Characterizing components of uncertainty in hydrologic modeling using an ensemble approach.

In hydrologic modeling, uncertainties are known to reside in model inputs, i.e., rainfall estimates, model parameters, observations of streamflow, and in some cases in the model structure itself. Estimation of the total prediction uncertainty for a hydrologic forecast first requires knowledge of the error characteristics of input rainfall estimates. Traditionally, evaluation of quantitative precipitation estimates (QPEs) has been accomplished by comparing remotely sensed rainfall to point rain gauge observations. In addition to errors associated with rain gauge measurements, it has been noted that sampling sizes between a typical radar pixel and a rain gauge orifice differ by about eight orders of magnitude (Droegemeier et al. 2000). It is thus highly desirable to design an objective, quantitative methodology that evaluates the skill of precipitation algorithms at the hydrologic scale of application, a watershed. QPEs from different algorithms are input to the Vflo(TM) hydrologic model. Thousands of simulations are performed in an ensemble fashion in order to "expose" each rainfall input to the entire parameter space. Probabilistic statistics are utilized to compare the predicted probability density functions (pdfs) to observations of streamflow. Results indicate the spatial variability of rainfall observed by radar is indeed important for skillful hydrologic predictions.The developed ensemble approach is also used to evaluate the propagation characteristics of error in rainfall estimates to hydrologic predictions. Predictions of peak discharge and time-integrated discharge volume are shown to be very sensitive to rainfall perturbations. Ensembles are then constructed to include the combined uncertainty in QPEs and model parameters. Several case studies are utilized to show how the total prediction uncertainty can be accurately estimated. Moreover, additional sources of uncertainty are identified for a case where simulation bounds derived from predicted pdfs do not replicate observed behavior during summer months. This may be due to inadequate parameterization (e.g., initial abstraction) or to model structure. Soil moisture observations from the Oklahoma Mesonet are introduced to reveal some explanation for conditions where the Green and Ampt submodel may not adequately characterize the infiltration behavior during summertime low flows

Probe Location within Interfacial Layer of CTAB Reverse Micelle System

To understand the chemistry of the interfacial region of reverse micelles (RM), we studied RM system made with the cationic surfactant cetyltrimethylammonium bromide (CTAB), alkanol cosurfactants dissolved in cyclohexane with water core. Spectroscopic methods, specifically UV-Vis absorption of Coumarin 343 (C343) as a probe molecule, were used to determine basic properties of RM systems. However, the probe location was difficult to determine because the spectrum (absorbance), when dissolved in RM solution, didn’t match the spectra in any of the pure components. Our data suggests that the interfacial layer of RM cannot be thought of behaving only the characteristic of single one of the components; rather, it behaves as a mixture of multiple components with unique characteristics. The interfacial layer appears to have roughly three distinct regions. By combining two components at a time, our data shows that C343 is most likely to reside in the middle or outer interfacial regions, which is surprising because C343 is polar enough that it would be expect to preferentially migrate into the water cor

Multi-Sensor Imaging and Space-Ground Cross-Validation for 2010 Flood along Indus River, Pakistan

Flood monitoring was conducted using multi-sensor data from space-borne optical, and microwave sensors; with cross-validation by ground-based rain gauges and streamflow stations along the Indus River; Pakistan. First; the optical imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) was processed to delineate the extent of the 2010 flood along Indus River; Pakistan. Moreover; the all-weather all-time capability of higher resolution imagery from the Advanced Synthetic Aperture Radar (ASAR) is used to monitor flooding in the lower Indus river basin. Then a proxy for river discharge from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA’s Aqua satellite and rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) are used to study streamflow time series and precipitation patterns. The AMSR-E detected water surface signal was cross-validated with ground-based river discharge observations at multiple streamflow stations along the main Indus River. A high correlation was found; as indicated by a Pearson correlation coefficient of above 0.8 for the discharge gauge stations located in the southwest of Indus River basin. It is concluded that remote-sensing data integrated from multispectral and microwave sensors could be used to supplement stream gauges in sparsely gauged large basins to monitor and detect floods.JRC.G.2-Global security and crisis managemen

Improving Flood Forecasting Skill with the Ensemble Kalman Filter

The purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak timecan be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days.The purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak timecan be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days

