Session HS6.8

[Programme]

HS6.8 | Applying Remotely Sensed Water Cycle Components in Hydrological Modelling, and Synthesizing them With In-Situ Data

Applying Remotely Sensed Water Cycle Components in Hydrological Modelling, and Synthesizing them With In-Situ Data

Convener: Zheng Duan | Co-conveners: Christina Anna OrieschnigECSECS, Jianzhi Dong, Hajar ChoukraniECSECS, Hongkai Gao, John W. Jones, Junzhi Liu

Orals

| Thu, 18 Apr, 08:30–10:15 (CEST)

Room 2.15

Posters on site

| Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00

Hall A

The hydrological cycle involves the continuous movement of water on, above, and below the surface of the Earth. In general, hydrological cycle components (e.g., precipitation, evaporation, water storage, and runoff) are characterized by large temporal and spatial variability. Accurate monitoring of various hydrological cycle components and the development of hydrological models are important for improving our understanding of hydrological processes. Acquiring this understanding is a crucial prerequisite to ameliorate resource management, optimize the development of infrastructure, and adjust land use practices to changing climate conditions and hazards such as floods and droughts.

With significant development of sensor technology and sharply growing platforms in past decades, remote sensing offers an enhanced capability to monitor various hydrological cycle components at different temporal and spatial scales to complement, or even replace, in situ measurements. Considerable efforts have been made to explore the potential of remotely sensed data from a vast range of different platforms (e.g., satellite, airborne, drone, ground-based radar) and sensors (e.g., optical, infrared, microwave) in advancing hydrology research, particularly in poorly gauged and ungauged regions. The application of remote sensing in hydrology is expected to increase with enhanced recognition of its potentials and continuous development of advanced sensors (e.g., new satellite missions) and retrieval methods (e.g., innovative machine learning and data assimilation techniques).

This session aims to present and discuss recent advances in the remote sensing of hydrological cycle components, their application in hydrological modeling, and their synthesis with in-situ data. We particularly welcome contributions that explore:
- The performance of remotely sensed data in multi-variable calibration and spatial evaluation of hydrological models
- The added-value of spatially downscaling remotely sensed data in improving hydrological modeling
- The combination of in-situ and remotely sensed data to analyze water cycle components and hydrological extremes such as floods and droughts
- The development of novel methods to gather in-situ benchmark data to combine with remotely sensed approaches
- Synthesized advances of remote sensing applications in hydrology, in natural and anthropized ecosystems

Session assets

Session materials Session summary

Orals: Thu, 18 Apr | Room 2.15

Chairpersons: Zheng Duan, Christina Anna Orieschnig, John W. Jones

08:30–08:35

5-minute convener introduction

08:35–08:45

EGU24-16494

Highlight

On-site presentation

CNES Earth Observation Programme and our vision for the monitoring of the water cycle

Sophie Le Gac, Selma Cherchali, Aurélien Carbonnière, Adrien Deschamps, Yannice Faugere, Philippe Maisongrande, and Annick Sylvestre-Baron

The French Space Agency, Centre National d'Etudes Spatiales (CNES), is responsible for shaping and implementing France’s space policy at national, European and international levels. Among its five key domains (launchers, science, Earth observation, telecommunications and defence), the Earth Observation programme is a fundamental pillar which covers a full scope of activities: from science and technology needs and priorities to the development of applications and services, with a strong core in infrastructure and data management.

Earth Observation (EO) is becoming increasingly accurate and essential to tackle major challenges for the future: advancing our understanding of the functioning of the Earth system, in particular the water, energy and carbon cycles, understanding and assessing climate change and its effects, and the impact of humans on the environment. This research and the development of new space missions both contribute to satisfying major societal needs for up-to-date and qualified environmental information.
CNES, along with its national and international partners, is working to develop and to renew the space infrastructure needed for continuous innovation to address those needs and, on the other hand, support new actors to develop the market.

In this presentation, we will show how CNES EO program addresses the challenges of the monitoring of the water cycle, from the science and climate drivers to the downstream applications, with a focus on current satellite missions’ achievements such as SWOT.
Ongoing developments and future missions addressing the different components of the water cycle will also be presented: Trishna, a thermal-infrared mission to measure surface temperature of land surfaces and coastal strips at high temporal and spatial resolution; The Atmosphere Observing System AOS, a mission to examine links between aerosols, clouds, convection and precipitation. C3IEL, an innovative mission dedicated to water vapor, convective clouds and lightning, and their impact on climate. ODYSEA, a mission to understand ocean currents and winds, and the sea-air interactions. SMASH, a high-revisit altimetry mission designed to provide river and lakes water level.

How to cite: Le Gac, S., Cherchali, S., Carbonnière, A., Deschamps, A., Faugere, Y., Maisongrande, P., and Sylvestre-Baron, A.: CNES Earth Observation Programme and our vision for the monitoring of the water cycle, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16494, https://doi.org/10.5194/egusphere-egu24-16494, 2024.

08:45–08:55

EGU24-12835

ECS

Virtual presentation

Improving Groundwater Recharge Estimation Using Remote Sensing Information in a Multiobjective Calibration.

Diego Cortes Ramos and Adriana Patricia Piña Fulano

This study aims to enhance groundwater spatiotemporal recharge estimation by incorporating three different sources of remote sensing information into a hydrological model. Traditional approaches for model calibration using flow rates often encounter equifinality issues, as aggregated variables may not adequately represent the spatial behavior of the watershed. To address these limitations, we hypothesized that including spatial information in the calibration process could lead to improved estimations.

The TETIS model was implemented in the Lebrija river watershed, located in the Magdalena middle valley of Colombia. R and Ostrich were used to couple the model with remote sensing data in a multiobjective calibration process with the Pareto archived dynamically dimensioned search algorithm. Subsequently, four calibration scenarios were executed, with the first one as a control scenario using only flow rates. The other three scenarios progressively integrated evapotranspiration and soil moisture remote sensing information. As a validation step, GRACE information was used to calculate recharge and compared with the simulations.

