Our capacity to observe hydrological states and fluxes at the catchment scale has greatly increased over the last decades. Novel technologies allow measuring the components of the terrestrial water budget at an unprecedented spatial and temporal resolution. Data assimilation and artificial intelligence have gained increasing interest as they allow for the optimal integration of various data and models. It is repeatedly demonstrated that they are ideally suited for forecasting hydrological processes and managing water resources. This session welcomes contributions that link observational data with models of hydrological, crop, land surface, vadose zone, or subsurface processes to improve our understanding of hydrological processes and real-time management of water resources.
Invited speaker: Christian Massari, Italy (christian.massari@irpi.cnr.it)
Over the last 40 years remote sensing has significantly changed the way we observe and predict the Earth system, particularly in the oceanographic and meteorological sciences. Today, every General Circulation model (GCM) relies upon advanced and well-established data assimilation (DA) techniques, and Land Surface Models (LSMs) – which are integral components of GCM – have been increasingly using DA to constrain the LSM model predictions with available remote sensing data of hydrological, carbon and energy cycles.
Despite this, the use of DA into hydrological models (HMs) is still operationally limited and the reasons for that lie in 1) the considerable variability between different HMs, with much uncertainty in their respective representations of processes (often conceptual) and their sensitivity to changes in key variables, 2) the contrast between the scale of application of HMs (often smaller than LSMs) and the coarse-scale information provided by remote sensing along with their associated accuracy and, 3) the variety of the data assimilation setups, specificity of the study areas, and pre-processing used by a plethora of studies on the topic which provide a blurred picture on the real benefit of DA of relevant hydrological variables into HMs (e.g. soil moisture, precipitation, snow, terrestrial water storage anomalies).
The recent exponential grown of high-resolution remote sensing data (the European Union's Earth observation Programme Copernicus with the constellation of the Sentinel satellites is a notable example) has potentially opened new opportunities for improving our HMs also for small scale applications. However, their usefulness is still limited by our ability to analyse and integrate efficiently a large volume of observations with current hydrologic models. In other words, most of the issues mentioned above have been not overcome with a consequent under-exploitation of potentially useful information to constrain HMs.
This contribution aims to summarize the main challenges and opportunities of DA into HMs from a hydrological perspective in light of the availaiblity of new and more skillful Earth observations. It identifies and explains critical challenges by using published literature by the author on European catchments as well as on-going studies, and offers insights for a productive research based on new available models and observations as to build a comprehensive hydrologic data assimilation framework that is a critical component of future hydrologic observation and modelling systems.
How to cite:
Massari, C.: Data assimilation of remote sensing observations into hydrological models: challenges and perspectives in light of a new era of Earth observations, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-6, https://doi.org/10.5194/egusphere-gc8-hydro-6, 2023.
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Shannon de Roos, Louise Busschaert, Michel Bechtold, Zdenko heyvaert, Sujay Kumar, Hans Lievens, Jonas Mortelmans, Dirk Raes, Samuel Scherrer, Maxime Van den Bossche, Elias Fereres, Margarita Garcia-Vila, Pasquale Steduto, Theodore Hsiao, Lee Heng, Maher Salman, and Gabrielle De Lannoy
Recent advances in gridded crop modeling and satellite observations help to improve the monitoring of crop growth and water requirements. In this contribution, we use AquaCrop v7 within the NASA Land Information System (i) to produce spatially distributed estimates of soil moisture, biomass and backscatter, and their uncertainty, and (ii) to assimilate backscatter observations from the Sentinel-1 satellite mission to improve soil moisture and biomass via state updating, at 1 km resolution over Europe. The results are evaluated against in situ observations of soil moisture and satellite-based vegetation products. We will discuss the opportunities and challenges of high-resolution gridded crop models and satellite-based active microwave data for agricultural applications.
How to cite:
de Roos, S., Busschaert, L., Bechtold, M., heyvaert, Z., Kumar, S., Lievens, H., Mortelmans, J., Raes, D., Scherrer, S., Van den Bossche, M., Fereres, E., Garcia-Vila, M., Steduto, P., Hsiao, T., Heng, L., Salman, M., and De Lannoy, G.: Assimilation of Sentinel-1 backscatter data into AquaCrop v7 for soil moisture and biomass updating over Europe, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-21, https://doi.org/10.5194/egusphere-gc8-hydro-21, 2023.
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Giovanni Francesco Santonastaso, Pasquale Marino, Daniel Camilo Roman Quintero, and Roberto Greco
In the area of Casamicciola, on the island of Ischia, in the Gulf of Naples, on November 26, 2022, heavy rain triggered landslides that killed people and caused great damage to buildings and roads. Rain gauges on the island recorded heavy rainfall starting at midnight on November 25. The 6-hour cumulative rainfall (between 00:00 on 25/11 and 06:00 on 26/11) resulted 126 mm. The peak hourly rainfall at the two nearest rain gauges was 51.6 mm in Forio and 50.4 mm in Monte Epomeo, attained just before the triggering of the major landslide. The attainment of critical rainfall depth was so sudden, that rain gauges recordings did not allow deploying timely risk mitigation measures. In this context, an effective Landslide Early Warning System (LEWS), based not only on rain gauges, would be an important tool to mitigate the impact of landslides. The goal of a LEWS is to provide timely information to individuals and organizations, so that they can take appropriate actions to reduce the risk. These systems typically use a combination of monitoring networks and modeling techniques, to issue real-time warnings when the probability of a landslide becomes high. A well-designed LEWS can save lives, reduce property damage, and minimize the economic impact of the events.
