Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot A
Effective management of eutrophication in inland lakes requires spatially continuous information on key water-quality variables at management-relevant scales. However, metre-scale mapping of total phosphorus (reported as “Phosphorus, Total”, PPUT; µg/L) remains difficult to achieve using conventional in-situ sampling, and nearshore gradients and tributary plumes are often poorly resolved by medium-resolution satellite sensors. In this study, we exploit multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid, physics-informed AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance is combined with short-term meteorological descriptors (3–7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure) and in-situ Secchi depth (SSD) to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes. Inclusion of SSD yields a substantial performance gain, with mean R² increasing from ~0.67 (SSD-free) to ~0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on phosphorus retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, we implement a teacher–student knowledge-distillation scheme in which an SSD-aware teacher transfers its representation to a student using only satellite and meteorological inputs. The optimal student, based on a compact subset of 40 predictors, achieves R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L on unseen monitoring stations, and is applied to 2020–2025 PlanetScope scenes to generate metre-scale PPUT maps. A 26 July 2024 case demonstrates that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but spatially coherent hotspots >20 µg/L coincide with tributary mouths and narrow channels, highlighting priority areas for management intervention. Although demonstrated here for phosphorus, the PlanetScope–KD framework is model-agnostic with respect to the target variable and can be retrained for other water-quality parameters with optical or hydro-meteorological controls, such as chlorophyll-a, dissolved oxygen, and surface water temperature. This opens a pathway toward unified, high-resolution, multi-parameter lake water-quality prediction to support adaptive monitoring and lake-basin management.
How to cite: Deng, Y., Pan, D., Yang, S., and Gharabaghi, B.: Knowledge Distillation of PlanetScope Imagery for Metre-Scale Lake Water-Quality Mapping, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8119, https://doi.org/10.5194/egusphere-egu26-8119, 2026.
- Rainfall-induced shallow landslides pose a persistent hazard in the Eastern Himalaya, particularly along the strategically important Balipara–Charduar–Tawang (BCT) corridor in western Arunachal Pradesh, India. This study develops a region-specific rainfall threshold framework by integrating long-term rainfall trend analysis with empirical landslide-triggering thresholds to enhance early warning capabilities in this data-scarce, high-relief terrain. Daily gridded rainfall data from the India Meteorological Department (2000–2020) and an inventory of 236 landslide events recorded between 2008 and 2015 were analyzed. Trend analysis reveals a statistically significant decline in annual rainfall (–81.05 mm yr⁻¹), accompanied by pronounced inter-annual variability and persistent monsoonal dominance. Empirical analysis indicates that short-term antecedent rainfall plays a critical role in slope failure initiation, with 3-day and 5-day cumulative rainfall showing the strongest correlation with landslide occurrence (R² = 0.508 and 0.480, respectively). Corresponding 80th percentile thresholds of ≥89.24 mm (3-day) and ≥118.80 mm (5-day) are proposed as practical triggering criteria. In addition, an intensity–duration (I–D) threshold derived from 95 rainfall-induced landslides follows a negative power-law relationship (I = 17.26·D⁻⁰·¹⁰), capturing the influence of short-duration, high-intensity rainfall events. The combined use of antecedent rainfall and I–D thresholds effectively represents both progressive soil saturation and rapid-onset rainfall triggers. This integrated threshold framework provides a robust and scalable basis for landslide early warning system development along the BCT corridor and offers broader applicability to similar monsoon-dominated Himalayan regions.
How to cite: kar, S., Chaturvedi, P., and Negi, H. S.: Integrating Intensity–Duration and Antecedent Rainfall Thresholds for Shallow Landslide Prediction in the Eastern Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1658, https://doi.org/10.5194/egusphere-egu26-1658, 2026.
Accurate runoff estimation is fundamental to improving streamflow forecasting, particularly in large river basins with sparse or uneven rain-gauge coverage. This study investigates the identification of representative rain gauges from a densely but randomly distributed network to support reliable runoff simulation in data-limited regions. The Middle Tapi Basin (MTB), comprising 26 operational rain gauges and extensive ungauged areas, is used as a case study. Four approaches—Hall’s method, K-means clustering, hierarchical clustering (HC), and self-organizing maps (SOM)—are applied to identify key rain gauges that effectively capture the spatial variability of basin-scale rainfall. Hall’s method selected 15 representative stations, whereas the clustering-based approaches identified nine stations each. The performance of the resulting rain-gauge networks is evaluated by simulating basin runoff using a lumped hydrological model. Results indicate that the rain-gauge network derived from Hall’s method consistently produces superior runoff simulations compared to the clustering-based networks, demonstrating improved representation of rainfall inputs at the basin scale. Based on these findings, the use of 15 key rain gauges identified through Hall’s method is recommended for runoff prediction in the Middle Tapi Basin. The proposed framework is transferable and can be applied to other large basins with heterogeneous rainfall patterns and limited monitoring infrastructure, offering a practical approach for optimizing rain-gauge networks to enhance hydrological modelling and flood forecasting.