The inclusion of remote sensing information improved the model spatial behavior in 47.9%. And comparations with GRACE also show an improvement representation of groundwater recharge in 31.9%. In conclusion, the incorporation of remote sensing data in the calibration process significantly increased the reliability of groundwater recharge estimations in the model.

How to cite: Cortes Ramos, D. and Piña Fulano, A. P.: Improving Groundwater Recharge Estimation Using Remote Sensing Information in a Multiobjective Calibration., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12835, https://doi.org/10.5194/egusphere-egu24-12835, 2024.

08:55–09:05

EGU24-16574

ECS

On-site presentation

Spatio-temporal mapping of groundwater-related flooding using two methods: piezometric levels and Sentinel-2 based remote sensing

Montana Marshall, Saleck Moulaye Ahmed Cherif, Emmanuel Dubois, Grégoire Mariéthoz, Charlotte Grossiord, and Paolo Perona

The purpose of this study is to assess the efficacy and validity of using piezometric data and remotely sensed data to spatially and temporally map groundwater-related flooding, using Nouakchott, Mauritania as a case study.

Despite a warm and dry climate, the city of Nouakchott in Mauritania has been experiencing constant flooding for nearly a decade, making portions of the city inhabitable and posing long-term health and socio-economic threats. During the rainy season, a combination of factors has led to the increasing frequency and duration of flooding events, including a shallow groundwater table, limitations of the domestic water system, reduced infiltration caused by rapid urbanization, and climate change.

The goal of the study is to better understand and quantify the extent of flooding in the developed areas of Nouakchott, both in space and in time, and to relate this flooding to seasonal and annual fluctuations in precipitation and hydrogeological conditions. To do this, we estimate the presence of flooding from two different perspectives: (1) by analyzing the piezometric levels from a network of 23 piezometers and comparing the interpolated piezometric surfaces to the topographic elevations, and (2) by using Sentinel-2 multi-spectral satellite imagery and machine learning with in-situ training data to identify pixels that are classified as flooded. Flooded area maps are then developed using these two methods for days with available data within the period of record (since 2015 for both data sources). These results are then used to develop a time series of flooded areas for both methods, allowing for comparison and potential validation of the results with each other and with the available in-situ data and observations. Preliminary results show that the piezometric analysis was sensitive to the topographic information and underestimated the flooded area compared to the remote sensing analysis. The remote sensing analysis showed satisfactory accuracy when compared to validation data but does not provide as detailed of information on the hydrogeological dynamics as the piezometric analysis. These findings demonstrate the complementarity of using both methods in tandem.

This estimation of groundwater-related flooding extents and seasonal variability was useful to better understand the relationships between the flooding dynamics and climatic factors, to identify vulnerable areas and communities, and to calibrate hydrogeological modeling. Additionally, this novel and open-source approach can produce critical data for flood risk assessment and planning in under-monitored and data-poor areas, mitigation scenario development, and urban management strategies. Next steps for the project include further linking the two methods by developing a piezometric record from the flooding information obtained from the remote sensing analysis using the temporal change in flooding extents and known topographic information.

How to cite: Marshall, M., Moulaye Ahmed Cherif, S., Dubois, E., Mariéthoz, G., Grossiord, C., and Perona, P.: Spatio-temporal mapping of groundwater-related flooding using two methods: piezometric levels and Sentinel-2 based remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16574, https://doi.org/10.5194/egusphere-egu24-16574, 2024.

09:05–09:15

EGU24-7833

ECS

On-site presentation

Benefits and challenges of daily GRACE(-FO) satellite Data Assimilation (DA) for predicting fast-evolving hydrological processes.

Leire Retegui-Schiettekatte, Maike Schumacher, and Ehsan Forootan

Terrestrial Water Storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE, 2002-2017) and its Follow-On mission (GRACE-FO, 2018-now) reflects a vertical summation of large-scale mass changes, globally. Better-than-monthly temporally resolved gravity solutions, such as daily GRACE(-FO) data, have the potential to reflect fast evolving hydrometeorological events. In the DansK-LSM project, supported by the Independent Research Fund Denmark (DFF), we will assess for the first time the benefits and challenges of daily GRACE(-FO) TWS Data Assimilation (DA) into a water-balance model (the modified 10 km resolution World-Wide Water Resources Assessment model, W3RA) for fast-evolving flood monitoring. Therefore, our particular interest is to assess to what extent a remotely sensed TWS data, which has a low spatial resolution, can help improving hydrological modelling during flood events. This experiment is performed within the region of the Ganges-Brahmaputra Delta for major floods occurred in 2004, 2007 and 2008. The daily DA is implemented in-house through a daily Ensemble Kalman Filter (EnKF) along with localization. When compared to the monthly solution, the daily TWS DA succeeds at transferring the High-Frequency (HF) GRACE TWS signal into the model (correlation coefficients of 0.97 between GRACE and daily DA TWS). However, the filter encounters some difficulties at accurately disaggregating the TWS into the different vertical water storage compartments (namely affecting the soil water) as well as horizontal grid cells. The observed irregularities are attributed to the very intensive use of ensemble statistics when the DA step is performed on the daily basis. To address this issue, a few possibilities for more stable filters and regularizations are explored and assessed. In this presentation, we will explore the spatial and temporal impacts of these choices.

How to cite: Retegui-Schiettekatte, L., Schumacher, M., and Forootan, E.: Benefits and challenges of daily GRACE(-FO) satellite Data Assimilation (DA) for predicting fast-evolving hydrological processes., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7833, https://doi.org/10.5194/egusphere-egu24-7833, 2024.

09:15–09:25

EGU24-819

ECS

On-site presentation

Unlocking precision in flash flood forecasting: A synergistic approach using machine learning and satellite-ground integration.

