Hydrologic Modeling as a Service (HMaaS): A New Approach to Address Hydroinformatic Challenges in Developing Countries

Abstract

Hydrologic modeling can be used to aid in decision-making at the local scale. Developed countries usually have their own hydrologic models; however, developing countries often have limited hydrologic modeling capabilities due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. However, large-scale forecasting systems come with their own challenges. These New hydroinformatic challenges can prevent these models from reaching their full potential of becoming useful in the decision making process. This article discusses these challenges along with the background leading to the development of a large-scale streamflow prediction system. In addition, we present a large-scale streamflow prediction system developed using the GloFAS-RAPID model. The developed model covers Africa, North America, South America, and South Asia. The results from this model are made available using a Hydrologic Modeling as a Service approach (HMaaS) as an answer to some of the discussed challenges. In contrast to the traditional modeling approach, which makes results available only to those with the resources necessary to run hydrologic models, the HMaaS approach makes results available using web services that can be accessed by anyone with an internet connection. Web applications and services for providing improved data accessibility, and addressing the discussed hydroinformamtic challenges are also presented. The HydroViewer app, a custom application to display model results and facilitate data consumption and integration at the local level is presented. We also conducted validation tests to ensure that model results are acceptable. Some of the countries where the presented services and applications have been tested include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. Overall, a HMaaS approach to operationalize a GSPS and provide meaningful and easily accessible results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

Verification of two hydrological models for real-time flood forecasting in the Hindu Kush Himalaya (HKH) region

“…In addition to forecast, the tools also provide an option to download historical discharge of the selected river section based on the ERA-Interim data. More detailed information on the models is available from earlier articles published elsewhere (Souffront Alcantara et al 2019).…”

“…Unless it is downscaled to capture local details, GloFAS outputs will not be meaningful for local decision-making where flood risks are the greatest. Advances in computing infrastructure, hydroinformatics, and communication technologies together with hydrologic models have greatly enhanced the capability in streamflow prediction (Souffront Alcantara et al 2019). Without these tools, technically less capable nations of the HKH region may not have access to cuttingedge technologies to address their hydroinformatic challenges.…”

Enhancing Flood Early Warning System in the HKH Region

“…The modeled predictions are consumed using intuitive web-based interfaces so as to extract and visualize flood-forecast data for specific areas of interest via customized web applications. Further localization is enabled through the implementation of REST API (Representational State Transfer-Application Program Interface) services (Souffront Alcantara et al 2019). The hydro-informatic workflow links the web applications with the back-end cyber infrastructure for model computation to access and display the forecast information sought by the users.…”

“…The forecast modeling tools have been calibrated and validated against several observed data sets collected from different locations around the world (Jackson 2018;Jackson et al 2019;Snow et al 2016;Swain et al 2016a, b;Souffront-Alcantara et al 2019;Nelson et al 2019). Results from earlier validation efforts were optimistic that the modeled predictions were consistent with outputs from other systems using the same set of meteorological forcings and land-surface model (LSM) fields.…”

Earth Observation Applications in the Hindu Kush Himalaya Region—Evolution and Adoptions

“…We are now accustomed to accurate hourly weather forecasts and to following satellite images of swirling storms hitting coasts and cities. A combination of satellite data and weather models has made it possible to forecast discharge in each stream segment and also the extent of flood two weeks ahead, thereby helping in better preparedness to tackle any potential disaster (Souffront et al 2019;.…”