Mapping global non-floodplain wetlands.

Non-floodplain wetlands - those located outside the floodplains - have emerged as integral components to watershed resilience, contributing hydrologic and biogeochemical functions affecting watershed-scale flooding extent, drought magnitude, and water-quality maintenance. However, the absence of a global dataset of non-floodplain wetlands limits their necessary incorporation into water quality and quantity management decisions and affects wetland-focused wildlife habitat conservation outcomes. We addressed this critical need by developing a publicly available "Global NFW" (Non-Floodplain Wetland) dataset, comprised of a global river-floodplain map at 90 m resolution coupled with a global ensemble wetland map incorporating multiple wetland-focused data layers. The floodplain, wetland, and non-floodplain wetland spatial data developed here were successfully validated within 21 large and heterogenous basins across the conterminous United States. We identified nearly 33 million potential non-floodplain wetlands with an estimated global extent of over 16×106 km2. Non-floodplain wetland pixels comprised 53% of globally identified wetland pixels, meaning the majority of the globe's wetlands likely occur external to river floodplains and coastal habitats. The identified global NFWs were typically small (median 0.039 km2), with a global median size ranging from 0.018-0.138 km2. This novel geospatial Global NFW static dataset advances wetland conservation and resource-management goals while providing a foundation for global non-floodplain wetland functional assessments, facilitating non-floodplain wetland inclusion in hydrological, biogeochemical, and biological model development. The data are freely available through the United States Environmental Protection Agency's Environmental Dataset Gateway (https://gaftp.epa.gov/EPADataCommons/ORD/Global_NonFloodplain_Wetlands/, last access: 24 May 2023) and through https://doi.org/10.23719/1528331 (Lane et al., 2023a).

Human alterations of the global floodplains 1992-2019.

Floodplains provide critical ecosystem services; however, loss of natural floodplain functions caused by human alterations increase flood risks and lead to massive loss of life and property. Despite recent calls for improved floodplain protection and management, a comprehensive, global-scale assessment quantifying human floodplain alterations does not exist. We developed the first publicly available global dataset that quantifies human alterations in 15 million km2 floodplains along 520 major river basins during the recent 27 years (1992-2019) at 250-m resolution. To maximize the reuse of our dataset and advance the open science of human floodplain alteration, we developed three web-based programming tools supported with tutorials and step-by-step audiovisual instructions. Our data reveal a significant loss of natural floodplains worldwide with 460,000 km2 of new agricultural and 140,000 km2 of new developed areas between 1992 and 2019. This dataset offers critical new insights into how floodplains are being destroyed, which will help decision-makers to reinforce strategies to conserve and restore floodplain functions and habitat.

River Basin Simulations Reveal Wide-Ranging Wetland-Mediated Nitrate Reductions.

River basin-scale wetland restoration and creation is a primary management option for mitigating nitrogen-based water quality challenges. However, the magnitude of nitrogen reduction that will result from adding wetlands across large river basins is uncertain, partly because the areal extent, location, and physical and functional characteristics of the wetlands are unknown. We simulated over 3600 wetland restoration scenarios across the ∼450,000 km2 Upper Mississippi River Basin (UMRB) depicting varied assumptions for wetland areal extent, physical and functional characteristics, and placement strategy. These simulations indicated that restoring wetlands will reduce local nitrate yields and nitrate loads at the UMRB outlet. However, the projected magnitude of nitrate reduction varied widely across disparate scenario assumptions─e.g., restoring 4500 km2 of wetlands (i.e., 1% of UMRB area) decreased mean annual nitrate loads at the UMRB outlet between 3 and 42%. Higher magnitude nitrate reductions correlated with best-case assumptions, particularly for characteristics controlling nitrate loading rates to the wetlands. These results show that simplified claims about basin-scale wetland-mediated water quality improvements discount the breadth of possible wetland impacts across disparate wetland physical and functional conditions and highlight a need for greater clarity regarding the likelihood of these conditions at river basin scales.

Wetland restoration yields dynamic nitrate responses across the Upper Mississippi river basin.