(withdrawn)

Paul Muñoz, David F. Muñoz, Johanna Orellana-Alvear, and Rolando Célleri

09:25–09:35

EGU24-8064

ECS

On-site presentation

Signatures-based appraisal of global rainfall datasets to capture hydrological trends in a meso-scale catchment

Muhammad Haris Ali, Markus Hrachowitz, Ioana Popescu, and Andreja Jonoski

Precipitation data is a critical input for hydrological models that regulates the spatio-temporal variability of other hydrological fluxes. However, in many regions worldwide, obtaining in-situ rainfall data remains a challenge. In such situations, global rainfall products can be valuable, providing global/regional coverage but these products are susceptible to errors from various factors. Previous studies have assessed the performance of different rainfall products to simulate hydrological models, primarily on their ability to reproduce time series of output variables (streamflow, groundwater level, evapotranspiration, or soil moisture), quantified using various error metrics. While the comparison of time series using error metrics provides insights into general model performance, it may not adequately highlight the capability of these products to simulate specific catchment characteristics, such as groundwater contribution to streamflow, catchment behaviour in high/low flows, etc. Utilizing hydrological signatures can offer additional insights into the hydrological behaviour of the modelled catchment. Therefore, this study aims to evaluate the potential of global rainfall datasets to capture catchment’s hydrological characteristics using a range of hydrological signatures for streamflow and groundwater levels, beyond the traditional time series comparisons.

The analysis was conducted on a meso-scale transboundary catchment, Aa of Weerijs, covering an area of 346 km². A fully distributed physically based hydrological model coupled with a hydrodynamic model was setup using the MIKE-SHE and MIKE-11 modelling tools of DHI, Denmark. The base model had a grid size of 500 by 500 m and fed with rainfall data from three local gauge stations (2010-2019). Four rainfall products (MSWEP, IMERG, ERA5 land and E-OBS) were shortlisted based on their comparative fine spatial resolution. To achieve the objective, firstly, a direct comparison of rainfall data from these products was conducted against rainfall data from the gauge stations using metrics such as probability of detection, false alarm ratio, equitable threat score and frequency bias. Secondly, the model was run with each dataset, and the performance assessment of the simulated outputs was done using hydrological signatures. The selected signatures included the flow duration curve's (FDC) high-flow segment volume, FDC's mid segment slope, groundwater duration curve, base flow index, runoff ratio, rising limb density, autocorrelation

The findings indicate that the performance of a rainfall product in direct comparison with a gauge station may not consistently align with its effectiveness in simulating model variables. Furthermore, the quantification of a product’s ability to simulate output variables varies depending on the evaluation criteria or metrics used. We advocate for the use of a range of hydrological signatures in the assessment criteria, as it provides additional insights into the capability of global datasets to simulate hydrological responses.

How to cite: Ali, M. H., Hrachowitz, M., Popescu, I., and Jonoski, A.: Signatures-based appraisal of global rainfall datasets to capture hydrological trends in a meso-scale catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8064, https://doi.org/10.5194/egusphere-egu24-8064, 2024.

09:35–09:45

EGU24-2814

On-site presentation

A new method for modelling key hydrological processes in paddy-dominated watershed based on water balance and remote sensing

Xiang Gao, Housheng Wang, and Rui Ren

This study addresses the need for accurate runoff data for sustainable water resource management in paddy fields, focusing on China, where agriculture consumes more than 62% of freshwater, and paddy rice is the most water-intensive crop. Given the risk of nitrogen and phosphorus loss through runoff, accurate models are crucial for enabling improved irrigation management and assessing agricultural non-point source pollution.

Modern hydrological models range from semi-empirical models, which are deficient in describing the growth phase of paddy, to process-based models that span either single large-scale paddy fields or the entire watershed. However, variations in historical models—and specifically, models such as SWAT-Paddy—indicate significant uncertainties due to the uniform application of irrigation date, amount and drainage outlet height.

This study introduces a novel method that synthesizes the spatial distribution patterns of drainage outlet height and irrigation information (date and amount), while integrating different irrigation and drainage management protocols across various phenological periods. This method uses Google Earth Engine to build a continuous spatiotemporal resolution evapotranspiration model based on multiple-source remote sensing satellites. It also leverages the water balance equation to automatically identify spatiotemporal patterns of runoff at the field scale.

We anticipate that this inclusive, accurate, and automated method will not only facilitate accurate quantification and assessment of paddy runoff but also provide critical data for studying agricultural non-point source pollution. These findings contribute to the existing body of knowledge on paddy water cycle dynamics and highlight the potential of remote sensing technology in addressing data scarcity challenges.

How to cite: Gao, X., Wang, H., and Ren, R.: A new method for modelling key hydrological processes in paddy-dominated watershed based on water balance and remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2814, https://doi.org/10.5194/egusphere-egu24-2814, 2024.

09:45–09:55

EGU24-14248

ECS

Highlight

On-site presentation

Landscape and Burn Severity Controls on Post-fire Snowpack Response in Montane Forests

David Rey and Graham Sexstone

Wildfire is becoming a more common landscape disturbance in snow-dominated watersheds as burned area extents have increased within high elevation areas that store key snow-water resources for down-stream communities. In snow-dominated watersheds, fire modifies the surface-energy budget that controls, in part, the magnitude and timing of snow accumulation and ablation. While there is growing recognition of fire-induced changes to seasonal snowpack dynamics, post-fire hydrologic studies have generally focused on changes in water quality or stream discharge as opposed to downstream impacts of fire-modified accumulation and ablation. To bridge these gaps, we use a combination of remotely sensed (i.e., satellite, fixed-wing), continuous plot-scale radiative and meteorological observations, and synoptic snow surveys to evaluate snowpack response to wildfire across a range of elevations, aspects, and canopy disturbances at several snapshots in time, and at eight continuously monitored north-south paired study sites. This approach demonstrated that topographic controls on snow distribution such as elevation and aspect still exhibit a stronger control on post-fire accumulation and ablation than wildfire induced changes. Nuanced radiative feedbacks also drove non-intuitive snow distribution patterns across burn-severities, particularly in areas where canopy was only partially combusted. In contrast, regions of lower burn severities where canopy remained unaffected, experienced insignificant changes in post-fire snow accumulation and ablation. Given the rising prominence of wildfire as a key land surface disturbance in snow-dominated watersheds, this work addresses key knowledge gaps currently inhibiting seasonal and long-term prediction of fire’s impact on snow water resources across burn-severities.

