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Showing papers in "Journal of Contingencies and Crisis Management in 2020"


Journal ArticleDOI
TL;DR: The resilience of small businesses and how that relates to community resilience, especially in rural communities has remained an under-researched aspect of community resilience as mentioned in this paper, and this exploratory study aims to understand the relationship between business resilience and community resilience.
Abstract: The resilience of small businesses and how that relates to community resilience, especially in rural communities has remained an under‐researched aspect of community resilience. This exploratory study aims to understand the relationship between business resilience and community resilience in rural communities. Firstly, the research seeks to understand the role that small businesses play in contributing to community resilience activities. The paper then sheds light on enabling and challenging factors that shape how small businesses prepare for and respond to weather‐related emergencies through the lens of flooding. Data were collected through in‐depth semi‐structured interviews and surveys with rural small businesses in Scotland. The analysis of the data suggests that businesses play an advisory and advocacy roles, make financial and material contributions to local community resilience activities and contribute to quick community recovery through various dimensions of corporate social responsibility activities. However, small businesses face formidable barriers and challenges in preparing for and responding to weather‐related emergencies that undermine their resilience to natural hazards. The paper suggests ways in which small businesses can enhance their resilience to natural hazards, while at the same time contributing to community resilience.

45 citations


Journal ArticleDOI
TL;DR: The unique challenges of university leadership when planning and aligning communications and activities before, during, and after crises are explored and a holistic crisis management system for university leadership to implement that addresses planning, response, and recovery is recommended.
Abstract: This paper explores the unique challenges of university leadership when planning and aligning communications and activities before, during, and after crises. We examined the three stages of a crisis and the unpredictable nature of large‐scale crises, including natural or man‐made disasters, that threaten people and structures. Compiled data exposed the frequency and complexity of critical events emphasizing the urgent need for attention and action. Case studies informed primarily by the literature, and a personal interview with a crisis strategist, detail specific alignment challenges and recommend a holistic crisis management system for university leadership to implement that addresses planning, response, and recovery. Consequences of misalignment include loss of message control, rumourmongering, prolonged disruption of operations, and the likelihood of reputational damage.

32 citations


Journal ArticleDOI
TL;DR: In this article, the authors describe the missteps and outline strategies that response organizations can take to improve communication for vulnerable groups in emergency risk communication is a crucial part responding to crises.
Abstract: Emergency risk communication is a crucial part responding to crises Right now, vulnerable groups are experiencing disproportionately negative outcomes from communication missteps surrounding COVID‐19 In this commentary, we describe the missteps and outline strategies that response organizations can take to improve communication for vulnerable groups

32 citations



Journal ArticleDOI
TL;DR: In this article, the authors explore the associations between perceived gratifications and protection motivations of using mental health chatbot services, active communicative action, and online and offline engagement behaviours of solving mental health problems after disasters.
Abstract: Chatbots are gaining their popularity in society and have triggered heated discussions in academia as well. Currently, few studies explored the applications of AI‐powered mental health chatbots in a mass‐shooting disaster context. Via integrating literature from multi‐disciplines such as crisis management, mental health and digital communication, this quantitative study intends to contribute to close this gap and explore the associations between perceived gratifications and protection motivations of using mental health chatbot services, active communicative action, and online and offline engagement behaviours of solving mental health problems after disasters. This study surveyed 1,114 US participants who ever used chatbot services from top healthcare companies. Implications of the results enhance theoretical discussions on how artificial intelligence has shaped individuals’ motivations, communicative action and engagement behaviour to treat mental health problems. This study also benefits professionals who want to learn more about chatbots for mental healthcare, crisis management and customer engagement.

