A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Impact of remote sensing upon the planning, management, and development of water resources

Principal water resources users were surveyed to determine the impact of remote data streams on hydrologic computer models. Analysis of responses demonstrated that: most water resources effort suitable to remote sensing inputs is conducted through federal agencies or through federally stimulated research; and, most hydrologic models suitable to remote sensing data are federally developed. Computer usage by major water resources users was analyzed to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era

2D Unsteady Routing and Flood Inundation Mapping for Lower Region of Brazos River Watershed

Present study uses two dimensional flow routing capabilities of hydrologic engineering center\u27s river analysis system (HEC-RAS) for flood inundation mapping in lower region of Brazo River watershed subjected to frequent flooding. For analysis, river reach length of 20 km located at Richmond, Texas, was considered. Detailed underlying terrain information available from digital elevation model of 1/9-arc second resolution was used to generate the two-dimensional (2D) flow area and flow geometrics. Streamflow data available from gauging station USGS08114000 was used for the full unsteady flow hydraulic modeling along the reach. Developed hydraulic model was then calibrated based on the manning\u27s roughness coefficient for the river reach by comparison with the downstream rating curve. Corresponding water surface elevation and velocity distribution obtained after 2D hydraulic simulation were used to determine the extent of flooding. For this, RAS mapper\u27s capabilities of inundation mapping in HEC-RAS itself were used. Mapping of the flooded areas based on inflow hydrograph on each time step were done in RAS mapper, which provided the spatial distribution of flow. The results from this study can be used for flood management as well as for making land use and infrastructure development decisions

Evapotranspiration Mapping for Forest Management in California's Sierra Nevada

We assessed the response of densely forested watersheds with little apparent annual water limitation to forest disturbance and climate variability, by studying how past wildfires changed forest evapotranspiration, and what past evapotranspiration patterns imply for the availability of subsurface water storage for drought resistance. We determined annual spatial patterns of evapotranspiration using a top-down statistical model, correlating measured annual evapotranspiration from eddycovariance towers across California with NDVI (Normalized Difference Vegetation Index) measured by satellite, and with annual precipitation. The study area was the Yuba and American River watersheds, two densely forested watersheds in the northern Sierra Nevada. Wildfires in the 1985-2015 period resulted in significant post-fire reductions in evapotranspiration for at least 5 years, and in some cases for more than 20 years. The levels of biomass removed in medium-intensity fires (25- 75% basal area loss), similar to magnitudes expected from forest treatments for fuels reduction and forest health, reduced evapotranspiration by as much 150-200 mm yr-1 for the first 5 years. Rates of recovery in post-wildfire evapotranspiration confirm the need for follow-up forest treatments at intervals of 5-20 years to sustain lower evapotranspiration, depending on local landscape attributes and interannual climate. Using the metric of cumulative precipitation minus evapotranspiration (P-ET) during multi-year dry periods, we found that forests in the study area showed little evidence of moisture stress during the 1985-2018 period of our analysis, owing to relatively small reliance on interannual subsurface water storage to meet dry-year evapotranspiration needs of vegetation. However, more-severe or sustained drought periods will push some lower-elevation forests in the area studied toward the cumulative P-ET thresholds previously associated with widespread forest mortality in the southern Sierra Nevada

Application of Advanced Information and Communication Technologies in a Local Flood Warning System

This paper deals with the practical application of a local flood warning system. The system is built on the mathematical model of a selected area. The rainfall-runoff processes are simulated in real-time. The warning system is designed as an on-line, real-time data inputs-processing system so that it can provide a timely warning. The warning system is based on a mathematical model and it uses modern information and communication technology tools. For the system to work properly, it is absolutely necessary to adhere to a real mathematical model, and therefore a calibration on real historical data and direct measurements is required. This article describes the tasks of data collection, of building the mathematical model of the rainfall-runoff process, and the monitoring system design. The composed algorithm is able, based on the measured input data and the modeled situation, send a notification message to the monitoring centre and warn respective civil protection authorities via SMS messages

Calibration and accuracy assessment of Leica ScanStation C10 terrestrial laser scanner

Requirement of high accuracy data in surveying applications has made calibration procedure a standard routine for all surveying instruments. This is due to the assumption that all observed data are impaired with errors. Thus, this routine is also applicable to terrestrial laser scanner (TLS) to make it available for surveying purposes. There are two calibration approaches: (1) component, and (2) system calibration. With the intention to specifically identify the errors and accuracy of the Leica ScanStation C10 scanner, this study investigates component calibration. Three components of calibration were performed to identify the constant, scale error, accuracy of angular measurement and the effect of angular resolution for distance measurement. The first calibration has been processed using closed least square solutions and has yielded the values of constant (1.2 mm) and scale error (1.000008879). Using variance ratio test (F-Test), angles observation (horizontal and vertical) for Leica C10 scanner and Leica TM5100A theodolite have shown significance difference. This is because the accuracy of both sensors are not similar and these differences are 0.01 and 0.0075º for horizontal and vertical measurements, respectively. Investigation on the resolution setting for Leica C10 scanner has highlighted the drawback of the tilt-and-turn target. Using the highest resolution, Leica Cyclone software only able to recognize the tilt-and-turn target up to 10 m distance compare to 200 m for the black and white target

Analyzing Remote Sensing Data in R: The landsat Package

Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.

