Morphologic characterization of urban watersheds and its use in quantifying hydrologic response

2009 Summer.Covers not scanned.Includes bibliographical references.Print version deaccessioned 2022.Current methods for hydrologic characterization of urban watersheds and analysis of the impacts of urbanization are primarily based on the description of imperviousness and how changes in this characteristic affect storage, infiltration, and runoff generation. The morphology of urban watersheds and the effects of urbanization on the structure of the drainage system have been much less studied. The overarching objectives of this study are to develop methodologies to characterize the morphology of urban drainage systems including the hillslopes, streets, pipes, and channels and to use this characterization to model the hydrologic response of the watershed. These objectives are accomplished through: (a) an exploration of potential applications of morphologic theories in the characterization of urban watersheds and the impacts of urbanization; (b) the development and testing of a methodology to generate urban terrains (i.e. a raster representation of the topography) in which the effects of conduits typically observed in urban areas are represented; and (c) the development and testing of a new rainfall-runoff model called the U-McIUH (Urban Morpho-climatic Instantaneous Unit Hydrograph). The model is based on the morpho-climatic instantaneous unit hydrograph theory, in which the hydrologic response is identified from the spatial structure of the watershed and the properties of the storm event. The morphologic approach adopted reveals significant impacts of urbanization on the internal structure of natural watersheds at a wide range of scales. This finding is relevant when building stormwater models intended to simulate and compare the pre- and post-development catchment response. The morphologic impacts should be incorporated into stormwater models through the redefinition of model parameters that characterize both the channelized and unchannelized portions of the catchment when the urbanized scenario is simulated. This research also shows the importance of incorporating artificial conduits into urban terrain for hydrologic modeling. A new method to incorporate the artificial conduits into the DEM based on the real elevation of these conduits proved to be superior to other previously available methods because it better represents the flow directions and flow paths. Finally, the new rainfall-runoff model developed in this study fills an existing gap in the field of distributed stormwater modeling. It provides a more thorough treatment of the flows in minor conduits and unchannelized portions of the watershed, which enhances the simulations of runoff accumulation that are traditionally used in conceptual models. The model is parsimonious and uses a simplification of kinematic wave routing that considers the dependence of the unit hydrograph on rainfall intensity and the effect of upstream contribution on the travel times without explicitly solving the flow equation at each cell for each time step. This simplification reduces the complexity of the model computations while still producing reasonable model performance

Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach

Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration’s Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1–3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction

Advanced numerical models for the propagation of floods with high-sediment concentrations in mountain rivers

Rapid floods induced by extreme precipitation are common events in regions near the Andes mountain range. Growing urban development, combined with the changing climate and the influence of El Niño, have increased the exposure of the population in many regions of South America. Simulations of flash floods in these watersheds are very challenging, due to the complex morphology, the insufficient hydrometeorological data, and the uncertainty posed by the variability of sediment concentration. To address these issues, we develop a high-resolution numerical model of the non-linear shallow water equations, coupled with the mass conservation of sediment, and considering the density effects and changes of rheology in the momentum equation. Based on these simulations we develop a real-time early-warning system, by creating a surrogate model or meta-model from the simulations. Using a small set of parameters, we define storms for a wide range of meteorological conditions, and utilize the high-fidelity model results to create a database of flood propagation under different conditions. Through this second model we perform a sophisticated interpolation/regression, and approximate efficiently the flow depths and velocities. This is the first application of its kind in the Andes region, which can be used to improve the prediction of flood hazard in real conditions, employing low computational resources. We also create a framework to develop early warning systems, and to help decision makers and city planners in these mountain regions

Advanced numerical models for the propagation of floods with high-sediment concentrations in mountain rivers

