Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
Cyanotoxins in agricultural waters pose a human and animal health risk. These chemical compounds can be transported to nearby crops and soil during irrigation practices; they can remain in the soils for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the randomForest algorithm in estimating microcystin concentrations from eight in situ water quality measurements at one active livestock water pond (Pond 1) and two working irrigation ponds (Pond 2 and 3) in Georgia, USA. Sampling was performed monthly from June of 2022 to October of 2023. Measurements of microcystin along with eight in situ sensed water quality parameters were used to train and test the machine learning model. The model performed better at Pond 1 (R2 = 0.601, RMSE =3.854) and Pond 2 (R2 = 0.710, RMSE = 2.310) compared to Pond 3 (R2 = 0.436, RMSE = 0.336). Important variables for microcystin prediction differed among the three ponds, temperature and chlorophyll, phycocyanin and turbidity, and temperature and phycocyanin in Ponds 1, 2 and 3, respectively. Separating nearshore and interior samples in Ponds 1 and 2 lead to better predictive capacity of the model in nearshore samples compared with the interior samples. Overall, the random forest algorithm explained 50% to 70% of the microcystin concentration variation in three Georgia agricultural ponds with data from in situ sensing. In situ sensing showed a potential to aid in the water sampling design for microcystin to characterize the spatial variation of concentrations in studied ponds using readily available in situ sensing data.
How to cite: Smith, J., Stocker, M., Hill, R., and Pachepsky, Y.: Microcystin concentrations and water quality in three agricultural ponds: A machine learning application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6696, https://doi.org/10.5194/egusphere-egu25-6696, 2025.
Escherichia coli (E. coli) is a key marker for monitoring microbial water quality, with significant consequences for both public health and agricultural practices. To address the challenges of traditional water quality assessments, remote sensing offers a promising alternative. In this research, we implemented the random forest (RF) algorithm to forecast E. coli levels in irrigation ponds using three distinct data sources: (1) conventional water quality measurements, (2) multispectral reflectance values from drones, and (3) remote sensing indices derived from these reflectance values. To enhance the model’s accuracy, a linear transformation was applied during postprocessing. The RF model achieved strong performance (R² = 0.74) with conventional water quality variables, while moderate results were obtained using multispectral reflectance values alone (R² = 0.56). The best outcomes were observed when remote sensing indices were used as inputs, yielding an R² of 0.76. Shapley additive explanations (SHAP) were employed to evaluate the importance of individual variables. Dissolved oxygen, pH, and Chlorophyll-a emerged as critical predictors among water quality parameters. Meanwhile, the visible atmospherically resistant index (VARI) and normalized difference turbidity index (NDTI) were the most significant remote sensing indices. Furthermore, location-based comparisons highlighted differences in the impact of VARI and NDTI between interior and nearshore sampling sites. These findings suggest that remote sensing indices effectively capture water quality features crucial for E. coli persistence. This study underscores the potential of using drone-derived multispectral data to enhance predictions of E. coli concentrations in irrigation ponds.
How to cite: Hong, S., Morgan, B., Stocker, M., Smith, J., Kim, M., Cho, K. H., and Pachepsky, Y.: Assessing the effectiveness of remote sensing indices for predicting E. coli concentrations in an irrigation pond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7313, https://doi.org/10.5194/egusphere-egu25-7313, 2025.