30 citations


Journal ArticleDOI
TL;DR: In this paper, the authors identify enabling strategies, impeding factors and trade-offs in the implementation of urban resilience, and find that transparent, inclusive and supportive governance reduces the risk of negative impact that resilience implementation will have on communities.
Abstract: Despite growing interest in urban resilience, there is a significant gap between discourse and the capacity to develop resilience in practice. This scoping review assembles and shares evidence and insights from empirical studies of attempts to implement urban resilience published between 2005 and 2017. More precisely, it seeks to identify enabling strategies, impeding factors and trade‐offs in the implementation of urban resilience. Findings are presented along the dimensions of urban resilience detailed in the City Resilience Framework (ARUP/Rockefeller Foundation): Health and Wellbeing, Economy and Society, Infrastructure and Environment, and Leadership and Strategy (which we present as a cross‐cutting theme). While some enabling and impeding factors in implementation are associated with a specific dimension, others are common to all three. Across dimensions, we find that transparent, inclusive and supportive governance reduces the risk of negative impact that resilience implementation will have on communities. Conflicting priorities of managing risk and meeting short‐term needs are found to diminish the potential for transformative resilience action. Integrating risk into planning appears as a promising strategy in all dimensions of resilience. Trade‐offs are found in resilience implementation, and range from adverse effects associated with infrastructure to power imbalances when the power to implement resilience privileges one system level over another.

26 citations



Journal ArticleDOI
TL;DR: This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively and experimental results obtained with a data set acquired during hurricane Florence demonstrate the effectiveness of the applied methods.
Abstract: Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster‐ and event‐related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state‐of‐the‐art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.

20 citations


Journal ArticleDOI
TL;DR: The authors argue that crisis teams tend to rely on procedures and responsibilities but largely underestimate the importance of the mindset of individual participants, and posit that crisis management should be regarded as an elite sport and adopt insights from that field accordingly.
Abstract: COVID-19 has spread around the world, and crisis teams are working full steam ahead to address its challenges. We argue that crisis teams tend to rely on procedures and responsibilities but largely underestimate the importance of the mindset of individual participants. We posit that crisis management should be regarded as an elite sport and adopt insights from that field accordingly.

19 citations


Journal ArticleDOI
TL;DR: The critical decision method was used to explore a series of decision points that were made for a low probability yet high consequence decision that was made by the commander of the Australian Urban Search and Rescue team deployed to Fukushima in 2011.
Abstract: This paper provides an insight into the complexities of decision‐making during an unprecedented disaster. We used the critical decision method to explore a series of decision points that were made for a low probability yet high consequence decision that was made by the commander of the Australian Urban Search and Rescue team deployed to Fukushima in 2011. The findings identified that in a situation with no similarities to previous experiences, the commander used a process of anomaly detection to trigger a situational assessment, following this with mental simulation and consultation of his actions. In this unparalleled case study, hazard‐specific expertise also supported the decision‐making process. The paper offers practitioners and academia an example of high consequence decision‐making in a unique situation as well as the opportunity to reflect on the models of decision‐making previously identified as useful in these operational environments.

17 citations


Journal ArticleDOI
TL;DR: A case study using Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage in the state and widespread evacuations of residents, identifies the role of Twitter during the damage containment stage of the flood.
Abstract: Combining machine learning with social network analysis (SNA) can leverage vast amounts of social media data to better respond to crises. We present a case study using Twitter data from the March 2019 Nebraska floods in the United States, which caused over $1 billion in damage in the state and widespread evacuations of residents. We use a subset of machine learning, deep learning (DL), to classify text content of 11,982 tweets, and we integrate that with SNA to understand the structure of tweet interactions. Our DL approach pre‐trains our model with a DL language technique, BERT, and then trains the model using the standard training dataset to sort a dataset of tweets into classes tailored to crisis events. Several performance measures demonstrate that our two‐tiered trained model improves domain adaptation and generalization across different extreme weather event types. This approach identifies the role of Twitter during the damage containment stage of the flood. Our SNA identifies accounts that function as primary sources of information on Twitter. Together, these two approaches help crisis managers filter large volumes of data and overcome challenges faced by simple statistical models and other computational techniques to provide useful information during crises like flooding.

