Displays

West Africa’s economy is mainly sustained on agriculture and over 70% of crops are rain-fed. Economic growth and food security in this region is therefore highly dependent on the knowledge of rainfall patterns. According to the IPCC, the Global South will seriously suffer from climate change. As traditional rainfall patterns shift, accurate rainfall information becomes crucial for farmers to optimize food production.

The scarce rain gauge distribution and data transmission challenges make rainfall analysis difficult in these regions. Satellites could offer a solution to this problem, but present satellite products do not account for local characteristics and perform poorly in West Africa.

A rainfall retrieval algorithm, developed within the Schools and Satellites (SaS) project, could overcome the lack of ground data and good rainfall satellite products through earth observation and advanced machine learning. However, to validate such an algorithm requires a high amount of rainfall data from ground stations. Since rain gauges are scarce in West Africa, a (temporary) high density observation network is necessary to strengthen the training and validation dataset provided by TAHMO and GMet ground measurements. SaS therefore engages with schools in Northern Ghana to build a Citizen Observatory. 

SaS is being funded by the European Space Agency as one of the pilot projects of CSEOL (Citizen Science and Earth Observation Lab). It is being developed in a cooperation between TU Delft, PULSAQUA, TAHMO Ghana, Smartphones4Water (S4W) and GMet. The Proof-of-Concept Algorithm will be fed with data collected in the Citizen Observatory during the rainy season of 2020.

This Citizen Observatory will be built around the already existing infrastructure of a classroom where Climate Change is amongst the topics in the Ghanaian teaching curriculum. We aim to provide a Climate Change educational module that can be used directly by the teachers. The educational module incorporates the building of their own low-cost rain gauge to be used for manual rainfall data collection. This rainfall collection method has already been highly tested by S4W in Nepal. Students will design their own research around the daily rainfall measurements, which they will submit via a web application called Open Data Kit (ODK). The data is being validated by including a picture of the rainfall measurement that is checked with the number passed on by the citizen scientist.

The Citizen Observatory will be placed under the existing TAHMO and S4W infrastructures to respectively continue the interaction with schools and to continue data collection, -validation and -visualization. If the algorithm proofs to indeed perform better than current satellite products for the pilot area in Northern Ghana, the Citizen Observatory could in the future help to validate and improve the product for the whole of West-Africa.

To enable the use of this Citizen Observatory for management of water resources and in this case more and better rainfall data, much effort is needed. We will demonstrate which measures we have taken to ensure that the Citizen Observatory performs with enough quality, and how (if done well) it has the potential to increase the impact of this study.

How to cite: de Vries, S. and Estebanez Camarena, M.: A Citizen Observatory at schools to train a rainfall retrieval algorithm based on Earth Observation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22111, https://doi.org/10.5194/egusphere-egu2020-22111, 2020.

One possibility to overcome the lack of data in hydrology is to engage the public in hydrological observations. Citizen science projects are potentially useful to complement existing observation networks to obtain spatially distributed streamflow data. Projects, such as CrowdHydrology, have demonstrated that it is possible to engage the public in contributing hydrological observations. However, hydrological citizen science projects have, so far, been based on the use of different kinds of instruments or installations. For stream level observations, this is usually a staff gauge. While it may be relatively easy to install a staff gauge at a few river sites, the need for a physical installation makes it difficult to scale this type of citizen science approach to a large number of sites because these gauges cannot be installed everywhere or by everyone. Here, we present the CrowdWater smartphone app that allows the collection of hydrological data everywhere without any physical installation or specialized instruments. The approach is similar to geocaching, with the difference that instead of finding treasures, hydrological measurement sites can be set up by anyone at any location and these sites can be found by the initiator or other citizen scientists to take additional measurements at a later time. This way time series of observations can be collected. For stream levels, a virtual staff gauge approach is used: a picture of a staff gauge is digitally inserted into a photo of a stream bank or a bridge pillar, and the stream level during a subsequent field visit to that site is compared to the staff gauge on the first picture. For intermittent streams, soil moisture and plastic pollution, qualitative scales are used to enable citizens to report their observations. Participants have already contributed more than 10 000 observations. In this pico-presentation, we report on our experiences after about four years with the CrowdWater project and discuss the use of the app by citizen observers, methods to ensure data quality, and illustrate how these data can be used in hydrological model calibration.