Antimicrobial resistance (AMR) in irrigation waters is a major worldwide health issue. Crops irrigated with waters containing antibiotic resistant bacteria (ARB) or related genes (ARG) can serve as a vector for AMR throughout food supply systems. The current extent of AMR in irrigation waters is poorly understood and even less so for small lentic waters such as farm ponds. The objectives of this work were to characterize the variability of ARG concentrations in an actively used irrigation pond and to determine if stable spatial patterns in the concentration data exist which can be used to inform monitoring designs. Water sampling was conducted on 9 dates between June and September 2023 at 20 locations within an actively used irrigation pond in Maryland, USA. The ARG tetracycline gene tetA was enumerated using dQPCR in all collected samples. Due to the presence of non-detects, the robust regression on ordered statistics (ROS) method was applied to the dataset to impute non-detectable concentrations on each date. Spatial variation of tetA concentrations was date-dependent with coefficients of variation ranging from 97 % to 377 % with an average of 181 %. Concentrations steadily declined throughout the observation period which significantly correlated with increases in water temperature (rs = - 0.738; p = 0.023). Rainfall events throughout the observation period did not result in higher concentrations of tetA in the pond. On a majority of dates, significant outliers in the data were identified according to the extreme studentized deviate test. The mean relative difference analysis revealed that samples collected at the pond banks contained higher tetA concentrations than those collected in the pond interior. Elevated concentrations of the ARG at bank sites were attributed to on-land activities as well as hydrological conditions within the waterbody. Sampling sites were identified that best represented the spatiotemporal average of the concentration data which is useful if large sample sets cannot be collected. This work is the first to evaluate fine-scale spatial variation of ARG in lentic waters used for irrigation and the results show that the choice of where to sample for ARG enumeration in ponds or lakes should not be made arbitrarily.
How to cite: Stocker, M., Smith, J., Pachepsky, Y., Gabriel, E., Sharma, M., and Gutierrez, A.: Fine-scale spatial patterns of antibiotic resistance gene concentrations in irrigation pond water, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7320, https://doi.org/10.5194/egusphere-egu25-7320, 2025.
Modeling is an efficient approach for predicting microbial water quality and suggesting related management practices. Escherichia coli or enterococci concentrations are commonly used to indicate microbial contamination and characterize microbial water quality. Small watersheds provide drainage into first- or second-order creeks, exhibit significant variation in land use, management, and conservation practices. Modeling microbial water quality in the small watersheds can help account for and mitigate the heterogeneity within larger hydrologic response units. A model for microbial water quality should incorporate key hydrologic components such as runoff, in-stream water fluxes, and meteorological inputs such as precipitation, air temperature, and solar radiation. Additionally, animal waste management, including the quantity and application schedule, are also important for microbial water quality simulations. The Agricultural Policy Environmental eXtender is a useful tool for hydrological, meteorological, and management drivers of microbial water quality, as it has been developed for small watersheds. Major microbial fate and transport processes include animal waste deposition, degradation, erosion, survival on soil, release from waste and transport by rainfall or irrigation, and microbial survival and resuspension in water or sediment. These processes can be simplified, for instance, by modeling proportional release of the indicators and animal waste during erosion. We can also use a two-phase survival model for manure and temperature-dependent rate of microbial survival in surface waters. Animal waste aging should also be considered in the microbial model, as daily bacterial survival and erodibility are influenced by it. The microbial module in APEX was used to the headwater watershed of Conococheague Creek in Pennsylvania, USA. The total watershed area is 34321.6 ha, with 15 subareas and the dominant land use is deciduous forest. Three years of hourly stage observations with rating curves and weekly E. coli concentrations at the outlet were available. The primary source of E. coli was animal waste from white-tailed deer, with an average density of 19 deer per square kilometers. Deer population dynamics reflect seasonal changes including fawn births, predation, pre-hunting, and post-hunting population phases. E. coli concentrations at the watershed outlet varied seasonally, ranging from 5 to 500 CFU (100 mL)-1. The model reasonably captured the temporal fluctuations in E. coli concentrations at the outlet. Ongoing improvements to the model include incorporating deer behavior patterns, animal waste preservation in snow, and runoff during snowmelt.
How to cite: Lee, J., Harriger, D., Hong, S., Jeong, J., Guber, A., Hill, R., and Pachepsky, Y.: Modeling fate and transport of indicator microorganisms in small rural watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7321, https://doi.org/10.5194/egusphere-egu25-7321, 2025.