How to cite: Yadav, S. and Panchal, A.: Designing efficient rain-gauge networks for improved flood forecasting in a large river basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3693, https://doi.org/10.5194/egusphere-egu26-3693, 2026.
Extreme hydro-meteorological events associated with the Coastal El Niño phenomenon represent a critical threat to the socioeconomic stability of the northern coast of Peru. In particular, the Piura River basin, characterized by its complex topography and short concentration times, requires precise monitoring and modeling to address these episodes. Currently, the National Meteorology and Hydrology Service of Peru (SENAMHI) employs a semi-distributed system (ARNO/VIC coupled with RAPID) for the operational assessment of flood risk. However, the increasing intensity and frequency of recent events highlights the need for tools that explicitly represent physical processes at higher resolution. This research proposes the implementation of the fully distributed WRF-Hydro model, focusing the methodology on the reconstruction and analysis of the main extreme flood events within the period covered by the PISCOp_h product, a gridded hourly precipitation observational dataset developed by SENAMHI for 2015–2020. The methodological strategy is based on generating a hybrid meteorological forcing to feed the hydrological model. For this purpose, an atmospheric simulation is carried out with WRF, forced by initial and boundary conditions from the GFS, obtaining high-resolution distributed atmospheric fields. Given the uncertainty of the modeled precipitation, the rainfall field generated by WRF is replaced by the hourly gridded observations from PISCOp_h, ensuring controlled and realistic forcing. With this configuration, model calibration and validation are performed. Calibration prioritizes the highest-magnitude events, highlighting the 2017 Coastal El Niño episode for the adjustment of physical parameters, while validation considers a set of floods recorded between 2015 and 2020, evaluating the robustness of the system. It is expected to demonstrate that this combination of atmospheric dynamics and observational accuracy constitutes a physically consistent and operationally viable tool for predicting intense floods, strengthening flood risk management in Peru.
How to cite: Tufino, J. C., Huerta, A., Lavado-Casimiro, W., De la Cruz, G., Saavedra, D., and Ibañez, A.: Implementation and Evaluation of the WRF-Hydro Model for Hydrometeorological Forecasting in the Piura River Basin, Peru, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3205, https://doi.org/10.5194/egusphere-egu26-3205, 2026.
Deep learning models, particularly LSTMs, have transformed large-sample hydrology by achieving high streamflow predictive performance, yet they remain largely black-box approaches with limited physical interpretability and no explicit representation of multiphysical hydrological processes. Differentiable, learnable process-based models (or δ-models) overcome these limitations by embedding neural networks within differentiable physics frameworks. While existing benchmarks like HBV-δ have proven this concept across 671 US basins, they rely on conceptual foundations (e.g., empirical beta-functions) that approximate, rather than resolve, underlying soil physics. This study introduces MILC-δ (Modular Differentiable Physic-Informed Learning), designed to bridge this gap. The MILC model utilizes continuous soil water retention curves and physically derived drainage laws, which can aid in more accurate hydrological flux simulation. Thus, we developed a MILC-δ - a hydrologic model embedded with neural networks and trained in a differentiable programming framework. Consequently, MILC-δ is anticipated to match or exceed HBV-δ by leveraging neural networks to map static catchment attributes directly to physically measurable properties (e.g., pore size distribution, hydraulic conductivity) rather than abstract calibration parameters. Initial testing of the developed model shows that the model performs at par in some basins and better than HBV-δ in other basins. This approach gives LSTM-level accuracy and generalizability as well as the clear physical story stakeholders actually need to explain the decline in baseflow, threats to the groundwater recharge, etc.
How to cite: Sharma, V., Barbhuiya, S., and Gupta, V.: Differentiable, Learnable MILC: Balancing Predictive Skill and Physical Interpretability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16119, https://doi.org/10.5194/egusphere-egu26-16119, 2026.
