Conceptual hydrological models play a central role in streamflow estimation, yet their lumped formulations often fail to represent spatial variability in hydrological processes, thereby limiting their performance. With the growing availability of satellite observations, global reanalysis products and high-resolution terrain datasets, grid-based conceptual modelling has become increasingly feasible. However, despite the widespread use of the Génie Rural à 4 Paramètres Journalier (GR4J) model, its grid-to-grid implementations remain limited, even though these frameworks offer clear advantages for capturing spatial heterogeneity and enabling modelling in data-scarce regions. This study presents a grid-based GR4J framework coupled with Muskingum-Cunge routing and driven entirely by remote sensing and reanalysis-based inputs, applied across four Australian catchments representing tropical, semi-arid, temperate, and humid subtropical climates. To implement this framework, the catchments were discretised into 0.1° grids aligned with the spatial resolution of GPM IMERG precipitation and GLEAM potential evapotranspiration, enabling these inputs to be applied at the grid level to generate runoff. Flow routing was done using Muskingum-Cunge method through the channel grids obtained based on flow-direction map derived from a digital elevation model (DEM), enabling sequential upstream-to-downstream runoff transfer across the grid network. Model calibration and validation were carried out using observed daily streamflow at the study catchment outlets for the periods 2005–2018 and 2018-2023 respectively. Model performance was evaluated using the Kling-Gupta Efficiency (KGE) under two configurations: the grid-based framework and the conventional lumped GR4J model. Both achieved calibration KGE values above 0.6; however, the grid-based model consistently showed superior performance during validation. The tropical basin exhibited the greatest improvement, with KGE increasing from 0.09 to 0.51, while the semi-arid and temperate basins showed 21% and 14% gains, respectively. Performance in the humid subtropical basin remained comparable across both configurations. Overall, the grid-based framework shows clear benefits in accounting for spatial variability.

How to cite: Nair, G. B. and Ramsankaran, R.: A Grid-Based Conceptual Hydrological Modelling Framework Using Remotely Sensed Inputs: Preliminary Insights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2131, https://doi.org/10.5194/egusphere-egu26-2131, 2026.

Accurate precipitation monitoring is critical for Small Island Developing States (SIDS) like Saint Lucia, where complex topography and high vulnerability to tropical cyclones necessitate precise data for disaster preparedness and water resource management. While ground-based radar provides high-resolution estimates, it is spatially limited. Conversely, satellite products offer global coverage but often suffer from accuracy issues at island scales. This study presents a comprehensive evaluation and calibration framework comparing NASA’s IMERG and ERA5 reanalysis products downscaled to 2km spatial resolution against Caribbean radar observations with 1km spatial resolution. The objective is to quantify satellite performance during extreme weather events and demonstrate a robust operational workflow for bias correction.

The analysis employs a dual-framework approach. First, we conducted a spatial and temporal validation across nine major hurricane and storm events (2007-2024), including Hurricanes Dean and Tomas. This phase utilized Root Mean Square Error (RMSE) and Structural Similarity Index (SSIM) to assess agreement between radar, IMERG, and ERA5 datasets, alongside a point-based comparison at the Grace Station utilizing rain gauge data. Second, we developed a seven-step operational workflow using continuous 2021 data to implement pixel-wise Quantile Mapping bias correction. This workflow involved parallel processing of raw radar imagery, precise temporal-spatial matching, and the training of reusable correction functions.

Analysis of the storm events reveals that uncorrected satellite and reanalysis products systematically underestimate rainfall intensity, particularly during hazardous convective peaks. Satellite storm-mean values often capture only 10-60% of radar-observed totals. While ERA5 and IMERG exhibit comparable performance, both struggle to resolve fine-scale convective structures, yielding modest SSIM scores (averaging 0.15-0.40) that degrade as storm intensity increases. Point-based analysis at Grace Station highlights distinct "smoothing" effects in gridded products. For example, during Hurricane Tomas, a rain gauge recorded a peak intensity of 1516 mm/h (likely an extreme burst or anomaly) and radar recorded 33 mm/h, whereas satellite and ERA5 estimates were smoothed to 15.33 mm/h and 19.18 mm/h, respectively.