Journal ArticleDOI
TL;DR: In this article, the authors identify three principles through which actors in a transboundary crisis can balance centralization with autonomy while shaping coordination along the way, and propose improvements for progressing current crisis management efforts.
Abstract: Boin (2019) argues that in transboundary crisis management it is almost impossible to achieve centralization and coordination. This article identifies three principles through which actors in a transboundary crisis can balance centralization with autonomy while shaping coordination along the way. We reanalysed three transboundary cases: the Dutch military mission in Afghanistan, the downing of MH17 and hurricane Irma striking Sint-Maarten. The principles we found are as follows: (a) reformulating key strategic priorities, (b) flexible adaptation of crisis management protocols and (c) the emergence of multifunctional units. With these three principles, we reflect on challenges in the Dutch crisis response to the corona outbreak and propose improvements for progressing current crisis management efforts.

Journal ArticleDOI
TL;DR: In this paper, the effects of warning type on behaviour can be studied on the basis of experimental (i.e. scenario‐based) research designs. But, it can be questioned whether scenario-based experiments are the appropriate methodology to test different warnings, for instance due to the absence of feelings catalysed by real events.
Abstract: The sharp increase in the use of smartphones and rapid advances in mobile communication offer new ways to warn the public about developing natural or technological hazards. So far, the effectiveness of different warning types, namely standard and impact‐based warnings (SW and IBW), were mainly tested in scenario‐based experiments and not in real life. However, it can be questioned whether scenario‐based experiments are the appropriate methodology to test different warnings, for instance due to the absence of feelings catalysed by real events. Therefore, we collected information about warning responses in real time via the smartphone application ‘Wetter‐Alarm’ of a Swiss weather provider. In the first phase of the study, we conducted a field experiment to investigate actual responses for SW and IBW. In the second phase, we compared these results with behavioural intentions collected via a scenario‐based experiment with an almost identical set‐up. The comparison shows that warning messages were perceived and understood very similar in both experiments. Also, we did not observe any significant interactions between warning type (SW vs. IBW) and study (field vs. scenario‐based experiment). These findings indicate that the effects of warning type on behaviour can be studied on the basis of experimental (i.e. scenario‐based) research designs. The paper ends with some reflections on the potential of big data in the social sciences and on a research agenda for real‐time data collection to improve warning effectiveness and, ultimately, climate risk management.


Journal ArticleDOI
TL;DR: In this paper, the nature of wildfire in the context of climate change and its framing effects on policy and public opinion are discussed. But the focus of media framing of wildfire has mainly been concerned with the nature and nature of the wildfire.
Abstract: Previous research on media framing of wildfire has chiefly been concerned with the nature of wildfire in the context of climate change and with framing effects on policy and public opinion. Empiric ...


Journal ArticleDOI
TL;DR: The work of enquiry commissions exhibits tensions that condition the subsequent opportunities of government to learn from crisis events as mentioned in this paper, and the lessons drawn by the enquiry commission investigating the attacks were shaped by the commission's dual function, by the dominant professional perspectives within the group, and by the specific models of decision-making and assessment standards that the commission adopted.
Abstract: The literature on crisis learning has thus far paid little attention to the institutional channels through which governments draw lessons from crisis events. This paper examines theoretically and empirically a key institutional site for crisis learning: enquiry commissions. The theoretical argument is illustrated by analysing the enquiry commission that examined the 2011 terrorist attacks in Norway. The paper argues that the work of enquiry commissions exhibits tensions that condition the subsequent opportunities of government to learn from crisis. The paper shows how the lessons drawn by the commission investigating the attacks were shaped by the commission's dual function, by the dominant professional perspectives within the group, and by the specific models of decision‐making and assessment standards that the commission adopted.

Journal ArticleDOI
TL;DR: This paper attempts to provide a CDROntology system built by concepts and relations, and make full use of the historical cases to drive the modelling of the target CDR with case‐based reasoning.
Abstract: With the acceleration of urbanization, cascading disaster risks (CDR) as a typical risk mode have become the main threat to cities. After experiencing several cascading disasters, such as typhoon Lekima, how to clarify the basic features of CDR and achieve risk modelling has turned to be increasingly significant for building resilient city. However, the complexity of CDR brings about the difficulty to quickly map such risk mode depending entirely on expertise. Therefore, this paper attempts to provide a CDROntology system built by concepts and relations, and make full use of the historical cases to drive the modelling of the target CDR with case‐based reasoning. Firstly, we describe the basic structure and content of CDR and give a three‐level CDROntology system with the explanation of modelling primitives. Then, taking CDROntology system as the basis, a case‐driven selection process is proposed to provide the modelling source for the target CDR. Furthermore, set covering and manual correction methods are adopted to model the evolutionary risk chain and the specific risk scenario of the target case. Finally, a case study is given to illustrate the use of CDROntology system and case‐driven method for building a predictive risk model in typhoon‐triggered cascading disasters.

