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A framework and pilot study for assessing usability of flood data portals for interdisciplinary research [1]
['Breanne K. Langlois', 'Friedman School Of Nutrition Science', 'Policy', 'Tufts University', 'Boston', 'Massachusetts', 'United States Of America', 'Aliya Magnuson', 'School Of Arts', 'Sciences']
Date: 2024-12
There is a lack of datasets to study the climate and human outcomes nexus. There are many flood data portals due to recent improvements in flood identification using satellites, providing opportunities to study the human impacts. The development of these portals is rapid and there is currently no standard for evaluating their usability for interdisciplinary research. This paper addresses this important data gap. We put forth a usability framework that includes data availability, approaches to flood identification, alignment, velocity, variety, and user feasibility aspects. We piloted it through an in-depth review and user survey of NASA Worldview (NW), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), Global Surface Water Explorer (GSWE), and Dartmouth Flood Observatory (DFO) GSWE and GloFAS were rated most favorably. Respondents had discrepancies in their opinions on the clarity of the goals and platform accessibility for GFMS, DFO, and NW, and in data and visualization quality for all portals. Historical data and measures of flood recurrence and other characteristics are needed. Flood data products should be provided in multiple formats, aggregated by sub-national boundaries, with mechanisms that delineate incomplete or unreliable data. Flood data portals should include interdisciplinary research as part of their mission. Their longevity and maintenance should be secured to preserve these important data sources for future research. This framework can be adapted and used to enable interdisciplinary spatial and survey data linkages.
Funding: This research was supported by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Cooperative State Research, Education, and Extension Service Fellowship (grant number 2020-38420-30724, ENN and TG); and the NSF International Research Experiences for Students (IRES) grant: US-Indonesian Research Experience for Students on Sustainable Adaptation of Coastal Areas to Environmental Change (grant number 1826939, ENN and MK). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright: © 2024 Langlois et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
All flood data portals included in this review are publicly accessible. This is not an exhaustive list or systematic review of all flood monitoring or flood data portals in existence, of which there are many. We selected five commonly used flood data portals that had global coverage based on a literature review and exploratory search for data in consultation with an expert in the application of Remote Sensing and Geographic Information Systems. Our criteria were that the data were freely and publicly available. We included:
We share a usability framework for flood data portals for research on human outcomes that incorporates the end user perspective. To showcase this framework, we conducted a pilot study using NASA Worldview (NW), Global Surface Water Explorer (GSWE), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), and Dartmouth Flood Observatory (DFO). This framework can be adapted and aids researchers wanting to link spatial and survey data to study how natural disasters and environmental changes are impacting human outcomes, e.g., health, economics, migration, food security, or nutrition.
Linking spatial and survey data is challenging due to usability aspects. While flood data portals classify whether an area is flooded, with varying approaches, the differing spatial and temporal attributes make it difficult to link this information to surveys which include community, sub-district, or district level boundaries. In contexts where flooding is recurrent and commonplace, such a measure could deepen understanding of the impacts and how to respond. The heterogeneity in the literature makes it difficult to compare studies across contexts to determine, broadly, the human impacts of floods. But also, this heterogeneity makes it difficult to compare across studies within the same context. Currently there is no universal definition or classification system of flood for use in disaster relief efforts or research.
Measurement of flood varies widely within the literature on nutrition and food security. One challenge is measuring flood at the community or household level. Another measurement challenge is distinguishing the type of flood–e.g. river, pluvial, coastal, or multiform floods–which can have different impacts [ 43 , 44 ]. Many studies that have examined nutrition and food security impacts of floods have focused on single flood disasters, comparing pre/post or flooded and non-flood areas [ 45 – 56 ]. Some studies have measured flood through self-report using surveys, having the unique advantage of directly identifying flood-affected households but raising the risk of bias due to potential overreporting of flood exposure [ 46 , 57 , 58 ]. Others have used direct measures from satellite or other spatial data, aggregated at various levels [ 10 , 59 – 61 ]. These studies typically do not differentiate the type of flood or main cause. Few studies in this body of literature have linked spatial data to household surveys. One recent study examined the effect of flooding on food security across broad regional, country, and all-continent scales in Africa and found both positive and negative impacts of floods and their related meteorological conditions. This study highlighted the important data gaps for assessment of flooding and food security outcomes across different spatial and temporal aggregations [ 44 ].