Rapid dam construction, rising water demand, climate change, and increasing pollution are exposing critical weaknesses in the governance of freshwater systems worldwide, particularly in shared river basins. Although environmental flows (e-flows) are widely recognised as essential for sustaining riverine ecosystems and long-term water security, their integration in transboundary water governance has remained largely symbolic, weakly enforced, and poorly adapted to climatic uncertainty. Even durable agreements in several regions prioritise volumetric allocation and procedural cooperation, offering limited mechanisms to safeguard e-flow regimes, as illustrated by treaties such as the Indus Water Treaty and the Ganga Water Sharing Treaty. This study argues that the persistent failure to operationalise transboundary e-flows in national and transboundary river basin governance frameworks reflects a deeper and systematic governance implementation gap that has not been adequately addressed in existing literature. Much of the literature examines legal provisions, economic instruments, and monitoring systems as separate domains rather than as interdependent components of operational governance. As a result, many transboundary river agreements pair legal allocation rules with flow monitoring but fail to link these to enforceable e-flow obligations or adaptive responses. To investigate this gap, the study undertook a structured comparative analysis of ten major international treaties and river basin agreements across Asia, Africa, Europe, and North America, covering both bilateral and multilateral transboundary river systems. Existing treaties were assessed to identify why most fail to deliver implementable e-flow solutions, while arrangements where elements of effective implementation exist were examined to extract transferable best practices for future transboundary water agreements. Based on the findings, the study proposes a three-tier governance framework to operationalise transboundary e-flows under climate uncertainty. The framework integrates climate-adaptive legal obligations, economic and financial mechanisms, and monitoring, reporting, and verification systems supported by remote sensing and GIS. By reframing e-flows as an implementable component of cooperative water security, this study makes both a conceptual and practical contribution to transboundary water governance, with implications for ecological resilience, conflict reduction, and long-term regional stability.
Keywords : Water demand, Climate change, River basin treaties, Ecological resilience
How to cite: Malhotra, K. B. and Nema, A. K.: Implementing Environmental Flows in Transboundary Rivers under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4740, https://doi.org/10.5194/egusphere-egu26-4740, 2026.
Lakes and associated hydrological processes are sensitive indicators of environmental change and climate variability. Variations in lake water level and storage reflect the combined effects of atmospheric forcing (precipitation, evaporation, and temperature regime) and anthropogenic interventions, including irrigation, drainage, and hydraulic infrastructure development. Continuous monitoring of lake level dynamics is therefore essential for water resources management, evaluation of regional climate impacts, and assessment of environmental risks in arid and semi-arid regions.
Central Asia has experienced pronounced hydrological transformations over recent decades as a result of climate warming, altered precipitation patterns, and intensified human water use. These changes are manifested in contrasting lake responses, ranging from the dramatic desiccation of the Aral Sea to the expansion of endorheic water bodies receiving anthropogenic inflows. Lake Sarikamish, one of the largest lowland lakes in the region, is located along the Uzbekistan–Turkmenistan border near the escarpment of the Ustyurt Plateau and represents a key example of such coupled natural–human system dynamics.
This study investigates water level variability of Lake Sarikamish over the period 2001–2024 using satellite altimetry observations from the Global Reservoirs and Lakes Monitor (G-REALM) database. The dataset, provided at a 10-day temporal resolution in NetCDF format, was processed to construct a continuous long-term time series. Short data gaps were filled using linear interpolation, a method previously shown to yield robust performance for altimetric lake level records. Descriptive statistics and trend analyses were applied to quantify intra-annual variability, interannual fluctuations, and long-term tendencies.
The minimum lake level during the observation period was recorded in February 2002 (4.23 m), while the maximum level occurred in April 2018 (8.84 m). The time series exhibits substantial interannual variability, with a standard deviation of 0.91 m. Four distinct phases of lake level evolution were identified: (i) a rapid increase during 2001–2007 at a rate of +0.56 m yr⁻¹, (ii) a short-term decline in 2008–2009 (−0.60 m yr⁻¹), (iii) a prolonged period of moderate increase during 2010–2020 (+0.15 m yr⁻¹), and (iv) a renewed decrease during 2021–2024 (−0.36 m yr⁻¹). Despite the recent downward trend, the overall period is characterized by a net positive trend of +0.16 m yr⁻¹.
The observed post-2020 decline suggests an increasing influence of regional climate change, particularly rising air temperatures and reduced effective precipitation. Continued water level lowering may have negative consequences for local ecosystems, biodiversity, and environmental stability. The results highlight the value of satellite altimetry for long-term lake monitoring and emphasize the need for integrated assessments of climatic and anthropogenic drivers of lake hydrological change in Central Asia.
How to cite: Umirzakov, G., Kalabaev, S., Gafurov, A., and Turgunov, D.: Remote Sensing–Based Monitoring of Lake Sarikamish Water Level Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17089, https://doi.org/10.5194/egusphere-egu26-17089, 2026.
