Data driven algorithms have been largely proven to be accurate tools for modelling many hydrological variables including aggregated river flows. Many studies have tested the suitability of a wide range of data-driven algorithms for predicting the recorded flows with times-steps ranging from a few minutes to monthly or even seasonal observations fed on a wide variety of inputs. They existing works often achieve brilliant performance indicators. In this work we pay our attention to a well-known hydrological process which is the flow hydrograph generation from rainfall hyetographs based on the mass conservation law within the catchment. Our assumption is that given many different physically based theories can provide accurate estimates of the expected flow hydrograph just providing the recorded hyetograph and a set of physical parameters of the catchment, data-driven approaches should also be able to successfully estimate the flow recorded hydrographs. For testing that hypothesis, we have selected two small mountain catchments (rivers Aragón in Canfranc and Valira Oriente in Andorra catchments in the Pirineos mountains in Spain and Andorra) easily parametrizable with no water depletion. We have checked the performance of different data-driven algorithms for predicting the 15-minutes recorded hydrographs fed on 15-minutes rainfall records and the set of physical variables involved in the Green-Ampt infiltration model. Over this process we have faced several issues and observed the data-driven algorithms are unable to provide the performance indicators commonly achieved in the published works.

How to cite: Zubelzu, S., Patricio, M. Á., Berlanga, A., and Molina, J. M.: Performance of data-driven approaches for estimating flow hydrographs from rainfall hyetographs in small mountain catchments. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4321, https://doi.org/10.5194/egusphere-egu25-4321, 2025.

The catchments of the Quéntar and Canales reservoirs are two adjoining valleys on the north-western side of the Sierra Nevada in Spain. Canales drains the northern slope of the river Genil, with 15 linear km of peaks above 3000 meters, culminating in the highest in the Iberian Peninsula, Mulhacen, at 3479 meters. With 83 km2 above 2000 m, this river exhibits a clear nival hydrological regime. In contrast, the Quéntar basin, which collects water from the Padules and Aguas Blancas rivers, drains a smaller area with a maximum altitude of 2336 m and only 7 km2 above 2000 m. Its regime is pluvio-nival, with a much more marginal influence of snow.

To understand and predict the hydrological behaviour of these catchments under climate change scenarios, we have calibrated two different hydrological models. These models will provide the predictive tools needed to calculate river temperature and substances, particularly nitrogen (N) and phosphorus (P). The first model, SWAT (Soil and Water Assessment Tool), is a well-known conceptual semi-distributed parametric model based on linear reservoir equations that simulates snow using a modified degree-day model. The second model, NIVAL, is a distributed model based on physical processes, featuring a specific snow module that relies on mass and energy balance, specifically designed for use in the Sierra Nevada.

The two models differ significantly in terms of preparation, calibration and performance. SWAT's advantages are those of any distributed model: fast computation, easy calibration (facilitated by automatic algorithms) and a reduced need for input data. These features make SWAT a practical choice for many applications. On the other hand, NIVAL offers a more detailed representation of the hydrological processes and greater robustness to changes in scenarios outside the calibration range. This makes NIVAL particularly valuable for studying individual processes and hypothetical future scenarios.

It was expected that the flow adjustment in SWAT would be less accurate than in NIVAL, especially in the Canales basin due to the significant snow influence. However, the calibration and validation of both models on daily flows for both basins yielded very similar results in the most common statistics. For instance, the Nash-Sutcliffe Efficiency (NSE) values were around 0.63/0.70, the Kling-Gupta Efficiency (KGE) was 0.70/0.74, and the Percent Bias (PBIAS) was 2.49/19.08 for the Canales and Quéntar cases. These results demonstrate that SWAT is a reliable option for calculating total flows in historical scenarios.

Nevertheless, NIVAL's detailed process representation makes it more reliable for studying individual processes or hypothetical future scenarios. The next step in this research is to compare these models against various climate change scenarios to assess the differences in their predictions. This will help us understand the strengths and limitations of each model and improve our ability to predict and manage water resources in snow-covered Mediterranean catchments under changing climate conditions.

