Runoff Storage Potential of Drained Upland Depressions on the Des Moines Lobe of Iowa

Abstract

We present estimates of the volumetric storage capacities of currently drained upland depressions and catchment depressional specific storage and runoff storage indices for the Des Moines Lobe of Iowa (DML‐IA) subregion of the Prairie Pothole Region of North America. Storage capacities were determined using hydrologically enforced Light Detection and Ranging‐derived digital elevation models, and a unique geoprocessing algorithm. Depressional specific storage was estimated for each 12‐digit Hydrologic Unit Code (HUC12) watershed in the region from total catchment‐specific depressional storage volume and catchment area. Runoff storage indices were calculated using catchment depressional specific storage values and estimates of the amount of rainfall likely to fall within each watershed during sub‐annual and 1‐, 2‐, 5‐, and 10‐year 24‐h events. The 173,171 identified drained depressions in the DML‐IA can store up to 903.5 Mm3 of runoff. Most of this capacity is in depressions located in the north of the region. Specific storage varies from nearly 109 mm in the younger landscapes to <10 mm in older more eroded areas. For 95% of the HUC12 watersheds comprising the region, depressional storage will likely be exhausted by rainfall‐derived runoff in excess of a 1‐year 24‐h event. Rainfall amounts greater than a 5‐year 24‐h event will exceed all available depressional storage. Therefore, the capacity of drained depressions in the DML‐IA to mitigate flooding resulting from infrequent, but large, storm events is limited.

Featured Collection Introduction: The Emerging Science of Aquatic System Connectivity II

“…In contrast to Ameli and Creed (), Green et al. () determined the drained upland depressions in this region have insufficient storage capacity to significantly alter regional and local flood events.…”

“…Green et al. () modeled the runoff storage potential of drained upland depressions on the Des Moines Lobe of Iowa using hydrologically enforced Light Detection and Ranging (LIDAR)‐derived Digital Elevation Models and a unique geoprocessing algorithm to determine storage capacities. In contrast to Ameli and Creed (), Green et al.…”

“…These findings can help managers prioritize wetland restoration efforts for flood or drought risk mitigation. Green et al (2019) modeled the runoff storage potential of drained upland depressions on the Des Moines Lobe of Iowa using hydrologically enforced Light Detection and Ranging (LIDAR)-derived Digital Elevation Models and a unique geoprocessing algorithm to determine storage capacities. In contrast to Ameli and Creed (2019), Green et al (2019) determined the drained upland depressions in this region have insufficient storage capacity to significantly alter regional and local flood events.…”

“…Green et al (2019) modeled the runoff storage potential of drained upland depressions on the Des Moines Lobe of Iowa using hydrologically enforced Light Detection and Ranging (LIDAR)-derived Digital Elevation Models and a unique geoprocessing algorithm to determine storage capacities. In contrast to Ameli and Creed (2019), Green et al (2019) determined the drained upland depressions in this region have insufficient storage capacity to significantly alter regional and local flood events. Jones, Ameli, et al (2019) review the current capabilities of hydrologic models and their ability to simulate hydrologic connectivity of non-floodplain wetlands.…”

Development of a novel framework for modeling field-scale conservation effects of depressional wetlands in agricultural landscapes

“…This precipitation-driven spill occurred because the antecedent water levels were very high from large runoff events in 2009, and precipitation in 2010 caused spill from the wetland. On the landscape scale, other research suggests that restoring all depressional wetlands might not be enough to store floodwater as Iowa continues to experience an increase in extreme precipitation years (Green et al 2019). Although depressional wetland restoration might not store all floodwater, Green et al (2019) also found that restoring wetlands could minimize flood severity and greatly reduce nutrient loads in the Mississippi River Basin.…”