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.

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.

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.