H23F-1672
Coupling of a Regional Atmospheric Model with an Ensemble Kalman Filter based Land Data Assimilation System and its application to North-East Asia during the 2003 Monsoon Season
A new ensemble Kalman filter based land data assimilation system is developed, which can be used to assimilate satellite-borne passive microwave brightness temperature data to update soil moisture and temperature. Fully driven by a regional atmospheric model, the assimilation system can provide new surface fluxes to the atmosphere and gets back its updated forcing. The system uses a new land surface model based on the Simple Biosphere Model, which is currently under development at the Japan Meteorological Agency. The observation operator is a radiative transfer model, which can estimate the land surface brightness temperature. Currently the Q/h model is used to estimate the soil brightness temperature. The data assimilation method employed is the ensemble Kalman filter technique, which is a Monte Carlo based sequential filter method. The atmospheric driver is the Advanced Regional Prediction System which is a 3-dimensional non-hydrostatic atmospheric model. The system was tested on a regional scale over North-East Asia (0 to 60 deg. North, 45 to165 deg. East) for a two weeks period during the 2003 Monsoon season using NCEP/GFS-FNL global data as boundary and initial conditions. Forcing from the atmospheric model drives the land surface model. Satellite brightness temperature observations from the Advanced Microwave Scanning Radiometer are compared to the simulated ones through the assimilation process to update the surface state and feed back the new simulated fluxes into the atmospheric model. A control run using only ARPS nested from the global model data without land surface assimilation is used for comparison. Furthermore the new land surface conditions and fluxes are compared to in-situ observation collected during the Coordinated Enhanced Observation Period. The results showed significant differences compared with standard regional atmospheric model outputs, and it is shown that the system can estimate land surface states more reasonable than uncontrolled modeling by merging the brightness temperature observations into land surface dynamics
H23F-1673
Forecasting Groundwater Level Fluctuations In a Costal Aquifer Using Support Vector Machine
Precise prediction of groundwater level fluctuations has been an important and challenging topic in hydrology. In coastal aquifer the groundwater level is influenced by a tide level as well as a precipitation, which renders the prediction more difficult. Support vector machine (SVM), a novel data-driven and artificial intelligence-based model, shows remarkable prediction performances for non-linear systems in many disciplines. Recently, researches using SVM for the prediction of water resource variables are increasing. In this study we developed a SVM based time series model, then we applied it to forecasting the groundwater level at the coastal aquifer of Mangsang in the western side of East Sea, Korea. We especially focused upon assessing the influence of input vector organizations on model performances. The results show that input vectors including past data of the groundwater level raise the model performance notably, with correlation coefficient over 0.9 in this case. This model performance is comparable or even superior to that of artificial neural network models or linear time series models. Sensitivity analyses for input vector sizes and prediction lag times emphasize that the input vector organization is necessary for a SVM time series model application to hydrologic fields.
H23F-1674
Reservoir Systems in Changing Climate
Climate change may cause more climate variability and further results in more frequent extreme hydrological events which may greatly influence reservoir's abilities to provide service, such as water supply and flood mitigation, and even danger reservoir's safety. Some local studies have identified that climate change may cause more flood in wet period and less flow in dry period in Taiwan. To mitigate climate change impacts, more reservoir space, i.e. less storage, may be required to store higher flood in wet periods, while more reservoir storage may be required to supply water for dry periods. The goals to strengthen adaptive capacity of water supply and flood mitigation are conflict under climate change. This study will focus on evaluating the impacts of climate change on reservoir systems. The evaluation procedure includes hydrological models, a reservoir water balance model, and a water supply system dynamics model. The hydrological models are used to simulate reservoir inflows under different climate conditions. Future climate scenarios are derived from several GCMs. Then, the reservoir water balance model is developed to calculate reservoir's storage and outflows according to the simulated inflows and operational rules. The ability of flood mitigation is also evaluated. At last, those outflows are further input to the system dynamics model to assess whether the goal of water supply can still be met. To mitigate climate change impacts, the implementing adaptation strategies will be suggested with the principles of risk management. Besides, uncertainties of this study will also be analyzed. The Feitsui reservoir system in northern Taiwan is chosen as a case study.