Aknowledments: This research has been supported by Grant TED2021-130744B-C22 funded by MICIU/AEI /10.13039/501100011033 and by the European Union Next GenerationEU/ PRTR

How to cite: Herrero, J., Galván, L., Fernández de Villarán, R., Gulliver, Z., López-Padilla, S., Pulido-Velázquez, D., and Rueda, F.: Comparison of a Semi-Distributed Empirical Model and a Distributed Physical Model in a Snow-Covered Mediterranean Catchment Under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19528, https://doi.org/10.5194/egusphere-egu25-19528, 2025.

Studying of surface water and groundwater interaction is crucial in understanding the changes in the ecosystems, thus affecting the quality as well as the quantity of hydrology of the catchment. Non -perennial rivers account around 50% of the world’s rivers and such interaction plays a prominent role in determination of seasonal availability and quality of such catchments. The present study aims to identify the river water and groundwater interaction using hydrogeochemistry and stable isotopes in Cauvery, a major non-perennial river of southern India. The river water as well as groundwater was collected once in four months from 2013 to 2021. The samples were analysed for major ions from 2013-2021 whereas stable isotopes δ¹⁸O and δ²H were analysed during 2018 and 2021. Inverse modelling was carried out to understand the hydrogeochemical reactions during surface water and groundwater interaction. Both river water and groundwater was  dominanted by Ca-Mg-HCO₃ and Na-Cl type. Seasonal variation of major ions in river water and groundwater shows similar variation. The inverse modelling indicates the weathering of hornblende, plagioclase, biotite, K-Feldspar into stable clay minerals along with the leaching of major ions into the water. The stable isotopes indicates that both river water falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. Also, the interaction between river water and surface water is more evident during wet seasons, whereas during dry periods the interaction persists in headwater regions. falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. The present study on river water and groundwater interactions acts a baseline framework in developing sustainable water management in non-perennial rivers. The temporal variation of major ions between groundwater and river water shows similar pattern, indicating their interrelationships. The isotope results shows that groundwater and river water falls between precipitation and the evaporation during wet seasons, whereas few samples have been isotopically enriched during the dry season as a result of evaporation, suggesting that groundwater contributes to the river water. The weathering of hornblende, plagioclase, biotite, K-feldspar occurs during groundwater -river water interaction which then transforms to stable clay minerals. It was evident that at the lower part of the basin, the river water discharges into groundwater during the wet periods and vice versa during dry seasons. Thus, this current study on river water- groundwater interactions act as a baseline knowledge in developing sustainable water management plan in the river basins.

How to cite: Ramesh, R., Lingaiah, K., and Lakshmanan, E.: Identification of surface water and groundwater interaction in a non perennial river using hydrogeochemistry and stable isotopes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20562, https://doi.org/10.5194/egusphere-egu25-20562, 2025.

Ensuring the safety and sustainability of drinking water sources is a critical component of modern water resource management. The recast Drinking Water Directive (EU 2020/2184) emphasizes the delivery of safe drinking water by strengthening protections along the entire supply chain, from source to tap, and adopting a risk-based approach to water safety as recommended by the World Health Organisation. Assessments of water treatment costs tend to focus on the current level of treatment, and not the potential additional costs associated with treatment of new emerging contaminants, many of which are of low molecular weight requiring specialist treatment technologies with expensive CAPEX and OPEX costs. The impacts of climate change on the raw water quality source water abstractions are also likely to result in increasing costs of water treatment systems. In Ireland, the inclusion of emerging substances on the 2023 Drinking Water Regulations and on the first European Commission’s Watch List reflects the evolving nature of water safety management in response to pollutants of emerging concern and environmental pressures. This study presents a robust methodology with a view to inform future funding and targeting of water quality measures and source protection work. Applied across six case studies, the four-stage process (pre-screening, coarse screening, fine screening, and final comparative analysis) guides decision-making. The framework incorporates open-source data from the Environmental Protection Agency (EPA) of Ireland, including land-use maps, Water Framework Directive (WFD) waterbody status and significant pressures such as agriculture, forestry, industry, and hydro-morphology, alongside local pressures on water sources. Source protection measures and treatment technologies were derived from extensive literature review of national and international projects and were tailored to specific goals for each case study, with independent evaluations for both strategies. The process concludes with a comparative analysis to identify optimal solutions for each scenario. The study provides recommendations, based on economic assessments and the evaluation of environmental and technological gaps to support the stakeholders in decision making and policy development. The selected strategy for each case is dependent on a suite of site-specific features, including the raw water source type, the catchment size, the mapped WFD pressures exerted into the water source and the latest WFD status and the Water Treatment Plant capacity. The findings highlight the importance of adopting integrated approaches to ensure the resilience of drinking water systems in the face of future uncertainties.