North China Plain is one of the agricultural region in the world with severe water shortage. Flood irrigation is still the most popular irrigation method in NCP, and have caused very low water use efficiency. Groundwater depletion becomes the most concerned issue for sustainable development. To determine the water & nitrate fluxes is important for better water resouces management. We built up a 48-m in depth of cassion and a 36 lysimeter group for this purpose to study the water budget and water/nitrate movement in the deep vadose zone. In this study, we will present the observation facts using these two facilities to reveal the differences between water transport velocity and celerity in the deep vadose zone of nearly 50 meters. This is the first time to observe the variations or responses of soil potential, moisture, temperature, and electricity conductivity to water inputs from land surface, such as extreme rainfall, directly in the deep vadose zone of 48 meters. We will also present the fresh observation results from the 36 lysimeters about ET and drainage fluxes of different cropping patterns, with different watering and fertilizing treatments. The latter experiment could provide useful information for improving the water/nutrients management for different cropping systems in NCP, and will be beneficial to sustainable groundwater management at the aspects of quantity and quality. The results of the observatoins using these new facilities is presented at international conference at the first time. We hope it could be interested by the colleagues worldwide.
How to cite: Shen, Y., Zhang, Y., Min, L., Wu, L., Li, H., and Li, H.: Water/nitrate fluxes and tranport in deep vadose zone of typical irrigated cropland in North China Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6894, https://doi.org/10.5194/egusphere-egu25-6894, 2025.
The growing need for environmentally friendly wastewater treatment technology has prompted researchers to look into natural alternatives. Among these, algal-bacterial systems have received attention for their capacity to combine biological treatment and biomass production. This study focuses on the use of algal-bacterial flocculent biomass for wastewater treatment in a 400 L pilot-scale raceway pond, with a focus on its potential as a sustainable option for lowering environmental impacts. The synergistic interactions between algae and bacteria in the consortia improve nutrient removal from wastewater, while also providing biomass for future use. The aim was to develop a high-concentration flocculent algal-bacteria biomass. The raceway system was placed in a greenhouse with water temperature 32±8oC for about 230 days. The pilot-scale experiment evaluates treatment efficiency of domestic wastewater in a batch mode procedure. The removal of chemical oxygen demand, ammonia, nitrate, and total phosphorus was over 95%. The biomass concentration stabilized at about 4 g/L after 70 days of operation. The implementation of algal-bacteria flocculent processes for the treatment of domestic or source-separated domestic wastewater shows great promise as a low-cost, sustainable, and efficient solution.
How to cite: Kakavas, D., Biliani, S., and Manariotis, I.: Long-term behavior of syntrophic algal-bacterial biomass in a pilot-scale raceway pond treating domestic wastewater , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6521, https://doi.org/10.5194/egusphere-egu25-6521, 2025.
Climate change significantly impacts water quality and quantity, intensifying extreme weather events, such as floods, droughts, and heat waves. Rising temperatures can increase humidity and dryness, disrupt the water cycle, cause saltwater intrusion into upstream lakes due to sea-level rise, and reduce dissolved oxygen in rivers, thereby deteriorating freshwater quality. Thus, accurate prediction of key climate variables, such as precipitation and temperature, is essential for mitigating detrimental impacts. This study evaluates three modeling approaches, including Process-Based (PB) models, Deep Learning (DL) models, and Process-Based Deep Learning (PBDL) models, to highlight their strengths and limitations.
Our assessment shows that PB models, which are based on physical laws and account for complex interactions between the atmosphere, land, and water bodies, require high parameterization and computational simplifications, which can lead to inaccurate results. DL models can uncover complex relationships from large datasets. They are effective in co-predicting variables, simulating General Circulation Model (GCM) outputs, optimizing PB models, and filling spatiotemporal data gaps. However, their performance depends on the availability of extensive temporal-spatial data, particularly for extreme events. The other group, PBDL models, known as physics-informed or hybrid models, can integrate the strengths of PB and DL approaches. Indeed, these models consider physical laws, such as mass balance and energy conservation, while leveraging DL's pattern recognition capabilities. Even with limited data, these models achieve superior predictions by combining pre-trained PB model outputs, which reduces computational demands.