H23F-1675
Quantifying the Hydrologic Effect of Climate Variability in the Lower Colorado Basin
Regional climate patterns are driven in large part by ocean states and associated atmospheric circulations, but modified through feedbacks from land surface conditions. The latter defines the climate elasticity of a river basin. Many regions that lie between semi-arid and semi-humid zones with seasonal rainfall, for instance, experience prolonged periods of wet and dry spells. Understanding the triggers that bring a river basin from one state (e.g. wet period of late 90s in the Colorado basin) abruptly to another state (multi-year drought initiated in 2001 to present) is what motivates the present study. Our research methodology investigates the causes of regional climate variability and its effect on hydrologic response. By correlating, using different monthly time lags, sea surface temperatures (SST) and sea level pressures (SLP) with basin averaged precipitation and surface temperature, we determine the most influential regions of the Pacific Ocean on lower Colorado climate variability. Using the most correlated data for each month, we derive precipitation and temperature distributions under similar conditions to that of the El Niño Southern Oscillation (ENSO). We compare the distributions of the climatic data, given ENSO constraints on SST and SLP, to the distributions considering non-ENSO years. Finally, we use observed stream flows and climatic data to determine the basin's climate elasticity. This allows us to quantitatively translate the predicted regional climate effects of ENSO on hydrologic response. Our presentation will use data for the Little Colorado as an example to demonstrate the procedure and produce preliminary results.
H23F-1676
Development and Evaluation of a Streamflow Forecasting Tool to Improve Seasonal Water Supply Forecasts on the Salt and Verde River Basin, Arizona
Researchers at the University of Arizona are conducting research aimed at improving seasonal water supply forecasts in the Salt and Verde River basins to help water managers at Salt River Project (SRP), in Phoenix make better water supply and reservoir operation decisions. The goal of the research is to improve the seasonal water supply forecast through the development, application and testing of a physically based distributed hydrologic model coupled to a regional climate model. The variable infiltration capacity (VIC) model was setup for the headwater basins with the intent to simulate historical observed streamflow at the outlet of Salt and Verde River basins. This model is forced by gridded observed precipitation and temperature data. A multi-objective calibration using the shuffled complex evolution, University of Arizona (SCE-UA) was implemented to calibrate the VIC model incorporating observed climate elasticities of the Salt and Verde River basins. In addition, the impact of climate change on future hydrologic variables of the Salt and Verde will be assessed through the use of future climate projections derived from statistically downscaled data from the coupled climate models participating in the Coupled Model Intercomparison Project (CMIP3). In this poster, results from this calibration procedure and scenarios based on future climate forcing data will be presented.
H23F-1677
Coupled Modes of Variability between Pacific and Atlantic Sea Surface Temperatures and Monthly Precipitation in Southwest Florida
Water supply managers in Florida are increasingly turning to alternative sources in order to minimize the environmental impact of groundwater withdrawals. Tampa Bay Water, the largest wholesale water supplier in Southwest Florida, makes decisions regarding how to meet its customers" demands by rotating and apportioning among existing sources (groundwater, surface water, the regional reservoir, and desalination) to minimize cost and environmental impacts while maximizing reliability. As part of a study to improve source allocation decision making by Tampa Bay Water an evaluation of Pacific and Atlantic Ocean sea surface temperatures (SSTs) and monthly precipitation was performed to identify coupled modes of SST and precipitation variability. The monthly patterns of precipitation were analyzed in relation to SST in the Atlantic and Pacific Oceans using singular value decomposition analysis (SVD). Time-lagged analysis of SSTs and precipitation were investigated in order to identify SST regions that show forecast potential for use in a suite of hydrologic forecast models used by Tampa Bay Water.
H23F-1678
Connecting climate, hydrologic and drought predictions to water resources management in Washington State
Washington State and its prime agricultural region, the Yakima River basin, are both highly dependent on seasonal water supplies, hence the application of improved climate predictions has potential to benefit water management in the basin and the state as a whole. In the Yakima River basin, the US Bureau of Reclamation manages reservoirs to support irrigation, environmental flows, and hydropower production, and the State Department of Ecology helps regulate this water management in times of drought. To strengthen the connection between climate information products and their applications on both the state and river basin levels, we are pursuing a two-pronged approach of developing and applying climate predictions to improve the hydrologic (streamflow) and drought products that are available to decision makers at each level. At the state level, we have implemented a hydrologic monitoring and forecasting system for the state of Washington domain that translates CPC climate predictions into estimates of future hydrologic status (e.g., soil moisture, snowpack, runoff), streamflow and drought conditions. In the Yakima River basin, we are interacting with the water-user community to understand current uses of climate information, assess the accuracy of climate forecasts, evaluate the usefulness of current forecast products, and explore the potential for new, decision-focused climate and streamflow forecast products. This presentation reports on the findings of the work to date. http://www.hydro.washington.edu/forecast/sarp/index.shtml
H23F-1679
Prediction of moisture availability in agricultural soils using probabilistic monthly forecasts
Despite technological advances in breeding and agricultural practice, crop yield remains subject to considerable inter-annual variability related to short-term (seasonal) climate fluctuations. Of utmost importance in this context are variations in soil water availability. Extreme conditions such as the heat wave observed in Europe during the summer of 2003 can lead to anomalous soil moisture depletion and induce considerable losses in crop production. The prediction of soil water levels using monthly forecasts could provide valuable means for risk assessment and mitigation. We present first results of a prediction system for soil moisture forecasts with a lead time of up to one month. The system uses dynamical, probabilistic forecasts of daily temperature, precipitation and global radiation from the European Centre for Medium-Range Weather Forecasts. The forecasts drive a bucket model of the water balance in the root zone. We show that the seasonal evolution of the soil water available to crops is well reproduced by the system. The prediction system was tested for a grid point in Switzerland (47N, 8E) using monthly hindcasts covering the time period 1994-2005. Simple downscaling of raw model data was performed by applying model anomalies from the model climatology to the observed climatology. Resulting soil moisture forecasts showed to be skilful over climatology (0 < skill < 0.6) up to a lead time of three weeks. The system allows for the probabilistic estimation of reaching a critical level of soil moisture within such a forecast period. This can serve as a valuable information for the farmers' decision-making process.
