H54B-01 INVITED
Demonstrating Integrated Forecast and Reservoir Management (INFORM) for Northern California in an Operational Environment
Considerable investments have been made toward improving the quality and applicability of climate, synoptic,
and hydrologic forecast information. In addition, earlier retrospective studies have demonstrated that the
management of water resource systems with large reservoirs can benefit from such information. However, prior
to this project no focused program has aimed to quantify and demonstrate these benefits in an operational
environment. As a result, few reservoir managers have been able or willing to dedicate the considerable effort
required to utilize new approaches and realize the benefits of improved forecast information. The purpose of the
Integrated Forecast and Reservoir Management (INFORM) Project is to demonstrate increased water-use
efficiency in Northern California water resources operations through the innovative application of
meteorological/climate, hydrologic and decision science. In accordance with its purpose, the particular objectives
of INFORM are to: (a) implement a prototype integrated forecast-management system for the primary Northern
California reservoirs, both for individual reservoirs as well as system-wide; and (b) demonstrate the utility of
meteorological/climate and hydrologic forecasts through near-real-time tests of the integrated system with actual
data and management input, by comparing its economic and other benefits to those accruing from current
management practices for the same hydrologic events.
To achieve the general objectives of the INFORM project, the authors performed the following technical tasks: (a)
Developed, implemented and performed validation of climate, weather, hydrology and decision INFORM
components for Northern California with historical data and real-time data; (b) Integrated INFORM system climate,
hydrology and decision components and performed initial operational tests producing real-time ensemble
forecasts out to lead times of 16 days four times daily for the wet season 2005-2006, and out to 9 months with
monthly resolution with start date of 1 March 2006; (c) Performed assessments of the integrated forecast-
decision system value via retrospective simulation experiments; (d) Held INFORM design, assessment and
training meetings with operational forecast and management agency staff. There are several technical and
specific conclusions that have been drawn from the outcomes of the project in the areas of meteorology/climate,
hydrology, and decision science. These conclusions will be discussed at the presentation. The most important
conclusion of the report is that, with available real-time availability of forecast information from the National
Centers for Environmental Prediction and with real-time observed precipitation and temperature as well as
hydrologic model state values from the California Nevada River Forecast Center, integrated forecast-
management systems are effective tools for assisting water managers in translating forecasts and their
uncertainty into a range of effective risk-based policies.
http:www.hrc-lab.org/projects/dsp_projectSubPage.php?subpage=inform
H54B-02
The effect of model scale in reconstructing snow water equivalent over complex terrain
Improving estimates of water and energy fluxes in the rugged landscapes of the American Cordillera is
particularly challenging given the considerable topographic heterogeneity and paucity of observations within the
region. In these complex systems, parameterizations of sub-grid variability in energy and mass transformations
are highly sensitive to relationships between model spatial resolution and the correlation-length scale of the
variable of interest - which is usually unknown. In the mountainous regions of the Western United States, the
processes controlling the distribution of snow water equivalent are likely better known than any other hydrologic
state. The observation record is relatively long (dating back to the early part of the 20th century) and snow cover is
relatively easy to detect using remotely sensed observations in the visible and near infrared. Hence, distributed
snowpack simulations provide an ideal case study for exploring relationships between process, model, and
observation scales; potentially guiding efforts regarding other hydrologic states (e.g. soil moisture). To that end,
this research uses a time series of fractional snow covered area (SCA) estimates from Landsat Enhanced
Thematic Mapper (ETM+), Moderate Resolution Imaging Spectoradiometer (MODIS), and Advanced Very High
Resolution Radiometer (AVHRR) data, in combination with a spatially distributed snowmelt model, to reconstruct
snow water equivalent (SWE) in the Rio Grande headwaters (3,419 km2) of Colorado, USA. In this
reconstruction approach, modeled snowmelt over each pixel is integrated over the time of satellite observed
snow cover to estimate SWE. Despite the considerable differences in the magnitude of SWE in 2001 versus
2002, model performance - using ETM+ data aggregated to 100-m resolution - was robust with a mean absolute
error (MAE) of 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated
when MODIS (MAE = 57%) and AVHRR (MAE = 90%) data were used and when simulations were run at coarser
resolutions; MAE = 26, 34, and 47%, for ETM+ simulations run at 250-m, 500-m, and 1-km resolution,
respectively. Basin-average maximum SWE using MODIS and AVHRR was 27% and 57% lower than ETM+
estimates, respectively. Maximum SWE decreased by 28% when ETM+ simulations were run at 1-km versus
100-m resolution. This research illustrates the utility and scale-dependent limitations of the reconstruction
technique for obtaining SWE estimates at larger scales (e.g. > 1000 km2) and in locations where detailed
hydrometeorological observations are scarce.
