Exploring the benefits of satellite remote sensing for flood prediction across scales

Abstract

Space-borne remote sensing datasets have the potential to allow us to progress towards global scale flood prediction systems. However, these datasets are limited in terms of space-time resolution and accuracy, and the best use of such data requires understanding how uncertainties propagate through hydrological models. An unbiased investigation of different datasets for hydrological modeling requires a parsimonious calibration-free model, since calibration masks uncertainties in the data and model structure. This study, which addresses these issues, consists of two parts: 1) the development and validation of a multi-scale distributed hydrological model whose parameters can be directly linked to physical properties of the watershed, thereby avoiding the need of calibration, and 2) application of the model to demonstrate how data uncertainties propagate through the model and affect flood simulation across scales. I based the model development on an interactive approach for model building. I systematically added processes and evaluated their effects on flood prediction across multiple scales. To avoid the need for parameter calibration, the level of complexity in representing physical processes was limited by data availability. I applied the model to simulate flows for the Cedar River, Iowa River and Turkey River basins, located in Iowa. I chose this region because it is rich in high quality hydrological information that can be used to validate the model. Moreover, the area is frequently flooded and was the center of an extreme flood event during the summer of 2008. I demonstrated the model's skills by simulating medium to high-flow conditions; however the model's performance is relatively poor for dry (low flow) conditions. Poor model performance during low flows To my sweet parents and siblings,

An integral-balance nonlinear model to simulate changes in soil moisture, groundwater and surface runoff dynamics at the hillslope scale

“…This system of ordinary differential equations is also based on the physical properties of the hillslope-channel coupling and has all its parameters determined from digital elevation models (DEM), geological maps and some theoretical considerations. Moreover, when simulated for large-scale river networks, it shows good approximation of flow data [4].…”

“…An initial attempt: a calibration-free integral-balance ODE system fails to reproduce the soil moisture data, at hillslope-scale In our quest for the construction of an ODE model at the hillslope-scale that is completely free of calibration, we have initially considered the model introduced in [4]. This system of ordinary differential equations is also based on the physical properties of the hillslope-channel coupling and has all its parameters determined from digital elevation models (DEM), geological maps and some theoretical considerations.…”

“…Given that the model from [4] is not the focus of our paper, and to ensure completion, we only summarize its equations below and list its fluxes and parameter values in Appendix B ds p dt ¼ c 7 ðp À e p À q pl À q pu Þ; dv dt ¼ c 8 ðq pu À q us À e unsat Þ;…”

“…Taking into consideration our discussion in A, h b is equal to the average soil depth times the soil porosity and the variable b; MaxInf and K SAT were obtained based on soil datasets (SSURGO), and n is the Manning roughness coefficient for forested area. We estimate k 1 and k 2 based on velocity measurements data provided by the USGS -see [4] for details. As this dataset does not comprise data for hillslopes (just for watershed larger than 10 km 2 ), we estimate v 0 based on the average hillslope concentration time (time difference between the peak of rainfall and the peak of streamflow) and flow path.…”

“…Given that the Shale Hills watershed data set was used in previous studies [23] to examine the ability of the spatially explicit PIHM model, it constitutes an ideal test case to determine the extent to which ODE-based models can reproduce the behavior of water movements in a first order watershed. Numerical simulations and comparison with data, presented in Section 3.1, show that a simple model [4] with a parameterization based on commonly available data is skillful enough to reproduce streamflow fluctuations at the outlet of the basin, but it is not capable of correctly reproducing the dynamics of the surface and subsurface water interactions. By contrast, the more complex ODE-based nonlinear model developed here (see Sections 3.3 and 4) is indeed capable of achieving the same level of accuracy obtained by PDE-based models presented in the literature [15,23].…”