Journal ArticleDOI
TL;DR: An automatic approach to comprehensive risk assessment is proposed that leverages on a semantic and spatiotemporal representation of knowledge of the urban area and relies on a software system including: a knowledge base; two components for quantitative and qualitative risk assessments, respectively; and a WebGIS interface.
Abstract: Risk assessment of urban areas aims at limiting the impact of harmful events by increasing awareness of their possible consequences. Qualitative risk assessment allows to figure out possible risk situations and to prioritize them, whereas quantitative risk assessment is devoted to measuring risks from data, in order to improve preparedness in case of crisis situations. We propose an automatic approach to comprehensive risk assessment. This leverages on a semantic and spatiotemporal representation of knowledge of the urban area and relies on a software system including: a knowledge base; two components for quantitative and qualitative risk assessments, respectively; and a WebGIS interface. The knowledge base consists of the TERMINUS domain ontology, to represent urban knowledge, and of a geo‐referenced database, including geographical, environmental and urban data as well as temporal data related to the levels of operation of city services. CIPcast DSS is the component devoted to quantitative risk assessment, and WS‐CREAM is the component supporting qualitative risk assessment based on computational creativity techniques. Two case studies concerning the city of Rome (Italy) show how this approach can be used in a real scenario for crisis preparedness. Finally, we discuss issues related to plausibility of risks and objectivity of their assessment.

Journal ArticleDOI
TL;DR: This paper explored the potential of verb tracking on social media to examine how linguistic categories can elucidate the intentional and/or unintentional communication of crisis attribution frames, finding that the level of linguistic abstraction reflected perceived attribution of responsibility through stability, locus and controllability.
Abstract: By applying the Linguistic Category Model (LCM) in crisis communication, this study explores the potential of verb tracking on social media to examine how linguistic categories can elucidate the intentional and/or unintentional communication of crisis attribution frames. Through a content analysis, linguistic categories used in both media posts reporting three clusters of crisis and public comments on Facebook were examined. Results indicated that linguistic abstraction in both media post and public comments describing the crisis varied based on crisis cluster, suggesting that the level of linguistic abstraction reflected perceived attribution of responsibility through stability, locus and controllability. Language used to describe preventable crisis tend to be more abstract than those used to describe accidental and victim crisis. Findings of this study empirically tested the integration of LCM in crisis communication and implied potential application of LCM in building automated environmental scanning and crisis prediction systems.

Journal ArticleDOI
TL;DR: In this paper, a data justice lens is used to analyse two locally-led civic technology projects in post-earthquake Nepal, and it is shown how such projects influence social justice outcomes.
Abstract: As disasters are becoming increasingly datafied, social justice in the context of disasters is increasingly bound up with data. A data justice lens reveals how data projects and social justice interlink. This paper approaches social injustice in the context of disasters as structural inequalities in terms of resilience and risk. The first refers to people's ability to prevent, prepare for, respond to and recover from disasters, and the second to the probability that people will be exposed to hazards to which they are vulnerable. A data justice lens draws attention to the ways in which these structural inequalities shape humanitarian data projects. It also shows how such projects influence social justice outcomes. This paper shows how this lens can be applied to concrete disaster settings and the insights this yields for designers and project managers of data projects. This paper uses a qualitative case study approach to analyse two locally led civic technology projects in post-earthquake Nepal. These progressive initiatives sought to give disaster-affected people a role and a voice in the disaster response—and made a valuable contribution to response and recovery efforts. However, as they were rolled out in a context marked by stark social and digital inequalities, they still ended up primarily benefitting those who were relatively more resilient and less at risk. This paper explains why this happened and concludes by recommending critical and strategic collaboration with local progressive digital elites towards greater data justice in disaster settings.