How to cite: Seibert, J., Etter, S., Strobl, B., and van Meerveld, I.: Citizen observers in hydrology – experiences from CrowdWater, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12793, https://doi.org/10.5194/egusphere-egu2020-12793, 2020.

The Open Hydrosystem Information Network (OpenHi.net) is a state-of-the-art information infrastructure for the collection, management and free dissemination of hydrological and environmental information related to Greece’s surface water resources. It was launched two years ago as part of a the national research infrastructure “Hellenic Integrated Marine Inland water Observing, Forecasting and offshore Technology System” (HIMIOFoTS), which also comprises a marine-related component (https://www.himiofots.gr/). The OpenHi.net system receives and processes real-time data from automatic telemetric stations that are connected to a common web environment (https://openhi.net/). In particular, for each monitoring site it accommodates stage measurements, raw and automatically post-processed. Furthermore, in some specially selected sites time series related to water quality characteristics (pH, water temperature, salinity, DO, electrical conductivity) are provided. The web platform also offers automatically-processed information in terms of discharge data, statistics, and graphs, alerts for extreme events, as well as geographical data associated with surface water bodies. At the present time, the network comprises about 20 stations. However, their number is continuously increasing, due to the open access policy of the system (the platform is fully accessible to third-parties uploading their data). In the long run, it is envisioned that a national-scale hydrometric infrastructure will be established, covering all important rivers, lakes and reservoirs of the country.

How to cite: Efstratiadis, A., Mamassis, N., Koukouvinos, A., Koutsoyiannis, D., Mazi, K., Koussis, A., Lykoudis, S., Dimitriou, E., Malamos, N., Christofides, A., and Kalogeras, D.: Open Hydrosystem Information Network: Greece’s new research infrastructure for water, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4164, https://doi.org/10.5194/egusphere-egu2020-4164, 2020.

Water and environmental monitoring, observation and decision support systems (DSS) are being transformed by a wealth of open and big data that are increasingly available, accurate and timely. Consolidated technologies of earth observation, remote sensing, geospatial modelling and visualization systems are stimulating earth, hydrological and environmental sciences that are reacting not only with increasing scientific production, but with novel solutions-oriented methods, tools and algorithms. Procedures, methods and tools are more and more available for analysis, interpretation and mapping of river and basin coastal landscape features and hydro-environmental dynamics. Citizen science are further empowering the capabilities of DSS by gathering and sharing data on the human behaviour component to better understand the nature-human-urban interplay. Citizens, empowered by mobile devices, act as data and information producers, receivers and transmitters supporting the assessment of the effects of human-derived observations, feedbacks and actions sensing. Emerging hardware and software technologies (e.g. machine learning, artificial intelligence, IoT, etc.) are creating amazing opportunities for these DSS linked to the development of the human-machine interface and its use for promoting practical environmental and social actions to manage and mitigate natural hazard and climatic risks. The National System for Environmental Protection (SNPA) by the Italian Institute for Environmental Protection and Research (ISPRA) is supporting and implementing a wide and diverse range of research, applied research, learning and communication activities, both at the national and international level, in collaborating with leading academic, professional and international organizations, for integrating citizen science, open data and big data into next generation water and environmental decision support systems. This contribution, while depicting the overall SINA framework (Italian Environmental Information System) and ongoing and planned activities by ISPRA SNPA and SINA, presents recent outcomes of research initiatives developed within the Water JPI, UNEP INFORAC, National Plan for Climate Adaptation (PNACC), Marine pollution, Biodiversity, the Water, Food and Energy Nexus among others. Insights from joint research efforts and working groups are presented and shared while pursuing further synergies and stimulate the discussion on this crucial topic for national and international agencies, like ISPRA, that seek to transfer research data, models and tools into institutional and operational activities.