Climate change, rising water demand, and ecosystem stress are intensifying the reliance on groundwater while limiting the capacity of many basins to effectively monitor and manage subsurface water resources. In data-scarce and conflict-affected regions where monitoring networks are sparse, decision-makers increasingly require reliable, high-resolution information to support drought preparedness, climate adaptation, and sustainable groundwater governance. The present study proposes an evidence-based machine-learning framework for the purpose of enhancing the monitoring of groundwater storage anomaly (GWSA) through the process of downscaling GRACE and GRACE-FO observations from ~3° to 0.1°. The reconstruction of monthly GRACE/GRACE-FO gaps was performed using a Seasonal-Trend Decomposition based on Loess (STL), and a Random Forest model was trained with hydroclimatic and land-surface predictors, including soil moisture, snow water equivalent, evapotranspiration, precipitation, land-surface temperature, and the normalized difference vegetation index (NDVI). The performance of the model was evaluated by comparing the model's results with the existing in-situ groundwater-level observations in the Kabul River Basin. The results indicate that satellite-inferred groundwater losses in Afghanistan are persistent, with a rate of −0.71 cm yr-1, ranging from basin-scale depletion of −0.77 cm yr-1 in the Helmand River Basin to −0.40 cm yr-1 in the Northern River Basin. Recent conditions indicate intensified depletion during 2018–2022, with year-sum GWSA declines reaching ~145 cm in the Harirod–Murghab River Basin, while the Northern River Basin shows comparatively lower losses (~80 cm). The 0.1° downscaled product improves agreement with observations (root mean square error (RMSE) reductions up to 77.8%) and reveals spatially heterogeneous hotspots that are not detectable at coarse GRACE resolution. Generally, the proposed framework translates coarse satellite gravimetry into actionable, basin-relevant information for climate-resilient groundwater management, while underscoring the necessity for uncertainty-aware, multi-source monitoring under increasing hydroclimatic extremes. The approach enables the early detection of emerging depletion hotspots, thereby supporting proactive planning for future water security. This includes targeted demand management, drought response, and adaptation investments in groundwater-dependent regions.
How to cite: Azizi, A. H., Akhtar, F., Borgemeister, C., and Tischbein, B.: Assessing Groundwater Storage Changes in Data-Scarce Basins of Afghanistan: A Machine-Learning Based Downscaling of GRACE(-FO) Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18956, https://doi.org/10.5194/egusphere-egu26-18956, 2026.
Water scarcity across the Mediterranean is increasingly forcing economies that are deeply integrated into global markets to balance export performance with long-term water sustainability. Research on “virtual water” and trade-related water footprints has grown rapidly, yet it remains fragmented: studies rely on diverse frameworks (physical accounting, MRIO, life-cycle assessment, and hybrids), use non-uniform scarcity metrics, and often treat climate projections and adaptation only implicitly. This review asks: which methods are currently used to estimate the water footprint of trade under climate constraints, what limits their comparability, and what methodological protocol is needed for a robust, policy-relevant application to Morocco? By framing trade-related water footprints as part of coupled human–water systems, the review highlights how economic structures, trade choices, and climate-driven water scarcity interact and generate feedbacks relevant for water governance and policy design.
We follow a PRISMA-type workflow based on systematic searches in Scopus and Web of Science, with explicit inclusion/exclusion criteria and standardized data extraction. Studies are coded along five dimensions: (i) data type (monetary vs. physical); (ii) modelling approach (IO/MRIO, LCA, hybrid); (iii) treatment of scarcity (stress factors, availability indicators, scarcity-adjusted footprints); (iv) integration of climate change (scenarios, downscaling, hydrological modelling); and (v) potential to inform policy (efficiency improvements, reallocation options, abstraction caps, and economic or trade-related instruments). Institutional sources are used in a complementary way to document indicator frameworks and datasets, without replacing the peer-reviewed evidence base.
The review delivers (1) an operational typology of methods used to quantify trade-related water footprints under climate stress; (2) a diagnosis of key comparability barriers (spatial resolution, upstream embodied water through inputs, and the limited use of dynamic approaches); and (3) a practical empirical agenda linking hydrological projections, water-extended input–output frameworks, and decision-relevant scarcity metrics. Outputs will be shared through a reusable coding grid and an analytical framework diagram to support country studies and comparative work across the Mediterranean.
How to cite: Tasra, F. and Mafamane, D.: How Much Water Is Embedded in Trade? A Systematic Review and Research Roadmap for Morocco under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14286, https://doi.org/10.5194/egusphere-egu26-14286, 2026.
Inter-state agricultural trade plays a critical role in redistributing water resources across India, particularly for water-intensive crops such as rice. This study examines the structure of inter-state rice virtual water trade in India using a directed, weighted complex network approach. Physical inter-state rice trade data covering all Indian states were obtained from the Directorate General of Commercial Intelligence and Statistics (DGCIS) and transformed into virtual water flows using crop-specific virtual water content coefficient for rice (m³/ ton), assumed to be uniform across states. This transformation enables an assessment of trade relationships in terms of embodied water transfers rather than physical commodity volumes. States are represented as nodes and directed edges denote rice virtual water flows from exporting to importing states, weighted by total virtual water volumes (m³). Network properties were analysed using strength-based measures to quantify import and export intensities, betweenness centrality to identify states functioning as key intermediaries in trade pathways, and PageRank to assess systemic importance within the national virtual water trade system. These metrics jointly allow differentiation between dominant exporting states, import-dependent states, and structurally central states influencing the overall redistribution of water through trade. The analysis reveals a highly centralized rice virtual water trade network, characterised by a small group of states accounting for a disproportionate share of total virtual water exports. States such as Punjab, Haryana, Andhra Pradesh, Chhattisgarh, Uttar Pradesh, Odisha, and Madhya Pradesh emerge as major exporters, while several other states rely predominantly on inter-state imports to meet rice demand. The concentration of virtual water exports among a limited number of producing regions indicates strong structural dependencies within the national trade network. Several major exporting states like Punjab are also subject to increasing pressure on water resources, the observed trade patterns raise concerns regarding the sustainability of current production-trade configurations. By integrating crop-specific virtual water accounting with complex network analysis, this study provides a quantitative framework for identifying key contributors, dependencies, and structural vulnerabilities in India’s inter-state agricultural water redistribution system. The methodology is transferable to other crops, years, and regional contexts and offers a basis for informing discussions on sustainable agricultural trade and water resource management.