To address the identified underestimation, the Quantile Mapping method applied to the 2021 dataset yielded significant improvements. The correction reduced systematic bias by 87% (from a relative bias of -185% to -23%) and decreased the Mean Absolute Error (MAE) by 43%. Crucially, the correction dramatically improved the spatial structure of the precipitation fields, raising the temporal SSIM by 70% (from 0.490 to 0.834). The methodology successfully extended the dynamic range of satellite estimates to match radar observations, correcting the "capped" maximum values (from ~8 mm to >87 mm) and enabling a more realistic representation of extreme events. This study confirms that while raw satellite and reanalysis products underestimate intense Caribbean precipitation, they can be effectively calibrated using ground-based radar. The proposed workflow establishes a reusable framework for training bias correction functions, allowing meteorologists and hydrologists in Saint Lucia to better model flood risks and enhance climate resilience.

How to cite: shafei, A. and Cioffi, F.: Bridging the Gap Between Radar and Satellite: A Multi-Source Validation and Bias Correction Framework for Precipitation Estimation in Saint Lucia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5151, https://doi.org/10.5194/egusphere-egu26-5151, 2026.

The coastal waters formed by the Saemangeum Dam are difficult to monitor and predict in terms of water quality because of complex mixing of seawater and freshwater caused by artificial water gate control, high optical variability, and other factors. The Saemangeum Dam area on South Korea's west coast is a representative artificial coastal water system where inflow of watershed water, seawater exchange through water gate operation, and nutrient accumulation interact nonlinearly, frequently leading to eutrophication and algal blooms.

This research developed a forecasting system for chlorophyll-a (Chl-a) and total phosphorus (T-P) levels in the Saemangeum aquatic region by integrating geostationary satellite GOCI data with artificial intelligence methods. We combined GOCI observations from 2011 to 2020 with in situ water quality measurements from 13 sites to compare machine learning and deep learning algorithms for estimating water quality.

To identify effective input variables for the optically complex Saemangeum environment, satellite reflectance was combined with meteorological information, gate-controlled water exchange, and nutrient indicators. Seven input scenarios were designed to evaluate how progressive variable integration influences prediction performance, and representative machine learning and deep learning models were compared.

Results showed that scenarios incorporating nutrient-related variables yielded the most robust predictions for both chlorophyll-a and total phosphorus. While deep learning models captured complex relationships under standard evaluation, spatially independent validation highlighted that machine learning approaches maintained more stable generalization under strong spatial heterogeneity and limited training data. This finding suggests that model suitability depends on data structure and validation context rather than algorithm complexity alone.

Overall, the artificial intelligence-based water quality prediction system presented in this study can effectively monitor fluctuations in chlorophyll a and T-P in embankment reservoir waters, and can be utilized as a practical tool for early warning systems and the development of water quality management policies. It is expected to contribute to strategies for responding to algal blooms and managing large-scale artificial coastal waters.

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00515357).

How to cite: Lee, Y., Kim, D., Kim, S., Han, D., Kim, H., Kim, S., and Yeom, J.-M.: Satellite-Based Estimation of Chlorophyll-a and Total Phosphorus in Saemangeum’s Hydrodynamically Complex Waters Using Machine and Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9162, https://doi.org/10.5194/egusphere-egu26-9162, 2026.

Sentinel-1 SAR enables high-resolution soil moisture estimation using the C-band backscatter coefficient. To account for attenuation and volume scattering effects in densely vegetated areas, soil moisture retrieval methods using the Water Cloud Model (WCM) are widely employed. Traditional WCM utilizes the Normalized Difference Vegetation Index (NDVI) and C-band Radar Vegetation Index (RVI) as vegetation parameters, which have limitations due to the saturation of the NDVI and low penetration of C-band. To overcome these problems, this study introduced SMAP L-band Vegetation Optical Depth (VOD) as a vegetation parameter for WCM and applied it to the complex mountainous terrain of the Korean Peninsula. In the parameter estimation process of the WCM, the quantitative relationship between in-situ observations and soil texture was established, enabling the dynamic spatial extension of model parameters to ungauged regions. The validation results with in-situ soil moisture data showed improved correlation coefficient R and ubRMSE compared to existing WCM methods. It was found to enhance the accuracy of soil moisture estimation by more precisely correcting signal attenuation caused by vegetation in complex terrain. This study demonstrates the validity of high-resolution hydrological parameter estimation in complex terrain through satellite data fusion and is expected to provide essential foundational information for precise drought monitoring and water resource management in the future.

Keywords: Soil Moisture, Sentinel-1, Vegetation Optical Depth, Water Cloud Model, Multi-source fusion, Complex terrain

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Climate, Energy and Environment (MCEE)(RS-2023-00230286). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339).