Wetland restoration is a primary management option for removing surplus nitrogen draining from agricultural landscapes. However, wetland capacity to mitigate nitrogen losses at large river-basin scales remains uncertain. This is largely due to a limited number of studies that address the cumulative and dynamic effects of restored wetlands across the landscape on downstream nutrient conditions. We analyzed wetland restoration impacts on modeled nitrate dynamics across 279 subbasins comprising the ∼0.5 million km2 Upper Mississippi River Basin (UMRB), USA, which covers eight states and houses ∼30 million people. Restoring ∼8,000 km2 of wetlands will reduce mean annual nitrate loads to the UMRB outlet by 12%, a substantial improvement over existing conditions but markedly less than widely cited estimates. Our lower wetland efficacy estimates are partly attributed to improved representation of processes not considered by preceding empirical studies - namely the potential for nitrate to bypass wetlands (i.e., via subsurface tile drainage) and be stored or transformed within the river network itself. Our novel findings reveal that wetlands mitigate surplus nitrogen basin-wide, yet they may not be as universally effective in tiled landscapes and because of river network processing.

The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset.

Floodplains provide essential ecosystem functions, yet >80% of European and North American floodplains are substantially modified. Despite floodplain changes over the past century, comprehensive, long-term land use change data within large river basin floodplains are limited. Long-term land use data can be used to quantify floodplain functions and provide spatially explicit information for management, restoration, and flood-risk mitigation. We present a comprehensive dataset quantifying floodplain land use change along the 3.3 million km2 Mississippi River Basin (MRB) covering 60 years (1941-2000) at 250-m resolution. We developed four unique products as part of this work, a(n): (i) Google Earth Engine interactive map visualization interface, (ii) Python code that runs in any internet browser, (iii) online tutorial with visualizations facilitating classroom code application, and (iv) instructional video demonstrating code application and database reproduction. Our data show that MRB's natural floodplain ecosystems have been substantially altered to agricultural and developed land uses. These products will support MRB resilience and sustainability goals by advancing data-driven decision making on floodplain restoration, buyout, and conservation scenarios.

Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling.

In watersheds located in semi-arid regions, vegetation dynamics, evapotranspiration (ET), and associated water and energy balances collectively play a major role in controlling hydrological regimes and crop yield. As such, it is challenging to predict the complex hydrological processes and biophysical dynamics. This challenge increases in areas with limited data availability. The key objective of this study was to evaluate the direct integration of remotely sensed Leaf Area Index (LAI) data into a hydrological model to improve streamflow, ET, and crop yield estimates. We also demonstrated how an improved model integrated with remotely sensed LAI data can inform water managers by predicting water productivity (WP) under different irrigation schemes. We took agricultural-dominated San Joaquin Watershed in California, United States, as our testbed and integrated the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution 4-day total LAI data into the SWAT (Soil and Water Assessment Tool) model. Results showed that, compared to conventional SWAT model that relies on semi-empirical equations and user inputs for simulating biophysical processes, direct LAI integration into SWAT model (SWAT-LAI) notably captured the actual vegetation dynamics and improved ET and crop yield estimations. The WP simulated by the improved SWAT-LAI model for almond and grape yields varied within a range from 0.363 to 3.81 kg/m3 and 0.32 to 4.76 kg/m3 across different irrigation applications. The outcomes of this study showed that deficit irrigation application could be a viable option in water stressed regions, since it can save a substantial amount of irrigation water and maintain the higher water productivity required for both almond and grape yield production. This study shows an evidence of how remotely sensed data integrated into hydrological models can serve as a decision support tool by providing quantitative information on crop water use and crop production.

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions.