In this paper, a novel approach to LEWS, based on machine learning and radar data, is proposed. Specifically, a random forest model is trained to define pre-alarm thresholds based on radar measurements available on the portal MISTRAL (Mistral portal Meteo Italian SupercompuTing poRtAL), and on rainfall measurements from four rain gauges on the island of Ischia. Two concentric monitoring areas around the island of Ischia are divided into 16 sectors, and the model evaluates every five minutes the percentage of nodes in each sector where the rainfall height detected by the radar exceeds assigned thresholds, corresponding to pre-alarm stages. Preliminary results show the prospects of using machine learning in LEWS.
How to cite:
Santonastaso, G. F., Marino, P., Roman Quintero, D. C., and Greco, R.: Landslide Early Warning System based on Machine learning and radar data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-64, https://doi.org/10.5194/egusphere-gc8-hydro-64, 2023.
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Sara Modanesi, Gabriëlle J. M. De Lannoy, Michel Bechtold, Louise Busschaert, and Christian Massari
Improving the knowledge of agricultural water uses is in the spotlight of hydrologic sciences and water management authorities due to an increasing amount of water used for irrigation. An efficient water management system has a crucial role also considering the climate change projections scenario and the large increase in the frequency, duration, and severity of droughts, especially over the Mediterranean basin, which has been recognized as a hotspot of extreme weather events. However, simulating irrigation through large scale land surface models is not trivial, because the simplistic model parameterization do not necessarily resolve field scale conditions. In particular, the main challenge is to reproduce the amount and timing of irrigation applications by farmers, because these are often not physically-based and effectively driven by water policies instead of root zone soil moisture conditions.
Some recent approaches have demonstrated the utility of remote sensing observations to either derive irrigation directly, or indirectly via their assimilation into land surface and hydrological models. Indeed, high resolution remote sensing offers an unprecedented opportunity to observe the soil/vegetation system and to consequently detect irrigation. However, although both methods seem promising, irrigation quantification and detection are still at their infancy due to limitations of both satellite data and models. In particular, recent data assimilation experiments have shown the crucial role of an accurate land surface model parameterization to optimally integrate models and satellite observations.
The aim of this study is to test the benefit of directly optimizing the irrigation parameters of a sprinkler irrigation module embodied in the Noah MP land surface model running within the NASA Land Information System framework. The experiment was conducted over a highly irrigated area in the Po Valley (Italy) using synthetic irrigation benchmark data and at a spatial resolution of 0.01°. The improvement of the poorly-parameterized sprinkler irrigation scheme through a proper calibration is intended to be a valid alternative to quantify agricultural water uses.
How to cite:
Modanesi, S., De Lannoy, G. J. M., Bechtold, M., Busschaert, L., and Massari, C.: Optimizing irrigation parameters to improve land surface model irrigation simulations: an example over the Po Valley, Italy, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-42, https://doi.org/10.5194/egusphere-gc8-hydro-42, 2023.
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Due to soil moisture and vegetation's critical role in controlling land-atmosphere interactions, detailed and accurate hydrological and ecological information is essential to understand, monitor, and predict hydroclimate extremes (e.g., droughts and floods), natural hazards (e.g., wildfires and landslides), irrigation demands, weather, and climate dynamics. While in-situ soil moisture and vegetation biomass measurements can provide detailed information, their representativeness is limited, and networks of sensors are not widely available. Multispectral satellite observations offer global coverage, but retrievals can be infrequent or too coarse to capture the local extremes. This observation data gap limits the use of such information to adequately represent land surface processes and their initialization conditions for seasonal to sub-seasonal (S2S) prediction models. To bridge this gap, the assimilation of remote sensing observations into land surface models at hyper-resolution spatial scales (< 100 meters) provides a pathway forward to (i) reconcile model and observation scales and (ii) enhance S2S hydroclimate predictability in Earth System Models.
To this aim, we introduce a scalable approach that leverages advances in machine learning, radiative transfer modeling, and in-situ observations to assimilate satellite observations into unstructured tile-based land surface models. In this approach, a machine learning model is trained to harness information from big environmental datasets and in-situ observations to learn how the physical model and satellite biases are related to specific hydrologic conditions and landscape characteristics and how these biases evolve over time. We demonstrate the added value of this approach for improving soil moisture and vegetation dynamics at the hyper-resolution scales by assimilating MODIS Leaf Area Index and NASA’s SMAP brightness temperature observations into the LM4.0 – the land model component of the NOAA-GFDL Earth System Model. To this end, we performed stand-alone LM4.0 simulations between 2000 to 2021 over the Continental United States, with the MODIS and SMAP assimilation performed from 2002 and 2015, respectively, until the present day. Soil moisture estimates are evaluated against independent in-situ observations. To quantify the approach added value for S2S predictability, we compare the impact of soil moisture and vegetation data assimilation on root zone soil moisture, runoff, vegetation biomass, surface temperature, and evapotranspiration.
How to cite:
Vergopolan, N.: Leveraging advances in hyper-resolution soil moisture and vegetation land data assimilation for S2S hydroclimate applications, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-82, https://doi.org/10.5194/egusphere-gc8-hydro-82, 2023.