Although these methods are used to evaluate (actual) evapotranspiration, snowmelt rate, soil permeability, hydraulic conductivity, and the effect of a warming climate on water temperature and streamflow, the interconnected influences on water systems, especially water quality indicators such as dissolved oxygen, heavy metals, nutrients, and water clarity, remain underexplored, presenting a critical research gap. Findings confirm that incorporating simultaneous predictions from DL models with proper variable selection and hyperparameter tuning can further enhance model robustness. Advancing PBDL models through integrating well-calibrated hydrological models, expanding spatiotemporal data coverage, and improving measurement accuracy yields more reliable climate change predictions and bolsters sustainable water resource management strategies.
To identify promising solutions, researchers are encouraged to address the non-stationary behavior of natural systems, considering not only meteorological factors (e.g., wind speed and solar radiation) but also the compound impacts of anthropogenic climate change on water resources. Additionally, selecting appropriate models and coupling them can improve an overall understanding of climate and water system interactions.
How to cite: Karimnejad, A., khorashadi zadeh, F., and Moghim, S.: Assessment of climate change-water resources interaction by different models , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7025, https://doi.org/10.5194/egusphere-egu25-7025, 2025.
Taiwan plays a crucial role in the global supply chain as a major semiconductor manufacturer. Semiconductor production depends heavily on water resources, making the stable supply of industrial water from upstream reservoirs essential to maintaining the global supply chain. However, international water risk assessments often fail to capture Taiwan’s regional hydrological variations due to their large spatial scale, obscuring the real physical and financial risks related to water resources under climate change. Given Taiwan's distinct climate with pronounced wet and dry seasons, short and fast-flowing rivers, and limited surface water retention, reservoirs are critical for regulating water supply. This study employs hydrological models and reservoir operational models to develop a reservoir risk assessment framework, which is the foundation of water resource management. The assessment procedure aids in understanding regional climate-related water risks. Utilizing this assessment tool to adjust reservoir operations will offer strategies for rational water resource management and enhanced climate resilience.
How to cite: Lai, Y.-P. and Lee, T.-Y.: A Framework for Assessing Water Availability and Risk of Reservoirs in Taiwan under Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12004, https://doi.org/10.5194/egusphere-egu25-12004, 2025.
Accurately capturing rainfall patterns is crucial for hydrometeorological applications, particularly in regions like Burkina Faso, West Africa, where rainfall variability significantly impacts water resources and agricultural productivity. However, challenges remain in identifying the most reliable rainfall products for such purposes. This research evaluates the effectiveness of satellite precipitation products (SPPs) and soil moisture-derived rainfall products (SM2RPPs) in representing rainfall patterns in Burkina Faso. Results show that SPPs generally perform better than SM2RPPs across daily to annual timescales. An analysis of total bias components highlights that hit biases dominate but are more pronounced in SM2RPPs. Systematic errors contribute significantly to these hit biases, indicating the potential for improvement through bias correction. Wavelet analysis reveals that both SPPs and SM2RPPs capture seasonal and annual rainfall variability effectively. However, all products exhibit limitations in accurately representing extreme rainfall indices, although SPPs demonstrate superior performance compared to SM2RPPs. While SM2RPPs currently underperform relative to SPPs in Burkina Faso, they show promise for hydrometeorological applications and could achieve comparable or improved results with enhanced bias correction techniques.
How to cite: Yonaba, R., Belemtougri, A., Fowé, T., Mounirou, L. A., Nkiaka, E., Dembele, M., Akpoti, K., Coly, S. M., Koïta, M., and Karambiri, H.: Rainfall Estimation in West Africa: A Performance Comparison of Satellite and Soil Moisture-Derived Products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2576, https://doi.org/10.5194/egusphere-egu25-2576, 2025.
At a local level, river sections maintenance represents a reduction condition of hydrological risk where soil defense work have been carried out.
In this context, this paper describes how the hydrological-hydraulic monitoring of a soil protection intervention can represent the first step for an integrated management strategy of the river ecosystem aimed at maintaining hydraulic safety at inter-municipal level and at the economic-financial sustainability of the interventions.
The case study concerns the soil defense work of - Celone valley - within the framework of agreement memorandum between the municipalities of Castelluccio Valmaggiore, Celle Di San Vito, Faeto and Troia.
