Francisco Olivera

Geographic Information Systems (GIS)-based spatially distributed model for runoff routing

A method is proposed for routing spatially distributed excess precipitation over a watershed to produce runoff at its outlet. The land surface is represented by a (raster) digital elevation model from which the stream network is derived. A routing response function is defined for each digital elevation model cell so that water movement from cell to cell can be convolved to give a response function along a flow path and responses from all cells can be summed to give the outlet hydrograph. An example application of analysis of runoff on Waller Creek in Austin, Texas, is presented.

UNIT HYDROGRAPH DERIVED FROM A SPATIALLY DISTRIBUTED VELOCITY FIELD

A unit hydrograph model is proposed in which the watershed is decomposed into subareas which are individual cells or zones of neighbouring cells. The unit hydrograph is found for each subarea and the response at the outlet to excess rainfall on each subarea is summed to produce the watershed runoff hydrograph. The cell to cell flow path to the watershed outlet is determined from a digital elevation model. A constant flow velocity is assigned to each cell and the time lag between subarea input and response at the watershed outlet is found by integrating the flow time along the path from the subarea to the outlet. The response function for a subarea is modelled as a lagged linear reservoir in which the flow time is equal to the sum of a time of translation and an average residence time in the reservoir. It is shown that the assumption of a spatially varying, but time-invariant, velocity field underlying this model produces a linear system model for all subareas whose outputs can be summed in the manner indicated. An example application is presented for the 8.70 km2 Severn watershed at Plynlimon in Wales using a 50 m digital elevation model in which the cell velocity is calculated by modifying an average watershed velocity according to the terrain slope and the drainage area of each cell. The resulting model reasonably reproduces the observed unit hydrograph.

Evaluation of the Global Land Data Assimilation System using global river discharge data and a source‐to‐sink routing scheme

[1]xa0Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the worlds major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge.

Global‐scale flow routing using a source‐to‐sink algorithm

In this paper, the development and global application of a new approach to large-scale river routing is described. It differs from previous methods by the extent to which the information content of high-resolution global digital elevation models is exploited in a computationally efficient framework. The model transports runoff directly from its source of generation in a land model cell to its sink on a continental margin or in an internally draining basin (and hence is referred to as source-to-sink routing) rather than from land cell to land cell (which we call cell-to-cell routing). It advances the development of earlier source-to-sink models by allowing for spatially distributed flow velocities, attenuation coefficients, and loss parameters. The method presented here has been developed for use in climate system models, with a specific goal of generating hydrographs at continental margins for input into an ocean model. However, the source-to-sink approach is flexible and can be applied at any space-time scale and in a number of other types of large-scale hydrological and Earth system models. Hydrographs for some of the worlds major river basins resulting from a global application, as well as hydrographs for the Nile River from a more detailed application, are discussed.

Extracting low-resolution river networks from high-resolution digital elevation models

[1]xa0Including a global river network in the land component of global climate models (GCMs) is necessary in order to provide a more complete representation of the hydrologic cycle. The process of creating these networks is called river network upscaling and consists of lowering the resolution of already available fine networks to make them compatible with GCMs. Fine-resolution river networks have a level of detail appropriate for analysis at the watershed scale but are too intensive for global hydrologic studies. A river network upscaling algorithm, which processes fine-resolution digital elevation models to determine the flow directions that best describe the flow patterns in a coarser user-defined scale, is presented. The objectives of this study were to develop an algorithm that advances the previous work in the field by being applicable at a global scale, allowing for the upscaling to be performed in a projected environment, and generating evenly distributed flow directions.

A derivation of the number of minima of the Griewank function

The Griewank function is commonly used to test the ability of different solution procedures to find local optima. It is important to know the exact number of minima of the function to support its use as a test function. However, to the best of our knowledge, no attempts have been made to analytically derive the number of minima. Because of the complex nature of the function surface, a numerical method is developed to restrict domain spaces to hyperrectangles satisfying certain conditions. Within these domain spaces, an analytical method to count the number of minima is derived and proposed as a recursive functional form. The numbers of minima for two search spaces are provided as a reference.