Richard Hoffmann, Klaus Görgen, Heye Bogena, and Harrie-Jan Hendricks-Franssen
The use of numerical models for real-time management of water resources is becoming increasingly popular as the increasing frequency and intensity of extreme weather events negatively affect society, agriculture and crop yields. Prolonged droughts are becoming the new normal, which, among other things, increase the need for operational, site-specific soil moisture forecasting. A model that provides accurate site-specific soil moisture forecasts can support agriculture by contributing to precision irrigation and the provision of important information for crop planning, yield maximization and the coordination of field operations. Soil moisture assimilation has proven potential to provide appropriate initial conditions for such a forecast model. However, the operational estimation of an initial condition requires model-specific protocols for continuously incorporating new observational data into models for hydrological, crop, land surface, vadose zone, or subsurface processes that are not yet widely available. In this study, we present an automated data pipeline for operational, site-specific soil moisture ensemble forecasting based on the Community Land Model Version 5.0 (CLM5) taking the TERENO agricultural research station "Selhausen" in western Germany as an example. CLM5 simulates vegetation states, carbon and nitrogen pools prognostically. We compare land surface model prediction quality (e.g., soil moisture, crop yield) with and without weather forecasts and with and without near real-time soil moisture data assimilation. Climatological mean time series and 10-day ensemble weather forecasts from the German Weather Service, aggregated to the grid cell, are the atmospheric forcings in simulating future states. Forecasts start from the states of the last simulation time step with on-site measurements of precipitation, wind speed, air temperature, air pressure, relative humidity, and global radiation as the atmospheric forcings. In parallel with forward simulations from 2011-2021 (open loop experiment), soil moisture assimilation is being performed for 2018-2021 to generate site-specific initial conditions for the land surface model with reduced uncertainty. Forecasts starting from initial conditions based on soil moisture assimilation are more reliable as model bias is reduced. Preliminary results show that the inclusion of site-specific weather forecast uncertainties in the model improves the simulation of soil moisture dynamics at the plot scale and is thus important for optimizing irrigation schedules while keeping crop productivity stable.
How to cite:
Hoffmann, R., Görgen, K., Bogena, H., and Hendricks-Franssen, H.-J.: From observations towards operational site-specific soil moisture ensemble forecasting, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-44, https://doi.org/10.5194/egusphere-gc8-hydro-44, 2023.
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Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco
The assessment of the response of slopes to precipitations is important for several applications: from drought associated problems to the evaluation of the occurrence of threatening events such as floods and landslides (Bogaard & Greco, 2016). This study aims at identifying the most important variables, that can be monitored in the field, suitable to describe the initial conditions that control the capability of a slope to store infiltrating water at the end of precipitation events. The case study of the slopes near the town of Cervinara, southern Italy, is presented, where field observations and laboratory experiments allowed the understanding of the water processes at different scales (Marino et al., 2020). A synthetic dataset, simulating the major hydraulic processes observed in the field, was generated to enrich the available data. It was built by simulating the response of the slope to a 1000-year long synthetic rainfall series, generated with the NSRP model, with a physically based model coupling the unsaturated flow in the coarse granular soil cover with the shallow aquifer hosted by the uppermost part of the underlying fractured limestone bedrock (Marino et al., 2021). The hydraulic behavior of the soil cover is modelled with the 1D Richards’ equation, while the aquifer, connected to the soil cover through its lower boundary condition, is modelled as a simple linear reservoir.
Two variables expressing underground antecedent conditions, one hour before any rainfall event, were analyzed: mean water content in the uppermost meter of the soil cover and aquifer water level. The slope response was quantified as the fraction of rainwater remaining stored in the soil cover at the end of any rainfall event. The non-linear relationships linking the three variables were studied with clustering and random forest techniques, allowing the identification of three major hydrological conditions. The first one is linked to dry seasons, when the lowest aquifer water level coincides with soil water content below field capacity: in this condition, rainwater tends to remain completely stored in the soil at the end of rain events. Once the soil cover overcomes the field capacity, two different conditions are found. When the aquifer water level is high, active drainage through the soil-bedrock interface limits the increase of water stored in the soil cover. Conversely, when the aquifer water level is low, it corresponds to impeded drainage, i.e., there is little leakage from the soil cover to the bedrock. In this condition, most rainwater tends to remain stored in the soil cover even when it is already wet at the beginning of the rain event.
References
Bogaard, T., & Greco, R. (2016). Landslide hydrology: from hydrology to pore pressure. Wiley Interdisciplinary Reviews: Water, 3(3), 439–459. https://doi.org/10.1002/wat2.1126
Marino, P., Comegna, L., Damiano, E., Olivares, L., & Greco, R. (2020). Monitoring the hydrological balance of a landslide-prone slope covered by pyroclastic deposits over limestone fractured bedrock. Water (Switzerland), 12(12). https://doi.org/10.3390/w12123309
Marino, P., Santonastaso, G. F., Fan, X., & Greco, R. (2021). Prediction of shallow landslides in pyroclastic-covered slopes by coupled modeling of unsaturated and saturated groundwater flow. Landslides, 18(1), 31–41. https://doi.org/10.1007/s10346-020-01484-6
How to cite:
Roman Quintero, D. C., Marino, P., Santonastaso, G. F., and Greco, R.: Identification of hydrological controls of slope response to precipitations using machine learning techniques, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-53, https://doi.org/10.5194/egusphere-gc8-hydro-53, 2023.