The intervention received funding from the Environment Italian Ministry as part of the Puglia Development Pact. The Implementing Body was the Government Commissioner for hydrogeological risk in Puglia.
The study area is located in northern Puglia as part of Celone basin, the portion closed by Torrebianca Dam. The area is surrounded in Daunia Apennines and is characterised by provincial roads that connect the municipalities affected by flooding phenomena. Specifically, we would like to recall the flood event of 12.13.2015 in which two Danish technicians died near the SP124, overwhelmed by a flood wave.
During the above-mentioned work, solid material transport was identified as a trigger for the landslide and its controlled removal could become a sustainable management strategy.
Therefore, starting from the post-operam monitoring, a solid transport indirect monitoring was planned in order to design the controlled extraction of material and its reuse, allowing the river sections upgrading and its hydraulic safety.
Preliminary and qualitative obtained results show the feasibility and economic sustainability of project. This strategy, codesigned and shared with all stakeholders, aims to become a long-term best practice for sustainable territorial management of the river ecosystem.
How to cite: Mita, L., Doria, A., and Godano, F.: Exploring a sustainable solid transport management strategy at local level, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16590, https://doi.org/10.5194/egusphere-egu25-16590, 2025.
The Peruvian coast is undergoing significant landscape transformations driven by environmental and climatic factors, with extreme precipitation events exerting a pivotal influence on the morphology of river channels and floodplains. This study leverages advanced technologies, including unmanned aerial vehicles (UAVs) and post-processing kinematic (PPK) techniques, to address these dynamic changes. The methodology involves co-registering point clouds using ground control points (GCPs) to produce high-resolution and temporally stable digital elevation models (DEMs).The research focuses on a 0.5 km² area within a coastal basin in Peru, with data collection scheduled across two distinct timeframes. The primary objective is to identify areas exhibiting minimal elevation changes and quantify rates of erosion and sediment deposition over a defined period. Specifically, the study measures erosion in gullies and riverbanks, as well as sediment deposition, enabling the estimation of volumetric changes in cubic meters (m³). These findings are critical for advancing the understanding of regional geomorphological processes and informing the development of effective management and mitigation strategies. By employing UAVs and PPK techniques, this research delivers actionable insights into sediment dynamics, supporting sustainable water resource management and land use planning in Peru’s coastal basins. Ultimately, the study contributes to mitigating the adverse impacts of extreme precipitation on the region’s landscapes.
How to cite: Cubas-Arteaga, E. and Cárdenas-Gaudry, M.: Monitoring Geomorphological Changes in the Peruvian Coast Using UAVs and PPK Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15513, https://doi.org/10.5194/egusphere-egu25-15513, 2025.
Analyzing deep soil water use (DSWU) response to precipitation change and its impact on tree physiology is necessary to disentangle tree mortality mechanisms, especially in drylands. In this study, a process-based model parameterized with in-situ measured fine root distribution data for 0-2000 cm and a root-cutting (below 200 cm) numerical experiment were used to explore DSWU strategies across different precipitation years and its contribution to total water consumption, as well as its relationship to tree gas exchange traits in mature apple (Malus pumila Mill) and black locust (Robinia pseudoacacia L.) plantations in both a wetter (Changwu, 583 mm) and a drier (Yan’an, 534 mm) sites on China’s Loess Plateau. Results showed that DSWU at 200-2000 cm depth in different precipitation years of both species mainly occurred during the early growing seasons. On average, DSWU contributed 22.9% and 25.1% to the total water consumption of apple trees and black locust, respectively, and its contribution increased to 26.0% and 36.7% in extremely dry years. Moreover, the lack of DSWU significantly decreased (p<0.05) stomatal conductance (by 16.9%, 16.9%, 47.4% and 11.4%, respectively) and photosynthetic rates (by 37.1%, 20.1%, 28.5% and 16.4%, respectively) of Changwu apple trees, Yan’an apple trees, Changwu black locust and Yan’an black locust in extremely dry years. Similar reductions occurred only in Yan’an for both tree species in normal years. In contrast, no significant differences were found in gas exchange traits in extremely wet years. Our results highlight that DSWU is an important strategy for plantations in deep vadose zone region to resist extreme drought.
