Poster session
The El Niño–Southern Oscillation (ENSO) is the strongest driver of monsoon variability that significantly affects agricultural production in India. This study evaluates the teleconnections between strong ENSO events during 1982-2017; the vegetation conditions and water availability over the Indian region. For this purpose, various indices (i.e., Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Precipitation Condition Index (PCI), Soil Moisture Index (SMCI), Temperature Condition Index (TCI), and Soil Moisture Agricultural Drought Index (SMADI)) are evaluated to determine agricultural drought conditions during the Kharif season (July to October) in India. Results indicate that vegetation conditions are strongly associated with Pacific Sea Surface Temperature (SST) and vary with geographical conditions. The correlation analysis between root zone soil moisture, precipitation, temperature, and vegetation conditions with SST delineates strong feedback with 1-2 months of lead time. Most of the indices show that drought severity was mainly pronounced in most of the parts of Peninsular, Central Northeast (particularly in Indo-Gangetic Plain), and West Central India during strong El Niño years. On the contrary, during strong La Niña years, some areas also experienced drought conditions in the Indo-Gangetic. Further investigation through empirical orthogonal teleconnections (EOT) and correlation analysis confirms the similar teleconnection impacts in the Indian region. These findings contribute to the understanding of drought dynamics in Indian regions. However, due to extreme heat as evident from TCI, the vegetation-based indices were not always consistent with precipitation and soil moisture-based indices. This study highlights that the impact of ENSO on agricultural conditions had a lead-time of up to 2 months in, which has significant potential to enhance the early warning system for agriculture and food security.
How to cite: Soni, A., Srivastava, A., Pillai, P., Rao, A. S., and Wagh, N.: Assessing the impact of ENSO on Vegetation during the Kharif Season over India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-852, https://doi.org/10.5194/egusphere-egu25-852, 2025.
Precision agriculture is essential to optimize the crop water use even in the environmental conditions with adequate water availability. It ensures the optimal water retention and reduced nutrient leaching for agricultural field. By doing so, precision agriculture also confirms tackling the situations like changing climate with varied seasonal weather patterns, availability of adequate water resources for crop growth and altering surface water quality due to runoff from agriculture.
Although, the agriculture in the Nordics is not under acute pressure of water scarcity however the situations of unprecedented weather conditions during the crop growing season requires necessary attention. Moreover, the subsequent effects of long-term changing climate predictions on the surface as well as groundwater availability in the region are concerning. Under such circumstances the conventional use of SSDS implementing controlled drainage (CD) approach with its impact on field-scale hydrology with a shallow groundwater state were assessed in recent studies. However, neither its subsequent effects on the field-scale crop productivity nor its integration with possible strategies to optimize crop water use under long-term predicted state of subsurface hydrology in the region were investigated.
This study presents the long-term climate assessment for agriculture in the Northern Finland with its impact on the seasonal crop yield. Also, with the use of process-based model approach the study attempts to present a possible eco-friendly strategy with necessary updates in the existing SSDS to optimize the crop water use under the adverse conditions of long-term forecasted state of subsurface hydrology in the region. In addition, the study presents the field scale crop productivity by ensuring the enhanced water retention and reduced nutrients from agriculture with precision in farm management practices to sustain or improve the crop productivity. The simulation results with crop water productivity tool over historical dataset showed over 40 to 60 percent rise in the seasonal crop yield under adverse climate conditions. Whereas the results for overall amount of soil water required to replenish the crop water need showed a difference of almost 0.016994 MCM per ha in case of effective integration of SSDS with more efficient water application systems for agriculture.
Moreover, this work introduces Data learning approach which talks about Integration of multi-source data (DI) and Machine Learning (ML) approach to real-world data. We present a broader perspective followed while developing applications based on Data Learning approach. We present a data learning method and results for a case study field which involves process based as well as machine learning approach.
How to cite: Ghag, K. S., Panchanathan, A., Md Touhidul, S. M., Liedes, T., Klöve, B., and Torabi Haghighi, A.: Field scale assessment of subsurface drainage systems (SSDS) for efficient crop water management with improved crop productivity for agriculture in the northern Finland., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-948, https://doi.org/10.5194/egusphere-egu25-948, 2025.
