A document by Nicolas Velásquez. Click on the document to view its contents.

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

In this study, the authors explore simple concepts of persistence in streamflow forecasting based on the real-time streamflow observations. The authors use 15-minute streamflow observations from the year 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers over the State of Iowa. The spatial scale of the basins ranges from about 7 km2 to 37,000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of different persistence schemes across spatial scales and lead-times. They show that temporal persistence forecasts skill has strong dependence on basin size and weaker, but non-negligible, dependence on geometric properties of the river network of the basin. The authors show that anomaly persistence forecasting can serve as a good reference for the evaluation of real-time streamflow forecasts at scales of order 100 km2. Building on results from this temporal persistence, they extend the streamflow persistence to space through flow-connected river network. It simply assumes that streamflow at a station in space will persist to another station which is flow-connected, and refer to it as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF derived streamflow forecasts is strongly dependent on basin area-ratio and lead-times, and weakly related to the downstream flow distance between stations. They show that the skill depicted in terms of Kling-Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.5 and lead-time up to three days. Adding complexities of hydrologic routing and rainfall QPF to the PSPF further improves the skill. The authors discuss the implications of their findings for improvements of rainfall-runoff models as well as data assimilation schemes.

This evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation allows a better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify overall model improvement. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors find discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.

Floods claim a high toll in fatalities and economic impacts. Despite their societal relevance, there is much more to learn about the projected changes in discharge and flooding. Here we force an operational hydrologic model over the state of Iowa with high-resolution convection-permitting climate-model precipitation to evaluate the response of 140 watersheds to climate change. At the end of the century, under the most aggressive scenario in terms of fossil fuel use, we show that the transition from snow to rainfall and a ~30% increase in extreme precipitation rates lead to a doubling of maximum discharge during the spring and extended the flood season into the fall. Total discharge volumes are also expected to increase. Our results suggest that flood projections based on extreme precipitation increases alone substantially underestimate future risk due to the nonlinearity of the hydrologic response explained by long-term soil moisture memory and its feedbacks with precipitation.