How to cite: Jeong, J., Kim, D., Lee, S., Kim, W., and Choi, M.: Integration of SMAP L-band VOD and Multi-source Satellite Data for Improved Sentinel-1 Soil Moisture Retrieval in Complex Terrains, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16272, https://doi.org/10.5194/egusphere-egu26-16272, 2026.

Annual monsoon inundations are essential ecosystem services in the Cambodian Mekong Delta. These floods support agricultural cycles, fish production, ecosystem regulation and potentially groundwater recharge. However, recent development of hydropower and irrigation infrastructures, land use and climate change have influenced hydrological processes including surface water and groundwater exchanges. Satellite imagery like Sentinel-1 has been widely used in previous studies to map inundation extent due to its temporal availability in all weather conditions. Nevertheless, Sentinel-1 is limited by unwanted interference from vegetation, terrain, and anthropogenic features. Data from the Surface Water and Ocean Topography (SWOT) mission launched in December 2022, equipped with Ka-band Radar Interferometer (KaRIn), provides a complementary perspective by providing two-dimensional water surface elevations and extent from which water volumes can be estimated. To help better characterise surface water flows that may percolate into the deep aquifer, this study presents a methodology for tracking river dynamics and floodplains inundation  over the Mekong Delta in Cambodia, using a combination of SWOT and Sentinel-1. The datasets obtained from the two satellites  are compared with in-situ water level and discharge data and evaluate their effectiveness for tracking the river dynamics, inundation extent, and flow quantification. This validation is performed over the Tonle Sap River, a tributary of the Mekong River, and provides insights into the functionality of the ecosystem within the study region. This study also explores potential estimation of the possible contribution of the groundwater recharge areas to the groundwater budget.   

How to cite: Bala Muhammad, I., Oubanas, H., Orieschnig, C., Malaterre, P.-O., Baudron, P., Massuel, S., Cazals, C., and Lun, S.: Tracking Fluvial Inundations and Groundwater Recharge Areas in the Upper Mekong Delta by Combining Sentinel-1, SWOT, and In-Situ Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12486, https://doi.org/10.5194/egusphere-egu26-12486, 2026.

Precipitation is important for hydrological monitoring and modeling. The accuracy of Mean Areal Precipitation (MAP) estimation relies largely on the the configuration of precipitation network. This study proposes a novel framework for optimizing rain gauge networks by leveraging high-quality reanalysis precipitation data to evaluate MAP estimation. Using the Qingyi River Basin as a case study, we employ the CMA Multi-source Precipitation Analysis System (CMPAS) data to characterize precipitation spatial patterns and establish a benchmark for network optimization. The framework introduces two metrics, i.e., bias of mean areal precipitation (MB) and Kullback-Leibler divergence (KL), to quantify differences between gauge-derived and CMPAS-derived precipitation fields. Evaluation of the current network reveals significant MAP estimation discrepancies in sub-basins with high precipitation variability. Through importance analysis of candidate gauges and hierarchical optimization, we demonstrate that strategic gauge placement guided by precipitation patterns markedly improves MAP estimation accuracy. The optimized network reduces MAP estimation bias by over 5\% in critical sub-basins. This framework offers advantages over traditional methods by enabling preliminary analysis of proposed gauge locations and explicitly incorporating spatial distribution considerations. The methodology proves effective for both network expansion and rationalization while maintaining computational efficiency through its hierarchical optimization strategy

How to cite: Fang, Y.-H., Qian, R., and Cao, Y.: Optimizing Precipitation Gauge Networks for Hydrological Modeling Using High-Quality Reanalysis Data: A Spatial Pattern-Based Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4334, https://doi.org/10.5194/egusphere-egu26-4334, 2026.