The intensified impacts of climate change, declining water resources, and rising food demand pose significant challenges to rainfed agriculture in the arid and semi-arid regions of China. The Loess Plateau, as a representative area, has long suffered from water scarcity, low water use efficiency, and mismatch between cropping structure and water resource distribution. To address these issues and promote high-quality, sustainable agricultural development, this study evaluated the crop-water mutual suitability to explore the strategies for water-adaptive cropping optimization. The research employs the following approaches: 1) Cropping structure on the Loess Plateau (2017–2022) were identified using an integrated phenology and machine learning approach. The resulting maps demonstrated high overall accuracies (0.83), showing strong agreement with municipal statistical data (R² ≥ 0.76). 2) The effective water supply and crop water demand were quantified using remote-sensing-based water balance assessment tool (RWBAT). A Crop Water Suitability (CWS) index was developed to quantitatively evaluate crop-water suitability across the region. The analysis revealed suboptimal water suitability (CWS < 0.35) in the central double-cropping zones and the hilly-gully regions of the Plateau. 3) A tightly coupled multi-objective optimization framework, integrating RWBAT model, was designed to optimize cropping structures. Compared to 2022 baseline condition, the optimization results indicated 25.6% improvement in crop water use efficiency and 5.3% increase in net income from crop production. The research results provide scientific basis and operational solutions for crop water resources management in the Loess Plateau and even similar arid areas, and provide an important reference for coping with climate change and agricultural water resources crisis.
How to cite: zhao, X., wang, J., gao, X., liu, Q., li, C., and gao, X.: Evaluation of crop-water mutual suitability and optimization of water-adaptive cropping for dryland farming in Loess Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1773, https://doi.org/10.5194/egusphere-egu25-1773, 2025.
Irrigation benefits crop yields directly by providing additional water and indirectly by reducing surface temperature (i.e., irrigation cooling benefit). To date, how irrigation cooling benefit is spatial-temporally distributed remains elusive at the global scale. Here, by synthesizing various datasets and data-driven models, we quantify global irrigation cooling benefits and explore the underlying mechanisms behind its spatial-temporal patterns. Results show that irrigation has exerted a cooling benefit (relative to its total benefit) of 7.76%, 9.43%, 6.92%, 5.0% and 2.82% for the globe, Northeastern China, North China Plain, Southern Great Plain and Northern India, respectively. A greater global-scale benefit of 8.62% is estimated for the year 2010 which is mainly achieved by reducing yield sensitivity to cooling rather than cooling magnitude. Maximum temperature and irrigation properties are found to modulate the spatial pattern of irrigation cooling benefit, while regional characteristics (e.g., the coefficient of variation and mean of irrigation yield increase) control its temporal differences. Additional analysis shows that the grid resolution and the number of maize patches are the main uncertainty sources of the estimated irrigation cooling benefits, suggesting the importance to develop more finer and long-term observational yield datasets under rainfed and irrigated conditions.
How to cite: Han, L. and Leng, G.: Global irrigation cooling benefits: the spatial-temporal patterns and possible mechanisms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2145, https://doi.org/10.5194/egusphere-egu25-2145, 2025.
Dry and warm snow drought has a lagging effect on vegetation browning during the growing season, but has not been systematically studied. This study quantified the propagation characteristics (propagation time and probability) of dry and warm snow drought on warm season vegetation browning, using the proposed three-dimensional conditional probability framework, and evaluated the potential driving mechanisms using random forest. Findings indicated that while the propagation time and probability were long in late summer and early autumn, they were short in late spring and early summer. The probability of vegetation productivity loss in the warm season was positively impacted by the severity of dry snow drought, whereas it was negatively impacted by warm snow drought. The warm snow drought had a more noticeable effect on warm-season vegetation than had dry-type in changing environment, with soil moisture and wind speed playing a major role.
How to cite: Wang, Z., Huang, S., Wei, X., and Liu, D.: Soil moisture and wind speed exacerbate the propagation from snow drought to vegetation browning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3467, https://doi.org/10.5194/egusphere-egu25-3467, 2025.
Effective water storage strategies are essential for reducing drought and flood risks, enhancing agricultural productivity, and fostering socioeconomic development. However, estimating future required water storage capacity (RWSC) is subject to great uncertainty due to varying model predictions of runoff variability (Rv). Here we integrate observations with the identified emergent constraint framework to refine global ΔRv estimates and reassess ΔRWSC across global basins. Under the SSP5-8.5 scenario for 2070-2099, the constrained RWSC increases by 24.39-29.93% across all three return periods (30, 50, and 100 years) compared to the historical period (1980-2014). Notably, the constrained ΔRWSC exhibits a significant decrease, particularly in lower-middle-income basins (by 11.66-22.12%) and low-income basins (by 7.95-14.69%), due to overestimations in ΔRv (by 26.98%). Our findings suggest a lower risk associated with Rv and a diminished need for water storage expansion, especially in basins with lower income levels, as shown by raw model projections.