Surface water storage in small yet abundant landscape depressions-including wetlands and other small waterbodies-is largely disregarded in conventional hydrologic modeling practices. No quantitative evidence exists of how their exclusion may lead to potentially inaccurate model projections and understanding of hydrologic dynamics across the world's major river basins. To fill this knowledge gap, we developed the first-ever major river basin-scale modeling approach integrating surface depressions and focusing on the 450,000-km2 Upper Mississippi River Basin (UMRB) in the United States. We applied a novel topography-based algorithm to estimate areas and volumes of ~455,000 surface depressions (>1 ha) across the UMRB (in addition to lakes and reservoirs) and subsequently aggregated their effects per subbasin. Compared to a "no depression" conventional model, our depression-integrated model (a) improved streamflow simulation accuracy with increasing upstream abundance of depression storage, (b) significantly altered the spatial patterns and magnitudes of water yields across 315,000 km2 (70%) of the basin area, and (c) provided realistic spatial distributions of rootzone wetness conditions corresponding to satellite-based data. Results further suggest that storage capacity (i.e., volume) alone does not fully explain depressions' cumulative effects on landscape hydrologic responses. Local (i.e., subbasin level) climatic and geophysical drivers and downstream flowpath-regulating structures (e.g., reservoirs and dams) influence the extent to which depression storage volume in a subbasin causes hydrologic effects. With these new insights, our study supports the integration of surface depression storage and thereby catalyzes a reassessment of current hydrological modeling and management practices for basin-scale studies.

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model's built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a "basic" traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model's LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Non-floodplain Wetlands Affect Watershed Nutrient Dynamics: A Critical Review.

Wetlands have the capacity to retain nitrogen and phosphorus and are thereby often considered a viable option for improving water quality at local scales. However, little is known about the cumulative influence of wetlands outside of floodplains, i.e., non-floodplain wetlands (NFWs), on surface water quality at watershed scales. Such evidence is important to meet global, national, regional, and local water quality goals effectively and comprehensively. In this critical review, we synthesize the state of the science about the watershed-scale effects of NFWs on nutrient-based (nitrogen, phosphorus) water quality. We further highlight where knowledge is limited in this research area and the challenges of garnering this information. On the basis of previous wetland literature, we develop emerging concepts that assist in advancing the science linking NFWs to watershed-scale nutrient conditions. Finally, we ask, "Where do we go from here?" We address this question using a 2-fold approach. First, we demonstrate, via example model simulations, how explicitly considering NFWs in watershed nutrient modeling changes predicted nutrient yields to receiving waters-and how this may potentially affect future water quality management decisions. Second, we outline research recommendations that will improve our scientific understanding of how NFWs affect downstream water quality.

Hydrologic model predictability improves with spatially explicit calibration using remotely sensed evapotranspiration and biophysical parameters.

A hydrologic model, calibrated using only streamflow data, can produce acceptable streamflow simulation at the watershed outlet yet unrealistic representations of water balance across the landscape. Recent studies have demonstrated the potential of multi-objective calibration using remotely sensed evapotranspiration (ET) and gaged streamflow data to spatially improve the water balance. However, methodological clarity on how to "best" integrate ET data and model parameters in multi-objective model calibration to improve simulations is lacking. To address these limitations, we assessed how a spatially explicit, distributed calibration approach that uses (1) remotely sensed ET data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and (2) frequently overlooked biophysical parameters can improve the overall predictability of two key components of the water balance: streamflow and ET at different locations throughout the watershed. We used the Soil and Water Assessment Tool (SWAT), previously modified to represent hydrologic transport and filling-spilling of landscape depressions, in a large watershed of the Prairie Pothole Region, United States. We employed a novel stepwise series of calibration experiments to isolate the effects (on streamflow and simulated ET) of integrating biophysical parameters and spatially explicit remotely sensed ET data into model calibration. Results suggest that the inclusion of biophysical parameters involving vegetation dynamics and energy utilization mechanisms tend to increase model accuracy. Furthermore, we found that using a lumped, versus a spatially explicit, approach for integrating ET into model calibration produces a sub-optimal model state with no potential improvement in model performance across large spatial scales. However, when we utilized the same MODIS ET datasets but calibrated each sub-basin in the spatially explicit approach, water yield prediction uncertainty decreased, including a distinct improvement in the temporal and spatial accuracy of simulated ET and streamflow. This further resulted in a more realistic simulation of vegetation growth when compared to MODIS Leaf-Area Index data. These findings afford critical insights into the efficient integration of remotely sensed "big data" into hydrologic modeling and associated watershed management decisions. Our approach can be generalized and potentially replicated using other hydrologic models and remotely sensed data resources - and in different geophysical settings of the globe.