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Harrie-Jan Hendricks-Franssen, Fang Li, Lukas Strebel, Haojin Zhao, Heye Bogena, and Harry Vereecken
The hydrological observatory for the Rur catchment (2400 km2) in Germany is highly equipped including 15 Cosmic Ray Neutron Sensors (CRNS) to measure soil moisture content, 6 eddy covariance stations with measurement of land-atmosphere exchange fluxes and further micrometeorological observations, and additional monitoring stations for river discharge and groundwater levels, amongst others. In addition, 3 intensive research sites at representative locations have been implemented with distributed soil moisture and temperature monitoring. These measurements allow for a better local verification of terrestrial model predictions, and the improvement of model predictions by model-data fusion methods. We did a series of studies on the assimilation of observations from the Rur observatory to improve predictions with the Terrestrial Systems Modelling Platform (TSMP), which models water, energy, carbon and nitrogen cycles of the land surface and subsurface. The data assimilation algorithm was in most cases the Ensemble Kalman Filter, but also the Particle Filter and Markov Chain Monte Carlo were used. Assimilated observations included soil moisture (from FDR-probes, CRNS or remote sensing), groundwater levels and net ecosystem exchange. We found that assimilation improved the characterization of the measured variable, also at verification locations. However, states and fluxes of variables that were not assimilated, such as evapotranspiration, often were not better characterized. The results suggest the importance of the joint assimilation of measurements for different variables, including remotely sensed information and vegetation information.
How to cite:
Hendricks-Franssen, H.-J., Li, F., Strebel, L., Zhao, H., Bogena, H., and Vereecken, H.: Assimilation of measurements from hydrological observatories for better terrestrial system model predictions: experiences and challenges, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-77, https://doi.org/10.5194/egusphere-gc8-hydro-77, 2023.
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Data assimilation techniques allow for optimally merging remote sensing observations in ecohydrological models, guiding them for improving land surface flux predictions. Nowadays freely available remote sensing products, like those of Sentinel 1 radar, Landsat 8, and Sentinel 2 sensors, allow for monitoring land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index, NDVI, for leaf area index, LAI) at unprecedented high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semi-arid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. An assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic-land surface model is proposed. It is based on the Ensemble Kalman filter (EnKF), and it is not limited to assimilate remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), the saturated hydraulic conductivity, and the grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture in an heterogeneous ecosystem in Sardinia, for 5 years period with contrasting hydrometeorological (dry vs wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared to the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors less than 4% and 15%, respectively, although the initial model conditions were extremely biased.
How to cite:
Montaldo, N. and Corona, R.: On the Assimilation of Remote Sensing Data for Soil Moisture and Leaf Area Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a tree-grass Mediterranean Ecosystem, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-71, https://doi.org/10.5194/egusphere-gc8-hydro-71, 2023.
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In recent years, important water-relevant research results have been achieved and numerous emerging issues have been identified. The posed challenges have to be elaborated, and solutions and measures must be developed. In this context, there is a great need to ensure a continuous competence building and to promote an exchange of results, protocols, infrastructure, and equipment as well as hydrological data. Among the different fresh water sources, groundwater is expected to play a key role in a resilient water future. For this reason, hydrogeological observatories are more necessary than ever.
Several existing, long-term hydrogeological observatories within Switzerland are currently being organized into a long-term national hydrogeological observatory network with the support of the Swiss groundwater network (CH-GNet; https://www.swissgroundwaternetwork.ch/en/). The current hydrogeologic observatories are complementary in terms of elevation, geology, use, and scientific as well as practical objectives. These observatories are managed by research groups or practice partners to ensure long-term operability. The observatories are heavily instrumented for long-term hydraulic and chemical water monitoring and are supplemented by field experiments and investigations. It is envisioned that synergies will be created to promote joint and future experiments by a large number of research groups with different expertise. In addition, these observatories are and will be an important tool to train students. National and international research groups have the opportunity to further develop approaches and improve their knowledge under "real" working conditions, as well as to develop and validate modelling and predictive numerical tools. The hydrogeological observatories are constantly further developed and we are open for any suggestions, remarks, and new partners. In our contribution, we present the already existing observatories, goals and the way forward.
How to cite:
Moeck, C.: A Swiss-wide network of hydro(geo)logical observatories, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-15, https://doi.org/10.5194/egusphere-gc8-hydro-15, 2023.
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Anke Hildebrandt, Christine Fischer-Bedtke, Johanna Clara Metzger, Gökben Demir, and Thomas Wutzler
Heterogeneity in below canopy precipitation has often been hypothesized to induce spatial variation of soil water content especially in forests. However, we are not aware of any observational study relating the spatial variation of soil water content directly to net precipitation or alternatively to deep percolation. Here, we investigate whether throughfall patterns affect the spatial heterogeneity of soil water response in the main rooting zone. We assessed rainfall, throughfall and soil water contents (two depths: 7.5 cm and 27.5 cm) in a very dense observation network on a 1‐ha temperate mixed beech forest plot in Germany during two growing seasons. Because throughfall and soil water content cannot be measured at the same location, we used kriging to derive the throughfall values at the locations where soil water content was measured.