How to cite: Badoni, A. and Reddy, M. J.: Mapping Inter-State Rice Virtual Water Trade in India Using Complex Network Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11912, https://doi.org/10.5194/egusphere-egu26-11912, 2026.
To address climate change and population growth, Central Asia must urgently adopt a holistic water resource management strategy, moving beyond traditional sectoral approaches to embrace a water-energy-food-ecosystems (WEFE) nexus approach. The WEAP model, a key research tool, integrates hydrological data and socioeconomic factors to create scenarios considering glacier melt, irrigation expansion, and energy generation. WEAP, unlike hydrological models, highlights unmet demand, demonstrating the sectoral impacts of water scarcity for decision-makers. The Nexus approach uses the WEAP model to optimize the Vakhsh hydropower cascade (Nurek and Rogun plants), balancing energy security, environmental flows, and predictable agricultural water supply. The WEAP model assesses innovative irrigation technologies in the Zarafshon basin to enhance food security and cross-border cooperation between Tajikistan and Uzbekistan. The scenario analysis shows that modernizing irrigation systems reduces the burden on the ecosystem and ensures stable harvests even in dry years. Integrating climate forecasts into WEAP allows for water availability scenarios, enabling adaptation measures like optimized cropping and expanded runoff management. WEAP modeling in the Vakhsh and Zarafshon basins highlights the importance of cross-sectoral considerations for water resource management in Tajikistan, providing a basis for sustainable water system decisions.
How to cite: Niyazov, J.: A Nexus-Based Approach to Water Resources Assessment: Practical Application of the WEAP Model in Tajikistan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9585, https://doi.org/10.5194/egusphere-egu26-9585, 2026.
The primary goal of hydrological modeling is to generate reliable forecasts of future changes in water resources. In the arid conditions of Central Asia, where irrigated agriculture demands significant water resources during summer, early forecasting is crucial for planning water allocation between upstream and downstream regions.
The WEAP model offers a flexible and user-friendly framework for addressing various water resource management challenges. It supports decision-makers and experts in constructing and selecting optimal solutions for water management. Accurate hydrological forecasts of water availability during the growing season are essential for effective water resource planning. National hydrometeorological services in Central Asia are adopting and adapting modern, effective methods for hydrological forecasting. The primary goal of developing a methodology for forecasting river water content in the Kyrgyz Republic, using the WEAP model, is to create a calculation algorithm, simulate a water management model, and implement this methodology into the practices of the Kyrgyz Republic's National Hydrometeorological Services. This approach will be applied to forecast water availability in the Naryn River during the growing season, monitor changes in the Toktogul Reservoir's water volumes, support hydroelectric power production, and facilitate agricultural irrigation. Advanced forecasts of low water availability during the growing season are vital for implementing preventive measures to ensure efficient water use by water and energy management organizations.
The WEAP model allows for the use of various scenarios, such as climatic ones, with a focus on the national level, while introducing various innovative technologies for irrigation and energy conservation in the upcoming years. This is significant for long-term planning in water management activities and the energy strategy of both the country and the region.
How to cite: Kalashnikova, O.: Predictive WEAP modeling for NEXUS management in the Naryn River basin (Kyrgyzstan), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9627, https://doi.org/10.5194/egusphere-egu26-9627, 2026.
How to cite: Cheung, P. K.: Is blue-green infrastructure effective in reducing urban flood depth and area?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8693, https://doi.org/10.5194/egusphere-egu26-8693, 2026.
The present study investigates the hydrological performance of two Nature Based Solutions (NBS) realised within the urban requalification project of the former military area “Caserma Gavoglio” (now public park), in one of the most heavily urbanized districts of the city of Genoa (Italy). The rapid expansion of urbanization has led to an increase in impervious surfaces and a consequent increase in runoff generation, flood volume and flood peak. Since the required expansion of the stormwater drainage capacity is neither economically nor environmentally sustainable, innovative stormwater management strategies are required. In this context, NBSs represent effective solutions to mitigate runoff generation and peak flows and restore natural infiltration processes.