In recent years many websites and data archives containing geospatial flood data products (i.e. flood data portals) have come into existence, each with their own mission and purpose [ 6 , 24 , 29 , 34 – 38 ]. These portals use various sources of information from satellite imagery, government, or media sources to provide information or generate usable products about floods. Some portals such as USDA’s Global Agricultural & Disaster Assessment System are interactive web map viewers that allow users to visualize floods without any special software [ 24 , 38 – 40 ]. Some offer data products in different formats [ 25 , 29 , 37 , 38 , 41 , 42 ]. The Dartmouth Flood Observatory, for example, provides a downloadable database of flood events [ 6 , 25 , 40 ]. Others, such as the Global Surface Water Explorer, provide high resolution map products of surface water changes [ 24 ].
Identifications and monitoring of floods and long-term changes remains challenging, despite availability of data provided by satellites [ 24 ]. Landsat imagery is limited by a wide temporal coverage (8 days), however, this has been recently resolved by a new product that combines Landsat and Sentinel-2 images to derive daily surface water extent maps with global coverage [ 24 , 28 ]. While MODIS sensors can monitor large floods daily, it cannot detect floods where there is persistent cloud cover, complex terrain such as urban environments, or smaller flash floods [ 6 ]. Sentinel-1’s SAR sensors are limited in flood detection capability in the presence of wind or vegetation as the contrast between flooded and non-flooded areas is reduced [ 19 , 30 , 31 ]. Due to the temporal cycle of Sentinel-1 satellites (2 to 3 days at best), SAR flood detection is more accurate for longer duration events rather than flash floods [ 19 , 30 , 31 ]. Several indices derived from satellite imagery, such as NDWI (Normalized Difference Water Index), WI (Water Index), RSWIR (Red and Short Wave Infra-Red), or GSWIR (Green and Short Wave Infra-Red), have been used for flood mapping, but each have limitations (e.g. tendency to classify cloud shadow as water) [ 32 , 33 ]. NDWI, for example, tends to underestimate inundated areas while GSWIR, in contrast, tends to overestimate [ 32 ].
Thirty meter resolution imaging (once considered high resolution, but currently considered moderate resolution) provided by optical sensors aboard Landsat satellites has been valuable to monitor seasonality and long-term changes in surface water over four decades [ 24 ]. With the launch of NASA’s Terra (1999) and Aqua (2002) satellites, daily tracking of wide areas (250 m resolution) of the Earth’s surface water has been made possible by the optical sensor MODIS (moderate-resolution imaging spectroradiometer) [ 6 , 25 – 27 ]. Synthetic aperture radar sensors (SAR) such as those aboard Sentinel-1 satellites, operated by the Copernicus program, have the advantage of penetrating cloud cover [ 19 , 27 , 28 ]. Multi-satellite precipitation data products from NASA’s Global Precipitation Measurement Mission are used in flood detection models [ 29 ].
To address this data gap, we propose a usability framework applied to flood data portals and pilot it among interdisciplinary graduate students with backgrounds predominantly in public health and nutrition who are potential end-users of these portals. We derived the framework from metrics defined in a prior paper for assessing modern dashboards using principles of evidence, efficiency, emphasis, and ethics. An important component of this framework is the end-user, or external perspective [ 20 ]. Other papers have evaluated data usability within public health and disaster relief, but interdisciplinary perspectives are lacking [ 21 – 23 ]. Findings from this pilot study may be of interest to developers of these portals to improve their data products. This framework can be adapted and may be useful for researchers interested in spatial and survey data linkages. We hope to raise awareness of these data sources to facilitate this critical interdisciplinary research.
Currently, datasets to study the impacts of environmental changes on food security, nutrition, and health outcomes are lacking. Spatial and survey data linkages–the process of assembling information from these different data sources into one dataset–are needed. Remotely sensed imagery from optical and synthetic-aperture radar sensors are instrumental for this purpose [ 13 , 18 , 19 ]. After decades of Earth observing satellite missions, abundant data are now available to the public for flood identification, monitoring, forecasting, and assessment of long-term environmental changes. These data present an opportunity for researchers to study the human impacts of disasters and environmental changes. The challenge, however, is integrating geospatial and population data that traditionally do not speak to each other. There is a heavy user burden for researchers outside the earth sciences to process geospatial data products into formats that can be linked to population surveys (e.g., extracting raster values into polygon attributes and aggregating data by administrative boundaries). With many flood data portals and products available, it is unclear which ones are best suited for use with population datasets.
