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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.

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets' performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies. [ABSTRACT FROM AUTHOR]

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation. Plain Language Summary: Natural hazards, such as flooding and extreme heat, can damage our infrastructure and local communities. These hazards often stem from extreme weather conditions such as heavy precipitation and hot temperatures. Global climate change is expected to affect precipitation and air temperature, leading to more extreme weather events and hazards in the future. Predicting frequency and severity of these future events is key to protect our infrastructure and communities. Climate models have been widely used to project statistics of future extremes. However, they come with uncertainties due to numerical approximations, low spatial resolutions etc. Consequently, predictions generated by these models are inherently uncertain. This paper used a new approach to improve future projections of extreme climatic events, focusing on South Florida. Our findings revealed substantial improvements in projecting future events, particularly for extreme precipitation. We also showed that nationally and globally derived simulations may not be suitable for accurately projecting heavy precipitation, but can suffice for air temperature and low/medium precipitation rates. This paper offers promising avenues for refining projections of future weather events. By enhancing our ability to anticipate upcoming weather events more accurately, we can better protect our infrastructure and communities against the challenges posed by climate change. Key Points: A hybrid statistical approach to improve bias correction of GCM precipitation and air temperature simulationsMajor improvements in both climatic variables, especially extreme precipitation, using our hybrid statistical approach in South FloridaCoarse resolution downscaled data may be insufficient for deriving high quantile precipitation at the regional scale [ABSTRACT FROM AUTHOR]