How to cite: Rey, D. and Sexstone, G.: Landscape and Burn Severity Controls on Post-fire Snowpack Response in Montane Forests , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14248, https://doi.org/10.5194/egusphere-egu24-14248, 2024.

09:55–10:05

EGU24-940

ECS

On-site presentation

Investigation of Spatio-Temporal Variations of Ground and Water Temperatures Using UAV-Based Thermal Camera in the High Arctic Catchments, SW Spitsbergen

Abhishek Bamby Alphonse, Tomasz Wawrzyniak, Nicole Hanselmann, and Marzena Osuch

The High Arctic region is experiencing rapid climate changes, with rising temperatures impacting one of the most fragile environments on the planet. This study employs Uncrewed Aerial Vehicle (UAV) technology equipped with Zenmuse H20T thermal camera to investigate the spatio-temporal variations of ground and water temperatures in the catchments of Southwest Spitsbergen. The UAV-based thermal imaging provides high-resolution and real-time data, allowing for a comprehensive understanding of temperature dynamics in this remote and challenging environment. The integration of UAV technology and in-situ measurements facilitates the collection of data at unprecedented spatial and temporal scales, allowing for a more detailed analysis of temperature trends and patterns.

The study focuses on assessing ground temperature variations across different land cover types to discern the influence of seasonal variations on these components. Moreover, this study extends its scrutiny to the thermal patterns of Arctic hydrological systems, encompassing channels and ponds. This multidimensional approach enables the identification of flow paths in the catchments, including groundwater intrusion and surface mixing. The study aims to contribute valuable insights into the complex interplay between cryo-, hydro-, and meteorological dynamics in the High Arctic.

The study was carried out with the SONATA BIS project financed by the Polish National Science Centre (grant no. 2020/38/E/ST10/00139).

Keywords: UAV, Zenmuse H20T, Land Surface Temperature, Hornsund, Spitsbergen

How to cite: Alphonse, A. B., Wawrzyniak, T., Hanselmann, N., and Osuch, M.: Investigation of Spatio-Temporal Variations of Ground and Water Temperatures Using UAV-Based Thermal Camera in the High Arctic Catchments, SW Spitsbergen, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-940, https://doi.org/10.5194/egusphere-egu24-940, 2024.

10:05–10:15

EGU24-20537

On-site presentation

Water clarity derived from multispectral imagery by semi-analytical algorithm in association with optical water types to classify inland waters into ecological classes: sensitivity study case in France.

Guillaume Morin, Nathalie Reynaud, Tristan Harmel, Arthur Coqué, and Thierry Tormos

Water clarity is a widely used indicator to monitor water quality and ecological status of lakes. It has been routinely assessed through in situ measurements of the Secchi disk depth (Z_SD). Remote sensing (RS) of this parameter could beneficially complement the sparse in situ data set but still remains dependent on the availability of ground-truth data to be validated.

Here, high-spatial resolution satellite missions were compared with in situ Z_SD in various water bodies in France . Our objective is to evaluate the contribution of remote sensing for densifying transparency monitoring in the framework of the WFD. First, Sentinel-2 MSI images were processed for atmospheric-correction (AC) and sunglint removal. Then, we applied a pixel-classification by Optical Water Types to infer bio-optical properties of waters, enabling to characterise optically-active compounds in waters to decipher the applicability, and ultimately tune standard Z_SD retrieval algorithms. Based on our in situ database, 577 matchups over 76 lakes and reservoirs were successfully established between in situ and satellite data.

Overall performances of the retrievals are satisfactory with RMSE: 1.91 m (40%), MAPE: 46 %, bias: -0.5% and r²: 0.52. This study shows that performances are highly variable with respect to the identified optical water types. Best performances are achieved in clear waters (Z_SD > 5m) with RMSE: 1.85 m (35%), MAPE: 37% bias : 8%. On the contrary, turbid waters exhibit larger discrepancies. In case of sediment-laden waters, performances fall to RMSE: 2.8 m (57%), MAPE: 71 %, bias = -34% and r²= 0.40 while it is even worth in case of hyper-eutrophic waters, due to massive phytoplankton bloom with RMSE: 0.9 m (75%), MAPE: 49 %, bias = -57% and r² = 0.06.

Nevertheless, those performances make it possible to critically map ecological classes between “high” and “bad”, and to monitor long term tendencies. Optical classification allows criticising the applicability and accuracy of generic RS retrieval algorithms to a country-scale area. It also brings a qualitative interpretation on the factors of degradation of the water quality related to the decrease of transparency, either by increasing sediment content, dissolved carbon inputs or during phytoplanktonic blooms events. Therefore, it provides a additional and valuable information for many users interested in evaluating the ecological status of inland water bodies from RS data such as academics, authorities and stakeholders.

How to cite: Morin, G., Reynaud, N., Harmel, T., Coqué, A., and Tormos, T.: Water clarity derived from multispectral imagery by semi-analytical algorithm in association with optical water types to classify inland waters into ecological classes: sensitivity study case in France., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20537, https://doi.org/10.5194/egusphere-egu24-20537, 2024.

