Author Archive for: mdick
About Margot Dick
This author has yet to write their bio.Meanwhile lets just say that we are proud Margot Dick contributed a whooping 81 entries.
Entries by Margot Dick
The authors analyzed the results of the Land Surface Model and the Routing Application for Parallel Computation of Discharge. The authors took the results – which showed that enhancements were still possible for the programs – and investigated the source of the errors. Their research resulted in an improvement in model performance. ElSadaani, M., W.F. […]
The authors propose a hydrologic evaluation framework for gridded rainfall products. The framework will aid in the compilation of rainfall data and lower the chance for errors with satellite rainfall products in hydrologic applications. ElSadaani, M., W.F. Krajewski, and D.L. Zimmerman, River network based characterization of errors in remotely sensed rainfall products in hydrological applications, Remote […]
The analysis of unique rainfall-runoff data can offer more in-depth knowledge than the current internal spatial variability of small-scale runoff yield. Through the use of 12 unique hills, the authors intend to reduce the error of runoff data. Chen, B., W.F. Krajewski, M. Helmers, and Z. Zhang, Spatial variability and temporal persistence of event […]
NEXRAD collects and stores rainfall data in a cloud, allowing the IFC to develop a program to retrieve the data and create a map. The map has the benefit of allowing researchers to choose a specific area of study. Seo, B.-C., M. Keem, R. Hammond, I. Demir, and W.F. Krajewski, A pilot infrastructure for searching rainfall […]
Reflectivity measurements are one example of a measurement that can be taken from GPM, DPR, and NEXRAD radars. This study demonstrates the potential use of the NASA’s Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) to examine ground radar (GR) miscalibration and other uncertainty sources (e.g., partial beam blockage). Keem, M., B.-C. Seo, W.F. Krajewski, […]
A stochastic model was created to create high resolution surface soil moisture information. Jadidoleslam, N., R. Mantilla,W.F. Krajewski and M. Cosh, Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture, Journal of Hydrology, 2019 (in press).
Iowa Flood Center
The University of Iowa
100 Stanley Hydraulics Laboratory
Iowa City, IA 52242
Contact: Breanna Shea