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Advances in computing, collection, and sharing of Earth Observations (EOs) have significantly improved the potential for integrating EO and water resources models. Inadequate observational data for the systems simulated have been a persistent limitation in developing robust water resources models. Although various EO datasets have been available for decades, they have been under-utilized for water resources modeling. This can be due to sensor and product limitations, including spatial, spectral, and temporal resolutions, and the reluctance of the water resources community to adopt the state-of-art quickly. Motivated by the dual agenda of engaging the water resources community on various aspects of integrating EOs with water resources modeling and understanding the likely factors that limit a deeper integration of EOs in water resources management, we investigated the communities' perception of water resources modeling and EO integration. This paper summarizes the findings of a web-based survey conducted at the annual ASCE-EWRI (American Society of Civil Engineers-Environmental Water Resources Institute) International Water Congress in 2022. The analysis of responses (n = 74) identified limited spatial resolution, atmospheric and cloud interference, and lack of in-situ validation data as the highest perceived barriers to integrating EOs with water resources modeling and management. Perceptions among different groups of participants and even within the groups were different. For example, the perceived barriers often differed between researchers and non-researchers (e.g., policymakers and practitioners). There were differences in perception among the remote-sensing and water resources researchers within the research community. Even among water resources communities, disparities existed between the perceptions of respondents who also identified as knowledgeable about remote sensing and those who didn't. These observations highlighted the need to intentionally develop a convergent group and domain to integrate the disciplines involved and capitalize on the advancements that have improved the EO for water resources management.

The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented about the use of GIS and remote sensing in watershed management and its role in impairment identification, model development, and planning and implementation. Then, ways in which GIS and remote sensing can be integrated with stakeholder engagement through (1) leveraging GIS and remote sensing datasets, and (2) stakeholder engagement approaches including outreach and education, modeler-led development, and stakeholder-led involvement and feedback, are discussed. Finally, future perspectives on the potential for transforming public participation and stakeholder engagement in the watershed management process through applications of GIS and remote sensing are presented.

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and durations. Probabilistic distribution of landslides for a given study area and rainfall intensity can be easily derived using the binomial method from these relations.

The inherent effects of global sea surface temperature (SST) anomalies on hydrological cycle and vegetation cover complicate the structure of tropical climate at the regional scale. Assessing hydrological processes related to climate forcing is important in Central America because it is surrounded by both the Pacific and Atlantic oceans and two continental landmasses. In this study, the use of high-resolution remote sensing imagery in wavelet analysis helps identify nonstationary characteristics of hydrological and ecological responses. The wavelet-based empirical orthogonal function (WEOF) further reflects the nonlinear relationship between the Atlantic and Pacific SST and the greenness of a pristine forested site in Panama, La Amistad International Park. Integrated WEOF and descriptive statistics for data analysis reveal a higher temporal variability in terrestrial precipitation relative to in situ land surface temperature and its probable effects on the presence of dry periods. Such teleconnection signals of SST were identified as a driving force of decline in tropical forest greenness during dry periods. The results of our remote sensing-based wavelet analysis showed intra-annual high-frequency and biennial to triennial low-frequency signals between enhanced vegetation index/precipitation datasets and SST indices in both Atlantic and Pacific oceans. A spatiotemporal priority search further confirmed the importance of the effects of the El Niño-Southern Oscillation (ENSO) over terrestrial responses in the selected study site. Coincidence of the effect of ENSO teleconnection patterns on precipitation and vegetation suggests possible impacts of El Niño-associated droughts in Central America, accompanied by reduced rainfall, especially during the first months of rainy season (June, July, and August), and decline in vegetation cover during the dry season (March and April). Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Delineating accurate nutrient fluxes and distributions in multimedia environments requires the integration of vast amounts of information. Such nutrient flows may be related to atmospheric deposition, agricultural runoff, and urbanization effect on surface and groundwater systems. Two types of significant undertakings for nutrient management have been in place for sustainable development. While many environmental engineering technologies for nutrient removal have been developed to secure tap water sources and improve the drinking water quality, various watershed management strategies for eutrophication control are moving to highlight the acute need for monitoring the dynamics and complexities that arise from nutrient impacts on water quality status and ecosystem state, both spatially and temporally. These monitoring methods and data are associated with local point measurements, air-borne remote sensing, and space-borne satellite images of spatiotemporal nutrient distributions leading to the generation of accurate environmental patterns. Within this context, several key water quality constituents, including total nitrogen, total phosphorus, chlorophyll-aconcentration, colored dissolved organic matter (dissolved organic carbon or total organic carbon), harmful algal blooms (e.g., cyanobacterial toxins or microcystin concentrations), and descriptors of ecosystem states, such as total suspended sediment (or turbidity), transparency (e.g., Secchi disk depth), and temperature, will be of major concern. Considering the advancements, challenges, and accomplishments related to remote sensing technologies in the past four decades, we present a thorough literature review of contemporary state-of-the-art technologies of remote sensing platforms and sensors that may be employed to support essential scientific missions, and provide an in-depth discussion and new insight into various inversion methods (or models) to improve the estimation accuracy. In this study, the spectrum of these remote sensing technologies and models is first divided into groups based on chronological order associated with different platforms and sensors, although some of them may be subject to mission-oriented arrangements. Case-based and location-based studies were cited, organized, and summarized to further elucidate tracks of application potential that support future, forward-looking, cost-effective, and risk-informed nutrient management plans. The comprehensive reviews presented here should echo real-world observational evidence by using integrated sensing, monitoring, and modeling techniques to improve environmental management, policy analysis, and decision making. [ABSTRACT FROM PUBLISHER]