A resin gravel permeable pavement (PP) was used for the paving of about 40% of the park surfaces while a bioswale was realised alongside the sport field to manage stormwater excess.
The PP was preliminarily tested in the laboratory by monitoring the outflow from a standardized test bed under various rainfall input and slope conditions. The results of the tests were interpreted mathematically using the analogy of the step response function of first- and second-order dynamic systems. This allows to transfer the laboratory results for comparison with field conditions, even if these were not precisely reproduced in the laboratory tests.
Both NBSs were monitored in the field with the objective to measure the outflow rate, representing the inflow to the urban drainage system, and to compare it with the corresponding rainfall input.
Two hydrometric measurement stations and one rain gauge station were installed. Since the stormwater drainage system was already in place, water stage probes were housed inside existing manholes equipped with suitable “V-shaped” weirs. Due to non-standard operational conditions, the measurement stations were preliminarily tested in the laboratory to verify their accuracy prior to field installation.
From the monitored rainfall events, direct comparisons between the measured precipitation and the outflow hydrographs were performed. These analyses enabled the quantification of the retention and detention effects due to the NBSs and their improvement relative to typical impervious paving solutions. The following performance indicators were derived for each significant precipitation event that exceeded the retention capacity of the NBS: (i) the outflow coefficient, defined as the ratio between total outflow and rainfall volumes, (ii) the peak reduction coefficient, i.e. the ratio between peak discharge and peak rainfall intensity and (iii) the system response delay, i.e. the time lag between the centre of mass of the flow hydrograph and that of the rainfall.
Acknowledgements
This work was conducted in the framework of the Urban Nature LABs (UNALAB) project, under the “HORIZON 2020” programme, Smart and sustainable Cities-SCC-02-2016-2017, as a collaboration between the University of Genova (DICCA) and the Municipality of Genova (project partner).
How to cite: Ferro, M., Chinchella, E., Cauteruccio, A., and Lanza, L. G.: Laboratory testing and in-situ monitoring of the hydrological response of a resin gravel permeable pavement and a bioswale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18311, https://doi.org/10.5194/egusphere-egu26-18311, 2026.
Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff
Yaren Ozturk 1, Derya Ayral Cınar 2
1 Marmara University, Istanbul, Turkiye
2 Gebze Technical University, Kocaeli, Turkiye
Abstract.
Due to urbanization and climate change, it has become common for urban runoff to carry pollutants to surface water bodies, wastewater treatment plants, infrastructure systems and groundwater. Pollutants transported include heavy metals, solids, nutrients, pathogens and various organic substances such as pesticides and polycyclic aromatic hydrocarbons (PAH). It is proposed to manage this pollutant load at source before it reaches receiving environments. Nature-based solutions such as filter ditches, infiltration ponds or rain gardens are considered more efficient to manage urban runoff. Among these methods, filter ditches have the highest potential to treat pollutants. It is thought that the use of organoclays, synthesized by the integration of surfactants into the clay mineral structure, as filter material may increase a common contaminant in urban runoff -PAH- removal compared to conventional clay minerals. In addition to treatment efficiency, another important parameter in designing filter ditches is the hydraulic permeability of the filter material. It is desirable that the infiltration rate of the surface flow is slow enough to allow time for pollutant removal and fast enough to prevent ponding on the filter. This study investigated how organoclays, which are proposed to enhance PAH removal from urban runoff, affect the hydraulic permeability of the filter material. Organoclay synthesized by Ca-montmorillonite and HDTMA is used at different percentages in the filter material mixture and hydraulic permeability was determined. Hydraulic conductivity of sand was 4.5x10-4 cm/s and it dropped to 2.4x10-5 cm/s and 2.3x10-5 cm/s when 10% and 20% clay was used, respectively. On the contrary, organoclay at 10% and 20% did not decrease the hydraulic conductivity significantly (to 1.5x10-4 cm/s and 1.4x10-4 cm/s, respectively). As hydraulic conductivity is suggested to be 0.3-1.4 x 10-4 cm/s for surface runoff treatment systems, it appeared that using 20% organoclay is promising to treat emerging pollutants such as PAHs without comprimising the hydraulic performance of the filter system.
Keywords: Nature based solutions, urban runoff, climate change, filter strips, organoclay
How to cite: Ozturk, Y. and Ayral Cınar, D.: Impact of Organoclay Content on Hydraulic Performance of Filter Strips to Treat Urban Runoff, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8965, https://doi.org/10.5194/egusphere-egu26-8965, 2026.
Urban water pollution remains a major challenge for sanitation management in Brazil and other tropical regions. In areas served by separate sewer systems, illicit domestic sewage connections to stormwater drainage networks represent a significant source of contamination of urban runoff and receiving water bodies. Conventional inspection techniques for identifying such contributions are often operationally complex, spatially limited, and therefore rarely applied. Distributed temperature sensing techniques have been successfully used in temperate regions to detect sewage inputs based on thermal contrasts; however, their applicability under tropical conditions remains poorly explored.
