In February of 2014 NASA has launched the core observatory of The Global Precipitation Measurement Mission (GPM). Since then, the mission has been providing a wealth of observation data collected by the core observatory along with other satellites belonging to the mission space constellation. One of the most important data products that GPM provides is the Level 4 (L4) rainfall data product called Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG is constructed using the raw data collected by the Microwave (MW) sensors on board the constellation satellites along with the Infrared (IR) sensors on board geostationary satellites and the advance Dual-frequency Precipitation Radar (DPR) on board the GPM core satellite. The IMERG product is available globally for all interested researchers to use. In this dissertation, I focus on the applicability of IMERG in hydrologic applications, and specifically in flood peak modeling.
In order to conduct a comprehensive evaluation of IMERG that is oriented towards hydrologic modeling. I have explored multiple hydrologic models which can be used to produce stream flow estimates using IMERG without the need of parameter calibration based on the model’s inputs. The calibration free capability is essential since model parameter calibration obscures the effect of the errors associated with the rainfall input on the estimated discharges, which in turn will limit our understanding about the distribution of the errors in IMERG over space and time. The two hydrologic models we used in this study are both physically based distributed models and were setup over the domain of the state of Iowa which is located in the United States’ Midwest. I also explored the performance of one of the hydrologic models’ component, which is the runoff-routing component, in order to estimate an additional portion of the errors in the discharge estimates that is not attributed to the model’s input but rather to the hydrologic model itself.
A significant portion of my dissertation is concerned with identifying and using accurate methods to evaluate both IMERG and the hydrologic models’ outputs in a hydrologic context that is useful for flood modeling. Several studies have evaluated other satellite rainfall products using methods that vary in complexity. Some studies used the simplest methods of evaluation, such as, mean aerial differences and standard deviation of the differences (additive or multiplicative) compared to a benchmark rainfall product. This is done without taking the spatial dependency of the errors in space into consideration. Other studies modeled the spatial dependency (correlation) between the errors in the rainfall product, however, using Euclidean distance based approaches that do not account for the hydrologic basins’ shape and size. Nevertheless, it is important to realize that hydrologic models will eventually aggregate the rainfall values, along with the errors associated with them, through a stream network that is dichotomous in nature and does not comply with Euclidean distance. Thus, we employed a stream based evaluation framework, called the Spatial Stream Network (SSN) approaches, to characterize the errors in IMERG taking into account the stream distances and the stream connectivity information between evaluation sites. Although previously used in applications such as modeling water temperatures and pollutant transport, to the best of my knowledge this approach has not been used in rainfall product evaluation before this study. The SSN analysis of IMERG allowed me to answer the question, “What is the proper basin scale which is capable of filtering out the correlated errors in IMERG by accumulating the rainfall values through the stream network?”
Finally, in order to add value to the current methods of evaluating model simulated stream flows. I proposed a time based evaluation that is capable of detecting peaks in both the observed and simulated flows and estimating the lag time of the simulated peaks. Typically, previous studies have used simple skill scores such as Root Mean Squared Errors (RMSE), correlation coefficient, and Nash-Sutcliff Efficiency (NSE) to evaluate hydrograph performance as a whole, or the difference in time to peak which involves primitive peak detection method (e.g., a moving or a defined time window). In this dissertation I propose a Continuous Wavelet Transform (CWT) based method to evaluate the peak times and shapes produced by the hydrologic model. The method is based on filtering the frequencies in the hydrograph by treating it as a signal and detecting sharp features in both the observed and time series and the phase difference between them. We also emphasized on the importance of the choice of wavelet shape used in the evaluation, and how different wavelet shapes can affect the inference about the time series.