Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling

Download or read book Improving Hydrologic Prediction for Large Urban Areas Through Stochastic Analysis of Scale-dependent Runoff Response, Advanced Sensing and High-resolution Modeling written by Amir Norouzi and published by . This book was released on 2019 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to urbanization and climate change, large urban areas such as the Dallas-Fort Worth Metroplex (DFW) area is vulnerable not only to river flooding but also flash flooding. Due to the nonstationarities involved, projecting how the changes in land cover and climate may modify flood frequency in large urban areas is a challenge. Part I of this work develops a simple spatial stochastic model for rainfall-to-areal runoff in urban areas, evaluates climatological mean and variance of mean areal runoff (MAR) over a range of catchment scales, translates them into runoff frequency as a proxy for flood frequency, and assesses its sensitivity to precipitation, imperviousness and soil, and their changes. The results show that the variability of MAR in urban areas depends significantly on the catchment scale and magnitude of precipitation, and that precipitation, soil, and land cover all exert influences of varying relative importance in shaping the frequency of MAR, and hence flood frequency, for different sizes of urban areas. The findings indicate that, due to large sensitivity of frequency of MAR to multiple hydrometeorological and physiographic factors, estimation of flood frequency for urban catchments is inherently more uncertain, and the approach developed in this work may be useful in developing bounds for flood frequencies in urban areas under nonstationary conditions arising from climate change and urbanization. High-resolution hydrologic and hydraulic models are necessary to provide location- and time-specific warnings in densely populated areas. Due to the errors in precipitation input, and model parameters, structures and states, however, increasing the nominal resolution of the models may not improve the accuracy of the model output. Part II of this work tests the current limits of high-resolution hydrologic modeling for real-time forecasting by assessing the sensitivity of stream flow and soil moisture simulations in urban catchments to the spatial resolution of the rainfall input and the a priori model parameters. The hydrologic model used is the National Weather Service (NWS) Hydrology Laboratory's Research Distributed Hydrologic Model (HLRDHM) applied at spatial resolutions of 250 m to 2 km for precipitation and 250 m to 4 km for the a priori model parameters. The precipitation input used are the Collaborative Adaptive Sensing of he Atmosphere (CASA) and the Multisensor Precipitation Estimator (MPE) products available at 500 m and 1 min, and 4 km and 1 hr spatio temporal resolutions, respectively. The stream flow simulation results were evaluated for two urban catchments of 3.4 to 14.4 km2 in Arlington and Grand Prairie, TX. The stream flow observations used in the evaluation were obtained from water level measurements via the rating curves derived from 1-D steady-state non-uniform hydraulic model. The soil moisture simulation result were evaluated for three locations in Arlington where observations are available at depths of 0.05, 0.10, 0.25, 0.50 and 1.00 m. The soil moisture observations were obtained from three Time Domain Transmissometry (TDT) and Time Domain Reflectometry (TDR)sensors newly deployed for this work. The results show that the use of high-resolution QPE improves stream flow simulation significantly, but that, once the resolution of QPE is increased to the scale of the catchment, no clear relationships are found between the simulation accuracy and the resolution of the QPE or hydrologic modeling, presumably because the errors in QPE and models mask the relationships. The soil moisture results suggest that there are disparate infiltration processes at work within a small area in Arlington, and that, while the near-surface simulation of soil moisture is generally skillful, the Sacramento soil moisture accounting model - heat transfer version (SAC-HT) in HLRDHM has difficulty in simulating the vertical dynamics of soil moisture. The findings point to real-time updating of model states to reduce uncertainties in initial soil moisture conditions, and the need for a dense observing network to improve understanding and to assess the impact at the catchment scale. Continuing urbanization will continue to alter the hydrologic response of urban catchments in the DFW area and elsewhere. To assess the impact of recent land cover changes in the study area and to predict what may occur in the future, stream flow and soil moisture were simulated using HLRDHM at 250 m and 5 min resolution with the National Land Cover Data of 2001, 2006 and 2011 for five urban catchments in Arlington and Grand Prairie, TX. The analysis indicates that imperviousness increased by about 15 percent in the DFW area between 2001 and 2011. The findings indicate that, in terms of peak flow, time-to-peak and runoff volume, small events are more sensitive to changes in impervious cover than large events, increase in peak flow is more pronounced for catchments with larger increase in impervious cover, increase in peak flow is also impacted by changes in antecedent soil moisture due to increased impervious cover, runoff volume is not significantly impacted by changes in impervious cover, and changes in time-to-peak relative to the response time of the catchment is impacted by the location of the land cover changes relative to the outlet and the time-to-peak itself. In particular, the Johnson Creek Catchment in Arlington (~40 km2), which has a time-to-peak of only 40 min, shows larger sensitivity in time-to-peak to land cover changes due presumably to the proximity of the area of increased land cover to the catchment outlet. For further evaluation, however, dense observation networks for stream flow and soil moisture, such as the Arlington Urban Hydrology Test bed currently under development, are necessary in addition to the CASA network of X-band polarimetric radars for high-resolution quantitative precipitation information (QPI).