How to cite: Yao, L., Leng, G., and Yu, L.: Observational Constraint Reveal Overestimation of Required Water Storage Expansion under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4296, https://doi.org/10.5194/egusphere-egu25-4296, 2025.
Satellite-based solar-induced chlorophyll fluorescence (SIF) has shown a promising skill for end-of-season crop yield prediction due to its close linkage with photosynthesis. However, the benefits of SIF have rarely been examined for in-season crop yield forecasts, which would depend on current-phase crop growing status and unknown-stage climate conditions. By leveraging SIF, seasonal climate forecasts and machine learning, we build an in-season maize yield forecast system at the 8-day scale in Northeast China (NEC). The value of SIF is demonstrated by comparing it against traditional vegetation indices (VIs). Overall, reliable yield forecasts can be achieved two months before the harvest (jointing–tasseling) in NEC, with an average bias of less than 2.5%. Assimilating SIF into the yield forecast system exhibits a better performance than VIs except in the medium-growing stage. The added value of SIF is more pronounced in the dry and hot years, especially under the early and early-medium growth phases. Attribution analysis reveals that the absorbing radiation signal carried by SIF is the main driver for its advantage over VIs under the early and early-medium phases, while its outperformance under the medium-late and late stages is related to both the reflection of photosynthetic rate and the absorbing radiation signal. This study provides a valuable framework for weekly yield predictions, which has great implications for early warning of yield loss risk in China.
How to cite: Lu, C., Leng, G., Han, L., Yu, L., Qiu, J., Yao, L., Liao, X., Huang, S., and Peng, J.: Combining sun-induced chlorophyll fluorescence and seasonal climate forecast for 8-day dynamic in-season maize yield prediction in northeast China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4617, https://doi.org/10.5194/egusphere-egu25-4617, 2025.
Potato cultivation is one of Europe's and also Finland’s most important agricultural sectors, and climatic variability highly affects its yield. Large-scale atmospheric phenomena, such as teleconnection patterns, are paramount for the regional climatic features to which temperature and precipitation are significant components of potato growth. This is due to the complex climatic interactions caused by teleconnections such as the North Atlantic Oscillation (NAO), El Niño-Southern Oscillation (ENSO), Arctic Oscillation (AO), and Scandinavian Pattern (SCA), which have not been fully discussed within the context of potato farming. This study aims to address possible potato yield predictions from the teleconnection patterns. The study employs machine learning techniques to investigate the relationship between different teleconnection indices and European potato yield variability. Advanced algorithms such as Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were applied to integrate historical climatic data, teleconnection indices, and potato yield records to develop robust predictive models to help identify the most important climatic drivers and capture nonlinear interactions among teleconnections and agricultural outputs. We examine the spatial and temporal dynamics of teleconnection patterns and their correlation with climate variations across European regions during potato growing seasons. By focusing on the relationship between teleconnections and agrarian outputs, the study seeks to contribute to developing climate-resilient farm strategies in Europe to achieve better food security in the face of changing climate.
How to cite: Naghdyzadegan Jahromi, M., Saboori, M., Gohari, A., Ghadimi, S., and Torabi Haghighi, A.: Teleconnection Patterns and Climate Variability: Insights into European Potato Growing Seasons, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6457, https://doi.org/10.5194/egusphere-egu25-6457, 2025.
Human activities significantly impact global water resource availability through alterations in terrestrial water cycle processes, with agricultural irrigation being a primary driver. Accurately quantifying irrigation water use is essential for understanding regional water resource dynamics, optimizing water resource allocation, and improving agricultural productivity. However, high-quality data on irrigation canal networks is often lacking at regional scales, hindering the precise delineate of river sources for irrigation. To address this, this study developed a large-scale canal system detection method using artificial intelligence (AI) techniques and large-scale satellite remote sensing images. The method enabled the identification of canal networks, clarified the irrigation intake points, and facilitated the calculation of irrigation water volumes supplied by various mainstream and tributaries in the basin. The Mekong River Basin, where riparian states heavily rely on tributaries for irrigation and face difficulties in acquiring canal data, is selected as the study area. The results show that the developed Convolutional Neural Network (CNN)-based method successfully detected 291 irrigation canals sourced from mainstream and tributaries of the Mekong River, with 43% of the main canals drawing directly from the mainstream and the remainder from tributaries. Spatial analysis reveals a higher canal density in the south compared to the north of the basin. Additionally, irrigation water use is markedly higher during the dry season from November to the following April, accounting for 69% of annual irrigation consumption, peaking in January and reaching a minimum in September. This research has the potential to address critical data gaps in irrigation in the Mekong River Basin, enhance the understanding of agricultural irrigation water use, and provide essential insights for effective water resource management and sustainable agricultural development.