How to cite: Sousa, D., Ali Khan, U., Bradshaw, S., and Grace, M.: Integrated catchment and treatment strategies for safeguarding drinking water quality: an adaptive decision-making tool, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10589, https://doi.org/10.5194/egusphere-egu25-10589, 2025.

This study explores an IoT soil moisture monitoring network designed to improve agricultural efficiency and sustainability. The system integrates LoRaWAN-enabled soil moisture and temperature sensors, strategically deployed across agricultural fields, with a Raspberry Pi 4 gateway that processes and transmits data to the cloud. The combination of low-power, long-range communication and dual connectivity options—Wi-Fi and LTE 4G—ensures reliable operation even in remote areas, making the system ideal for large-scale agricultural monitoring.

The core of the network is a robust edge processing framework that enhances data accuracy, security, and efficiency. The framework begins with noise filtering, using techniques such as median filtering to remove anomalies from raw data. Once filtered, the data is aggregated over specific time periods to reduce transmission bandwidth and provide actionable summaries of soil conditions. Adaptive data rate adjustments further optimize resource use by increasing data collection frequency during significant environmental changes and reducing it during periods of stability.

Data security is ensured through encryption at the edge, protecting sensitive environmental information from unauthorized access. Local processing also supports predictive analytics, using models like decision trees or linear regression to forecast future soil moisture and temperature conditions based on historical trends. These forecasts enable proactive decision-making, such as adjusting irrigation schedules to maintain optimal soil moisture levels, improving resource efficiency and crop health.

Anomaly detection is another critical component of the system, identifying unusual patterns in sensor readings that could indicate malfunctions or unexpected environmental changes. This ensures data integrity by flagging or excluding erroneous data. In addition, real-time event-driven alerts notify users of critical thresholds, such as dangerously low soil moisture or rapid temperature changes, allowing for immediate interventions. Alerts are delivered through SMS, email, or cloud dashboards for maximum accessibility and responsiveness.

The system's scalability supports the seamless addition of sensors, accommodating expanding agricultural operations without significant modifications. Local data logging provides redundancy, preserving raw and processed data even during network outages. This ensures uninterrupted monitoring and allows for post-event analysis, enhancing reliability and resilience.

The network’s design offers substantial benefits for agriculture. Adaptive resource management conserves bandwidth, power, and computational resources, reducing operational costs while extending system lifespan. By combining edge processing with cloud analytics, the system provides timely and actionable insights, empowering farmers to make data-driven decisions. Enhanced security through encryption protects sensitive data, while predictive analytics and anomaly detection ensure proactive and accurate responses to changing field conditions.

Overall, the IoT soil moisture monitoring network is a robust and efficient solution for modern agriculture. It enhances real-time monitoring, decision-making, and resource management, enabling farmers to optimize irrigation, improve crop health, and boost productivity. The system's scalability and adaptability make it a practical choice for addressing the growing demands of precision agriculture, contributing to sustainable farming practices and improved food security.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under ScaleAgData project (Grant Agreement No. 101086355).

How to cite: Vlachos, M., Mitro, N., and Amditis, A.: Enhancing Agricultural Efficiency through an IoT-Based Soil Moisture Monitoring Network Utilizing LoRaWAN and Edge Computing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15376, https://doi.org/10.5194/egusphere-egu25-15376, 2025.

The Piedmont Plain (NW Italy) is characterized by a shallow phreatic aquifer hosted in fluvial complex (gravel and sand),  overlying a fluvial-lacustrine and marine complex (gravel and sand with silty clayey levels) containing deep confined/semiconfined aquifers.