Throughfall spatial patterns were related to canopy density. Although spatial auto-correlation decreased with increasing event sizes, temporally stable throughfall patterns emerged, leading to reoccurring high and lower input locations across precipitation events. A linear mixed effect model analysis showed, that soil water content patterns were only poorly linked to throughfall spatial patterns, and it was rather shaped by unidentified but time constant factors. Instead the increase soil water content after rainfall corresponded more closely to throughfall input patterns. Furthermore, soil water patterns additionally affected how much water was stored, and ancillary data suggest that this was related to preferential flow.
In this comprehensive study we show that throughfall patterns imprint less on soil water contents and more on soil water dynamics shortly after rainfall events, therefore only partly confirming previous modelling with data. Our findings highlight at the same time systematic patterns of times and locations where the capacity to store water is reduced and water probably conducted quickly to greater depth. Our results indicate percolation patterns may already be triggered in the canopy.
How to cite:
Hildebrandt, A., Fischer-Bedtke, C., Metzger, J. C., Demir, G., and Wutzler, T.: Spatial patterns for canopy drainage translate into soil moisture dynamics – empirical evidence, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-114, https://doi.org/10.5194/egusphere-gc8-hydro-114, 2023.
Fang Li, Heye Reemt Bogena, Bagher Bayat, Wolfgang Kurtz, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Cosmic-ray neutron sensors (CRNS) measure soil moisture in real-time at the field scale, bridging the gap between in situ measurements and remote sensing products. This is promising and has the potential to enhance hydrological model predictions through the assimilation of CRNS data and improve the estimation of model parameters. In this study, soil moisture measurements from a network of 13 CRNS in the Rur catchment (~2000km2, Germany) were assimilated into the integrated model Terrestrial System Modelling Platform (TSMP) by the ensemble Kalman filter (EnKF). In total 128 ensemble members were generated by perturbing atmospheric forcing variables and soil textures to account for the uncertainties. The data assimilation experiments (with and without soil hydraulic parameter estimation) were carried out in both a wet year (2016) and a dry year (2018), and later validated using an independent year (2017) without assimilation. The objectives of this study were to investigate the potential of CRNS assimilation for improving soil moisture and evapotranspiration (ET) characterization, estimation of soil hydraulic parameters at the catchment scale, and analysis of whether the data assimilation performance differs between wet and dry years. The data assimilation experiments showed that soil moisture estimation was significantly improved during the assimilation period at measurement locations, with a root mean square error (RMSE) reduction (compared to open loop simulations without assimilation) in the range of 36-60% either in the dry or wet year, and the improvements were limited by the measurement error of CRNS (0.03 cm3/cm3). The joint state-parameter estimation gives better performance than state estimation alone (more than 15% RMSE reduction), and 9% RMSE reduction in the verification period with the updated parameter. The jackknife experiments revealed that the measurement network (~1 site per 200 km2) was insufficiently dense because soil moisture characterization at independent verification locations only improved marginally with large differences between wet and dry years (with an RMSE reduction of 40% in 2016 and 16% in 2018). The improved predictions from the jackknife experiments, however, imply that the assimilation of soil moisture data from a CRNS network still has the potential to improve the soil moisture characterization on the catchment scale by updating the spatially distributed soil hydraulic parameters of the subsurface model. The comparison of simulated ET with the data from eddy covariance (EC) stations demonstrates that it is challenging to achieve great improvements in ET simulations through CRNS soil moisture assimilation (with the RMSE reduction of monthly ET ranging between 6% and 21%).
How to cite:
Li, F., Reemt Bogena, H., Bayat, B., Kurtz, W., Vereecken, H., and Hendricks Franssen, H.-J.: Assimilation of soil moisture data from cosmic-ray neutron sensors into the integrated Terrestrial System Modeling Platform TSMP (case study: Rur catchment in Germany), A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-17, https://doi.org/10.5194/egusphere-gc8-hydro-17, 2023.
Giacomo Bertoldi, Georg Niedrist, Alessandro Zandonai, Nikolaus Obojes, Veronika Fontana, Stefano Brighenti, Francesco Comiti, and Ulrike Tappeiner
The Long Term (Socio-) Ecological Research LT(S)ER site IT25 - Val Mazia/Matschertal is a catchment covering an elevation range between 900 and 3700m a.s.l., in South Tyrol (Italian Alps). While nivo-glacial processes dominate runoff production, lower sideslopes have a relatively dry climate, (ca. 500 mm at 1500m a.s.l.), mainly as summer convective precipitation, and therefore the site is appropriate for space-to-time substitution experiments for understanding mountain eco-hydrologic processes along climatic gradients.
For a better understand of the ecological, hydrological, and climatic processes in the catchment, a spatially distributed micro-meteorological network has been installed since 2009. The measurement infrastructure consists of about 20 stations among all dominant land-use types (grassland, forest, river, proglacial area) covering an elevation range from 1000 to 2700 m a.s.l.. The parameters monitored are mainly related to the 1) Microclimate (air temperature, humidity, wind) 2) Hydrological cycle (soil moisture, soil water potential, runoff, evapotranspiration, solid/liquid precipitation) 3) Energy balance (short/longwave/net radiation, surface heat fluxes) 4) Optical reflectance (Phenocam, NDVI/PRI Sensors). 5) Vegetation (sap-flow. O-H stable isotope monitoring).