The frequency and duration of flood events is increasing globally, presenting threats to public health and nutrition through vector borne diseases, water contamination, and agricultural losses [ 1 – 10 ]. Flooding due to river overflow, coastal seawater intrusion, or flash floods are all impacted by climate factors such as extreme precipitation and sea level rise [ 11 – 13 ]. Other factors such as urbanization, land use, mitigation infrastructures, and socioeconomic conditions also impact flood risk [ 14 , 15 ]. With a growing global population exposed, floods are among the most impactful environmental hazards [ 6 , 16 , 17 ]. The destructive nature of floods warrants data for situational awareness and disaster planning efforts, and research of the human impacts.
We used Stata 17 (StataCorp LLC, College Station, TX) for analysis of the external user assessment survey. Survey responses were recorded using a 5-point Likert scale defined as: Strongly Disagree, Disagree, Agree, Strongly Agree, Not clear/ Difficult to assess. For each of the 25 metrics in the survey, we calculated percent agreement (i.e. rated either ‘Strongly Agree’ or ‘Agree’) for each portal. Then, we assessed interrater agreement using a generalization of weighted Kappa allowing for the situation of more than 2 raters, more than 2 ratings, and an incomplete design. We calculated the Kappa statistic for each flood data portal and interpreted the level of agreement as 0 to .20 = slight, .21 to .40 = fair, .41 to .60 = moderate, .61 to .80 = substantial, and .81 to 1 = perfect. Survey respondents were also asked to leave notes about each of the portals they reviewed. We summarized common themes from these notes inductively.
From May to September 2022, we recruited graduate students from Tufts University and Boston University to review each portal and complete a webform survey (see Appendix). For each portal, participants were asked to spend no more than 20 minutes exploring the portal and rating the assessment metrics using 5-point Likert responses. The survey was created and conducted using the Kobo Toolbox platform.
From February to March 2022, we explored the flood data portals in depth and completed a structured form containing the usability characteristics defined in Table 1 (see Appendix for data gathering form). The forms were completed with as much detail as possible that could be obtained from the websites and associated articles. We summarized the information in a table.
From the above principles, Zhou et al. designed metrics to assess functionality and operation features of nutrition dashboards, which communicate nutrition data through interactive visualizations ( Table 2 ) [ 20 ]. While these metrics were designed mainly for the evaluation of visualization dashboards, they can be applied more broadly to other types of data platforms. The functionality and operation aspects aligned with our purposes related to the practicality of flood data portals and provided the opportunity and structure to develop a survey instrument to capture end-users’ opinions in a quantifiable way, which is critical for making recommendations to improve the portals. We developed the survey by reviewing the criteria from Zhou et al. and tailoring it to the application of flood data portals.
The user feasibility component included a survey of graduate students reviewing and rating each of the portals for several criteria. The survey captured participants’ opinions about the portals’ goals and scope, data and visualization quality, and platform accessibility from an external user perspective. We developed the survey using metrics adapted from a previous study which were based on principles of evidence, efficiency, emphasis, and ethics ( Table 2 ) [ 20 ]. Under the evidence principle are metrics assessing the quality of a portal’s data products and whether the data is presented in line with the goals and objectives. Under the efficiency principle are metrics assessing the content organization and visual properties of a portal. Under the emphasis principle are metrics assessing a portal’s “user-friendliness” and ability to recognize the needs of a target audience and tailor the content to their perceptions, abilities, and interests. Finally, the ethics principle seeks to evaluate whether best practices are applied.
Our framework incorporated data usability aspects as well as user feasibility. The data usability component included an in-depth review of the portals for data accessibility and availability, approaches to flood identification through the data sources and flood detection methods, and data quality through alignment, velocity, and variety ( Table 1 ).
Ethical approval for the external user survey was obtained from the Tufts Social, Behavioral & Educational Research Institutional Review Board. The study met criteria for exempt status. All participants were informed of the study by reviewing a consent script on the first page of the webform and selecting “I consent to participate” to begin the survey.