Deep aquifers are essential for the supply of drinking water in the Piedmont Plain. However, detailed information on deep aquifers is lacking, such as a regional piezometric map, a continuous monitoring of the water table variations over time and a regional characterization of GW quality. Moreover, the deep groundwater chemical values in the Piedmont Po Plain show significant temporal variability and need to be characterized.

The aim of this study was to analyze the trends (period 2000–2021) in the main physicochemical parameters (electrolytic conductivity (EC), pH) and main ions (Ca, Mg, HCO₃, Na, Cl, NO₃ and SO₄) in 70 wells in the deep aquifers in order to identify the main ongoing processes. Furthermore, to gain a deeper understanding of specific processes, the temporal distribution of threshold exceedances ​​for the sum of pesticides (period 2009-2021) was evaluated. The potential interaction with shallow aquifers was evaluated making a comparison of the average concentrations for the main ions and parameters between shallow and deep aquifers. In general, shallow aquifers are exploited for agricultural purposes and show higher concentrations compared than  deep aquifers.

Additionally, the temporal trends of ion exchange (Ca+Mg/Na index) were evaluated to highlight the contribution from silty-clayey layers, which represent the less permeable portions of the deep aquifers.

Results highlight relevant increasing trends for EC, Ca, Mg and Cl in more than 60% of the monitored wells, and increasing trends for HCO₃ and Na in more than 40% of the monitored points. For these parameters, decreasing trends exist for less than 10% of the monitored points. SO₄, NO₃ and pH show heterogeneous trends. In particular, several monitored wells show significant variation over time, with concentrations doubling from the beginning of the time series. The sum of pesticides shows greater exceedances of the threshold values in the most recent period (2016-2021) compared to the previous one (2009-2015).

The temporal trends of ion exchanges reveal the presence of trends in 61% of the monitored wells, with a prevalence of increasing trends, corresponding to direct ion exchange. For the main ions, the comparison between the average concentrations in the shallow and deep aquifers shows higher values in the shallow aquifers.

These results suggest an increase in the recharge of the deep aquifers by the shallow aquifers and an increased contribution from silty-clayey layers of the deep aquifers. These processes are consistent with excessive withdrawal from deep aquifers. Furthermore, the increasing concentrations represent a significant issue, leading to the progressive deterioration of deep groundwater quality. In conclusion, the main processes responsible for the variation in groundwater chemistry in the deep aquifers were identified, defining the existence of impacting and worrying processes at a regional scale.

How to cite: Cocca, D., Lasagna, M., and De Luca, D. A.: Groundwater chemical trends analyses in the deep aquifers of the Piedmont Po Plain (NW Italy): preliminary evaluation of ongoing processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15983, https://doi.org/10.5194/egusphere-egu25-15983, 2025.

Soil Moisture (SM), acknowledged by the Global Climate Observing System (GCOS) and the European Space Agency’s Climate Change Initiative (ESA CCI) as an Essential Climate Variable (ECV), is a fundamental hydrological parameter that plays a pivotal role in bridging Earth's surface and atmospheric interactions. Understanding SM status and dynamics is critical for various meteorological, hydrological, and climatological applications. Furthermore, it provides insights into the water, energy, and carbon cycles while aiding in the forecasting of extreme climatic events, such as droughts and floods. In consequence, accurate global monitoring of SM with suitable temporal and spatial resolutions is imperative.

This study focuses on the validation of multiple satellite-derived near-surface SM products against field measurements to evaluate their accuracy and reliability. The research was conducted over the northeastern Spain and southern France, covering a 7-year span from January 2015 to December 2021. Ground truth data were obtained from the International Soil Moisture Network (ISMN) database, which included observations from 30 stations across four networks (COSMOS, FR-Aqui, IPE, and SMOSMANIA). The analysis assessed four microwave-based sensors, encompassing both active and passive systems: ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive), and CCI.