The talk will focus on the capability of the collected observations to clarify if the water used by vegetation is coming from snowmelt or mainly from summer precipitation, which is one of the key open research questions in the perspective of a future elevational increase of the rain-snowfall transition zone.
In this contribution, we would like to highlight the potential of the site in a network of hydrological observatories in Europe that allows the testing of hydrologic hypotheses for different environments and climatic regions.
How to cite:
Bertoldi, G., Niedrist, G., Zandonai, A., Obojes, N., Fontana, V., Brighenti, S., Comiti, F., and Tappeiner, U.: An observatory to monitor long-term eco-hydrological changes in Alpine environments: the LTER Matschertal/Val di Mazia., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-93, https://doi.org/10.5194/egusphere-gc8-hydro-93, 2023.
Silvano Fortunato Dal Sasso, Maria Rosaria Margiotta, Beniamino Onorati, Biagio Sileo, Alonso Pizarro, Salvatore Manfreda, Ruggero Ermini, and Mauro Fiorentino
Hydrological observations provided by in situ monitoring networks are essential to better understand hydrological processes and to improve water resource management. This is even more precious for small basins where large spatial coverage or remotely sensed data are not enough to represent hydrological behavior in space and time. In addition, the availability of several years of hydrological data is particularly useful for the application of hydrological models that usually requires long calibration data series in order to provide reliable results. Starting from 2002 and continuing for the subsequent two decades, the "Fiumarella of Corleto" basin, which spans an area of 32.5 km2 and is situated in the Basilicata region of Southern Italy, has been under observation (Manfreda et al., 2011). The basin is located on two slopes with differing land use patterns: the left slope is mostly comprised of agricultural land, while the right slope is predominantly covered by forests. The hydrometeorological network consists of three automated weather stations equipped with various sensors to monitor rainfall, snow depth, temperature, wind speed and direction, air temperature, relative humidity, solar radiation, atmospheric pressure, and hydrometric data. From 2006, a TDR100 system connected to 22 probes located at 11 different sampling sites was used to monitor soil moisture in the sub-basin. The system was set up along a transect measuring approximately 60 meters in length, with probes located at two different depths of 30 and 60 cm. In addition to this, a high-resolution (1x1 m) DSM of the basin was derived using LiDAR to provide a detailed characterization of the morphology of the two slopes. The catchment pedology was investigated through field campaigns and laboratory measurements to identify the primary soil types and units in the basin (Romano et al., 2002; Santini et al., 1999). Monitoring activities were conducted with reference to two different spatial scales: the entire basin (32.5 km2) and the sub-basin (0.65 km2). Hydrological signatures were used to characterize the hydrological behavior of the two drainage areas. Peak flow analyses were performed to define lag-time, soil moisture conditions before flood events evidencing the different hydrological responses of both basin and sub-basin. Some flow indicators (e.g., base flow and recession constant) were used to constrain a semi-distributed hydrological model in order to optimize performances in calibration and validation. In this contribution, an overview of the main results of hydrological data analyses and modeling obtained at different spatial scales is presented.
How to cite:
Dal Sasso, S. F., Margiotta, M. R., Onorati, B., Sileo, B., Pizarro, A., Manfreda, S., Ermini, R., and Fiorentino, M.: Twenty years of hydrological observations at Fiumarella of Corleto basin: experimental data, analysis and modeling, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-102, https://doi.org/10.5194/egusphere-gc8-hydro-102, 2023.
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János Tamás, Zsolt Zoltán Fehér, Nikolett Kiss, Dávid Pásztor, and Attila Nagy
Vehicle-mounted, wide-angle cameras combined with deep learning algorithms are proving to be a powerful mapping tool (e.g. Google Street View). Such tasks include facilitating adaptation to the increasingly common extreme rainfall events attributed to climate change. Repeatable rapid surveys of agricultural parcels have the potential to combine ground and satellite information over large areas, enabling cost-effective planning of cultivation tasks such as more efficient nutrient supplementation, pest management, irrigation water use. In this paper we describe our experience with a photogrammetric data acquisition system (FODAR).
The vehicle mounted Geometer device takes images every two metres along the routes travelled, which can be evaluated on its cloud-based geographic information platform. The geospatial data can then be displayed and evaluated interactively. On-the-move survey control is also provided. A powerful on-board computer can be used to monitor the recording during fieldwork. After the survey, the software automatically converts the recording metadata for cloud-based processing. It is also possible to determine the geographic position of point objects and measure distances and areas. The artificial intelligence used by the system uses deep learning algorithms to recognize and pinpoint with high accuracy on the map various objects on the surveyed road sections, such as traffic signs, but also fire hydrants, sewer covers, stormwater drains and other objects related to urban hydrology.
The use of such equipment in precision agriculture is not yet widespread, despite the fact that due to its vehicle mountability also can be used for mapping of ploughs or orchards and for effective assessment of crop growth. Our aim in using the tool was to build a prototype workflow to evaluate the data set that is expected to become available in the near future.
Patterns in the data, such as vegetation health, that are present in the data and have not been investigated so far, could lead to an increase in the profitability of management decisions by including the near-infrared band in photogrammetric analyses. In contrast to teaching deep learning algorithms, the object detection and image classification itself can be done with relatively little hardware effort, but future trends must be taken into account. For processing a large number of images submitted by cloud-connected vehicles, it may be worth considering a dynamically scalable hardware infrastructure. In addition, objects of agricultural interest identified by the technology could also serve as calibration data for aerial or even spaceborne imagery.