How to cite: Monacelli, G., Cipolloni, C., Babbini, L., Sole, M. C., Lotti, A., Annis, A., Spasiano, A., and Nardi, F.: Integrating citizen science, open and big data into water and environmental decision support systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21928, https://doi.org/10.5194/egusphere-egu2020-21928, 2020.

The Oceans cover 70% of the surface of our planet and contain 99% of the living space on the planet. Surveying the blue planet  is a very demanding and expensive activity since requires large infrastructures and trained personnels. Research  institutions, on the contrary, have very limited funding to perform their studies  so that the seas remain, still, mostly unexplored. This urges for  a bold step towards a new paradigm for marine data acquisition. MaDCrow (Marine Data Crowdsourcing) is a marine technology research and development project co-funded by the European Regional Development Fund (ERDF), aiming  to create an innovative technological infrastructure for the acquisition, integration and dissemination of data on the marine ecosystem. This is coupled with the goal to increase  public awareness of environmental issues and in particular of climate changes as drawn within goal 13.1 of the UN Sustainable Development Goals. MaDCrow sensors acquire Temperature, Salinity, pH and Oxygen data in real time. These are placed in ad hoc housing  that can be installed  on citizen’s  vessels . Data acquired are transmitted onshore, stored, processed and integrated with other information sources in order to provide end-users with an App- or web-site-based  clear picture of the status of the marine environment to address relevant social questions (e.g.: where is a good place to swim?; is there an oil spill?; are the seawater conditions good for aquaculture and fishery?) The main idea behind the project is to bridge the gaps among three actors who are mutually interdependent, namely: (I) Researchers, (II) Policy makers and (III) and the Citizens.

From the point of view of the scientific community, data acquisition by volunteers is a mechanism that has many advantages. It keeps costs low while at the same time generates large quantities of information. We will discuss the pros and the cons of MaDCrow approach and the future development of this multi-stakeholder initiative.

How to cite: Diviacco, P., Nadali, A., Malfatti, F., Iurcev, M., Carbajales, R., Busato, A., Pavan, A., Grio, L., and Nolich, M.: Citizen science and crowdsourcing in the field of marine scientific research – the MaDCrow project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9030, https://doi.org/10.5194/egusphere-egu2020-9030, 2020.

Streamflow is the fundamental variable for any hydro-informatics based decision making to manage catchment-scale water resources. However, with the significant reduction in the number of streamflow gauging stations in many world-rivers, emphasis has now been shifted toward obtaining river discharges along the ungauged / scantily-gauged river reaches using innovative hydroinformatics tools. Many rivers which were gauged in the past, are now ungauged. In this context, this study considers a typical real-river, namely, the 48 km Bolani-Gomlai reach of the Brahmani River in eastern India, where a few historical concurrent streamflow hydrographs are available at the upstream and downstream gauging stations, which are defunct at present. Therefore, the main focus of this study is to generate spatially distributed high-frequent daily-scale river discharges along the selected ungauged river reach using the real-time optical remote sensing (RS) based imageries. To achieve this objective, the MIKE11 hydrodynamic (HD) model is setup and used in the selected reach to route the past streamflow records, available at the upstream section, so as to obtain the corresponding spatially distributed past discharges at 1 km resolution downstream. These routed historical streamflow records at each 1 km interval form the observed flow database for that specific RS-based virtual streamflow measurement station (VMS). For establishing the VMSs at each 1 km interval to estimate daily-scale river discharges, an RS-based methodology has been advocated that uses the spectral reflectances of the fused MODIS and Landsat satellite imageries and the MIKE11-HD derived corresponding routed past streamflows for calibration and validation. The different spectral behavior of land (C) and water (W) pixels in the near infrared of the electromagnetic spectrum is exploited by computing the (C/W) ratio of the fused imageries between two pixels located within (W) and outside (C), but close to the river. The values of C/W increase with the presence of water and, hence, with discharge. Moreover, in order to reduce the noise effect, an exponential smoothening filter is applied to obtain C/W_*. Finally, the real-time filtered pixel ratios are used in the RS-based framework to estimate recent high-frequent streamflows in the ungauged river reach. The results reveal that the developed model has a very good potential which can be extended for high-frequent discharge estimation at any ungauged world-river reaches.