Application of a stochastic snowmelt model for probabilistic decisionmaking

A stochastic form of the snowmelt runoff model that can be used for probabilistic decision-making was developed. The use of probabilistic streamflow predictions instead of single valued deterministic predictions leads to greater accuracy in decisions. While the accuracy of the output function is important in decisionmaking, it is also important to understand the relative importance of the coefficients. Therefore, a sensitivity analysis was made for each of the coefficients

Enhanced watershed modeling and data analysis with a fully coupled hydrologic model and cloud-based flow analysis

2014 Summer.Includes bibliographical references.In today's world of increased water demand in the face of population growth and climate change, there are no simple answers. For this reason many municipalities, water resource engineers, and federal analyses turn to modeling watersheds for a better understanding of the possible outcomes of their water management actions. The physical processes that govern movement and transport of water and constituents are typically highly nonlinear. Therefore, improper characterization of a complex, integrated, processes like surface-subsurface water interaction can substantially impact water management decisions that are made based on existing models. Historically there have been numerous tools and watershed models developed to analyze watersheds or their constituent components of rainfall, run-off, irrigation, nutrients, and stream flow. However, due to the complexity of real watershed systems, many models have specialized at analyzing only a portion of watershed processes like surface flow, subsurface flow, or simply analyzing local monitoring data rather than modeling the system. As a result many models are unable to accurately represent complex systems in which surface and subsurface processes are both important. Two popular watershed models have been used extensively to represent surface processes, SWAT (Arnold et al, 1998), and subsurface processes, MODFLOW (Harbaugh, 2005). The lack of comprehensive watershed simulation has led to a rise in uncertainty for managing water resources in complex surface-subsurface driven watersheds. For this reason, there have been multiple attempts to couple the SWAT and MODFLOW models for a more comprehensive watershed simulation (Perkins and Sophocleous, 1999; Menking, 2003; Galbiati et al., 2006; Kim et al., 2008); however, the previous couplings are typically monthly couplings with spatial restrictions for the two models. Additionally, most of these coupled SWAT-MODFLOW models are unavailable to the general public, unlike the constituent SWAT and MODFLOW models which are available. Furthermore, many of these couplings depend on a forced equal spatial discretization for computational units. This requires that one MODFLOW grid cell is the same size and location of one SWAT hydrologic response unit (HRU). Additionally, many of the previous couplings are based on a loose monthly average coupling which might be insufficient in natural spring and irrigated agricultural driven groundwater systems which can fluctuate on a sub-monthly time scale. The primary goal of this work is to enhance the capacity for modeling watershed processes by fully coupling surface and subsurface hydrologic processes at a daily time step. The specific objectives of this work are 1) to examine and create a general spatial linkage between SWAT and MODFLOW allowing the use of spatially-different existing models for coupling; 2) to examine existing practices and address current weaknesses for coupling of the SWAT and MODFLOW models to develop an integrated modeling system; 3) to demonstrate the capacity of the enhanced model compared to the original SWAT and MODFLOW models on the North Fork of the Sprague River in the Upper Klamath Basin in Oregon. The resulting generalized daily coupling between a spatially dis-similar SWAT and MODFLOW model on the North Fork of the Sprague River has resulted in a slightly more lower representation of monthly stream flow (monthly R2 = 0.66, NS = 0.38) than the original SWAT model (monthly R2 = 0.60, NS = 0.57) with no additional calibration. The Log10 results of stream flow illustrate an even greater improvement between SWAT-MODFLOW correlation (R2) but not the overall simulation (NS) (monthly R2 = 0.74, NS = -0.29) compared to the original SWAT (monthly R2 = 0.63, NS = 0.63) correlation (R2). With an improved water table representation, these SWAT-MODFLOW simulation results illustrate a more in depth representation of overall stream flows on a groundwater influenced tributary of the Sprague River than the original SWAT model. Additionally, with the increased complexity of environmental models there is a need to design and implement tools that are more accessible and computationally scalable; otherwise their use will remain limited to those that developed them. In light of advancements in cloud-computing technology a better implementation of modern desktop software packages would be the use of scalable cloud-based cyberinfrastructure, or cloud-based environmental modeling services. Cloud-based deployment of water data and modeling tools assist in a scalable as well as platform independent analysis; meaning a desktop, laptop, tablet, or smart phone can perform the same analyses. To utilize recent advancements in computer technology, a further focus of this work is to develop and demonstrate a scalable cloud-computing web-tool that facilitates access and analysis of stream flow data. The specific objectives are to 1) unify the various stream flow analysis topics into a single tool; 2) to assist in the access to data and inputs for current flow analysis methods; 3) to examine the scalability benefits of a cloud-based flow analysis tool. Furthermore, the new Comprehensive Flow Analysis tool successfully combined time-series statistics, flood analysis, base-flow separation, drought analysis, duration curve analysis, and load estimation into a single web-based tool. Preliminary and secondary scalability testing has revealed that the CFA analyses are scalable in a cloud-based cyberinfrastructure environment to a request rate that is likely unrealistic for web tools