We present an exploratory descriptive and qualitative assessment of graduate student raters from the external user assessment survey. Eight raters participated in the survey, with 6 completing ratings for all 5 portals. Fig 1 shows the percent agreement. According to raters’ opinions, GSWE and GloFAS were ranked most favorably for their usability on average (73% average agreement), while DFO and NW were ranked least favorably (39% and 37%, respectively) and GFMS was in the middle (58%). Fig 1 shows the breakdown by the assessment metrics and areas where the portals can improve their usability. For goals and scope, raters agreed for GSWE, GloFAS, and GFMS, but did not seem to agree for DFO and NW. Ratings for data and visualization quality were mixed across the portals, with raters agreeing on different aspects for each portal. For platform accessibility, raters agreed for GSWE, but less so for the other portals. For contacts, communication, and conflicts of interest, raters agreed for GFMS, GloFAS, and GSWE and did not seem to agree for DFO and NW. Inter-rater reliability for each of the portals ranged from slight to fair. When examined across all 5 rating categories and across 2 rating categories (either agree or disagree), respectively, Kappa coefficients were 0.06 and 0.40 for GSWE, 0.03 and 0.16 for GloFAS, 0.25 and 0.40 for GFMS, 0.05 and 0.08 for DFO, and 0.08 and 0.11 for NW.
All portals update their data products regularly. GFMS, NW, GloFAS, GFMS, and DFO (River and Reservoir Watch), provide daily updates. DFO may have some poor or missing data in the 1990s. The flood data products provided by NW cover the 1985–2003 and January 2022-present timespans, but there is a gap between 2003 to January 2022.
We assessed data quality through alignment, velocity, and variety ( Table 2 ). All portals provide data products structured by spatiotemporal attributes, but only DFO provides a dataset of flood events. All provide daily or Near Real Time data except for GSWE which classifies the whole timespan. DFO is the most important source of historical data about flood events from the 1980s. NW and GSWE also provide historical data products from the 1980s, but the products available through NW are derived from DFO data and EM-DAT. While GSWE has historical data, it captures changes over time and is not specific to flood events. GSWE and GloFAS provide the highest resolution data products, but only DFO provides detailed locations including names of rivers and towns.
Unlike the other portals, the DFO historical archive is based mainly on media sources (news reports, governmental and international relief agency websites). DFO captures large flood events spanning large geographic extents which are mostly hand drawn based on where the media reported flooding. In some instances, the archive might also have satellite observations to determine the flood extents. While it is a good source for identifying where large flood events occur globally, it is not useful for identifying which specific locations or communities are affected, making it prone to type 1 error. The differences in news coverage by country and region may impact the accuracy of information about a particular flood event.
GFMS uses a real-time multi-satellite precipitation data product from NASA’s Global Precipitation Measurement Mission to identify floods using a dual land surface with river runoff model–the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity Environment (DRIVE) model [ 29 ]. Flood is defined when the routed runoff is greater than the flood threshold of that grid cell. Precipitation biases and the size and duration of flood events can impact GFMS flood detection capability [ 29 ]. False positives can occur in areas with dams or related to winter surface features in higher latitudes. False negatives can occur due to underestimation of orographic rainfall (due to hills or mountains). Generally, GFMS has improved flood detection performance with longer, larger floods.
GloFAS captures river flooding using Sentinel-1 SAR satellite data and a combination of 3 separate flood detection algorithms [ 64 ]. A final ensemble algorithm generates the final flood product based on a consensus of the 3 flood detection algorithms [ 64 ]. Like NW, a reference water mask is used to differentiate flooding from permanent surface water [ 64 ]. The ensemble approach helps prevent flood classification errors attributed to any one of the flood detection algorithms. However, classification errors are possible due to limitations inherent with SAR data. False positives can occur from land characteristics such as very dry soil, frozen ground, wet snow, or impervious surfaces or from radar shadowing [ 64 ]. False negatives can occur from urbanization or dense vegetation or strong, heavy winds and rainfall [ 64 ]. To address these classification challenges, GloFAS produces exclusion mask (for effects due to ground surface characteristics) and advisory flag (for effects due to weather conditions) layers along with their flood product layers [ 64 ]. Users can apply these layers to determine which pixels to disregard.
GSWE uses high resolution Landsat satellite imagery to classify changes in surface water from 1984–2021 [ 24 ]. This portal differs from the others in its goal to detect and classify surface water, not flood events, over multiple decades. Challenges include variations in measurement across the Landsat sensors (TM, ETM+, OLI) and in the conditions in which they’re observed [ 24 ]. False water detection may be caused by shadows (e.g. from buildings, terrain, or cloud), or lava flow. GSWE applies a combination of expert systems, visual analytics, and evidential reasoning techniques to classify pixels and prevent water classification errors [ 24 ]. This is followed by manual visual inspection and cleaning of the maps to remove false detections, and further validation to handle remaining errors [ 24 ]. An important feature of GSWE data is its classification over an extended period. Because classifications are made over time, the temporal behavior of the pixels is also used in determining their likelihood of being water, thus strengthening the accuracy of the classification.