How to cite: Sahoo, B., Sahoo, D. P., and Tiwari, M. K.: A Hydrodynamics and Remote Sensing based Framework for Establishing Virtual Streamflow Measurement Stations in Scantily-gauged River Reaches, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18311, https://doi.org/10.5194/egusphere-egu2020-18311, 2020.

Remote sensing and crowdsourcing data are new sensing methods that have the potential to improve significantly inundation modelling. That is especially true in data-scarce situations, for example when resources for acquiring sufficient traditional data are limited or when field conditions are not favourable. Crowdsourced water depths and velocities have been demonstrated to be useful for improving inundation models, ranging from the calibration of 1D models to data assimilation in 2D models. In this study, we aim to further evaluate how much the amount and type of crowdsourced data influence model calibration and validation, in comparison with data from traditional measurements. Further, we aim to assess the effects of combining both sources. For that, we developed a 2D inundation model of the Sontea-Fortuna area, a part of the Danube Delta in Romania. This is a wetland area, where data was collected during two 4-day field campaigns, using boat navigation together with the involved citizens. Citizens obtained thousands of images and videos that were converted into water depth and velocity data, while technicians collected ADCP data. We calibrated and validated the model using different combinations of data (e.g. all water depth data, half water depth and half water velocity). Results indicated that velocity data by themselves did not yield good calibration results, being better used in conjunction with water depths or by combining them into discharge. We also observed that calibration by crowdsourced water depths is comparable to the use of water depths from traditional measurements.

How to cite: Popescu, I., Assumpção, T. H., Jonoski, A., and Solomatine, D. P.: Calibrating and validating an inundation model with and without crowdsourced water depths and velocities, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18321, https://doi.org/10.5194/egusphere-egu2020-18321, 2020.

Measuring discharge in a river system involves tedious procedures. Over the last few decades, innovations in technology has improved discharge estimation methods in rivers due to the use of improved velocity measurement equipment, such as the ADCP and thereby easing   and improving the discharge estimation at the river sites. However, adopting these technologies prove to be expensive for its use in very many sites. To overcome this expensive approach of river discharge estimation, a new approach of discharge estimation based on the use of hydrodynamic principle to estimate discharge using only the observed stage data at a river site is proposed in this study.  This method is derived in this study using the Approximate Convection-Diffusion equation combined with the Diffusive Wave model. This method can be considered as a more generalized approach ideally suited for field applications.  Due to its simplicity, easy applicability, versatility and accuracy of estimating discharge for a wide range of roughness and channel bed slope conditions, this method can be considered ideal for field applications. Moreover, the method can be applied to estimate discharge in a channel characterized by varying cross-section or the roughness parameter along the river reach The proposed method has been tested for a number of hypothetical flood scenarios in hypothetical channels. Further its accuracy, and its applicability has been evaluated using the well-established evaluation criteria. After its evaluation using hypothetical flood scenarios in hypothetical channels, the field applicability of the method is evaluated by applying the method for the real data to estimate discharge using only the observed stage data at the desired site. The limitation of the method arises due to uncertainty of the used Manning’s roughness coefficient(s) at the desired station and, therefore, to avoid this problem it may be prudent to carry out the sporadic velocity measurements at the desired river site during the passage of a flood wave, for the confident use of the proposed model or formula for estimating discharge using only the stage data.

How to cite: Perumal, M. and Adhikari, K.: Approximate Convection-Diffusion Wave Application to Compute Discharge Using only Stage Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21198, https://doi.org/10.5194/egusphere-egu2020-21198, 2020.