The Himalayan River systems sustain the livelihoods of millions of downstream inhabitants by providing water for diverse needs. However, the Himalayas are among the most adversely impacted ecosystems in the world due to global warming and climate change. Many Himalayan basins have reported an increase in extreme precipitation events and floods. As concerns grow about the changing hydrological regime and future water availability, accurately estimating water yield and streamflow under climate change scenarios becomes essential. Hydrological modelling in the Himalayan basins, however, is challenging due to the lack of in-situ measurements. With limited rain gauge networks in these areas, and the majority of observation stations located in valley bottoms, higher-elevation climates remain underrepresented. In recent times, satellite precipitation products (SPPs) and reanalysis products (RAPs) have emerged as alternatives to ground-based observations, offering precipitation estimates with good spatial and temporal resolution. In this study, several SPPs and Re-Analysis Products (RAPs)—including APHRODITE, CHIRPS, ERA5, ERA5-LAND, IMDAA, GPM-IMERG, and PERSIANN—were evaluated for their prediction accuracy and hydrological applications by comparing them with IMD gridded data in the Upper Beas Basin, located in the western Himalayas. The precipitation products (PPs) were assessed based on their ability to capture daily, seasonal, and annual precipitation, as well as extreme precipitation indices (90th, 95th, and 99th percentile rainfall) using statistical metrics such as correlation coefficient (CC), RMSE, R² and relative bias (RB). Their performance in detecting rainfall events was evaluated using Categorical metrics such as POD, FAR, and CSI. Their statistical performance was ranked as: APHRODITE > ERA5-LAND > PERSIANN > ERA5 > GPM > IMDAA > CHIRPS. Overall statistical performances of APHRODITE, ERA5-LAND, ERA5, GPM and PERSIANN were found to be satisfactory. Further, to assess the hydrological utility of these PPs, the SWAT model was employed to generate basin water yield and water balance components using different products. APHRODITE, PERSIANN and GPM satisfactorily reproduced streamflow well (NSE = 0.88, 0.65, & 0.61, RSR = 0.34, 0.59, & 0.62; and PBIAS = -2.18%, -10.86%, & -18.98% respectively). PERSIANN and APHRODITE generated the water yield with an error of 1.68% and 3.91%. Only through hydrological validation, it was revealed that ERA5-LAND, despite ranking second in the statistical evaluation, exhibited poor hydrological performance (PBIAS = -39.14%), while GPM proved to be capable of reproducing streamflow although it performed poorly in statistical evaluation.  Hydrological validation not only revealed such discrepancies but also provided insights into water balance components, water yield, and flow extremes such as high flows and low flows. Therefore, this study recommends hydrological validation in addition to statistical evaluation for selecting reliable precipitation datasets for hydrological modelling in complex mountainous regions.

Keywords: Beas basin, Precipitation products, categorical metrics, Hydrological evaluation, streamflow, water yield

How to cite: Somisetty, A., Singh, V., and Sharma, A.: Hydrological Validation of Satellite-based and Reanalysis Precipitation Datasets in a Himalayan River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-689, https://doi.org/10.5194/egusphere-egu26-689, 2026.

With the challenges of cliamte change and increasing food demand, North China has produced more than 65% of cereal productions of China, and is of great importance in food security. However, North China is facing with significant ecological degradation subsequent to water withdrawal for irrigation. As a result, lots of rivers run drying-up and groundwater table declined fast in the past several decades. There is a observed positive precipitation trend in past 2 decades over North China, and most rivers' flow has been measured increasing, accompanied with vegeration revigorous, i.e. increase in vegetatoin cover.  Under this background of climate and land cover change, we find groundwater continued to subject overpumping and has played a key role to support the gain of food production and vegetation restoration during this period. In this study, we will present the results in detail to use combined method of ground-based and GRACE observations to screening up groundwater drought risk under the increasing precipitation or wetting background. Using some machine learning algoriths, we extropolated the GRACE observed Terrestiral Water Storage (TWSA) and Groundwater Storage (GWSA) back to 1960s, and proposed a groundwater drought index (GDI) to investigate the groundwater drough characteristics under the effects of climate change and human exploitation. And we found that in major basins across North China, such as Tarim River Basin, Yellow River Basin, Hai River Basin, and Songhua River Basin, groundwater experienced more frequent and severe drought in recent 20 years, than it was in the past 4 decades before 2000. This is mainly caused by agricultural withdrawal and vegetation restoration. And in future, groundwater would be likely encounter with more severe drought threats.

How to cite: Shen, Y., Liu, M., and Guo, Y.: Monitoring groundwater drought risks in major agricultural regions of China: A combined perspective of GRACE- and groundbased obervations and modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2166, https://doi.org/10.5194/egusphere-egu26-2166, 2026.

The mountainous “water towers” of Northern China are crucial for regional water security but face threats from climate change and ecological restoration projects. Understanding their hydrological responses requires tools that capture both intense climatic events and long-term land cover changes. This study presents an integrated assessment using multi-source remote sensing data and hydrological modeling across two critical regions: the Taihang Mountains and the broader Beijing-Tianjin-Hebei (BTH) mountainous area.