The abstract was funded by European Union’s Horizon 2020 “WATERAGRI Water retention and nutrient recycling in soils and steams for improved agricultural production” research and innovation programme under Grant Agreement No. 858375. Project no. TKP2021-NKTA-32 has been implemented with the support provided from the Na-tional Research, Development and Innovation Fund of Hungary, financed under the TKP2021-NKTA funding scheme.
How to cite:
Tamás, J., Fehér, Z. Z., Kiss, N., Pásztor, D., and Nagy, A.: Mobile photogrammetric raster clouds to apply classification of time series data, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-104, https://doi.org/10.5194/egusphere-gc8-hydro-104, 2023.
Soil drainage flux is crucial for determining agrochemical loading and groundwater recharge. Because soil drainage is difficult to measure, it is typically predicted using soil moisture models. However, different soil moisture models have been shown to produce different drainage values although they all respected the same soil measurements well, leading to a non-uniqueness problem. To address this issue, this study used groundwater level, stream flow, and tile drainage measurements along with soil moisture data to constraint soil drainage estimation through a coupled soil and groundwater modeling framework in the Cross River watershed in Prince Edward Island, Canada. A 1D Richards equation model, LEACHM, was developed to predict soil drainage and calibrated using soil moisture data. A 3D watershed-scale MODFLOW model was built and calibrated against groundwater level data. The two models were loosely coupled using the soil drainage predicted by LEACHM as recharge. Forward coupled LEACHM and MODFLOW simulations were performed until simulated daily soil moisture, groundwater level, baseflow, and tile drainage values simultaneously matched the 2011–2014 observed values within prescribed error ranges by fine tuning the hydraulic parameters in coupled models. The coupled models were then verified using 2015–2016 data. The resulting LEACHM simulations matched the soil moisture data with less than 15% error, and MODFLOW simulations matched the groundwater level and base flow data, except for a few short periods when LEACHM overestimated soil drainage under deep snow cover. Although the timing of simulated soil drainage corresponded with the occurrence of tile drainage, the simulated soil drainage was generally higher than the tile drainage, which is considered reasonable because tiles intercept only a portion of the overall soil drainage. This exercise demonstrates that the coupled modeling respected multiple hydrological data sources instead of soil moisture alone, and thus enhanced soil moisture estimation.
How to cite:
Jiang, Y. and the Yefang Jiang: Using multiple hydrological data sources to reduce uncertainty in soil drainage modeling, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-30, https://doi.org/10.5194/egusphere-gc8-hydro-30, 2023.
Jan Lukas Wenzel, Christopher Conrad, Thomas Piernicke, Kristin Haßelbusch, Falk Böttcher, and Julia Pöhlitz
In the view of global freshwater availability and an increasing water demand in agriculture to secure world nutrition, efficient water use is a key factor for sustainable irrigation management. Irrigation decision support systems often show a lack of awareness on intra-site and variety-specific optimum ranges of plant available water content, which enhances the ineffective use of irrigation water. In potato production, all phenological stages are sensitive to insufficient water supply, with optimum soil water contents ranging between 40% and 90% plant available water content. Hence, observations and simulations of soil moisture dynamics are crucial information for irrigation management. In a study to be presented we aim (i) to assess the optimum irrigation level for starch potatoes in terms of plant available water dynamics, and (ii) to compare the suitability of three different model environments for simulating soil moisture dynamics.
Four test plots (each 172 m x 72 m) were installed during the growing seasons 2021 and 2022 on two loamy sands (27 ha and 35 ha) in Mecklenburg-Western Pomerania, Germany, within one gun sprinkler irrigation lane. In each test plot, one irrigation level was applied: the longtime used irrigation level of the local farmer (100%), two deficit irrigation levels (80%, 90%), and one abundant irrigation level (120%). The 100% irrigation level was 119.2 mm in 2021 and 132.8 mm in 2022. The soil hydraulic properties determined in laboratory are typical for a loamy sand with soil moisture of 0.196 m3 m-3 at field capacity and 0.038 m3 m-3 at permanent wilting point. Hourly and daily simulations of root-zone (0-60 cm) soil moisture dynamics were performed using the evapotranspiration-based “Agrarmeteorologisches Modell zur Berechnung der aktuellen Verdunstung” (AMBAV) model and the soil hydraulic properties-based HYDRUS-1D and HYDRUS-2D model environments. In-situ soil moisture measurements, observed in three-time replicates per test plot in 10 cm increments up to a depth of 60 cm, were used for validation.
Field measurements confirmed that all irrigation levels impacted plant available water contents. They ranged between 25% and 65% at the 80% irrigation level, between 42% and 94% at the 90% irrigation level, between 50% and field capacity at the 100% irrigation level and between 64% and 109% at the 120% irrigation level. All three model environments provide reliable simulation results at all irrigation levels, with an average coefficient of determination (R2) of 70.13% (AMBAV), 76.62% (HYDRUS-1D) and 81.13% (HYDRUS-2D). Simulated soil moisture dynamics varied stronger in topsoil than in subsoil layers, mainly due to the soil hydraulic properties of a potato dam and the effects of evapotranspiration.