Posters on site: Thu, 18 Apr, 16:15–18:00 | Hall A

Display time: Thu, 18 Apr 14:00–Thu, 18 Apr 18:00

Chairpersons: Hajar Choukrani, Jianzhi Dong, Hongkai Gao

A.51

EGU24-790

ECS

Progressive assessment of multivariate parameter estimation in distributed hydrological modelling using spatial patterns of remote sensing data

Vinicius Moreira, Francisco Silva, and Camila Welerson

Hydrological models are traditionally calibrated using observed flow records. This challenge is magnified in distributed modeling due to the increased dimensionality of the problem, where parameters must be estimated for hydrologically homogeneous subareas (HRUs). Regardless, the estimated flows can present significant inconsistencies, even though the modeled flows are quite similar to those observed. Products derived from remote sensing techniques have been employed to circumvent this problem. In summary, spatial patterns of hydroclimatological variables are used to assess the consistency of the modeled flows. In this study, the mesoscale hydrologic model (mHM) was used to simulate a mesoscale basin in Brazil, located in the savanna biome. In addition to the flows monitored at different fluvial stations, estimates of actual evapotranspiration (AET) and total water stored in the basin (TWS) obtained from remote sensing were used for parameter calibration. Comparison occurred from scenarios where these variables were considered independently, in pairs, and together. The similarity between monitored and modeled flows was assessed using KGE metric, while spatial similarity was characterized through the SPAEF. In the multi-objective scenarios, these indices were aggregated using weightings to compose a single objective function. The results obtained demonstrated that similar degrees of agreement between flows can be obtained for completely disparate spatial flows. Furthermore, the spatial consistency of these flows generally implies a reduction in the similarity between flows. Lastly, the weightings proved to be an interesting alternative, meriting further analysis, as they facilitate the definition of priorities in the search for optimal solutions.

How to cite: Moreira, V., Silva, F., and Welerson, C.: Progressive assessment of multivariate parameter estimation in distributed hydrological modelling using spatial patterns of remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-790, https://doi.org/10.5194/egusphere-egu24-790, 2024.

A.52

EGU24-2076

ECS

Enhancing flash flood simulations through appropriate assimilation of remotely sensed soil moisture

Yan Liu, Yong Chang, Ingo Haag, Julia Krumm, Visakh Sivaprasad, Dirk Aigner, Harry Vereecken, and Harrie-Jan Hendricks Franssen

The precondition of a catchment, especially soil wetness that can affect remaining soil water storage capacity and infiltration rate, is crucial for flash flood generations. Remotely sensed (RS) soil moisture (SM) can provide valuable information on soil wetness, but typically for the top 5 cm soil. Many flash flood hydrological models only have few or even a single soil layer. How to appropriately represent wetness of the entire soil by RS SM becomes crucial for enhancing flash flood simulations with data assimilation (DA). In this study, we propose a new approach to use a certain amount of historical RS SM to derive total soil water storage such that we can assimilate it into flash flood simulations. We applied this approach for the Körsch and Adenauer catchments in Germany, where we assimilated RS SM from the Soil Moisture Active Passive (SMAP) Mission into the Large Area Runoff Simulation Model (LARSIM). Our results show that we can build a good relationship between RS SM considering different antecedent and present data and soil storage using random forest regression compared to linear, polynomial and long short-term memory (LSTM) regressions, resulting in R² of 0.85 and 0.94 for Körsch and Adenauer, respectively. Using our approach to assimilate RS-derived soil storage into flash flood simulations, performance of flash flood event simulations was improved by an increase of ~0.19 in KGE (Kling-Gupta efficiency) for our study sites. Errors in flash flood peak can be reduced up to 15% compared to simulations without assimilating RS SM. The uncertainty of soil wetness over space was reduced as expected. We examined the possibility of transferring our approach to other RS SM products. We also noticed that despite of the enhancement by assimilating RS SM, the simulation of flash flood is still primarily affected by precipitation uncertainty. In general, we provided a feasible way to use RS SM for hydrological models only with a single soil layer. Future studies applying it to more catchments and events can help to better verify the general validity of our proposed approach.

How to cite: Liu, Y., Chang, Y., Haag, I., Krumm, J., Sivaprasad, V., Aigner, D., Vereecken, H., and Hendricks Franssen, H.-J.: Enhancing flash flood simulations through appropriate assimilation of remotely sensed soil moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2076, https://doi.org/10.5194/egusphere-egu24-2076, 2024.

A.53

EGU24-4861

ECS

Application of Multi-mission Satellite Remote Sensing Data for Land Surface Data Assimilation

(withdrawn after no-show)

Mehdi Khaki

A.54

EGU24-9452

ECS

The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain

(withdrawn after no-show)

Shoaib Ali and Jiangjun Ran

A.55

EGU24-21704

An observational constraint of global soil moisture droughts

(withdrawn after no-show)

Guoyong Leng, Lei Yao, Xiaoyong Liao, Jieyong Wang, Zehong Li, Ruixing Hou, You Li, Dongqi Sun, Jiali Qiu, and Jian Peng

A.56

EGU24-10585

ECS

A fusion retrieval approach for monitoring upstream suspended sediment fluctuations using Sentinel-2 imagery

bowen Cao, Lorenzo Picco, and Xiankun Yang

Remote sense data are increasingly being developed also to accomplish environmental monitoring activities. In the context of climate change, a growing number of extreme events are being observed worldwide, leading to significant changes in surface hydrological processes. This shift is a crucial factor in the alteration of suspended sediment concentration (SSC) in large rivers. Particularly, large rivers originating in the high mountainous regions of Asia and flowing into densely populated areas of Southeast Asia are more susceptible to erosion due to their geomorphic characteristics and intensive human activities. Faced with grand and complex geomorphic conditions, there is a need to adopt a basin-wide perspective by employing a broader array of monitoring methods. This is particularly important for the Pearl River, a major river in southern China. Retrieval of SSC using remote sensing is one of the popular monitoring methods in the past decades. Unfortunately, its application has mainly focused on the estuary and coastal open water. In this study, we place a stronger emphasis on the basin-wide scale, specifically focusing on the upstream and major tributaries. We recalibrated model parameters using a general index model (G_index) and a regional high-precision model (C_SSC). These recalibration results were combined with Sentinel-2 imagery and field data to establish a basin-wide suspended sediment monitoring program. The results of the integrated model fit (n = 29), R² = 0.95, RMSE = 14.15 mg/L. The modeling results indicated that the spatial distribution of SSC in the upstream and tributaries of the Pearl River showed a clear concentration pattern, with markedly different concentrations in the upstream and downstream reaches. In the upstream, the SSC distribution was clearly divided into two parts, with concentrations of 104.85 mg/L and 13.43 mg/L, respectively, reflecting a substantial difference. It is worth noting that the monthly SSC statistics were clearly seasonal related to the precipitation. May and June were two months with high SSC concentrations in the whole river, with median values of 85.51 mg/L and 106.48 mg/L, respectively. In addition, we observed an abrupt change in suspended sediment downstream of the large reservoirs. This difference is likely caused by the streamflow resulting from the high drop of the dam or channel narrowing. Consequently, we have analyzed the suspended sediment dynamics of both the mainstem and major tributaries of the entire Pearl River. The results will enhance the understanding of suspended sediment changing in large rivers, serving as a valuable complement to water resource management and soil erosion risk assessment.