http:cee.ucla.edu/faculty/molotch.htm
H54B-03
Evaluation of Snowmelt-Dominated Forest Hydrology Simulations in a Mountainous Watershed with the Swat Model
The Soil and Water Assessment Tool (SWAT) has a long track record of successful application in lowland and temperate environments, but little is known about the model's performance in forested mountain watersheds where hydrologic patterns are dominated by seasonal cycles of snow accumulation and melt. In this study, the ability of SWAT to simulate annual, monthly, daily, and seasonal streamflow in a snow-dominated upland watershed that represents conditions commonly found in high elevation environments in the Rocky Mountains of North America was evaluated. The model was calibrated with 4 years of continuous daily climate data collected within the research watershed, and corresponding streamflow records measured at the defined outlet. Hydrologic flow predictions were assessed graphically and with relative error (RE), mean paired deviation (DV), and Nash- Sutcliffe model efficiency (NS) statistics. The calibrated model was validated with an independent dataset spanning an additional 4 years, using both traditional performance criteria obtained over the calibration and validation time periods, and objective regression-based methods. After calibration, model performance was very good, with a relative simulation period error of 2%, mean deviations of 36% and 31%, and Nash-Sutcliffe efficiencies of 0.90 and 0.86 for monthly and daily streamflow predictions, respectively. Model predictions were validated over annual, monthly, and daily time steps using both traditional and objective procedures, with RE 4%, DV 43 and 32, and NS 0.90 and 0.76. Seasonally, SWAT performed well during the snowmelt-induced runoff periods, but could not be validated for baseflow simulation. Assessment of key factors indicated that adjustment of snow process parameters contributed most significantly to model calibration. Other important parameters were surface runoff lag, groundwater, soil, and curve number parameters, in decreasing order of influence. Ultimately, model results indicate that when calibrated SWAT can predict annual, monthly, and daily hydrologic processes in forested mountain watersheds with efficiency levels that are similar to those obtained in other regions where it has been applied.
H54B-04 INVITED
Hydroclimatic Teleconnections in the American Cordillera: Case Studies and Considerations for Future Assessment
Understanding the trends and low frequency oscillation modes of hydrologic variables such as precipitation and storage volumes in lakes and snow peaks along the American Cordillera is an aim of enormous relevance for the adequate management of its water reserves. Many hydroclimatic patterns affecting the cordillera are teleconnected in turn to quasi periodic changes in sea surface temperatures on the Pacific Ocean such as ENSO and the PDO. The spatiotemporal influence of these teleconnections was quantified by applying Principal Components (PC) and Multichannel Singular Spectrum Analysis (MSSA) techniques to two cases that exhibit different climatic regimes: the seasonal and annual variability of precipitation over the Colorado River Basin (CRB) in North America, and the seasonal fluctuations of storage at the Tota Lake in the Colombian Andes. The coupled impact of the PDO-ENSO signals on the annual precipitation series of the CRB indicates the presence of a trend and two oscillation modes around five and 15 years that explain a significant fraction of its variance. Common enhancement phases between the three signals also favor the occurrence of regionalized droughts and wet years in the basin. The Tota lake exhibits in turn a continuous decline in its mean annual levels and a delayed ENSO-related response in which pronounced drops occur during severe El Nino episodes. The two cases mentioned exemplify the situation faced by many water bodies in the cordillera, especially under the current projections of global climate change. Addressing the expected hydrologic changes in a coordinate manner will help improving the monitoring, modeling and forecasting skills necessary to make better informed decisions about the use and protection of these resources.
H54B-05
Sustainable Water Resources Management in a Complex Watershed Under Climate Change Scenarios
The Aconcagua River Basin in central Chile supplies water for over one million people, high-return agriculture, mining and hydropower industries. The Aconcagua river basin has Mediterranean/semi-arid climate, its hydrologic regime varies along its path from snow- to a rainfall-dominated, and significant stream-aquifer interaction is observed throughout the river path. A complex water market operates in the Aconcagua River Basin, where private owners hold surface and subsurface water rights independently of land ownership and/or intended use. The above yield integrated watershed management critical for the sustainability of basin operations, moreover under conditions of significant precipitation interannual variability and uncertain future climatic scenarios. In this work we propose an integrated hydrologic-operational model for the Aconcagua River in order to evaluate sustainable management scenarios under conditions of climatic uncertainty. The modeling software WEAP (Water Evaluation and Planning System) serves as the platform for decision support, allowing the assessment of diverse scenarios of water use development and hydrologic conditions. The hydrologic component of the adopted model utilizes conceptual functions for describing the relations between different hydrologic variables. The management component relies on economic valuation for characterizing the space of efficient operational policies.
H54B-06 INVITED
Uncertainties in Seasonal and Long Range Climate in Hydrologic Forecasts
The analysis of uncertainties in seasonal and long-range climate and hydrologic forecasts in the American Cordillera will be presented. Particular emphasis will be on the impact of these uncertainties on water resource systems and their management in California.
H54B-07
Evaluation of Digital Elevation Model Uncertainty in Flood Inundation Modeling
Flood is one of the most life threatening natural hazard on the earth, therefore it is very important to estimate flood magnitudes and to map areas under inundation. Flood inundation modeling is the final stage of a flood study in which inundation area for a certain flood magnitude is determined. In the last decade integration of Geographic Information Systems (GIS) with flood studies increased the efficiency and visualization of basin and river modeling. Since all GIS datasets suffer from error, when they are used as input to a GIS operation, then the errors in the input propagate to the output of the operation. In GIS integrated flood inundation modeling, where topographic conditions of the river network are obtained from a Digital Elevation Model (DEM), error inherent in the DEM will propagate through the analysis till its outputs. In this study propagation of DEM uncertainty in flood inundation modeling is investigated. Monte Carlo Simulations method is utilized for uncertainty propagation modeling, and flood inundation modeling is performed by integrating HEC-RAS and ArcView softwares. The methodology is applied to a small area selected within Ulus Basin which is located in the West Black Sea region of Turkey. At the end of the study, results of uncertainty propagation modeling are compared with the results of GIS integrated flood inundation modeling of the same study site.