Journal ArticleDOI
TL;DR: In this article, the authors explored the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015 in order to examine social implications of news media's roles during rumour propagation.
Abstract: Purpose: This paper explores the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015 in order to examine social implications of news media's roles during rumour propagation. There was an alarming level of public fear during the disease outbreak due toan information crisis, resulted by the government's holdback of vital information and the widespread MERS rumours on social media. By paying attention to news coverage patterns of rumours and comparing them across the outbreak period, the paper examines the following research questions: (a) Under what media frames were the MERS rumours reported by the online news? (b) Which media frame did the online news use most frequently? (c) How did the media frames change during and after the information vacuum?. Methods: Content analysis of news articles that covered MERS rumours during the outbreak has been conducted. Inductive open coding has been performed to investigate what reactions and media frames the news coverages have demonstrated to report the rumour propagation. Sample: The article samples were retrieved for big data analysis from the Big Kinds or the Korea Integrated News Database System (www.bigkinds.or.kr), by using the search terms, “MERS” and “SNS (Social Network Services).” A total of 142 articles have been sampled. Results: The paper found 7 reaction variables and categorized them into 2 risk-reporting media frames: risk-alarming frame(Anxiety, Criticisms and Damage) and risk-mitigating frame (Government, Correction, Remedies and Causes). The paper discovered that anxiety was the most frequently observed reaction variable across all phases. The paper also concluded that there has been a decrease in risk-alarming media frames and an increase in attempts to analyze causes for the rumour propagation (Causes), as the outbreak proceeds to the second phase, when the information vacuum finally ended. Conclusion: By exploring a disease outbreak in which ineffective risk management and absence of official information caused significant problems, the paper underlines the need for systematic risk communication measures, endorsed by effective collaboration among political leadership, media and the public.

Journal ArticleDOI
TL;DR: In this article, the authors studied the differences in public, private and nonprofit employees' perceptions of the kinds of preparedness measures their organizations adopt and the factors motivating these organizations to adopt different kinds of measures.
Abstract: Few studies have sought to understand the different kinds of preparedness measures public, private and nonprofit organizations adopt and the factors motivating these organizations to adopt different kinds of preparedness measures. The present study addresses these gaps in research using perceptions from 1,960 public, private and nonprofit employees. Results indicate significant variations in public, private and nonprofit employees' perceptions of the kinds of preparedness measures their organizations adopt. Findings also suggest there are variations in the factors motivating public, private and nonprofit organizations to adopt different kinds of preparedness measures. The results provide important insights to emergency managers aiming to increase the levels of disaster preparedness among organizations within their communities.

Journal ArticleDOI
TL;DR: A model‐based AI framework for describing collaborative situations and the associated formal metamodel dedicated to be instantiated to characterize collaborative situations in a very wide range of application domains is presented.
Abstract: Identifying, designing, deploying and maintaining accurate collaborative networks of organizations (e.g. responders in a crisis situation) are key activities in nowadays ecosystems. However, there is a lack regarding formal approaches dedicated to characterize collaborative networks of organizations. Formal descriptions of collaborative situations, that could be used, transformed, computed and exploited would be of great benefit for the quality of such collaborative networks. This article presents a model‐based AI framework for describing collaborative situations and the associated formal metamodel dedicated to be instantiated to characterize collaborative situations in a very wide range of application domains. This metamodel (describing collaborative situation between organizations) is structured according to four complementary dimensions: the context (social, physical and geographical environment), the partners (the involved organizations, their capabilities resources and relations), the objectives (the aims of the network, the goals to be the achieved and the risks to avoid, etc.) and the behaviour (the collaborative processes to be implemented by the partners to achieve the objectives in the considered context). Besides, this metamodel can be extended for some precise application domains. This article focuses on this mechanism in the specific context of crisis management.