How to cite: Cao, B., Picco, L., and Yang, X.: A fusion retrieval approach for monitoring upstream suspended sediment fluctuations using Sentinel-2 imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10585, https://doi.org/10.5194/egusphere-egu24-10585, 2024.

A.57

EGU24-11127

ECS

Performance of ASCAT soil moisture and MODIS snow cover satellite data for calibration of hydrologic models in poorly gauged catchments

Asma Khalil and Juraj Parajka

A.58

EGU24-11661

ECS

Trend analysis of reference evapotranspiration: Does the original, downscaled ERA5 Land reanalysis dataset have the potential to bridge the gap in agrometeorological data for North African basins?

(withdrawn after no-show)

Youness Ouassanouan, Mohamed Hakim Kharrou, Vincent Simonneaux, Younes Fakir, Bouchra Ait Hssaine, Mohamed Wassim Baba, Chouaib El Hachimi, Laura Sourp, and Abdelghani Chehbouni

A.59

EGU24-11758

ECS

Snow Cover Area and Snow Water Equivalent Relation in Estonia to Model Surface Water-Groundwater Interactions

Marlen Hunt and Andres Marandi

Hydrological processes, especially snowmelt-related ones, are crucial in water resource management in cold climate regions. Process-based hydrological models, such as the Precipitation-Runoff Modeling System (PRMS), commonly utilize snow depletion curves to describe snowmelt dynamics. Understanding the seasonal variability of snowmelt processes and accurately simulating them through models is essential for assessing water resource sensitivity to various environmental changes in our climate. This study elucidates the relationship between normalized snow water equivalent (SWE) and snow cover area (SCA) in Estonia, northeastern Europe. The snow depletion curves were constructed for 40 gauged river catchments using SWE measurements from the eleven meteorological stations throughout Estonia and SCA data derived from Sentinel-1 and Sentinel-2 satellite imagery from 2016–2022. The resulting snow depletion curve provides valuable insights into the connection between SWE and SCA, allowing for estimating snow water equivalent using remote sensing data in regions lacking on-site measurements. This approach enables the assessment of snowmelt processes in these areas, contributing to improved streamflow and groundwater level forecasts through hydro(geo)logical models. Ultimately, integrating remotely sensed and in-situ data enhances our ability to understand and model the complex interactions between surface water and groundwater at regional and local scales. This research contributes to advancing the field of hydrology and supports informed water resource management decisions.

How to cite: Hunt, M. and Marandi, A.: Snow Cover Area and Snow Water Equivalent Relation in Estonia to Model Surface Water-Groundwater Interactions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11758, https://doi.org/10.5194/egusphere-egu24-11758, 2024.

A.60

EGU24-12415

ECS

Applicability of cloud radar observations for an intra-event analysis of runoff on a steep vineyard

Tatiana Nomokonova, Alexander Myagkov, Marcella Biddoccu, Giorgio Capello, Gerrit Maschwitz, Davide Canone, and Stefano Ferraris

Studying runoff on steep vineyards is an important topic in the agricultural science. Steep vineyards are especially susceptible to soil erosion due to the force of water runoff. Runoff can also carry sediment, pesticides, and fertilizers thus affecting nutrient efficiency and water quality. Understanding of how runoff depends on properties of rain and state of soil can help to improve soil management practices.

There is a number of studies investigating how runoff depends on properties of rain and soil. Typically, the amount of runoff is related to maximum rain intensity, total precipitation, soil water content, etc. Based on a large number of runoff events, often collected over multiple years of observations, the runoff can be represented as a linear regression of the abovementioned variables. However, these linear regressions are highly variable from time period to time period and also among different sites. In order to better understand possible reasons of such variability, a more detailed analysis of single events is required.

In this study we make an attempt to characterize the water budget within a single rain event. From summer 2023 we have run a measurement campaign at an operational site in the Alto Monferrato vine-growing area (Piedmont, NW Italy). For the campaign the site, which is already well equipped with state-of-the art soil and runoff sampling tools, was complemented by a polarimetric cloud radar. The cloud radar can obtain range-resolved profiles of drop-size distribution with high spatial and temporal resolution. The radar observations can be used to characterize rain and to check how variable rain properties are over the field. The main advantages of cloud radars over conventional in-situ rain sampling devices are much larger sampling volume and range profiling of rain properties.

During the summer and autumn seasons we have already collected more than 10 runoff events with different duration and intensity. The collected dataset allows us to relate runoff to rain and soil properties on intra-event scale. The rain intensity is characterized based on cloud radar observations. The water content is measured by moisture sensors located at three different depth levels. Finally, the amount of runoff is measured using 12 L tipping buckets. The site is split into plots with different soil management in the inter-row. This makes it possible to also investigate how different cover affects the water budget.

How to cite: Nomokonova, T., Myagkov, A., Biddoccu, M., Capello, G., Maschwitz, G., Canone, D., and Ferraris, S.: Applicability of cloud radar observations for an intra-event analysis of runoff on a steep vineyard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12415, https://doi.org/10.5194/egusphere-egu24-12415, 2024.