This study investigates the thermal signature of domestic sewage in a tropical urban environment and evaluates the detectability of illicit sewage discharges in stormwater systems using a simplified thermal mixing model. Sewage temperature was monitored using thermocouples connected to a data logger with 1-minute temporal resolution in a sewer interceptor located at the São Carlos School of Engineering, University of São Paulo, Brazil, in an area characterized by student housing and food service facilities. Two monitoring campaigns were conducted. Mean sewage temperatures of 27.45 ± 0.45 °C (November 2024–April 2025) and 24.21 ± 0.54 °C (September–November 2025) were observed. A moderate Pearson correlation between sewage temperature and local air temperature (r = 0.58, p < 0.05, n = 140) indicates that atmospheric conditions partially influence sewage thermal variability.
Based on the monitored sewage temperatures (T₂) and stormwater temperature data (T₁) from the literature, a preliminary theoretical model was developed using an instantaneous energy balance approach. The model relates the detectable temperature variation (ΔT) to the sewage fraction (f), defined as the ratio between sewage discharge (Q₂) and stormwater flow (Q₁). Results indicate an exponential relationship between f and ΔT for different thermal contrasts (T₂ − T₁). The minimum detectable sewage discharge was found to be highly sensitive to ΔT, associated with the thermal resolution of the sensing system, while showing direct proportionality to stormwater flow and inverse proportionality to the thermal contrast between sewage and runoff. Future work will focus on model validation under field conditions and its extension to non-stationary flow regimes.
How to cite: de lima neto, E., Bertotto, L. E., and Wendland, E. C.: Applicability of distributed thermal sensing for identifying illicit sewage connections in urban drainage networks under tropical climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15359, https://doi.org/10.5194/egusphere-egu26-15359, 2026.
Accurate drought forecasting is crucial for effective agricultural risk management in semi-arid regions, particularly in drought-prone regions of Maharashtra, India, where the majority of the population relies on farming. This study develops a one-month-ahead drought forecasting using random forest regression, an ensemble tree-based machine-learning algorithm, using the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple temporal scales. Random Forest regression models were trained to forecast SPEI-3, SPEI-6, and SPEI-12, incorporating rainfall, temperature, and derived hydro-climatic predictors. Model performance exhibits clear timescale-dependent predictability, with skill increasing for longer accumulation periods: SPEI-3 (R² = 0.55, RMSE = 0.81), SPEI-6 (R² = 0.65, RMSE = 0.69), and SPEI-12 (R² = 0.87, RMSE = 0.38). Corresponding generalization ratios of 62.4%, 71.8%, and 90.5% indicate improved robustness and reduced overfitting at short (SPEI-3) to long (SPEI-12) timescales. Feature importance analysis consistently highlights the current SPEI state, contributing approximately 35–40% of the total importance, followed by the precipitation minus potential evapotranspiration (PPET) balance and other hydro-climatic variables, reflecting the dominant role of drought persistence and climatic memory in one-month-ahead forecasting. The models successfully capture spatial drought patterns, though reduced accuracy is observed for extreme drought magnitudes at shorter timescales, likely due to inherent climate non-stationarity and rapidly evolving predictor relationships. Overall, this study demonstrates the effectiveness of machine-learning-driven, one-month-ahead drought forecasting across multiple SPEI time scales, enabling near-real-time monitoring and early warning depending on the selected accumulation period. The proposed framework provides a scalable foundation for operational drought early-warning systems in Maharashtra and other drought-prone hydro-climatic regions worldwide.
Keywords: SPEI, Drought forecasting, Random Forest
How to cite: Ganjir, G., Reddy, M. J., and Karmakar, S.: Multi-Timescale SPEI Drought Forecasting Using Random Forest Regression over Maharashtra, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12410, https://doi.org/10.5194/egusphere-egu26-12410, 2026.
Accurate forecasting of reservoir inflow is crucial for managing water resources, maintaining a balance between water supply and demand, preventing floods, supporting hydropower production, and planning irrigation. India, ranking third globally, with more than 5,000 dams, faces challenges in reservoir operations due to hydrological variability caused by the monsoon. While ensuring demand, supply, and flood security requires high water levels, the reservoir also needs to maintain a certain amount of free storage to accommodate high inflows. While Long-Short Term Memory (LSTM) models have been widely used for inflow forecasting, traditional LSTM models often limit their ability to capture sudden hydrological extremes and accurately represent peak timings. Therefore, a comparative evaluation of various advanced LSTM variants is necessary to identify architectures that are more reliable for modelling nonlinear inflow dynamics. Our study introduces a specialised type of recurrent neural network, specifically the LSTM framework, for forecasting daily reservoir inflow. Our methodology uses a structured feature engineering strategy that integrates hydrometeorological forcings, hydrological state variables, and outputs from the CaMa-Flood hydrodynamics model. A permutation-based feature importance analysis, in terms of the increase in mean absolute error, highlights that antecedent precipitation and lagged upstream reservoir outflow are the main influencing factors for the inflow forecast within a multivariate sequence-to-one LSTM framework. Overall, this framework provides a strong, scalable, and practical solution for inflow forecasting. By supporting timely operational decisions for water release, flood preparedness and storage optimisation, the framework serves as an effective tool for managing reservoirs.