Rapid floods induced by extreme precipitation are common events in regions near the Andes mountain range. Growing urban development, combined with the changing climate and the influence of El Niño, have increased the exposure of the population in many regions of South America. Simulations of flash floods in these watersheds are very challenging, due to the complex morphology, the insufficient hydrometeorological data, and the uncertainty posed by the variability of sediment concentration. To address these issues, we develop a high-resolution numerical model of the non-linear shallow water equations, coupled with the mass conservation of sediment, and considering the density effects and changes of rheology in the momentum equation. Based on these simulations we develop a real-time early-warning system, by creating a surrogate model or meta-model from the simulations. Using a small set of parameters, we define storms for a wide range of meteorological conditions, and utilize the high-fidelity model results to create a database of flood propagation under different conditions. Through this second model we perform a sophisticated interpolation/regression, and approximate efficiently the flow depths and velocities. This is the first application of its kind in the Andes region, which can be used to improve the prediction of flood hazard in real conditions, employing low computational resources. We also create a framework to develop early warning systems, and to help decision makers and city planners in these mountain regions

Meteorological Characterization of Large Daily Flows in a High-Relief Ungauged Basin Using Principal Component Analysis

Decision making and hydrologic design for coping with floods are complex tasks in poorly gauged high-relief basins. The response of such basins is driven by precipitation and temperature, which controls the freezing level elevation and size of the runoff-contributing area. Moreover, early warning of floods based solely on real-time in situ monitoring is impractical. This study presents a meteorological characterization of daily flows based on off-site daily precipitation and temperature data in a high-relief catchment in central Chile. The results show that the variables that best explain daily discharges are the cumulative precipitation over the previous 3 days measured at a high elevation and the minimum temperature on the day of the maximum discharge measured at a lower elevation in the valley. These variables were used to build three multivariate regression models, based on principal component analysis, which are able to predict the occurrence of daily flows, particularly for low exceedance probabilities. Although developed for a particular catchment, and despite the specific meteorological threshold magnitudes identified for the catchment, the analysis is easily extendable to other similar high-relief locations

Daily and seasonal variation of the surface temperature lapse rate and 0 degrees C isotherm height in the western subtropical Andes

The spatial distribution of surface air temperatures is essential for understanding and modelling high-relief environments. Good estimations of the surface temperature lapse rate (STLR) and the 0 degrees C isotherm height (H0) are fundamental for hydrological modelling in mountainous basins. Although STLR changes in space and time, it is typically assumed to be constant leading to errors in the estimation of direct-runoff volumes and flash-floods risk assessment. This paper characterizes daily and seasonal temporal variations of the in-situ STLR and H0 over the western slope of the subtropical Andes (central Chile). We use temperature data collected during 2 years every 10 min by a 16 sensors network in a small catchment with elevations ranging between 700 and 3,250 m. The catchment drains directly into Santiago, the Chilean capital with more than seven million inhabitants. Resulting values are compared against those obtained using off-site, operational data sets. Significant intra- and inter-day variations of the in-situ STLR were found, likely reflecting changes in the low-level temperature inversion during dry conditions. The annual average in-situ STLR is -5.9 degrees C/km during wet-weather conditions. Furthermore, STLR and H0 estimations using off-site gauges are extremely sensitive to the existence of gauging stations at high elevations.Fondo de Financiamiento de Centros de Investigación de Aéreas Prioritarias 15110009 15110017 15110020 Fondo de Fomento al Desarrollo Científico y Tecnológico IT13i20015 Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 116143

A Dynamic, Multivariate Sustainability Measure for Robust Analysis of Water Management under Climate and Demand Uncertainty in an Arid Environment

Considering water resource scarcity and uncertainty in climate and demand futures, decision-makers require techniques for sustainability analysis in resource management. Through unclear definitions of “sustainability”, however, traditional indices for resource evaluation propose options of limited flexibility by adopting static climate and demand scenarios, limiting analysis variables to a particular water-use group and time. This work proposes a robust, multivariate, dynamic sustainability evaluation technique and corresponding performance indicator called Measure of Sustainability (MoS) for resource management that is more adapted to withstand future parameter variation. The range of potential future climate and demand scenarios is simulated through a calibrated hydrological model of Copiapó, Chile, a case study example of an arid watershed under extreme natural and anthropogenic water stresses. Comparing MoS and cost rankings of proposed water management schemes, this paper determines that the traditional evaluation method not only underestimates future water deficits, but also espouses solutions without considering uncertainties in supply and demand. Given the uncertainty of the future and the dependence of resources upon climate and market trajectories, the MoS methodology proposes solutions that, while perhaps are not the most optimal, are robust to variations in future parameter values and are thus the best water management options in a stochastic natural world