How to cite: Zhao, X., Shao, X., and Gao, X.: Deep soil water use can compensate drought effect on gas exchange in dry years than in wet years for dryland tree plantations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1681, https://doi.org/10.5194/egusphere-egu25-1681, 2025.
Concerning water supply in mountainous regions where surface water plays an important role, the understanding of lake stratification or even hypolimnia can be important for water treatment actions.
The historical dam reservoirs were used for the continuous water supply to the ore mines in the Upper Harz Mountains. The first reservoirs were built in the 16th century. The dam heights reach up to 15 m and the stored water volumes are between 10,000 and 600,000 m3. There are about 70 of such lakes around Clausthal-Zellerfeld. Today only few of them are directly used for drinking water supply in the surrounding communities.
Hydrogeochemical data of the lakes have been investigated for about ten years. The specific electrical conductivity (SEC) of the lakes’ surface water ranges between 30 and 280 µS/cm (Bozau et al., 2015, Schäfer et al. 2024). Three lakes (Kiefhölzer, Langer and Oberer Grumbacher Teich) differing in chemical composition and morphometry (area, mean depth and maximum depth) were selected for the investigation of seasonal changes in the water columns. Samples were taken by boat with a Ruttner sampler. SEC and pH were measured on the boat. The titration for HCO3 was done directly after sampling. The main ions were analyzed by ion chromatography and the trace elements by ICP-MS.
Stratification during summer could be clearly observed in all of the three lakes. The degradation of organic material and accompanying redox reactions are seen in the measured pH, SEC, HCO3-, Fe(II), NO3-, NH4+ and SO42- concentrations. Each lake showed a characteristic temporal and chemical behaviour. The development of an anoxic hypolimnion above the lake sediments was obvious in the two shallower lakes Langer Teich (max. depth ~ 5 m) and Kiefhölzer Teich (max. depth ~ 7 m) as being accompanied by H2S-odor in the water column starting ~ 1 m above sediment. This feature was absent in the deepest lake Oberer Grumbacher Teich (max. depth ~ 9 m), which also showed weaker increase of SEC and HCO3- in the profile. The aeration of the hypolimnion started in autumn leading to a well mixed, chemically uniform water column.
Bozau, E., Licha, T., Stärk, H.-J., Strauch, G., Voss, I., Wiegand, B. (2015): Hydrogeochemische Studien im Harzer Einzugsgebiet der Innerste. Clausthaler Geowissenschaften, 10, 35-46.
Schäfer, T., Bozau, E., and Hutwalker, A.: Reservoir lakes in the Upper Harz Mountains (Germany): GIS Implementation and hydrochemical development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5085, https://doi.org/10.5194/egusphere-egu24-5085, 2024.
How to cite: Schäfer, T., Bozau, E., and Hutwalker, A.: Seasonal Changes in Water Columns of Historical Reservoir Lakes in the Upper Harz Mountains (Germany), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2730, https://doi.org/10.5194/egusphere-egu25-2730, 2025.
Temperature plays a critical role in the functioning of river ecosystems. Hence, understanding the processes that control water temperature in river networks across daily to multi-year scales is important when trying to manage river thermal regimes. This is particularly urgent in alpine semi-arid basins with substantial human impact, and, especially within the context of global change, where river ecosystem integrity is at risk. A process-based model has been developed to simulate water temperature in lakes and rivers at a regional (watershed) scale. The physically based and fully distributed hydrological model provides comprehensive hydrological and hydraulic simulations of river flow, including contributions from snowmelt, groundwater, and direct runoff at each node of the network. Additionally, the model accounts for the discharge of urban wastewater at its respective nodes. To overcome the computational cost and numerical problems associated with Eulerian methods in long-term simulations, the model uses a semi-Lagrangian approach to discretize the one-dimensional heat conservation equations in river reaches. Reservoir stratification and withdrawal temperatures are simulated with a 1D Lagrangian model (General Lake Model). This methodology ensures the accurate and detailed simulation of water temperature dynamics in rivers by integrating meteorological, hydrological, and hydraulic data, along with the impact of urban wastewater discharges and reservoir outflows. The model is applied to simulate water temperature in a small semi-alpine watershed upstream of the city of Granada that includes two water-supply reservoirs (Canales and Quéntar). Autonomous temperature sensors deployed at different sites are used for model validation. The model is forced with climate databases (reanalysis, regional climate simulation conducted with WRF, and measured data bases) and used in hindcast/forecast exercises to assess the impact of climate change on the thermal regime of inland waters.