Journal ArticleDOI
TL;DR: In this article, the authors identify the inhibitors of risk information sharing in a supply chain by using practical-side evidence and demonstrate possible solutions for reluctance in risk sharing among supply chain partners based on managers' experiences.
Abstract: One of the greatest threats faced by organizations is disruption in the supply chain arising from not sharing risk information among the supply chain partners. The aim of this study is to identify the inhibitors of risk information sharing in a supply chain by using practical‐side evidence. An exploratory multiple case design was utilized to investigate why supply chain partners in Turkey may be reluctant to share risk information among their members and provide solutions to overcome these barriers. The results of the study indicate that the inhibitors of risk information sharing fall into three categories: risk‐related, organization‐related, and management‐related. Furthermore, the findings demonstrate possible solutions for reluctance in risk information sharing among supply chain partners based on managers' experiences.

Journal ArticleDOI
TL;DR: Rescue MODES is developed and implemented, a communication system oriented to support situation awareness amongst French emergency actors in rescue operations, based on an application ontology.
Abstract: Efficient rescue operations require a high level of situation awareness amongst decision‐makers and first responders for the purpose of achieving operations successfully and reducing losses. Moreover, a common operational picture between involved actors is required in order to support decision‐making. Therefore, different organisations and agencies have to collaborate, cooperate, and coordinate their actions with each other. Hence, effective interactions and communications between participants are vital to fulfil these essential needs. However, emergency actors still lack backing to exchange information effectively and ensure a common operational picture in order to reach shared situational awareness. For this reason, we aim to develop and implement Rescue MODES, a communication system oriented to support situation awareness amongst French emergency actors in rescue operations. In this paper, we examine and analyse actors’ activities and interactions, so that the system will be based on the real needs of actors. We start by studying and modelling the communications, interactions, and information flow. This modelling is based on an application ontology. Then, we define requirements for good communication in these operations and present some existing systems in France and how each system responds to these requirements.

Journal ArticleDOI
TL;DR: This work proposes an ontology‐based messaging service on the basis of the Emergency Data Exchange Language (EDXL) standards that will resolve inconsistencies and enhance mutual understanding among EROs by ensuring semantic translation of the exchanged information.
Abstract: Disaster response requires the cooperation of multiple emergency responder organizations (EROs). However, after‐action reports relating to large‐scale disasters identity communication difficulties among EROs as a major hindrance to collaboration. On the one hand, the use of two‐radio communication, based on multiple orthogonal frequencies and uneven coverage, has been shown to degrade inter‐organization communication. On the other hand, because they reflect different areas of expertise, EROs use differing terminologies, which are difficult to reconcile. These issues lead to ambiguities, misunderstandings, and inefficient exchange of data and information among those involved, which can impede the response process and slow decision making. We, therefore, hypothesize that promoting semantic interoperability across ERO information systems might improve information exchange among stakeholders and thereby allow a more coherent response to the disaster. We propose an ontology‐based messaging service on the basis of the Emergency Data Exchange Language (EDXL) standards. The parties involved will continue to use the terminologies to which they are accustomed, but the system will resolve inconsistencies and thereby enhance mutual understanding among EROs by ensuring semantic translation of the exchanged information. The evaluation of the semantic translation demonstrated the effectiveness and accuracy of the proposed service.

Journal ArticleDOI
TL;DR: This turns out to be a practical method to fill the missing data in streamflow records which are critical to the sustainable management of water and the development of hydrological models.
Abstract: Uncertainty analysis of hydrological models often requires a large number of model runs, which can be time consuming and computationally intensive. In order to reduce the number of runs required for uncertainty prediction, Bayesian networks (BNs) are used to graphically represent conditional probability dependence between the set of variables characterizing a flood event. Bayesian networks (BNs) are relevant due to their capacity to handle uncertainty, combine statistical data and expertise and introduce evidences in real-time flood forecasting. In the present study, a runoff–runoff model is considered. The discharge at a gauging station located is estimated at the outlet of a basin catchment based on discharge measurements at the gauging stations upstream. The BN model shows good performances in estimating the discharges at the basin outlet. Another application of the BN model is to be used as a reverse method. Knowing discharges values at the outlet of the basin, we can propagate back these values through the model to estimate discharges at upstream stations. This turns out to be a practical method to fill the missing data in streamflow records which are critical to the sustainable management of water and the development of hydrological models.