A.61

EGU24-13370

ECS

Evaluation of Remote Sensing and Reanalysis based Precipitation Products for hydrological studies in Semi-arid Tropics of Tamil Nadu

Aatralarasi Saravanan, Niels Schuetze, Daniel Karthe, and Selvaprakash Ramalingam

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Semi-Arid regions of Tamil Nadu in India, particularly focusing on Coimbatore, Madurai, Tiruchirapalli and Tuticorin. The study regions lack sufficiently long-term and spatially representative observed precipitation data, which is a crucial component for hydrological management. Hence, the present study evaluates the accuracy of five remote sensing-based precipitation products viz. Tropical Rainfall Measuring Mission (TRMM), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Records (PERSIANN CDR), CPC MORPHing technique(CMORPH), Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) and three reanalysis-based precipitation products viz. National Center for Environmental Prediction (NCEP2) Reanalysis 2, European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis version 5 Land (ERA 5 Land), Modern-Era Retrospective analysis for Research and Application version 2 (MERRA 2) against the station data obtained from the archives of respective Public Works Department. Initially, precipitation products and ground station data were gridded to a common spatial resolution of 0.1 by linear interpolation. The products were then statistically evaluated at multiple spatial (grid and district-wise) and temporal (daily, weekly, monthly and yearly) resolutions for the period 2003-2014. We found that district-wise analysis at monthly and yearly temporal resolution provided better correlation and significantly reduced biases and errors. Evaluation results showed that in terms of overall statistical metrics, ERA 5 Land, MSWEP, PERSIANN CDR and GPM were the best-performing precipitation products, while NCEP2 performed the worst. ERA 5 Land and MSWEP better represented the daily rainfall characteristics with lower Mean Absolute Error and Root Mean Square Error. This study has significant implications for managing hydrological resources by providing valuable guidance when choosing alternative precipitation products in data-scarce regions.

How to cite: Saravanan, A., Schuetze, N., Karthe, D., and Ramalingam, S.: Evaluation of Remote Sensing and Reanalysis based Precipitation Products for hydrological studies in Semi-arid Tropics of Tamil Nadu, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13370, https://doi.org/10.5194/egusphere-egu24-13370, 2024.

A.62

EGU24-13465

ECS

Proposal of Hourly Rainfall Estimation Method by CNN Using Meteorological Satellite Himawari and Its Evaluation of Areal Rainfall in a Watershed Scale

Kansei Fujimoto and Taichi Tebakari

Many regions, including developing countries, have limited meteorological observation networks and still lack quantitative rainfall data with basin-scale accuracy that can contribute to water-related disaster prediction.

This study aims to develop satellite precipitation products with quantitative accuracy in basin-averaged precipitation for water-related disaster forecasting. In recent years, deep learning has been utilized in many fields as an experiential statistical model, and CNN is a useful model for estimating precipitation from meteorological satellites. The purpose of this study is developing a satellite precipitation estimation method that can be used for predicting water-related disasters by using CNN and the brightness temperature of clouds and water vapor from the Himawari meteorological satellite.

The data used were precisely geometrically corrected data from the Himawari meteorological satellite and elevation data from MERIT DEM. The training period was four months during the summer of 2015 through 2021 (July through October), and the validation period was the summer of 2022. The training domain was the northeastern part of Japan, and the validation watersheds were the Arakawa River in the Kanto region (within the training domain) and the Chikugo River in the Kyushu region (outside the training domain). As a result, this study was able to reproduce the basin-averaged precipitation quantitatively with high accuracy within the training domain. Outside of the training domain, precipitation of rainfall events could be reproduced qualitatively and generally, and some rainfall cases were more accurate than GSMaP's accuracy, however there were cases where no rainfall events were misclassified as rainfall events, therefore we still have room of improvement.

How to cite: Fujimoto, K. and Tebakari, T.: Proposal of Hourly Rainfall Estimation Method by CNN Using Meteorological Satellite Himawari and Its Evaluation of Areal Rainfall in a Watershed Scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13465, https://doi.org/10.5194/egusphere-egu24-13465, 2024.

A.63

EGU24-18211

ECS

Inferring the storage of aquifer systems from InSAR measurements via flow and geomechanical modelling

Yueting Li, Baris Caylak, Alper Elçi, Hakan Ören, Claudia Zoccarato, Elif Aysu Batkan, Pietro Teatini, and Claudia Meisina

In hydrogeological science, it is widely acknowledged that the response of an aquifer to groundwater pumping is predominantly influenced by two key parameters characterizing the aquifer system: saturated hydraulic conductivity K_s and the oedometric bulk compressibility c_m. The response must be viewed in terms of changes of pore water pressure p and the deformation of the pore volume which traduces in a movement of the land surface. In a confined aquifer system subjected to groundwater pumping, the variation in groundwater pressure is linearly dependent on K_s, while the deformation of the pore volume is linearly dependent on c_m. However, c_m also impacts p, particularly the speed of pressure variation over time, as aquifer specific storage S_s is also dependent on c_m. The dependency p - c_m can be considered “weaker” than that p - K_s. The RESERVOIR Project, funded by the EU-PRIMA Programme, aims to characterize aquifer properties, with a focus on S_s, by optimizing the use of the available measurements. Pressure measurements from piezometers provide fundamental information to quantify K_s through the groundwater flow equation. Additionally, displacement measurements of the land surface provided by InSAR can be optimally used in equilibrium equations to constrain c_m (and consequently S_s). This objective is achieved through a novel procedure utilizing a 3D groundwater flow simulator (MODFLOW) and a 3D geomechanical simulator (GEPS3D) in an iterative one way coupled approach. Spatial variations of S_s and K_s are mathematically described as stationary Gaussian random fields. The procedure is applied to characterize the properties of the alluvial aquifer system in the eastern portion of the Gediz River basin, Turkey. In this region, groundwater withdrawal for irrigation has led to a general decline in pore water pressure and land subsidence of up to 10 cm/year over the past decade. The convergence of the procedure was achieved after four iterations, highlighting the presence of considerable heterogeneity in the distribution of parameters. This heterogeneity cannot be effectively constrained without the aid of satellite-based earth observation measurements.