Acknowledgments: This research has been supported by the project: STAGES-IPCC (TED2021-130744B-C22) from the Spanish Ministry of Science, Innovation and Universities
How to cite: Gulliver, Z., López-Padilla, S., Herrero, J., Huertas-Fernández, F., Collados-Lara, A. J., García-Valdecasas Ojeda, M., Ramón, C. L., Esteban-Parra, M. J., Pulido-Velázquez, D., and Rueda, F. J.: Long-term water temperature modeling in semi-arid alpine basins , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18985, 2025.
Coffee-banana intercropping, widely practiced by smallholder farmers in South America and East Africa, is recognized for its potential to combine sustainability with resilience to climate change. This practice promotes crop diversification, but may also enhance water-use efficiency. However, its effectiveness may vary depending on the local conditions and agricultural practices. The lack of quantitative data on drought stress and the complexity of interactions within coffee-banana intercropping systems pose significant challenges in modelling and optimizing water use efficiency. This study aims to develop and refine innovative methods to assess drought stress in coffee-banana intercropping systems, with a focus on stable carbon isotope values (δ¹³C), leaf temperature, and mid-infrared spectroscopy (MIRS). While stable carbon isotope analysis is a promising tool, its application may face challenges due to factors such as crop size, canopy heterogeneity, banana-coffee canopy overlapping, leaf age, orientation, or position (leaf morphological aspects), leading to variable competition for water and light. These factors affect the way sampling for stable carbon isotope and leaf temperature analysis should be conducted, in addition to physiological differences between coffee genotypes, agronomic practices, and complexities in data interpretation. Sampling and analytical protocols must be adapted to address these factors and their effects, while accounting for leaf morphology and microenvironmental parameters. Initially, we evaluated the influence of these factors on δ¹³C variability in coffee leaf samples, in addition to their correlation with leaf temperature. Samples were collected from a 0.15 hectares experimental farm managed by the Agricultural Research Company of Minas Gerais (EPAMIG) in Brazil, an intercrop of Arabica coffee and Cavendish banana plants at 3.6 a distance apart. Coffee leaves were sampled using a metal puncher and leaf temperature was measured using an infrared thermometer, considering varying levels of sunlight exposure. Ten plants of the Catuaí Vermelho IAC 44 coffee cultivar were randomly selected: five under conventional management (chemical fertilizers) and five under organic management (cattle manure). For each plant, samples were taken at three different heights (Top, Middle and Bottom), three orientations (South, East and West), and two branch sides, including young and mature leaves, resulting in 36 leaves per plant. The poster presents key findings on the variability of δ¹³C isotopes in coffee leaves within a banana-coffee intercropping system and their relationship with leaf temperature under different management practices (organic and conventional). This presentation highlights the observed effects of leaf sampling parameters, such as age, position, and sunlight exposure, on δ¹³C values, as well as the implications for improving drought stress screening methodologies.
How to cite: Bernardo, T., Vezzone, M., Felizardo, J. P., Rodrigues, C., Moura, W., Gomes Soares, L., Sebastião Sant' Anna Andrade, H., Victor Vieira Queiroz, C., Nakamya, J., Vantyghem, M., Dercon, G., and Meigikos dos Anjos, R.: Unravelling Sampling Bias in δ¹³C Isotope Variability in Coffee-Banana Intercropping for Drought Stress Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9556, https://doi.org/10.5194/egusphere-egu25-9556, 2025.