According to European approach (art. 9 WFD 2000/60/EC), the collection of data relating to the quantification of water abstraction represents an important phase to promote efficiency in the use of water resources. Through the collection and subsequent study of the data provided, in fact, it is possible to apply a water pricing policy, based on the volumes currently used, to cope environmental sustainability and agricultural resilience to climate change, in a context of water scarcity. 
In Italy, for agricultural sector, guidelines to collect and monitoring data are in force at national scale and detailed methodologies are applied at regional scale. A WebGIS platform called SIGRIAN (National Information System for Water Management in Agriculture (https://sigrian.crea.gov.it/sigrian/)), managed by CREA- Research Centre for Agricultural Policies and Bio-economy and realized in collaboration with Italian Regions, is adopted as national reference database for the collection and share of data resulting from the monitoring of water volumes for irrigation. 
SIGRIAN also fits in the logic of Integrated Water Management (IWRM) approach. In order to coordinate the development and management of water and related resources, this platform is setup to link itself with Google satellites and Copernicus programme in order to obtain and process satellite information and earth observation data. In addition, SIGRIAN website (https://sigrian.crea.gov.it/index.php/cosesigrian/) provides an OPEN DATA section, ( in this section is possible to use a WMS (Web Map Service) Enquiry service and a WFS (Web Feature Service) Service, both related to the borders of authorities irrigation. 
All data collected and monitored in this system are useful to support planning, programming and management processes of policy making and enforcement, such as CAP common indicators, water pricing based on water uses, monitoring and evaluation of investment programs, support economic analysis for Agricultural sector in the context of the Water framework directive.
Otherwise, SIGRIAN data can be useful to support the definition and application of Sustainability standards related to water use in agriculture through defining reference parameters for territories that uses water for irrigation in a sustainable way and in a multidisciplinary approach.
Keyword: SIGRIAN, irrigation volumes, sustainability standards, open data, monitoring, resilience.

How to cite: zucaro, R., manganiello, V., and ferrigno, M.: Monitoring and data collection of agricultural water uses in Italy to face water scarcity: approach and tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21777, https://doi.org/10.5194/egusphere-egu2020-21777, 2020.

The post-event understanding and reconstruction of flooding dynamics and impacts is not trivial task. This is especially critical for ungauged basins, lacking river flow monitoring networks, that are characterized by inundation dynamics falling outside specifications of Earth Observation (EO) and large-scale disaster emergency management systems. Satellite data provide, in fact, radar and multi-spectral images supporting inundation extent rapid mapping, but the coverage and quality of EO-based flood mapping is often not adequate for effective riverine inundation assessments in many complex urban, coastal and rural ecosystems where important flood damages occur. Rainfall-driven flash flooding occurring in most cities provide further cases with temporal (e.g. fast hydrologic response to extreme rainfall events) and spatial scales (e.g. floodplain landscape feature complex morphologies; urban micro-features like walls and culverts) determining an increased level of un-observed and uncertain, yet crucial, flood modelling variables. As a result, while technological progresses of remote sensing and flood modelling data and tools create advanced opportunities and support numerical simulations for real time or post event analysis, it is still often challenging to understand, reconstruct and accurately simulate flooding dynamics and related effects.  Distributed and timely flood event observations are always taken by citizens fostering new means for real time or post event analysis of extreme events. This wealth of “new data”, namely Volunteer Geographic Information (VGI) or crowdsourced data, are surely a value for flood risk management, but several and diverse technical, administrative and procedural barriers are impacting their uptake. This work illustrates preliminary tests developed in using crowdsourced data for post-event simulation of flooding impacts in ungauged basins. Videos and images from social networks are used for calibrating both a detailed 2D hydraulic model and a cost-effective geomorphic floodplain extent rapid mapping algorithm to investigate on novel procedures, methods and tools of post-event flood hazard assessment and impact mapping.

How to cite: Castelli, F., Annis, A., and Nardi, F.: Testing the use of crowdsourced data for supporting post-event understanding and simulation of flooding impacts in ungauged basins, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19858, https://doi.org/10.5194/egusphere-egu2020-19858, 2020.