Abstract. The lack of a comprehensive, up-to-date emission inventory for the Himalayan region is a major challenge in understanding the extensive regional air pollution, including its causes, impacts and mitigation pathways. This study describes a high-resolution (1 km × 1 km) present-day emission inventory for Nepal, developed with a higher-tier approach. The complete study is divided into two parts; this paper covers technologies and combustion sources in residential, industrial, commercial, agricultural diesel-use and transport sectors as Part I (NEEMI-Tech), while emissions from the open burning of municipal waste and agricultural residue in fields and fugitive emissions from waste management, paddy fields, enteric fermentation and manure management for the period 2001–2016 will be covered in Part II (NEEMI-Open). The national total energy consumption (except hydropower, solar and wind energy) estimated in the base year 2011 was 374 PJ, with the residential sector being the largest energy consumer (79 %), followed by industry (11 %) and the transport sector (7 %). Biomass is the dominant energy source, contributing to 88 % of the national total energy consumption, while the rest is from fossil fuel. A total of 8.9 Tg of CO2, 110 Gg of CH4, 2.1 Gg of N2O, 64 Gg of NOx, 1714 Gg of CO, 407 Gg of NMVOCs, 195 Gg of PM2.5, 23 Gg of BC, 83 Gg of OC and 24 Gg of SO2 emissions were estimated in 2011 from the five energy-use sectors considered in NEEMI-Tech. The Nepal emission inventory provides, for the first time, temporal trends of fuel and energy consumption and associated emissions in Nepal for a long period, 2001–2016. The energy consumption showed an increase by a factor of 1.6 in 2016 compared to 2001, while the emissions of various species increased by a factor of 1.2–2.4. An assessment of the top polluting technologies shows particularly high emissions from traditional cookstoves and space-heating practices using biomass. In addition, high emissions were also computed from fixed-chimney Bull's trench kilns (FCBTKs) in brick production, cement kilns, two-wheeler gasoline vehicles, heavy-duty diesel freight vehicles and kerosene lamps. The monthly analysis shows December, January and February as periods of high PM2.5 emissions from the technology-based sources considered in this study. Once the full inventory including open burning and fugitive sources (Part II) is available, a more complete picture of the strength and temporal variability in the emissions and sources will be possible. Furthermore, the large spatial variation in the emissions highlights the pockets of growing urbanization, which emphasize the importance of the detailed knowledge about the emission sources that this study provides. These emissions will be of value for further studies, especially air-quality-modeling studies focused on understanding the likely effectiveness of air pollution mitigation measures in Nepal.

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.

Combinatorial Testing (CT) is a systematic way of sampling input parameters of the software under test (SUT). A t-way combinatorial test set can exercise all behaviors of the SUT caused by interactions between t input parameters or less. Although combinatorial testing can provide fault detection capability, it is often desirable to isolate the input combinations that cause failures. Isolating these failure-inducing combinations aids developers in understanding the causes of failures. Previous work directly uses classification tree analysis on the results of combinatorial testing to model the failure inducing combinations. But in many scenarios, the effectiveness of classification depends upon whether the analyzed test set is sufficient for classification. In addition, generating combinatorial tests for more-than-6-way combination is generally expensive. To address these issues, we propose an approach that uses existing combinatorial testing results to generate additional tests that enhance the effectiveness of classification. In addition, our approach also includes a technique to reduce the complexity of the resulting classification tree so that developers can understand the nature of failure-inducing combinations. We present the preliminary results of our approach applied on the TCAS benchmark.

Tethys Platform is an open source framework for developing web-based applications for Earth Observation data. Our experience shows that Tethys significantly lowers the barrier for cloud-based app development, simplifies the process of accessing scalable distributed cloud computing resources and leverages additional software for data and computationally intensive modeling. The Tethys software development kit allows users to create web apps for visualizing, analyzing, and modeling Earth Observation data. Tethys platform provides a collaborative environment for scientists to develop and deploy several Earth Observation web applications across multiple Tethys portals. We work in partnership with leading regional organizations worldwide to help developing countries use information provided by earth-observing satellites and geospatial technologies for managing climate risks and land use. This paper highlights the several Tethys portals and web applications that were developed as part of this effort. Implementation of the Tethys framework has significantly improved the Application Readiness Level metric for several NASA projects and the potential impact of Tethys to replicate and scale other applied science programs.

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