Transpiration significantly depletes terrestrial subsurface water stores and plays a crucial role in
the hydrological cycle. While extensive research has been conducted in the Maimai M8 catchment
(New Zealand) and across many catchments on streamflow generation processes and streamflow
sources, we still know little about the sources of transpiration and when transpiration and
streamflow sources are hydrologically connected. Here we leverage M8, a long-term studied
catchment with well-described streamflow generation mechanisms, to investigate the transpiration
source water of Pinus radiata and its connectivity to streamflow sources. We combined monthly
observations of isotopic signatures (δ18O and δ2H) of xylem, bulk soil water, mobile water,
subsurface flow, and stream water with continuous monitoring of tree water stress across a
hillslope to answer: (1) What is the seasonal source of transpiration at Maimai? And (2) how does
transpiration source water interact with streamflow sources? Our data showed that transpiration
sources across the hillslope were not distinct but changed seasonally. During summer, when trees
showed greater periods of water stress, trees relied on shallow soil water. In contrast, during the
winter, trees’ isotopic signatures plotted along the local meteoric water line (LMWL), overlapping
with mobile soil and stream water. Xylem isotopic signatures were not statistically distinct from
stream signatures in the winter, contrasting with distinct isotopic signatures during the summer.
Our results showed that transpiration source water in the Maimai M8 catchment changes
seasonally, influenced by tree water stress and wetness conditions. Overall, our findings suggest
an ecohydrological connectivity between transpiration and streamflow sources during winter
months in this wet temperate climate.
How to cite: Simmons, C., Dudley, B., McDonnell, J., and Nehemy, M.: Seasonal transpiration source water and ecohydrological connectivity with streamflow sources in the Maimai M8 Catchment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14282, https://doi.org/10.5194/egusphere-egu25-14282, 2025.
This work introduces a new webGIS tool to estimate the Crop Water Requirement (CWR), using time series of satellite images and meteorological data, at high spatial resolution and a global scale. This CWRweb tool provides users with information on the temporal evolution of the CWR, as a first approach of the crop evapotranspiration, as well as other parameters of interest. This process is implemented via web and requires no proficiency in remote sensing.
The implemented calculation of the evapotranspiration under standard conditions (ETc) stands on the robust FAO-56 methodology, based on the relationship between the Crop Coefficient and the Reference Evapotranspiration (Kc-ETo). The CWRweb tool adopts the single crop coefficient approach, combining the effects of both, crop transpiration and soil evaporation into a single coefficient (Kc). These Kc values derive from the NDVI time series of Sentinel-2 multispectral satellite images, for a broad range of crops (horticulture, woody crops, and other crops) and natural vegetation, assuming a general component for the soil evaporation.
The CWRweb tool benefits from the potential of the Sentinel-2A & B satellite constellation to provide users with free time series of images with a spatial resolution of 10m × 10m and a revisit frequency of 2-3 days. The high frequency of Sentinel-2 imagery allows to obtain daily Kc values through interpolation of NDVI data from cloud-free images at high spatial resolution. Online access to massive databases of satellite images, such as those of the Copernicus Data Space Ecosystem program (https://dataspace.copernicus.eu/), together with recent advances on meteorological numerical models to provide global ETo layers at different gridding size, are boosting the operational use of the CWRweb tool.
The CWRweb tool runs and graphically displays daily ETc, as well as NDVI, Kc, and ETo values used in its calculation, for a selected time interval. Results can be provided at both, field and pixel scales. An assessment of the CWRtool was conducted by comparison against the OpenET tool on a selection of crops-sites in California, USA. An average uncertainty of RMSE=0.9 mm·d-1, with a negligible bias, was obtained in a performance analysis using the OpenET ensemble outputs as a reference, using 15 different locations, and data for the period 2016-2024. These results are promising and reinforce the potential of the CWRweb tool for the operational estimation of global evapotranspiration at a high spatial resolution.
How to cite: Campoy, J., Sánchez, J. M., Beltrán, A., Pérez, Y., Molina, A., and Calera, A.: Remote-Sensing based Global Evapotranspiration estimates at high spatial resolution through Sentinel-2 satellite imagery and meteorological data. The CWRweb tool., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12878, https://doi.org/10.5194/egusphere-egu25-12878, 2025.
