in land use, and nitrogen and water UE. Since 2015, JULES has been used to determine land-atmosphere interactions, changes in land use and GHG impacts on agriculture. Since 2008, ORYZA modellers have mainly predicted nitrogen and water UE, salinity impacts, and toxicity to rice. STICS was developed in 1996, and since 2008, it has been primarily used to simulate fertilization and irrigation systems, nitrogen leaching, and water availability. Since 2000, researchers have used WOFOST to analyze water availability, crop growth, and productivity under temperature changes. Crop models are fast and reliable resources when simulating crop production and food availability.

How to cite: Gavasso Rita, Y., Papalexiou, S., Li, Y., Elshorbagy, A., Li, Z., and Schuster-Wallace, C.: The use of crop models to assess crop production and food security, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20643, https://doi.org/10.5194/egusphere-egu24-20643, 2024.

Estimating crop yield can help farmers plan for equipment, labor, and other crop production input requirements. Forecasting crop yield is also useful for analyzing weather-related variability to guide decisions such as irrigation water and fertilizer management. This work discusses the application of Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) machine learning algorithms for seasonal cotton yield prediction. Simulation results from the crop model AquaCrop, consisting of irrigation depth, soil moisture content, and crop growth stage data from 2003 to 2021 were used to train the algorithms. The two developed yield-prediction models were tested against data collected from an irrigated cotton field located at Clemson University Edisto Research and Education Centre (EREC), near Blackville, South Carolina, USA during the 2023 growing season. The values of hidden layers, hidden units, dropout, learning rate and batch size hyperparameters were set to respectively, 3, 64, 0.2, 10E-3 and 64 for the GRU model and 3, 128, 0.4, 10E-3 and 64 for the LSTM model. Analysis suggested that the tested algorithms resulted in very good to excellent performance. We concluded that machine learning algorithms are useful tools that can provide insights into how much yield to expect in an upcoming season and help farmers optimize energy, water, and fertilizers applications.

How to cite: Umutoni, L., Samadi, V., Payero, J., Koc, B., and Privette, C.: Application of Machine Learning Approaches for Cotton Seasonal Yield Estimation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20876, https://doi.org/10.5194/egusphere-egu24-20876, 2024.

Climate change is expected to impact the maritime sector, including the port industry. Ports are on the frontline when it comes to experiencing operational challenges from the increased sea levels and extreme weather conditions, associated with increased infrastructure investments. For instance, rising sea water levels are expected to change the accessibility of channels and increase the need for higher quay walls, while the increased intensity or/and frequency of events, such as fog, high winds, and waves, may increase the frequency of port operation disruptions; but changes are uncertain, and with regional variation.

The present study focuses on the Port of Heraklion, one of the main ports of national importance in the Greek Maritime Network, located in the North side of the island of Crete, and aims at assessing the impacts of climate change on port operations associated with: 

• Changes in mean sea level, storm surges and wave characteristics (i.e. wave height, period, frequency of occurrence).
• Reduced visibility caused by intense precipitation and/or fog.
• Disruption of port operations due to high wind speeds, drainage system induced flooding, as well as river discharges and sediment transfer in the harbor basin.

To assess the effects of climate change on winds we use climate change factors (CCFs) obtained using climate model data at 3-hourly temporal resolution over the Island of Crete (i.e. sub-country level) from EURO-CORDEX ensemble, and more in particular from HIRHAM5 RCM (Regional Climate Model) nested in (downscaled from) EC-EARTH GCM (Global Climate model), for two Representative Concentration Pathways of future emissions: RCP 4.5 for the period 2071-2100 and RCP 8.5 for the period 2041-2070. These are also the RCM-GCM combination and time periods used to assess the effects of climate change on the sea state and wave characteristics.

For rainfall, we make direct use of the climate change factors reported in the context of SWICCA program (Service for Water Indicators in Climate Change Adaption, 2015 - 2018), which was financed by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Copernicus Agency within the framework of the Copernicus climate change service (C3S). Over the island of Crete, the corresponding factors are available for 9 GCM - RCM combinations (i.e. 5 for the RCP 4.5 scenario and 4 for the RCP 8.5 scenario).

We find that the increase of the mean sea level, as well as the increase in the frequency of intense storms significantly affect the frequency of port operation disruptions, particularly due to breakwater overtopping, storm induced flooding, as well sediment deposition in the harbor basin.

Acknowledgements

The presented work has been conducted under the project Climate Risk and Vulnerability Assessment (CRVA) for the Heraklion Port Authority" (project code: AA 011391-002/CC15302), which has been financed by the EIB under the InvestEU Advisory Hub. 

How to cite: Perdios, A., Boutatis, A., Langousis, A., Biniskos, P., Kypraiou, E., Korda, K., and Zacharof, A.: Climate Risk and Vulnerability Assessment (CRVA) for the Port of Heraklion in Greece , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21145, https://doi.org/10.5194/egusphere-egu24-21145, 2024.