Following data acquisition and processing for both satellite images and ground observations, a comprehensive validation was performed using statistical metrics, scatter plots, and linear regression analysis of the respective time series. Results highlighted that the SMAP mission delivered the most reliable outcomes, achieving a near-unity slope, an intercept close to zero, a correlation coefficient of R = 0.72, and a Root Mean Square Error of RMSE = 0.07 m³/m³. The CCI product followed, while ASCAT and SMOS showed larger uncertainties and weaker correlations, respectively. In addition, an analysis of the in situ depth effect using SMAP indicated that measurements at 0–6 cm (integrated) and 5 cm (point-specific) depths yielded optimal results. Nevertheless, despite remarkable advances in SM monitoring, this work underscores the need for further research to align satellite-derived data more closely with field-level precision.

Acknowledgements: This study was carried out in the framework of the PID2020-118797RBI00 (Tool4Extreme) project, funded by MCIN/AEI/10.13039/501100011033, and also the PROMETEO/2021/016 project, funded by Conselleria d’Educació, Universitats i Ocupació de la Generalitat Valenciana.

How to cite: Tomás-Portalés, G., Valor, E., Niclòs, R., and Puchades, J.: Validation of Satellite-Derived Soil Moisture Products Using Ground Observations in Southern Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5173, https://doi.org/10.5194/egusphere-egu25-5173, 2025.

Accurate error characterization is essential for validating satellite-based geophysical products. Triple Collocation (TC) estimates random error variances of three mutually independent datasets but assumes a common spatial scale—a condition rarely met in practice. Spatial heterogeneity in the ground truth and mismatches in spatial resolution introduces "spatial representativeness errors", whose influence on error variance estimates remains unexamined. In this study, we have analyzed the sensitivity of the triple collocation estimates using the synthetically generated soil moisture dataset under varying sample sizes and spatial heterogeneity. Our results indicate that sample size (N) affects the TC estimates, with % bias decreasing from ±15% to ±2% for N ranging from 100 to 1000. The study finds that % bias also varies with the degree of spatial heterogeneity across the area under consideration. Additionally, the TC framework exhibits an equal likelihood of overestimation and underestimation. These findings underscore the critical importance of addressing spatial heterogeneity to enhance the reliability and robustness of error characterization in geophysical measurement systems. The study provides valuable insights for improving the applicability of TC in satellite product validation and underscores the need for more advanced approaches to handling spatially diverse datasets.

How to cite: Gupta, D. and Dhanya, C. T.: Influence of Spatial Heterogeneity in Error Characterization Using Triple Collocation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3139, https://doi.org/10.5194/egusphere-egu25-3139, 2025.

Soil moisture is a crucial parameter that influences various environmental and socioeconomic processes, including flood and drought mitigation, sustainable agricultural productivity, and industrial applications. This study analyses soil moisture dynamics using data from 25 sensing stations distributed across various regions of Karnataka State. These sensing stations were installed under the REWARD (Rejuvenating Watersheds for Agricultural Resilience through Innovative Development Programme) project funded by World Bank. These stations encompass diverse topographic, soil, rainfall, and crop characteristics. High-frequency data collected from these stations at 15-minute intervals is aggregated into daily averages to analyse soil moisture responses to rainfall, recovery times, and depth-wise correlations between 5 cm and 50 cm. This study also validates soil moisture products from SMAP and EOS-04 satellites using ground-based measurements at these 25 locations. The validation was performed for both raw satellite data and data filtered using the Soil Wetness Index (SWI). The Soil Wetness Index (SWI) filter is applied as a background layer to effectively capture soil moisture dynamics across different spatial scales. The accuracy of soil moisture retrievals is evaluated for SMAP products at spatial resolutions of 9 km, 1 km, and 400 m, as well as for EOS-04 data at a 500 m resolution. When the SWI filter is applied, the remotely sensed retrievals show the strongest agreement with in-situ measurements across cultivated crop areas throughout the year. The findings from this study enhance the understanding of soil moisture dynamics and offer actionable recommendations for selecting the best satellite soil moisture products and optimizing soil moisture modelling. These insights are valuable for agricultural planning, water resource management, and disaster mitigation strategies in regions with diverse environmental conditions.

How to cite: Parekattuvalappil Shaju, A., Gupta, V., and Muddu, S.: Validation of SMAP and EOS-04 Soil Moisture Products Over Karnataka’s Heterogeneous Agricultural Landscapes Using Ground Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15812, https://doi.org/10.5194/egusphere-egu25-15812, 2025.