NW classifies floods via the MODIS Near Real Time Global Flood Product and the Flood Hazard: Frequency and Distribution layer. The Global Flood Product is derived from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor datasets from both Terra and Aqua satellites. Flood identification is based on time compositing water detections over 3 periods (1, 2, and 3-day) and differentiating them from surface water using a reference mask [ 63 ]. If a water detection does not match the reference layer, it is classified as flood. The compositing approach minimizes false flood detections by combining multiple observations, thus reducing error from clouds. For each period, all observed water detections are combined, and pixels are classified as water if they exceed thresholds of 1, 2, or 3 observations based on the periods, respectively. While this approach minimizes error, the Global Flood Product still has limitations and false detections cannot be entirely removed, especially if cloud cover is persistent over multiple observations [ 63 ]. False-negative detections may result from obscuration from buildings, canopy, or clouds, while false-positives may still result from cloud or terrain shadow or dark rock or soil. Users are encouraged to compare flood maps to reflectance imagery to determine potential sources of false-positives or false-negatives [ 63 ]. Another potential source of error is the reference mask used to differentiate surface water from floods, which is outdated and planned for improvement [ 63 ].
In addition to data on flood occurrence, NW and GSWE capture recurrence and NW, GloFAS, GFMS, and DFO provide data on other characteristics of floods ( Table 3 ). The Flood Hazard: Frequency and Distribution gridded data from NW measures frequency of floods between 1985–2003 using DFO data. The Recurrence dataset from GSWE measures inter-annual frequency of surface water between 1984 to present time, capturing areas that were episodically flooded. NW offers Mortality Risk and Economic Risk Flood Hazard layers, providing information on mortality and economic losses. Both are derived from the International Emergency Events Database (EM-DAT). GloFAS flood monitoring has data layers of the affected population and affected land use. GFMS provides data on flood depth. DFO provides the most information about floods in its Global Archive of flood events including main cause, area affected, detailed locations, names of rivers, duration, known number of deaths and displaced, and damages in USD. None of the portals’ data products distinguish or classify the type of flood, but DFO does provide the main cause and a measure of flood severity. GloFAS data only includes river floods.
All portals make their data products accessible directly from the site but NW and GloFAS require login. The portals offer 18 different flood-related data products ( Table 3 ). Most provide information about flood occurrences from satellite imagery. For example, the MODIS NRT Global Flood Product from NW is a daily global map of flooding derived from Terra and Aqua satellite imagery. The collection of Global Surface Water datasets from GSWE capture changes in surface water occurrence at 30m resolution (previously considered high, but currently considered moderate resolution) from Landsat imagery since 1984. While not specifically measuring flood events, GSWE datasets can be used to identify flooded areas. GloFAS data products measure ongoing and upcoming river floods from Sentinel-1 imagery. While the other portals offer multiple data products, GFMS offers only 1, flood intensity (in depth), derived from multi-satellite precipitation data. DFO data provide information on occurrence of large flood events, compiled mostly from media sources.
All five portals in this review are in the public domain and free to users. All are currently maintained but it was unclear whether they receive long-term funding. The aims and missions varied and included flood monitoring (NW, GloFAS), flood forecasting (GloFAS), disaster preparedness (GloFAS), humanitarian (DFO), water-management decision-making and applications (DFO, GSWE). Only DFO specified a research purpose. The GFMS is an experimental system and did not specify an aim or mission.
Discussion
We addressed an important data gap in interdisciplinary research of how climate events are impacting human outcomes. We provided a usability framework for flood data portals, which previously did not exist. We piloted this framework using 5 flood data portals, which resulted in a detailed comparison that is useful to researchers and host organizations seeking to improve these important and valuable data products. Our framework can be adapted to other climate data products. We did not evaluate the correctness of the flood detection algorithms in our framework, as it depends largely on the intended use and our focus was on the end-users of these data products. We found considerable heterogeneity in the data products and information provided, and identified areas where these portals could improve. Overall, these portals should include research within their scope and provide products that facilitate interdisciplinary research.