Uncertainty in hurricane surge simulation due to land cover specification

Hurricane storm surge is one of the most costly natural hazards in the United States. Numerical modeling to predict and estimate hurricane surge flooding is currently widely used for research, planning, decision making, and emergency response. Land cover plays an important role in hurricane surge numerical modeling because of its impacts on the forcing (changes in wind momentum transfer to water column) and dissipation (bottom friction) mechanisms of storm surge. In this study, the hydrodynamic model ADCIRC was used to investigate predicted surge response in bays on the central and lower Texas coast using different land cover data sets: (1) Coastal Change Analysis Program for 1996, 2001, and 2006; (2) the National Land Cover Dataset for 1992, 2001, and 2006; and (3) the National Wetlands Inventory for 1993. Hypothetical storms were simulated with varying the storm track, forward speed, central pressure, and radius to maximum wind, totaling 140 simulations. Data set choice impacts the mean of maximum surges throughout the study area, and variability in the surge prediction due to land cover data set choice strongly depends on storm characteristics and geographical location of the bay in relation to storm track. Errors in surge estimation due to land cover choice are approximately 7% of the surge value, with change in surge prediction varying by as much as 1 m, depending on location and storm condition. Finally, the impact of land cover choice on the accuracy of simulating surges for Hurricane Bret in 1999 is evaluated.

Relative Importance of the Different Rainfall Statistics in the Calibration of Stochastic Rainfall Generation Models

Stochastic rainfall generators are used in hydrologic analysis because they can provide precipitation input to models whenever data are not available, and their parameters are calculated so that the long-term statistics of the synthetic rainfall time series match those of the rainfall records. However, although mentioned in the literature, the relative importance of each rainfall statistic on the watershed response has not been addressed yet, and no guidance on how to account for it has been provided. In this paper, this relative importance is estimated and used to ponder each statistic differently in the calibration of rainfall generators so that it better reflects the watershed hydrology. Rainfall records of 1,249 rain gauges throughout the contiguous United States were used in the study. It was found that when synthetic rainfall time series are generated by weighting the precipitation statistics according to their relative importance, predicted runoff depths and peak flows are underestimated by 4 and 3%, respectively, whereas when they are generated by giving the same weight to all statistics, the underestimation is by 20 and 14%, respectively. These results, based on a significant number of rain gauges, confirm the benefit of weighing the statistics differently for watershed analysis. DOI: 10.1061/(ASCE)HE.1943-5584.0000453.

WaterNet: A GIS Application for the Analysis of Hydrologic Networks Using Vector Spatial Data

Traditionally, stream and sub-watershed characterization in GIS has been accomplished using a DEM-based terrain analysis approach; however, there is a large amount of existing vector hydrographic data difficult to accurately reproduce using DEMs. WaterNet is a GIS/hydrologic application for the integration and analysis of stream and sub-watershed networks in vector format. Even with vector data, hydrologic inconsistencies between streams and sub-watersheds do exist, and are revealed in the form of streams crossing drainage divides and sub-watersheds with more than one outlet. WaterNet rectifies these inconsistencies and couples the two datasets. Most algorithms involving traces of dendritic networks employ a form of tree traversal which requires topologic information to be organized into specialized data structures. On the contrary, WaterNet develops topologic relationships from GIS attribute tables, which, in combination with sorting and querying algorithms, make the calculation process efficient and easy to implement. With the topologic relationships of the streams and sub-watersheds, WaterNet can perform traces to calculate cumulative network parameters, such as flow lengths and drainage areas. WaterNet was applied to the catchment of the Texas Gulf coast for a total of 100 cataloging units (411,603 km 2 ) and 60,145 stream lines (183,228 km).

Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 1. Impact on peak and extreme rainfall values

A noble approach of stochastic rainfall generation that can account for inter-annual variability of the observed rainfall is proposed. Firstly, we show that the monthly rainfall statistics that is typically used as the basis of the calibration of the parameters of the Poisson cluster rainfall generators has significant inter-annual variability and that lumping them into a single value could be an oversimplification. Then, we propose a noble approach that incorporates the inter-annual variability to the traditional approach of Poisson cluster rainfall modeling by adding the process of simulating rainfall statistics of individual months. Among 132 gage-months used for the model verification, the proportion that the suggested approach successfully reproduces the observed design rainfall values within 20xa0% error varied between 0.67 and 0.83 while the same value corresponding to the traditional approach varied between 0.21 and 0.60. This result suggests that the performance of the rainfall generation models can be largely improved not only by refining the model structure but also by incorporating more information about the observed rainfall, especially the inter-annual variability of the rainfall statistics.