Flooding is one of the most devastating natural disasters, significantly impacting human lives, infrastructure, and ecosystems. Severe rains when combined with a lack of proper infrastructure in urban areas can lead to floods. Thereby accurate flood predictions and modelling are essential for efficient flood control in such environments. A critical component of this process is obtaining reliable hydrological outputs over watersheds, which forms the foundation of precise flood forecasting. Flood inundated areas can be generated by hydrological and hydraulic modelling to provide valuable insights into high-risk zones. Modelling helps in interpreting timely and reliable flood information from the generated flood maps to reduce damages in flood areas. In this study Hydrological Response in the form of runoff is computed for a region of the Upper Ganga basin, India by using HEC Series and thus flood inundation maps were generated for different return periods. Data sets required for the study included satellite images, digital elevation model, daily precipitation and soil map. To model flood inundated areas for a return period of 2,5,10,25,50,100 years, HEC-HMS and HCE-RAS were employed. Flood inundation maps were generated and flood risk areas were identified for different return periods. Results showcased that 2-years return period flood inundates approximately 0.29 sq. km, accounting for nearly 2% of the total study area and 100-years return period flood inundates approximately 4.42 sq. km covering nearly 31% of the study area. This study provides a framework for similar research in other flood prone areas and suggest implementation of low-impact development strategies for regions prone to frequent flooding in the study area. The findings underscore the importance of integrating advanced flood modelling techniques with historical data to enhance disaster preparedness and resilience.

Keywords: Climate Change, Hydrological Modelling, Flood Inundated Areas, Return Period.

How to cite: Thakur, K. and Pathak, S.: Flood Frequency Analysis on Ganga Basin Catchment using Geospatial Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3986, https://doi.org/10.5194/egusphere-egu25-3986, 2025.

The Himalayan belt contains over 12,000 glaciers that have witnessed accelerated glacial melt due to concomitant climate change, leading to the formation of numerous unstable glacial lakes. These lakes, dammed by glacial deposits, pose significant mountain hazards due to their potential for sudden discharge of water and debris, causing devastating floods in the downstream reaches. To address the precipitous Glacial Lake Outburst Flood (GLOF) risks, there is a dire need to account for the impacts at near-real time, given their lesser warning times. The study proposes to develop a pre-simulated GLOF inundation library through a set of scenarios based on breach depths, breach widths, and moraine failure times to model extreme GLOF events over Safed Lake, a sensitive glacial lake in the Uttarakhand, India. At the first place, a geospatial analysis is carried out with a set of Landsat 9 images to ascertain the spatio-temporal dynamics. Using a set of scenarios within the 1D-2D coupled MIKE+ model, we perform flood inundation simulations to create a GLOF inundation library. This library will facilitate the selection of the closest inundation map based on near-real-time data; Thus, enhancing effective flood risk communication and preparedness. This innovative approach to GLOF modeling and flood risk communication is crucial for managing unstable glacial lakes with high flooding probabilities and short warning times. The findings underscore the importance of advanced modeling and timely communication in mitigating the impacts of glacial lake outburst floods and improving resilience in the Himalayan region.

Keywords: Climate change; Flood Risk Management; Glacial Lake Outburst Flood; Inundation library; Landsat 9; MIKE+;

How to cite: Saha, S., Pandey, A., Rao, B. S., and Prakash Mohanty, M.: Development of a GLOF forecasting system through a novel concept of pre-simulated library over the Hindu Kush Himalaya region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15646, https://doi.org/10.5194/egusphere-egu25-15646, 2025.

Cotton is a one of the major crops in the southeastern United States. It significantly impacts regional water resources since it consumes a large amount of freshwater for irrigation. Current irrigation practices fail to optimize water use accurately since they are largely dependent on soil moisture sensors and grower experience. They do not consider dynamic factors such as soil texture, prevailing weather conditions, and the crop's phenological stage. In this paper we propose an innovative approach to enhance the irrigation efficiency through the use of Deep Reinforcement Learning (DRL) model. It takes into consideration the dynamic variables and optimizes irrigation. We utilize a crop growth simulation model as a learning environment to devise an optimal irrigation strategy. By continuously learning from crop feedback and environmental inputs, the DRL system dynamically modifies irrigation amount to optimize production while consuming the least amount of water. Our approach presents a viable alternative for sustainable irrigation decisions in water-intensive crops, since preliminary findings indicate that it can greatly conserve water without sacrificing crop health or productivity. The goal of this research is to aid in the advancement of precision irrigation technologies that guarantee cotton production's sustainability and resource efficiency. 

