Thaine H. Assumpção

Bio: Thaine H. Assumpção is an academic researcher from UNESCO-IHE Institute for Water Education. The author has contributed to research in topics: Citizen science & Crowdsourcing. The author has an hindex of 4, co-authored 8 publications receiving 152 citations.

Citizen observations contributing to flood modelling: opportunities and challenges

Abstract: . Citizen contributions to science have been successfully implemented in many fields, and water resources is one of them. Through citizens, it is possible to collect data and obtain a more integrated decision-making process. Specifically, data scarcity has always been an issue in flood modelling, which has been addressed in the last decades by remote sensing and is already being discussed in the citizen science context. With this in mind, this article aims to review the literature on the topic and analyse the opportunities and challenges that lie ahead. The literature on monitoring, mapping and modelling, was evaluated according to the flood-related variable citizens contributed to. Pros and cons of the collection/analysis methods were summarised. Then, pertinent publications were mapped into the flood modelling cycle, considering how citizen data properties (spatial and temporal coverage, uncertainty and volume) are related to its integration into modelling. It was clear that the number of studies in the area is rising. There are positive experiences reported in collection and analysis methods, for instance with velocity and land cover, and also when modelling is concerned, for example by using social media mining. However, matching the data properties necessary for each part of the modelling cycle with citizen-generated data is still challenging. Nevertheless, the concept that citizen contributions can be used for simulation and forecasting is proved and further work lies in continuing to develop and improve not only methods for collection and analysis, but certainly for integration into models as well. Finally, in view of recent automated sensors and satellite technologies, it is through studies as the ones analysed in this article that the value of citizen contributions, complementing such technologies, is demonstrated.

Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions

Abstract: Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

Abstract: Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve man...

Citizens’ Campaigns for Environmental Water Monitoring: Lessons From Field Experiments

Abstract: Advanced sensing technologies, combined with wireless sensor networks, have already demonstrated their value in monitoring urban water systems, where management is rather centralized within water utility organizations. Environmental water resources, characterized by more diverse stakeholders and overlapping management responsibilities of different agencies, present more challenging contexts for implementing novel sensing technologies. Crowdsourcing by citizens has been proposed as an alternative approach for adaptive data collection that can augment the amount of data collected, as well as bring together the diverse stakeholders and citizens in more participatory water resources management processes. This article first introduces the challenges of designing citizens' campaigns for collecting data on environmental waters. A set of developed mobile phone and web applications is then introduced, integrated within a specific platform, as it was used in the execution of citizens' campaigns for data needed in flood analysis and management. Experiences and lessons learned are presented from the field execution of citizens' campaigns in two pilot areas located in Europe - the Danube Delta in Romania, and the Kifissos catchment in Greece. Two of the campaigns are on river data collection - water levels and water velocities, and two on collecting land use/land cover data. Surveys carried out with campaign participants indicate their appreciation of the initiative, but challenges remain regarding user-friendliness of the applications. Logistic issues such as timing, duration, and pathways for data collection impacted the motivation of participants. Overall, a unique and large dataset was obtained in terms of quantitative water measurements, despite data losses due to low raw data quality. Further work lies in evaluating the usability of this dataset for local authorities.

Citizens AND HYdrology (CANDHY): on the application of a transdisciplinary framework for assessing citizen science projects addressing hydrological challenges

Abstract: <p>Earth and water monitoring and observation systems provide open geo data to scientists and professionals supporting distributed knowledge of major hydromet dynamics and extremes. Mobile technologies, at the same time, are empowering citizens who are nowadays informed and involved in volunteering actions designed and implemented to make our communities more safe and sustainable. Citizen science, as a consequence, is gaining momentum empowering the general public, from the “pleasure of doing science” to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water-related challenges. This work illustrates a conceptual transdisciplinary assessment model that was designed with the goal of standardizing the use of citizen science for advancing hydrology. This work was promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS), and that is composed by a diverse group of hydrological, computer and social science experts. A community paper (Nardi et al., in press) presented the conceptualization of this transdisciplinary framework by identifying the shared constituents, interfaces and interlinkages between hydrological sciences and other academic and non-academic disciplines. Particular emphasis was given to the integration of human sensing and behavioural mechanisms into citizen science programs addressing hydrological problems. The proposed CANDHY transdisciplinary framework is here further tested and applied to assess some selected citizen science programs to understand the knowledge gaps and opportunities arising from ongoing citizen science programs. This comparative assessment shows some interesting preliminary results demonstrating the capacity of the proposed framework in homogenizing and accumulating knowledge from the collaboration of diverse participatory programs addressing similar or complementary hydrological challenges.</p><p> </p><p>Nardi F. et al., in press. Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges. Hydrological Sciences Journal, https://doi.org/10.1080/02626667.2020.1849707</p>

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

Abstract: On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Three Years of Country-Wide Rainfall Maps from Cellular Communication Networks

Abstract: Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.

A Review of the Artificial Neural Network Models for Water Quality Prediction

Abstract: Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

Designing Participation Processes for Water Management and Beyond. Synthesis, part of a special feature on implementing participatory water management: recent advances in theory, practice and evaluation

Abstract: This article addresses the question of how to design participation processes in water management and other fields. Despite a lot of work on participation, and especially its evaluation, this question has received little attention in the research literature. However, it is important, because previous research has made it clear that participation may yield important benefits for humans and the environment but that these benefits do not occur automatically. One precondition is sound design. The design of participation processes has been addressed in detail in the so-called "craft" literature but more rarely in the scientific literature. This article helps close this gap by systematically analyzing and comparing five design guides to determine whether it is possible to combine them into a more robust guide. The article confirms that possibility and presents a preliminary outline for such a guide. Principles for participatory process orientation are presented, as well as numerous partially iterative steps. The adaptive process is laid out in a way intended to help designers determine the objectives of the participation process and the initial design context, and make preplanning choices that eventually lead to the selection of suitable participation mechanisms. There are also design tools that facilitate this work. We discuss how our findings are largely compatible with previous research on participation, notably the work on criteria for "good" or "effective" participation processes. We also argue that our article advances research on an important remaining question in the scientific literature on participation: What process should be chosen in which context? (Resume d'auteur)

Pluvial flood risk and opportunities for resilience

Abstract: Environmental Sciences Initiative, Advanced Science Research Center at the Graduate Center, City University of New York, New York, New York Julie Ann Wrigley Global Institute of Sustainability, Arizona State University, Tempe, Arizona Department of Geography, Portland State University, Portland, Oregon Arizona State University, Tempe, Arizona Department of Chemical, Biochemical, and Environmental Engineering and Center for Urban Environmental Research and Education, University of Maryland, Baltimore County, Baltimore, Maryland Cary Institute of Ecosystem Studies, Millbrook, New York Georgia State University, Atlanta, Georgia Department of Civil and Environmental Engineering, Syracuse Center of Excellence in Environmental and Energy Systems, Syracuse University, Syracuse, New York