How to cite: Mani, D. and Mishra, V.: Data-Driven LSTM Architectures for Reservoir Inflow Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16207, https://doi.org/10.5194/egusphere-egu26-16207, 2026.
Streamflow provides critical support for the biodiversity of aquatic and riparian ecosystems, sediment transport, and nutrient cycling. Therefore, a minimum streamflow in rivers is crucial for sustaining the proper functioning of aquatic habitats. However, lean or low-flow regimes have been significantly altered by various human activities, such as dam construction, flow diversion for irrigation purposes, industrialisation, and urbanisation. Moreover, changing climate is causing erratic monsoons, increased temperatures, and more prolonged droughts, thereby maintaining ecological flow has become increasingly challenging and urgent to preserve the riverine ecosystems. Our aim is to develop a robust, data-driven framework for estimating environmental flows (E-flows) across 55 stations in major Indian river basins. The primary objective is to assess the quantity and timing of streamflow required to sustain the various river ecosystems, utilising hydrological indicators and long-term datasets, such as temperature and precipitation from the Indian Meteorological Department (IMD). Changes in streamflow characteristics are assessed by comparing observed and machine learning-based naturalised flows, enabling the isolation of reservoir-induced impacts on the streamflow regime, magnitude, duration, and seasonal timing. The study hypothesises that, with the use of observed streamflow data, naturalised streamflow reconstruction and a multi-indicator hydrologic approach, integrating Indicators of Hydrological Alteration (IHA), the Range of Variability Approach (RVA), and Flow Duration Curve (FDC) analysis, can provide reliable E-Flow estimates at regional and national scales. By comparing indicator-based benchmarks derived from observed and naturalised streamflow, the stations are classified according to the degree of hydrologic alteration, thereby supporting scientifically informed river management and policy decisions.
How to cite: Sahil, S. and Mishra, V.: Estimation of Ecological Flow for Major Indian River Basins under Changing Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16490, https://doi.org/10.5194/egusphere-egu26-16490, 2026.
The Vu Gia – Thu Bon River Basin (VGTBRB), Central Vietnam’s largest river basin (about 10,350 km2), flows through Quang Nam Province and Da Nang City. It supplies water for multiple purposes, including hydropower generation (with around 20 operational upstream hydropower plants), irrigation, and domestic use (accounting for almost 70% of domestic use for Da Nang). While this multifunctional role supports the regional socio-economic development, the basin is increasingly challenged by intensifying water, energy, and food (WEF) demands driven by population growth, urban expansion, tourism development, and salinity intrusion, highlighting the need for an integrated Water-Energy-Food nexus approach.
Despite growing global research on the WEF nexus, no comprehensive statistical WEF nexus models have been developed for the VGTBRB. Previous studies in the region have largely focused on individual sectors, overlooking the role of salinity intrusion and its implications for water demand, food production, and tourism-related resource use. This study addresses this gap by employing a WEF nexus framework combined with System Dynamics Modelling (SDM) to capture sectoral interactions, feedback mechanisms, and trade-offs in water allocation under future climate and socio-economic scenarios. The analysis incorporates historical data from 2010 to 2024 for model calibration and validation, and projections for 2025–2050 aligned with climate change scenarios and the regional Master Plan for 2021–2030 with a vision to 2050.
Results indicate pronounced seasonal variability in water demand, critical feedback between temperature and domestic water use, and interactions between rainfall and water use that influence the risks of salinity intrusion at downstream water supply intakes. In addition, a positive relationship is identified between tourism growth and water demand, particularly during dry seasons, which exacerbates water stress.
By explicitly integrating salinity and tourism dynamics, this study pioneers a WEF nexus-based modelling approach for the VGTBRB. The findings provide policy-relevant insights to enhance water system resilience under climate and socio-economic change, support progress towards the Sustainable Development Goals, and inform integrated resource governance in a tourism-dependent, salinity-affected river basin.
How to cite: Hoang, L., Shamseldin, A. Y., Henning, T. F. P., Latu, K., Zorn, C., and Dong, S.: Assessing the Water-Energy-Food Nexus under Climate and Socio-economic Change in Vu Gia – Thu Bon River Basin, Vietnam, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15614, https://doi.org/10.5194/egusphere-egu26-15614, 2026.
