•We provide insights on hydrologic model calibration using remotely sensed ET data.•The commonly followed lumped approach to use ET data produces an equifinal model.•We propose a spatially explicit ...approach that noticeably improves model performance.•Including the often disregarded biophysical parameters can influence model accuracy.•We demonstrate how the model state shifts within the continuum of equifinality.
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.
Cities evolve through phases of construction, demolition, vacancy, and redevelopment, each impacting water movement at the land surface by altering soil hydrologic properties, land cover, and ...topography. Currently unknown is whether the variable physical and vegetative characteristics associated with vacant parcels and introduced by demolition may absorb rainfall and thereby diminish stormwater runoff. To investigate this, we evaluate how vacant lots modulate citywide hydrologic partitioning by synthesizing a novel field dataset across 500+ parcels in Buffalo, New York, USA. Vacant lot infiltration rates vary widely (0.001 to 5.39 cm h
), though parcels are generally well-vegetated and gently sloped. Extending field estimates to 2400 vacant parcels, we estimate that vacant lands citywide may cumulatively infiltrate 51-54% additional annual rainfall volume as compared to pre-demolition state, in part by reducing and disconnecting impervious areas. Our findings differentiate vacant lots as purposeful landscapes that can alleviate large water fluxes into aging wastewater infrastructure.
Urbanizing environments alter the hydrological cycle by redirecting stream networks for stormwater and wastewater transmission and increasing impermeable surfaces. These changes thereby accelerate ...the runoff of water and its constituents following precipitation events, alter evapotranspiration processes, and indirectly modify surface precipitation patterns. Green infrastructure, or low‐impact development (LID), can be used as a standalone practice or in concert with gray infrastructure (traditional stormwater management approaches) for cost‐efficient, decentralized stormwater management. The growth in LID over the past several decades has resulted in a concomitant increase in research evaluating LID efficiency and effectiveness, but mostly at localized scales. There is a clear research need to quantify how LID practices affect water quantity (i.e., runoff and discharge) and quality at the scale of catchments. In this overview, we present the state of the science of LID research at the local scale, considerations for scaling this research to catchments, recent advances and findings in scaling the effects of LID practices on water quality and quantity at catchment scales, and the use of models as novel tools for these scaling efforts. WIREs Water 2018, 5:e1254. doi: 10.1002/wat2.1254
This article is categorized under:
Engineering Water > Sustainable Engineering of Water
Science of Water > Hydrological Processes
Science of Water > Water Quality
Visualization of the scaling effects of green infrastructure, or low‐impact development (LID) practices, on downstream waters from plot to nested catchment scales (not to scale).
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.
Key Points
We present the first hydrologic model incorporating surface depression and wetland water storage in one of the world's major river basins
Surface depressions, often overlooked in conventional large‐scale hydrologic modeling, significantly alter landscape water yields
Cumulative hydrologic effects of surface depression storage on downstream waters depend on a combination of local drivers
Geographically isolated wetlands (GIWs), those surrounded by uplands, exchange materials, energy, and organisms with other elements in hydrological and habitat networks, contributing to landscape ...functions, such as flow generation, nutrient and sediment retention, and biodiversity support. GIWs constitute most of the wetlands in many North American landscapes, provide a disproportionately large fraction of wetland edges where many functions are enhanced, and form complexes with other water bodies to create spatial and temporal heterogeneity in the timing, flow paths, and magnitude of network connectivity. These attributes signal a critical role for GIWs in sustaining a portfolio of landscape functions, but legal protections remain weak despite preferential loss from many landscapes. GIWs lack persistent surface water connections, but this condition does not imply the absence of hydrological, biogeochemical, and biological exchanges with nearby and downstream waters. Although hydrological and biogeochemical connectivity is often episodic or slow (e.g., via groundwater), hydrologic continuity and limited evaporative solute enrichment suggest both flow generation and solute and sediment retention. Similarly, whereas biological connectivity usually requires overland dispersal, numerous organisms, including many rare or threatened species, use both GIWs and downstream waters at different times or life stages, suggesting that GIWs are critical elements of landscape habitat mosaics. Indeed, weaker hydrologic connectivity with downstream waters and constrained biological connectivity with other landscape elements are precisely what enhances some GIW functions and enables others. Based on analysis of wetland geography and synthesis of wetland functions, we argue that sustaining landscape functions requires conserving the entire continuum of wetland connectivity, including GIWs.
The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and ...other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales.
•A fully automated algorithm was developed to map wetland inundation dynamics.•Multiple wetland inundation maps (1-m) were produced for the Prairie Pothole Region.•Mapped wetlands show high accuracy when compared to existing surface water products.•The algorithm is scalable for mapping wetland inundation at large geographic scales.
Climate variations and human modifications of the water cycle continue to alter the Earth's surface water and energy exchanges. It is therefore critical to ascertain how these changes impact water ...quality and aquatic ecosystem habitat metrics such as river temperatures. Though river temperature trend analyses exist in the literature, studies on seasonal trends in river temperatures across large spatial extents, e.g. the contiguous United States (US), are limited. As we show through both annual and monthly trend analyses for 20 year (
= 138 sites) and 40 year (
= 40 sites) periods, annual temperature trends across the US mask extensive monthly variability. While most sites exhibited annual warming trends, these annual trends obscured sub-annual cooling trends at many sites. Monthly trend anomalies were spatially organized, with persistent regional patterns at both reference and human-impacted sites. The largest warming and cooling anomalies happened at human impacted sites and during summer months. Though our analysis points to coherence in trends as well as the overall impact of human activity in driving these patterns, we did not investigate the impact of river temperature observation accuracy on reported trends, an area needed for future work. Overall, these patterns emphasize the need to consider sub-annual behavior when managing the ecological impacts of river temperature throughout lotic networks.
This paper evaluates the current state of life cycle impact assessment (LCIA) methods used to estimate potential eutrophication impacts in freshwater and marine ecosystems and presents a critical ...review of the underlying surface water quality, watershed, marine, and air fate and transport (F&T) models. Using a criteria rubric, we assess the potential of each method and model to contribute to further refinements of life cycle assessment (LCA) eutrophication mechanisms and nutrient transformation processes as well as model structure, availability, geographic scope, and spatial and temporal resolution. We describe recent advances in LCIA modeling and provide guidance on the best available sources of fate and exposure factors, with a focus on midpoint indicators. The critical review identifies gaps in LCIA characterization modeling regarding the availability and spatial resolution of fate factors in the soil compartment and identifies strategies to characterize emissions from soil. Additional opportunities are identified to leverage detailed F&T models that strengthen existing approaches to LCIA or that have the potential to link LCIA modeling more closely with the spatial and temporal realities of the effects of eutrophication.
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.
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