Toward a user-centered design of a weather forecasting decision-support tool

Hazard Services is a software toolkit that integrates information management, hazard alerting, and communication functions into a single user interface. When complete, National Weather Service forecasters across the United States will use Hazard Services for operational issuance of weather and hydrologic alerts, making the system an instrumental part of the threat management process. As a new decision-support tool, incorporating an understanding of user requirements and behavior is an important part of building a system that is usable, allowing users to perform work-related tasks efficiently and effectively. This paper discusses the Hazard Services system and findings from a usability evaluation with a sample of end users. Usability evaluations are frequently used to support software and website development and can provide feedback on a system’s efficiency of use, effectiveness, and learnability. In the present study, a user-testing evaluation assessed task performance in terms of error rates, error types, response time, and subjective feedback from a questionnaire. A series of design recommendations was developed based on the evaluation’s findings. The recommendations not only further the design of Hazard Services, but they may also inform the designs of other decision-support tools used in weather and hydrologic forecasting. Incorporating usability evaluation into the iterative design of decision-support tools, such as Hazard Services, can improve system efficiency, effectiveness, and user experience

Effects of display design on signal detection in flash flood forecasting

The Flooded Locations and Simulated Hydrographs (FLASH) project is a suite of tools that use weather radar-based rainfall estimates to force hydrologic models to predict flash floods in real-time. However, early evaluation of FLASH tools in a series of simulated forecasting operations, it was believed that the data aggregation and visualization methods might have contributed to forecasting a large number of false alarms. The present study addresses the question of how two alternative data aggregation and visualization methods affect signal detection of flash floods. A sample of 30 participants viewed a series of stimuli created from FLASH images and were asked to judge whether or not they predicted significant or insignificant amounts of flash flooding. Analyses revealed that choice of aggregation method did affect probability of detection. Additional visual indicators such as geographic scale of the stimuli and threat level affected the odds of interpreting the model predictions correctly as well as congruence in responses between national and local scale model outputs

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters

Integrated Multi-Satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Integrated multi-sensor assessment is proposed as a novel approach to advance satellite precipitation validation in order to provide users and algorithm developers with an assessment adequately coping with the varying performances of merged satellite precipitation estimates. Gridded precipitation rates retrieved from space sensors with quasi-global coverage feed numerous applications ranging from water budget studies to forecasting natural hazards caused by extreme events. Characterizing the error structure of satellite precipitation products is recognized as a major issue for the usefulness of these estimates. The Global Precipitation Measurement (GPM) mission aims at unifying precipitation measurements from a constellation of low-earth orbiting (LEO) sensors with various capabilities to detect, classify and quantify precipitation. They are used in combination with geostationary observations to provide gridded precipitation accumulations. The GPM Core Observatory satellite serves as a calibration reference for consistent precipitation retrieval algorithms across the constellation. The propagation of QPE uncertainty from LEO active/passive microwave (PMW) precipitation estimates to gridded QPE is addressed in this study, by focusing on the impact of precipitation typology on QPE from the Level-2 GPM Core Observatory Dual-frequency Precipitation Radar (DPR) to the Microwave Imager (GMI) to Level-3 IMERG precipitation over the Conterminous U.S. A high-resolution surface precipitation used as a consistent reference across scales is derived from the ground radar-based Multi-Radar/Multi-Sensor. While the error structure of the DPR, GMI and subsequent IMERG is complex because of the interaction of various error factors, systematic biases related to precipitation typology are consistently quantified across products. These biases display similar features across Level-2 and Level-3, highlighting the need to better resolve precipitation typology from space and the room for improvement in global-scale precipitation estimates. The integrated analysis and framework proposed herein applies more generally to precipitation estimates from sensors and error sources affecting low-earth orbiting satellites and derived gridded products

A cloud-based global flood disaster community cyber-infrastructure: Development and demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-ofthe- art cloud computing and crowd-sourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters

Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth’s climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982–2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes. The ET record shows a significant upward global trend of 0.88 mm yr−2 (P \u3c 0.001) over the 32-year period, mainly driven by vegetation greening (0.018% per year; P \u3c 0.001) and rising atmosphere moisture demand (0.75 mm yr−2; P = 0.016). Our results indicate that reduced ET growth between 1998 and 2008 was an episodic phenomenon, with subsequent recovery of the ET growth rate after 2008. Terrestrial precipitation also shows a positive trend of 0.66 mm yr−2 (P = 0.08) over the same period consistent with expected water cycle intensification, but this trend is lower than coincident increases in evaporative demand and ET, implying a possibility of cumulative water supply constraint to ET. Continuation of these trends will likely exacerbate regional drought-induced disturbances, especially during regional dry climate phases associated with strong El Niño events