How to cite: Zhao, H.: Intelligent Remote Sensing Canal System Detection and Irrigation Water Use Estimation: A Case Study in the Transboundary Mekong River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6604, https://doi.org/10.5194/egusphere-egu25-6604, 2025.
Increasing food demand and water scarcity pose critical challenges to food and water security, exacerbated by climate change. Although substantial progress has been made in crop yield forecasting, limited research has investigated how irrigation water requirements (IWR) and water productivity (WP) respond to changing climate conditions at a basin scale. To address this knowledge gap, we developed an agro-hydrological model to estimate IWR and WP for maize and wheat across the Yellow River Basin (YRB)—a major cereal-producing region in China—under different climate scenarios from CMIP6. Projections indicate that 70% of maize-irrigated areas will experience an increase in IWR of more than 20%, particularly in the lower YRB due to reduced rainfall during the growing season. In contrast, spring wheat IWR is projected to decrease by 12–16% in the western YRB and 5–8% in the northern YRB, depending on irrigation frequency, due to increased rainfall. Although over 90% of the winter wheat-irrigated areas may require less irrigation water in the near future (2021–2060), non-negligible increases in IWR are expected in the far future (2061–2100) in the southern YRB due to increasing reference evapotranspiration (ET0). These effects increase with higher levels of radiative forcing and longer time horizons. In addition, changes in IWR are most pronounced in humid areas (low ET0/P ratios), while increases in WP are most pronounced in areas with ET0/P values around 1.7 for maize and 4 for spring wheat. The implementation of high-frequency irrigation could mitigate large-scale negative effects. These results highlight the need for water-saving irrigation practices to improve water and food security in the YRB.
How to cite: Liu, Y., Huo, Z., and Gobin, A.: Impacts of climate change on cereal irrigation and water productivity in the Yellow River Basin, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10230, https://doi.org/10.5194/egusphere-egu25-10230, 2025.
The Blue Nile Basin, shared by Ethiopia and Sudan, is a region of significant agricultural importance, supporting the livelihoods of millions who rely on its resources for farming. However, this area faces critical challenges linked to climate change, including rising temperatures and changes in rainfall patterns, which lead to increased crop yield variability. These factors have increased the unpredictability of farming, making it difficult for farmers to plan planting, irrigation, and harvesting schedules effectively. Moreover, the growing population in the Blue Nile region further intensifies the pressure on agricultural systems to produce sufficient food. These challenges highlight the pressing need for crop yield forecasts to enhance agricultural planning, ensure resource efficiency, and strengthen food security in this vulnerable region.
This research aims to address these challenges by adopting crop yield forecasts at a subseasonal timescale, integrating process-based crop models into a high-resolution atmospheric simulation framework. Specifically, the Weather Research and Forecasting (WRF) model, coupled with the Noah-MP land surface model, is used to simulate land-atmosphere interactions, including soil moisture, temperature, rainfall, and solar radiation. The output from the WRF-NoahMP system is then fed into a process-based crop model to simulate crop growth and estimate yields.
The study seeks to produce an accurate model capable of reproducing water availability and crop yields in the Blue Nile region, considering the limited availability of observational data, and to compare the performance of the process-based crop model against traditional statistical approaches. By providing early warning signals for potential yield fluctuations, this research offers practical tools for improving agricultural decision-making. The findings have implications not only for farmers and policymakers in the Blue Nile Basin but also for regions facing similar climate-induced challenges, paving the way for adaptive strategies in a rapidly changing global environment.
How to cite: Hageltom, Y., Arnault, J., and Kunstmann, H.: Crop Yield Forecasting in the Subseasonal Timescale: Case Study of the Blue Nile Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11319, https://doi.org/10.5194/egusphere-egu25-11319, 2025.