How to cite: Panthi, K., Samadi, V., and Toxtli, C.: Optimizing Irrigation for Cotton Crops using Deep Reinforcement Learning Algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14673, https://doi.org/10.5194/egusphere-egu25-14673, 2025.

Agro-hydrological modeling is crucial for designing climate change adaptations such as irrigation management. However, the accuracy of the simulation results greatly relies on the availability and accessibility of reliable ground data. Many countries extremely vulnerable to climate change have limited ground data as input for agro-hydrological modeling that restricts the validity of model results. A ‘model inversion’ technique can potentially tackle this data-scarce situation. Here, we combine alternative data sources, such as remote sensing for the estimation of crop development, with intense simulations to find missing input data such as irrigation.

The present study aims to assess the performance of the model inversion technique using the AquaCrop model under different synthetic scenarios. The main research question is, ‘Is an inverted AquaCrop model able to identify the irrigation pattern of the crop growing period?’ The different synthetic scenarios for testing the performance include variations in the rainfall amount, irrigation amount and interval, soil texture, and initial soil moisture conditions. Preliminary results for synthetic scenarios show that inverse modeling is feasible for the estimation of irrigation patterns. The results indicate that under conditions of zero rainfall and dry initial soil moisture state, best inversion results were produced in both scenarios where continuous and non-continuous irrigation was applied. The scenarios near real-world conditions yielded the best results when continuously using uniform irrigation. Further research will investigate whether integrating remote sensing-based crop growth indicators like LAI or NDVI into the inverse modeling approach can improve scenarios' simulation with non-continuous irrigation.

How to cite: Saravanan, A., Karthe, D., and Schütze, N.: Assessing the Performance of Crop Model Inversion Technique in the AquaCrop Model under Different Synthetic Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18154, https://doi.org/10.5194/egusphere-egu25-18154, 2025.

The vast network of glaciers in the Himalayas serves as a vital source of freshwater for the main river systems. These are essential in determining a region's climate and hydrology. Ecological balance, agricultural output, and hydrological systems all depend on these glaciers. However, the stability of hydrological systems and long-term water availability have become major concerns in recent decades due to the acceleration of glacier melting brought on by climate change. In this study, globally available gridded satellite and reanalysis datasets, including ERA5, IMDAA, IMD, APHRODITE, and others, were evaluated to identify the most accurate dataset for the Bhilangana Basin. A thorough performance evaluation was conducted to assess the suitability of these datasets for the region. Furthermore, a hybrid rainfall dataset was developed using a bias correction approach to improve accuracy and reliability, ensuring a more robust representation of precipitation dynamics. The Spatial Processes in Hydrology (SPHY) model was utilized to examine the dynamics of snow-glacier melt during the years 2020–2023. The performance matrix revealed that the ERA5 dataset performed better than other datasets except the hybrid precipitation dataset. The average variation during 2000-2023 in snow q was found in the range of 15 to 26 percent, rain q from 12 to 58 percent, glacier q from 56 to 18 percent and base q from 8 to 18 percent. The analysis further revealed that 11 parameters were found to be critical in influencing the model's output e.g. Degree day factor for snow(DDFS), Glacier debris degree day factor(DDFG), Tcritical, Glacier melt frac runoff. The SPHY model's applicability for studying snow-glacier melt runoff dynamics and the significance of combining various climate datasets to precisely forecast the water resource scenarios in glaciated basins are further highlighted by this study.

How to cite: Joshi, B., Singh, V., Chandola, V. K., and Singh, A.: Modelling Snow-Glacier Melt Runoff Dynamics In Bhilangana Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19490, https://doi.org/10.5194/egusphere-egu25-19490, 2025.