The in-situ measured soil moisture dynamics confirm the capability of a 90% irrigation level for starch potatoes. AMBAV´s lower input parameter requirements ensure a greater dispersion of simulated soil moisture dynamics, when compared to more precise estimations by both HYDRUS environments. The inclusion of soil hydraulic properties in irrigation scheduling provides practice-relevant information, e.g., the actual irrigation demand of a specific crop, and enables the use of hydrological models for irrigation scheduling instead of in-situ measurements.
How to cite:
Wenzel, J. L., Conrad, C., Piernicke, T., Haßelbusch, K., Böttcher, F., and Pöhlitz, J.: Observing soil moisture dynamics on starch potato fields for improving irrigation management based on hydrological simulations, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-87, https://doi.org/10.5194/egusphere-gc8-hydro-87, 2023.
Vittorio Miraglia, Maria Fabrizia Clemente, Valeria D'Ambrosio, and Ferdinando Di Martino
Urban and metropolitan settlements, due to the growing impacts of climate change, are highly at risk from critical hydro-meteorological hazards (HMHs), such us floods and heatwaves. Future climate change scenarios require the implementation of resilient design solutions taking into account the climate projections, as well as vulnerability and exposure. In this context, we propose a GIS-based framework aimed at supporting decision-makers in designing long-term climate adaptive design solutions. The framework is developed starting from input data assimilation; then, using AI machine learning and decision-making techniques, are executed aggregations and classifications of urban physical features in order to assess the spatial distribution of vulnerability and risk indicators. In particular, it is proposed a method to verify the resilient efficacy of nature-based solutions in reducing potential economic damages produced by coastal floods events, and simultaneously improving the open spaces heatwave vulnerability.
How to cite:
Miraglia, V., Clemente, M. F., D'Ambrosio, V., and Di Martino, F.: GIS-based Framework and AI Approaches to support Decision Makers in Implementation of Climate-adaptive Design Solutions, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-68, https://doi.org/10.5194/egusphere-gc8-hydro-68, 2023.
El houssaine Bouras, Abdelhakim Amazirh, Zoubair Rafi, Lionel Jarlan, Said Khabba, Olivier Merlin, and Salah Er-Raki
Accurate estimation of the partitioning of actual evapotranspiration (ETa) into plant transpiration (Tr) and soil evaporation (E) is difficult but important for assessing biomass production and the allocation of increasingly scarce water resources. This work aims to evaluate the performance of the AquaCrop model to estimate actual crop ETa and its components (Tr and E) over drip irrigated wheat fields in the semi-arid region of Morocco. Field experiments were carried out during 2016-2017 season on an irrigated winter wheat field in semi-arid region of Morocco. Wheat ETa and its partitioning components (Tr and E) were measured by using the eddy covariance (EC) system and the sap flow system (SF). The obtained results showed that the AquaCrop model adequately simulated canopy cover (CC), ETa and wheat biomass. The coefficient of determination (R2) between observed and measured CC, ETa and biomass were 0.98, 0.72 and 0.98 respectively. With regard to the ETa partitioning, the results indicate that the estimate of Tr using the AquaCrop model is well consistent with those of the in-situ measurements with SF. The Root mean square error (RMSE) between the observed and simulated Tr was about 0.60 mm.day-1. This work demonstrates that the AquaCrop model has reliable accuracy in simulating wheat growth, production and ETa partitioning. As a result, this model provides a technical means of application to formulate optimal irrigation schedules.
How to cite:
Bouras, E. H., Amazirh, A., Rafi, Z., Jarlan, L., Khabba, S., Merlin, O., and Er-Raki, S.: Evapotranspiration partitioning over irrigated wheat in a semi-arid region using in-situ measurements and AquaCrop model., A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-62, https://doi.org/10.5194/egusphere-gc8-hydro-62, 2023.
Carmelo Cammalleri, Chiara Corbari, and Marco Mancini
Actual evapotranspiration (ET) is one of the key quantities of the hydrological cycle, with a central role in many applications including crop water stress assessments, analysis of green water scarcity and studies on drought conditions. Due to the sparseness of ET measurements, large scale estimates are often based on models, which outputs are usually validated only on a limited number of sites. This results in a large variety in the estimates, with differences in magnitude that can limit engineering applications based on volumes. In this study, five ET datasets are compared over Italy, with the final goal to design a strategy for a robust assessment of a combined product over the climatological reference period 1991-2020 at monthly scale and at a moderate spatial resolution (i.e., 1-km). The datasets analyzed in this study include estimates from: 1) the BIG BANG water balance project; 2) the MODIS satellite product MOD16; 3) the LSA SAF product based on Meteosat; 4) the CEMS-LISFLOOD hydrological model; and 5) the SSEBop simplified surface energy balance. Preliminary results show a good spatial coherence between all the datasets over winter (DJF) and summer (JJA) – mainly driven by the marked north-south gradients during these months – but also non negligible systematic differences in the modeled ET magnitudes. A good consistency between anomaly values is also observed for many datasets. With the aim to preserve both the inter-annual variability and the temporal consistency of the time series, a strategy based on the separation between the climatological dynamic and the monthly anomalies is proposed for the combined dataset.
How to cite:
Cammalleri, C., Corbari, C., and Mancini, M.: A comparison of multi-source actual evapotranspiration estimates to derive a combined dataset over Italy, A European vision for hydrological observations and experimentation, Naples, Italy, 12–15 Jun 2023, GC8-Hydro-70, https://doi.org/10.5194/egusphere-gc8-hydro-70, 2023.
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