H23F-1680
The Effect of Hydrologic Model Calibration on Seasonal Streamflow Forecasts for the Western U.S.
Forecasts of seasonal streamflow, particularly for the spring and summer period which are dominated by snowmelt runoff, are central to the management of the water resources infrastructure of the western U.S. Operational approaches to seasonal streamflow forecasting, like the Ensemble Streamflow Prediction (ESP) method used by the U.S. National Weather Service, rely heavily on manpower and/or computationally intensive calibration of conceptual streamflow models. We suggest an alternative approach, in which a priori (e.g., based on regional information) model parameters are used for streamflow forecasting, and a post processing bias correction is applied using a percentile mapping approach which utilizes the past history of model errors associated with the uncalibrated model. We evaluate intensively the impact of calibration on ESP forecasts at eight forecast points carefully selected to span a range of basin sizes and hydroclimatic conditions across the western U.S. At each of these sites, we apply the ESP approach, and evaluate forecast errors for a range of forecast dates and lead times. We use both the root mean squared error (RMSE) and coefficient of prediction Cp (which essentially is a measure of the fraction of variance explained by the forecast) to evaluate the effects of model calibration on seasonal streamflow forecast accuracy. We find that while the bias correction approach captures most of the accuracy achievable by model calibration, for most forecast points, forecast dates, and lead times there remains a modest increase in forecast accuracy that can only be captured by model calibration.
H23F-1681
Modeling and Forecasting Snowmelt Runoff in California Mountain Watersheds using NASA Satellite Data Products
This study describes research using both the USDA Snowmelt-Runoff Model (SRM) and the CASA (Carnegie- Ames-Stanford) ecosystem model coupled with a surface hydrologic routing scheme to validate and generate daily forecasts for snowmelt runoff in California mountain watersheds. To assess CASA's ability to estimate actual water flows in both extreme and non-extreme precipitation years, we have compared gridded model results with gauge station data throughout the state and with predictions from the SRM. Satellite remote sensing studies of land cover types and snow cover extent are presented for selected watersheds in the Sierra Nevada Range of California. This research supports the development of a near-term (4-6 weeks) forecast system for snow melt runoff and flood potential from California mountain watersheds. http://geo.arc.nasa.gov/sge/casa/
H23F-1682
Verification of a Downscaling Sequence Applied to Medium Range Meteorological Predictions for Global Flood Prediction
We describe a prototype system for medium range (up to two week lead) flood prediction in large rivers, which is intended for global implementation – particularly in river basins having limited in situ meteorological observations. The procedure draws from the experimental North American Land Data Assimilation System (NLDAS) and the University of Washington West-wide Seasonal Hydrologic Forecast System for streamflow prediction. Meteorological forecasts based on a numerical weather prediction model serve both as the forcing for hydrologic model initialization and forecasts for lead times up to fifteen days. The hydrologic component of the system is the Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype, VIC is spun up for forecast initialization using daily ERA-40 precipitation, wind, and surface air temperature. In hindcast mode, VIC is driven by global NCEP ensemble 15-day re-forecasts (NOAA/ESRL) that are bias corrected with respect to ERA- 40 and spatially disaggregated using two higher spatial resolution satellite products: Global Precipitation Climatology Project (GPCP) 1DD daily precipitation and Tropical Rainfall Measuring System (TRMM) 3B42 precipitation are used to spatially disaggregate NCEP re-forecasts precipitation during the 15-day forecast period. The use of forecast models and satellite remote sensing data in this procedure reduces the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient, but where a global hydrologic forecast capability arguably would have the greatest value. The prototype system was implemented at one-half degree spatial resolution and tested during the 1979-August 2002 period. For the Mississippi R. Basin (where ample data for model evaluation exist) we evaluate the spatial disaggregation step in which observed precipitation products (NARR) are first aggregated to a coarser resolution (for the sole purpose of the evaluation) and then used in the spatial disaggregation step. The output of this procedure is compared to the original high resolution data. We also compare our disaggregation scheme with the analog technique of Hamill and Whitaker. Finally, we verify forecast error statistic¬s resulting from the application of the entire downscaling sequence.