How to cite: Li, Y., Caylak, B., Elçi, A., Ören, H., Zoccarato, C., Batkan, E. A., Teatini, P., and Meisina, C.: Inferring the storage of aquifer systems from InSAR measurements via flow and geomechanical modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18211, https://doi.org/10.5194/egusphere-egu24-18211, 2024.

A.64

EGU24-18649

Development and Evaluation of A Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM

Shuai Han

High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is used to correct temperature forecasts produced by ECMWF. The multi-grid variation formulation method is then used to fuse the data from corrected temperature forecasts and ground automatic station observations. The fused dataset covers the area over (0–60°N, 70–140°S), where different un-derlying surfaces exist, such as plains, basins, plateaus, and mountains. The spatial and tem-poral resolutions are 1 km and 1 h, respectively. The comparison of the fusion data with the verification observations, including 2400 weather stations, indicates that the accuracy of the gridded temperature is superior to European Centre for Medium-Range Weather Forecasts (ECMWF) data. This is because a more significant number of stations and high-resolution terrain data are used to generate the fusion data than are utilized in the ECMWF. The obtained dataset can describe the temperature feature of peaks and valleys more precisely. Due to its continuous temporal coverage and consistent quality, the fusion dataset is one of China’s most widely used temperature datasets. However, data uncertainty will increase for areas with sparse observa-tions and high mountains, and we must be cautious when using data from these areas.

How to cite: Han, S.: Development and Evaluation of A Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18649, https://doi.org/10.5194/egusphere-egu24-18649, 2024.

A.65

EGU24-19748

ECS

Integrating Remotely Sensed and Field-monitored Soil Moisture Data for High Resolution Agricultural Drought Monitoring

Akash Atnurkar and Meenu Ramadas

Agricultural drought monitoring using high resolution soil moisture information is particularly useful for management of precision agriculture and drought early warning studies. The soil moisture products obtained from different active microwave remote sensing satellites may not be appropriate for local-level drought studies due to their limited spatial resolution. This study aims to accurately estimate surface soil moisture (SSM) by utilizing high-resolution multispectral imagery available from the Landsat 8 OLI (Optical Land Imager) mission for monitoring agricultural droughts using soil water deficit index (SWDI). The study demonstrates that using the Landsat-derived SWDI at a spatial resolution of 30 m, and at bimonthly scale, can provide drought information for use in precision irrigation especially at watershed-scale. The red, green, near infrared (NIR), and short-wave infrared (SWIR) bands of Landsat 8 after atmospheric and geometric correction, are utilized for estimating SSM in this study, by considering popular vegetation indices such as normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and normalized difference water index (NDWI), as inputs. Field-monitored soil moisture data available for an agricultural watershed in eastern India during 2016-2017 are utilized for SSM model development. The SSM estimation model is developed using conventional linear regression and artificial neural networks (ANN) models. The conventional linear regression algorithm gave correlation coefficient (R) of 0.60 and mean square error (MSE) of 0.012 cm3/cm3. Whereas, the machine learning-based ANN model has performed SSM estimation with R and MSE of 0.67 and 0.011 cm3/cm3 respectively. Further, the study utilized SSM based on the ANN technique for estimation of SWDI at 30 m resolution for long-term drought monitoring over the study watershed. Based on the computed SWDI, seasonal variations in agricultural drought patterns are also evaluated for the study area.
Keywords: Agricultural Drought Monitoring, Surface Soil Moisture, Remote Sensing, Landsat-8, Vegetation Indices, Machine Learning

How to cite: Atnurkar, A. and Ramadas, M.: Integrating Remotely Sensed and Field-monitored Soil Moisture Data for High Resolution Agricultural Drought Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19748, https://doi.org/10.5194/egusphere-egu24-19748, 2024.

A.66

EGU24-20083

Enhancing the rain/no-rain identification in remote sensing-based rainfall products: what impact on streamflow simulation

Hamza Ouatiki, Taoufik Shit, and Abdelghani Chehbouni

The daily remote sensing-based rainfall estimates have often been problematic in several regions around the globe. This is particularly prevalent in semi-arid regions where, in addition to misestimating the magnitude of the rain events, the spatial rainfall products (SRP) often fail to detect many events correctly. Whether missed or falsely detected, misestimating many events is a real constraint in running (calibrating/validating) hydrological models. Thus, here we are attempting to enhance the capability of some of the well-known SRPs (GPM IMERG, PERSIANN-CDR, and CHIRPS) in rain/no-rain identification (using ancillary data) and how that can impact predicting the hydrological response. To this end, the SRPs were used to drive the HBV and GR4j conceptual hydrological models in watersheds from different climatic contexts.

Using the raw SRPs, the performance of the HBV and GR4j models was relatively poor and temporally unsteady. This was primarily due to uncertainties associated with the SRP estimates. Even the best-performing product (GPM IMERG), was found to largely misestimate rainfall up to 50%. In particular, a prevalence was also observed in terms of detection capacity with non-negligible missed events (according to POD; Probability Of Detection) and many rainfall events detected as false alarms (according to FAR; False alarm Ratio). However, the SRPs blended with remote sensing-based ancillary data allowed us to relatively enhance the streamflow simulation, particularly using the HBV model. This enhancement was possible as using ancillary data allowed us to reduce the number of false alarms and recover some of the missed events. Still, some bias persists in the SRPs, which can be addressed by incorporating in-situ observations employing conventional (e.g., Scaling Factor, CDF matching…) and AI-based bias correction techniques.

How to cite: Ouatiki, H., Shit, T., and Chehbouni, A.: Enhancing the rain/no-rain identification in remote sensing-based rainfall products: what impact on streamflow simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20083, https://doi.org/10.5194/egusphere-egu24-20083, 2024.