Wetlands protect downstream waters by filtering excess nitrogen (N) generated from agricultural and urban activities. Many small ephemeral wetlands, also known as geographically isolated wetlands (GIWs), are hotspots of N retention but have received fewer legal protections due to their apparent isolation from jurisdictional waters. Here, we hypothesize that the isolation of the GIWs make them more efficient N filters, especially when considering transient hydrologic dynamics. We use a reduced complexity model with 30 years of remotely sensed monthly wetland inundation levels in 3700 GIWs across eight wetlandscapes in the US to show how consideration of transient hydrologic dynamics can increase N retention estimates by up to 130%, with greater retention magnification for the smaller wetlands. This effect is more pronounced in semi-arid systems such as the prairies in North Dakota, where transient assumptions lead to 1.8 times more retention, compared to humid landscapes like the North Carolina Pocosins where transient assumptions only lead to 1.4 times more retention. Our results highlight how GIWs have an outsized role in retaining nutrients, and this service is enhanced due to their hydrologic disconnectivity which must be protected to maintain the integrity of downstream waters.

Inundation area is a major control on the ecosystem services provisioned by geographically isolated wetlands. Despite its importance, there has not been any comprehensive study to map out the seasonal inundation characteristics of geographically isolated wetlands over the continental United States (CONUS). This study fills the aforementioned gap by evaluating the seasonality or the long-term intra-annual variations of wetland inundation in ten wetlandscapes across the CONUS. We also assess the consistency of these intra-annual variations. Finally, we evaluate the extent to which the seasonality can be explained based on widely available hydrologic fluxes. Our findings highlight significant intra-annual variations of inundation within most wetlandscapes, with a standard deviation of the long-term averaged monthly inundation area ranging from 15% to 151% of its mean across the wetlandscapes. Stark differences in inundation seasonality are observed between snow-affected vs. rain-fed wetlandscapes. The former usually shows the maximum monthly inundation in April following spring snowmelt (SM), while the latter experiences the maximum in February. Although the magnitude of inundation fraction has changed over time in several wetlandscapes, the seasonality of these wetlands shows remarkable constancy. Overall, commonly available regional hydrologic fluxes (e.g. rainfall, SM, and evapotranspiration) are found to be able to explain the inundation seasonality at wetlandscape scale with determination coefficients greater than 0.57 in 7 out of 10 wetlandscapes. Our methodology and presented results may be used to map inundation seasonality and consequently account for its impact on wetland functions.

Climate change may accelerate the water cycle at a global scale, resulting in more frequent extreme climate events. This study analyzed changes in extreme precipitation events employing climate projections statistically downscaled at a station-space scale in South Korea. Among the CMIP5 climate projections, based on spatial resolution, this study selected 26 climate projections that provide daily precipitation under the representative concentration pathway (RCP) 4.5. The results show that a 20-year return period of precipitation event during a reference period (1980∼2005) corresponds to a 16.6 yr for 2011 to 2040, 14.1 yr for 2041 to 2070, and 12.8 yr for 2071 to 2100, indicating more frequent extreme maximum daily precipitation may occur in the future. In addition, we found that the probability density functions of the future periods are located out of the 10% confidence interval of the PDF for the reference period. The result indicates that the design standard under the reference climate is not managed to cope with climate change, and accordingly the revision of the design standard is required to improve sustainability in infrastructures.

Although many studies have sought to characterize future meteorological droughts, a few efforts have been done for quantifying the uncertainty, inter-model variability, arises from global circulation models (GCM) ensemble. A clear understanding of the uncertainty in multiple GCMs should be preceded before future meteorological droughts are projected. Therefore, this study evaluates the uncertainty in future meteorological drought characteristics that are induced by GCM ensemble using the custom measure “the degree of GCM spreading”. Future meteorological drought indices, the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), were computed to five different time scales: 3, 6, 9, 12 and 24 months using statistically downscaled 28 GCMs under Representative Concentration Pathway (RCP) 4.5 and 8.5 at 60 weather stations in South Korea. The frequency, duration, and severity of drought events were estimated for three different future periods; F1 (2010–2039), F2 (2040–2069), and F3 (2070–2099). It was found that the uncertainty increases as the time scale lengthens regardless of a choice of drought indices or RCP scenarios. It also turned out that the SPI exhibits larger uncertainty rather than the SPEI, because temperature data exhibit a relatively much smaller variability comparing to precipitation data. Moreover, there was a shift of regions having larger values of the increasing rate between F1 and F2, which is shift from the north-western to southern region of South Korea.

Artificial Intelligence (AI) has been extensively utilized for streamflow prediction, primarily in gauged watersheds using meteorological and historical streamflow data. However, its application in data-limited regions requires innovative approaches due to the reliance on extensive monitoring data. Physically based models, while comprehensive, are labor-intensive and inherently uncertain. Our study leveraged AI to address these limitations, focusing on direct streamflow drought estimation using statistical threshold levels without a physically based model. Models were developed and tested using inflow data and meteorological variables from four major South Korean dams. The Threshold Level Method (TLM) was applied to daily inflow data to define drought events, creating a time series for model training. We utilized the XGBoost algorithm, integrating comprehensive meteorological data to enhance the accuracy and reliability of the drought predictions. Our findings show that AI models can effectively identify and forecast streamflow droughts, even with limited streamflow data, by using meteorological inputs. The results demonstrated significant drought patterns and characteristics across different threshold levels and time resolutions. This application provides a robust framework for integrating advanced AI techniques in hydrological studies, offering practical insights into water resource management and drought planning, particularly in semi-gauged basins where baseline data is available but limited.