There are several case examples of how data from these portals have been used in prior research (Table 5). DFO and GloFAS have been used more frequently than the other portals. DFO has had a broader range of applications in the literature including spatiotemporal variability of floods, impacts on mortality, displacement, and economy, while GloFAS has been used mainly for early warning systems [65–73]. Several studies have linked DFO to other datasets [70, 72, 74]. It appears GFMS, NW, and GSWE have been used less frequently for research. Few studies seem to use GSWE, and these have focused on changes in delta morphology and coastal morphology over time due to human activity [75, 76]. We are not aware of any studies using GSWE variables to measure flooding. These case examples point to the need for improvements to usability so these important data products can be used more efficiently in longitudinal studies to examine human impacts of flooding.
Historical data products that span several decades are needed for longitudinal studies examining impacts of flooding on health, nutrition, or other population outcomes. A few of the portals provided historical data products, with DFO providing an important historical archive of floods since the 1980s. Historical data from NW was derived from DFO data and from EM-DAT but is not ongoing. GSWE also provided important historical data taken from satellite imagery, but it captured the overall change over time. Data products that capture flood events over a long period of time are valuable for population studies, though deriving such products retrospectively is difficult due to quality of data from earlier decades.
Most data captures flood occurrences, but measures of flood recurrence are needed. Only GSWE and NW provided this type of information. GSWE provides a ‘recurrence’ layer, which captures information about the inter-annual behavior showing areas that are inundated regularly versus those that are flooded episodically [42]. NW captures flood recurrence through its ‘Flood Hazard: Frequency and Distribution’ layer, providing information about flood frequency using data from the DFO historical archive [37]. Additionally, a planned future improvement to the NW Global Flood Product is to include a ‘recurring flood’ layer to identify regularly flooded areas [63]. Incorporating other information about floods is also important, such as size, duration, depth, or damages. Only DFO and NW provided other information about floods, including the human and economic impacts. Other classifications of flood exist that incorporate potential damages [13, 25, 77]. For example, the US National Weather Service uses a 3-level classification system based on the level of damage caused [13]. If flood data portals can extend beyond flood monitoring, to incorporating these other elements, the usability of their products will improve greatly.
Spatial and survey data linkages are challenging and not intuitive for researchers in public health and nutrition related fields. All the data products in this review require extra steps to be linked to surveys. None provide aggregations by administrative boundaries, which could facilitate this process. Having multiple data formats available that are accessible to a broad range of researchers could also facilitate this process. High resolution data products are important for data linkages, as aggregating up to community, sub-district, or district levels is more advantageous than downscaling coarse data. A deeper exploration of data integration methods is needed, including their challenges, benefits, and disadvantages. This is a future direction.
Survey respondents highlighted areas where the portals could improve or provide better clarification. DFO and NW could improve the clarity of their goals and scope. All portals could improve with respect to classifying flood types, providing more information about floods, and measuring recurrence. Sub-national and sub-yearly classifications are also needed to facilitate data linkages. GSWE was the most accessible platform, with raters agreeing on some aspects for GloFAS and improvements to user-friendliness, outdated links, tutorials, and data accessibility and formats could be made to the other portals. Taken together, this external user review can help these portals determine where they could improve either in terms of their products or the clarity of their websites. It has wider application to other types of data portals or environmental hazards. Addressing scalability is another future direction.
The low inter-rater agreement could reflect either the variability of the raters or of the portals themselves. The raters in this review were graduate students who were familiar with spatial and survey data, although some had more expertise than others. The low agreement was more likely due to the variability of the portals, indicating a need for common standards to improve their usability. Some criteria may have been easier to judge than others. Regardless, the percent agreement still provides valid and important information. A larger sample size would yield more precision.
We did not conduct an extensive review of all flood or disaster data portals in existence, of which there are many. We highlighted major aspects of 5 common flood data portals that we identified. However, it is possible that other flood data portals not included in this review have different usability aspects. While not specific to other flood data portals, our findings may provide general guidance on how to improve these important data sources. The portals included in this review–NW, GloFAS, GFMS, GSWE, DFO–may take specific recommendations from these findings.
Flood data portals should include research as part of their purpose and mission and should incorporate the interdisciplinary user perspective. Usability of the data products could be improved by providing sub-national aggregations, different data formats such as statistical datasets in Stata or R format, and mechanisms that clearly indicate where data may be incomplete or unreliable. Historical data products that span decades can provide an opportunity for longitudinal research of the human impacts of climate changes and extreme events. Measures of flood recurrence, not just occurrence, are needed. Data on other characteristics of flooding such as type of flood, depth, duration, and damages are also needed. Finally, the longevity and maintenance of these data portals should be secured through long-term funding for future generations and research.
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