We employed the InVEST model to simulate water yield (WY). For the Taihang Mountains (1990–2020), we introduced a detrending analysis framework coupled with the Optimal Parameters-based Geographical Detector (OPGD) to attribute drivers. Results show a significant WY decline (-0.66 mm/yr), primarily (86.46%) driven by climate change. Crucially, OPGD analysis revealed that in areas of sharp decline, precipitation intensity (Q=0.369) was a more dominant factor than total precipitation (Q=0.305), highlighting the key role of changing precipitation patterns.

To assess the long-term impact of large-scale vegetation restoration, we extended the analysis to the BTH mountains over four decades (1980–2020). Multi-period scenario analysis showed that while land use/cover change (LUCC) exerted a short-term negative effect on WY during initial afforestation (2000–2020), it shifted to a positive contribution over the full 40-year period, especially in the Bashang region (+37.80%). This indicates that ecological restoration, despite initial water consumption, can enhance water retention and yield benefits over decadal scales.

Our integrated approach demonstrates that combining process-attribution tools (OPGD) with long-term scenario analysis provides a holistic view of mountain hydrology. The findings underscore that sustainable water management must simultaneously address increasing precipitation extremes and harness the long-term hydrological benefits of ecological restoration.

How to cite: yang, H. and Cao, J.: Integrated Assessment of Water Yield in Northern China's Mountain Using Remote Sensing and Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3210, https://doi.org/10.5194/egusphere-egu26-3210, 2026.

Climate warming is reshaping cryospheric processes, hydroclimatic extremes, and ecosystem stability across the arid regions of Central Asia. This study presents an integrated assessment of climate change impacts on glacier dynamics, snow regimes, hydroclimatic extremes, and associated water–ecological risks.

Results indicate a pronounced decline in snowfall amount and duration since the late 20th century, accompanied by a widespread transition in snow drought regimes from precipitation-limited to temperature-driven conditions. Projections based on glacier evolution models suggest that glacier runoff in much of the Tianshan region is approaching, or has already passed, peak water. Peak glacier runoff in the Western Tianshan is projected to occur between the late 2020s and mid-century, depending on emission pathways, while the Eastern Tianshan likely entered a post-peak phase in the early 2020s. Meanwhile, the accelerated expansion of glacial lakes has raised the risk of glacial lake outburst floods (GLOFs), which are currently 3–4 times higher in the western subregion compared to other areas.

Concurrently, hydroclimatic extremes are intensifying. Heatwaves have become more frequent, longer-lasting, and more severe since the 1980s, particularly in the drylands of Central Asia, where declining soil moisture amplifies surface warming. Compound drought–heatwave events are projected to increase markedly under high-emission scenarios, with prolonged durations exceeding several weeks in some regions. Snow droughts are expected to occur more frequently, with warm snow droughts emerging as the dominant type and accounting for approximately two-thirds of future snow drought events by mid-century. These shifts signal a fundamental reorganization of drought dynamics, with cascading effects on hydrological, agricultural, and ecological systems.

Overall, this research highlights the escalating water-ecological risks in arid Central Asia driven by accelerated cryospheric change and intensifying heat extremes, and shifting drought regimes. The findings emphasize the importance of adaptive water management and climate resilience strategies to support sustainable development in this highly vulnerable region.

How to cite: Chen, Y., Li, Z., and Wang, C.: Water–Ecological Risks in Arid Central Asia Under Climate Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3314, https://doi.org/10.5194/egusphere-egu26-3314, 2026.

Ecological and water conservation projects, such as Returning Agricultural Land to Forest (RAF) and Returning Agricultural Land to River (RAR), have significantly altered land surface conditions and hydrological processes in many river basins. However, quantifying their spatial and temporal impacts remains challenging due to the complexity of land-use/cover change (LUCC) and the need for high-resolution data. This study focuses on the Qin River Basin, a major tributary in the middle reaches of the Yellow River, where RAF and RAR projects have been extensively implemented. We integrate multi-source remote sensing data, including the China Land Cover Dataset (CLCD) and SRTM DEM, with the Soil and Water Assessment Tool (SWAT) to simulate hydrological responses from 2010 to 2018. The model performed robustly (NSE: 0.70–0.72, R²: 0.71–0.79) and revealed that RAF reduced total runoff by 3.00%, with spatially heterogeneous effects: surface runoff increased in northern subbasins, lateral flow decreased in central regions, and groundwater flow rose dramatically (2366.67%). RAR scenarios showed that converting agricultural land to water bodies enhanced runoff components, with greater efficacy on slopes <15° compared to <6°. The study demonstrates the critical role of remote sensing in capturing LUCC dynamics and highlights the importance of spatially explicit planning for sustainable water resource management in semi-humid basins under intensive human intervention. Our approach provides a scalable framework for integrating remote sensing into hydrological modeling to assess and optimize ecological restoration strategies in data-scarce or heterogeneous regions.