Agricultural systems in regions with previously low scale irrigation such as Thuringia, Germany, face an increase of droughts and weather extremes through climate change. Farmers are expected to adapt by increasing irrigation as a means of securing incomes. For Thuringia this entails water security implications due to limited groundwater resources and strong reliability on surface water highlighting the need to understand the feedbacks between human and natural systems in order to ensure efficient allocation and protection of water resources. So far, few studies have simultaneously combined hydro-economic models of the agricultural sector with hydrological models on a high spatial disaggregation to inform the future resilience of human-natural systems in historically water abundant regions such as Central Europe. We apply the DroughtMAS model, which simulates agricultural agents representing the production conditions of the local area, to Thuringia on a 4x4 km grid. We calibrate the model with plot-level remote sensing data using Econometric Mathematical Programming (EMP) to simulate cropping and irrigation decisions. Potential yields under climate change are calculated using machine-learning models. Socio-economic and climatic futures are simulated based on a plausible set of downscaled scenarios for Thuringia expanding upon the Shared Socio-Economic Pathways (SSPs) and Regionalized Concentration Pathways (RCPs). Regional water demand is linked to models of available groundwater to assess water security. We find a locally differentiated increase in irrigation demand and water insecurity under scenarios of drought and socio-economic change, implying the need for demand-side interventions or a provision of sufficient reservoir capacities. A spatially explicit coupled hydro-economic multi-agent approach enables an economic valuation of demand and supply side management options to inform the adaptation options towards climate resilient agricultural and hydrological systems.
How to cite: Werner, S., Heilemann, J., Klassert, C., Nagpal, M., Klauer, B., and Gawel, E.: Simulating irrigation demand under climate change applying a high-resolution hydro-economic Multi-Agent-System model in Thuringia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11792, https://doi.org/10.5194/egusphere-egu25-11792, 2025.
Climate change has significantly affected terrestrial water cycle, but future runoff change response to global warming remains uncertain among CMIPs. Here, we estimate the runoff sensitivity to global mean temperature (GMT) change and recognize global runoff response hotpots from existing CMIPs. Results show that global mean runoff increases linearly with GMT rise (3.3%/℃) in CMIP6, which is more sensitive than CMIP3 (1.9%/℃) and CMIP5 (2.9%/℃). Albeit with difference in the magnitude of regional runoff sensitivity, CMIPs exhibit consistent spatial pattern in terms of the direction of runoff response. Furthermore, exiting CMIPs projected that the significant negative runoff response hotspots mostly occur in the extended subtropics, while hotspots to experience significant wetting are mainly found in the northern high-latitude and some water-scare areas. Our results highlight global hotpots of runoff response to climate change from CMIP3 to CMIP6, which may benefit to develop associated climate change mitigation and adaptation strategies.
How to cite: Zhang, X. and Qu, Y.: Global Hotspots for Runoff Sensitivity to Climate Change , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14078, https://doi.org/10.5194/egusphere-egu25-14078, 2025.
The increasing impacts of climate change are causing serious challenges for global food security and sustainable agriculture. A key concern is how changing climate conditions, such as precipitation and temperature, might influence the suitability of croplands and agricultural systems, with significant consequences for future food production and related policies. This issue is particularly relevant in Switzerland, as mountainous regions and lowlands are especially vulnerable to foreseen climate changes, including rising temperatures and changes in precipitation patterns, characterized by reduced summer rainfall and increased winter precipitation. Furthermore, soil properties, such as pH and organic carbon, are also expected to change due to increased aridity and warming. In this study, by establishing relations between soil and climate factors and crop yield, we evaluate the suitability of five major crops produced in Switzerland (namely rye, wheat, barley, vines, and maize) via a data-driven model. We derive spatially explicit results for current and future scenarios. Findings for the reference year 2000 show that the leading drivers affecting the suitability are mostly related to climate rather than soil conditions. The relative effect of precipitation, temperature, and solar radiation varies depending on the crop and its geographic location, highlighting context-specific impacts of climate variations and their interlinkages. Regarding future projections, we assess how shifts in projected temperature and rainfall regimes under three Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) translate into spatial variations in crop suitability compared to the year 2000. Regions facing the strongest warming by 2090 are projected to lose suitability for all the crops considered here, with temperature emerging, overall, as the dominant driver of such shifts, particularly in the Swiss lowlands.
How to cite: Bassani, F., Wakjira, M., Peleg, N., and Bonetti, S.: Climate change impacts on Swiss cropland suitability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16347, https://doi.org/10.5194/egusphere-egu25-16347, 2025.