How to cite: Cao, J. and yang, H.: Quantifying Hydrological Impacts of Ecological Restoration Projects in a Yellow River Tributary Using Remote Sensing and SWAT Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3640, https://doi.org/10.5194/egusphere-egu26-3640, 2026.

The Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) data set provides a long-term, consistent suite of global ocean-atmosphere climate variables over ice-free oceans derived from satellite passive microwave observations. Designed to support climate monitoring, air-sea interaction studies, and model evaluation, HOAPS offers more than three decades of key parameters such as vertically integrated water vapor, evaporation, near surface specific humidity, near surface wind speed, freshwater flux, latent heat flux, and, recently implemented, liquid water path.

A defining feature of HOAPS is its inter-sensor calibration and physically consistent algorithms, ensuring that all products are produced using a uniform retrieval framework, minimizing artificial trends and discontinuities.

In this presentation, we will focus on the upcoming HOAPS release (HOAPS v5.0), outline recent methodological updates such as newly implemented data sources, updates of the retrieval and radiative transfer model including a new bias correction scheme, improved uncertainty propagation, and more. Additionally, we will demonstrate the usefulness of the dataset through selected examples that highlight variability in the global water cycle. We will also discuss the implementation of a continuous extension of the dataset. HOAPS sustains to serve as a robust satellite-based reference for climate studies of air-sea fluxes and ocean-atmosphere coupling and more.

How to cite: Niedorf, A., Sikorski, T., Fennig, K., Konrad, H., Schröder, M., Bärlin, J., Hollmann, R., Bennartz, R., and Fell, F.: HOAPS: A Satellite based Climate Data Record of Ocean-Atmosphere Interaction Parameters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6780, https://doi.org/10.5194/egusphere-egu26-6780, 2026.

Water vapour is the single most important natural greenhouse gas in the atmosphere, thereby constraining the Earth’s energy balance, directly and indirectly through the water vapour feedback mechanism. In addition, water vapour is a key component of the water cycle. There is consequently the need to consolidate our knowledge of natural variability and past changes in water vapour and to establish climate data records (CDRs) of both total column and vertically resolved water vapour for use in climate research. This is the objective of the ESA Water Vapour Climate Change Initiative (WV_cci).

Within WV_cci a global total column water vapour (TCWV) data record was generated by combining microwave-based TCWV observations over the ice-free ocean with near-infrared imager-based TCWV over land, coastal ocean and sea-ice. The data record relies on microwave imager observations, partly based on a fundamental climate data record from EUMETSAT CM SAF and on near-infrared observations from MERIS, MODIS and OLCI. The microwave and near-infrared data streams are processed independently and combined in a postprocessing, retaining the individual TCWV values and their uncertainties. The precursor version of the data record is freely available to the public via 10.5676/EUM_SAF_CM/COMBI/V001. A new version, which relies on more sensors and features improved stability and uncertainty characterisation, will be released in the coming months. In addition, a high-resolution regional product was generated covering three regions in the sub-tropics and tropics with a spatial resolution of 0.01°.

This presentation will briefly introduce WV_cci and new developments and improvements related to the data record generation. The TCWV over land is reliably possible in clear-sky conditions only. Thus, after aggregation and comparison to all-sky data a clear-sky bias is present. Results from the clear-sky bias analysis will be shown as well. A focus will be on results from intercomparisons and validation, including results from uncertainty validation through comparisons against radiosonde and GNSS observations.

How to cite: Konrad, H., Bärlin, J., Niedorf, A., Danne, O., Schröder, M., Preusker, R., Trent, T., Fischer, J., Brockmann, C., Hegglin, M., and Hollmann, R.: A combined global total column water vapour data record from microwave and near-infrared imager observations: new developments and results from validation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6474, https://doi.org/10.5194/egusphere-egu26-6474, 2026.