H23F-1683
Climate Change on Sediment Yeild of the ChiChiaWan Creek Watershed
Soil erosion and transport capacity determines sediment yield, which may be influenced by climate change. Because the climate change impacts on factors of soil erosion have not been well established for the selected study area yet, it may not be realistic to use soil erosion model, such as USLE, and a transport model to simulate sediment yield. Instead, a rating curve is established to describe the relationship between streamflow and sediment yield and applied to project sediment yield under different climate conditions. Future climate derives from several GCMs. Then, the GWLF model is applied to simulate streamflows under different climate conditions. Those simulated flows are further applied to project the climate change impacts on sediment yield based on rating curves. The uncertainty of using a rating curve is discussed in this study. The ChiChiaWan Creek watershed is an important habitat for Formosa Salmon and is chosen as a case study. Furthermore, the impacts on habitat due to the changes of sediment yield are also assessed.
H23F-1684
National Integrated Drought Information System (NIDIS), United State Drought Portal (USDP): A Window on Drought Information, Impacts and Implications
The NIDIS Act of 2006 calls for an interagency approach to improve drought monitoring, forecasting and early warning. Led by NOAA, NIDIS focuses on the consolidation of physical, hydrological and socio-economic impacts data; integrated observing networks; development of a suite of drought decision support and simulation tools; and interactive delivery of standardized products through an internet portal. The vision for NIDIS is a dynamic and accessible drought risk information system that informs user decisions in preparing for and mitigating of the effects of drought. In support of this vision, the U.S. Drought Portal (USDP) will be a national resource for data, models, risk information and impacts of drought, with responsibility for integrating, archiving, and disseminating data via the internet. A portal environment, defined as a "site on the World Wide Web that typically provides personalized capabilities for their visitors," is critical, as it allows selected drought information from multiple authorities to be consolidated and interrogated, while simultaneously using metadata references to identify emerging information from the drought community. The USDP will provide reliable information on drought conditions at county, regional and national scales and serve as the primary point of entry for drought-related queries (through the already secured drought.gov URL) for a variety of user groups. Such questions include: -Where are drought conditions now and where might they develop? -Does this drought event look like other events in the past? -Will the drought continue? -How is the drought affecting me? -How can I plan for and manage the impacts of drought? The USDP will be comprised of information tailored for various user communities. The portal will work by combining NIDIS-related data and information with tools necessary to exchange and integrate data on various space and time scales, and among various formats. These portal data will incorporate a spectrum of information from both federal interagency and non-federal sources.
H23F-1685
Hydrological Forecasting in Mexico: Extending the University of Washington West-wide Seasonal Hydrologic Forecast System
Hydrologic forecasting in areas constrained by the availability of hydrometeorological records is a notable challenge in water resource management. Techniques from the University of Washington West-wide Seasonal Hydrologic Forecast system www.hydro.washington.edu/forecast/westwide) for generating daily nowcasts in areas with sparse and time-varying station coverage have been extended from the western U.S. into Mexico. The primary forecasting approaches consist of ensembles based on the NWS ensemble streamflow prediction method (ESP; essentially resampling of climatology) and on NCEP Coupled Forecast System (CFS) outputs. These in turn are used to force the Variable Infiltration Capacity (VIC) macroscale hydrology model to produce streamflow ensembles. The initial hydrologic state utilized in the seasonal forecasting is generated by VIC using daily real-time hydrologic nowcasts, produced using forcings derived via an 'index-station percentile' approach from meteorological station data accessed in real time from Servicio Meteorológico Nacional (SMN). One-year lead time streamflow forecasts at monthly time step are produced at a set of major river locations in Mexico. As a case study, the streamflow forecasts, along with forecasts of reservoir evaporation, are used as input to the Simulation-Optimization (SIMOP) model of the Rio Yaqui system, one of the major agricultural production centers of Mexico. This is the first step in an eventual planned water management implementation over all of Mexico.
H23F-1686
Climate Change Impacts on Water Resources in the Lake Victoria Basin
The success of water resources planning and management decisions is predicated on decision maker access to reliable and conclusive information, including climate forecasts. This is becoming increasingly critical as climate changes are predicted to take place over the same time horizons used in water and energy resources planning. The results of climate scenario analysis for Lake Victoria in East Africa are presented and used in a detailed climate change impact assessment for water resources. This region is shown to be particularly vulnerable to climate changes through projected impacts on net basin supplies, lake levels, lake releases, and energy generation.
H23F-1687
ESP forecasts for water resources: Model Uncertainty, Climate Uncertainty or Both?
Operational water resources management relies on the streamflow forecasts of reservoir inflow. In the Western U.S, where seasonal snowmelt represents the larger portion of water supplies, these forecasts are traditionally obtained through statistical regression-based estimates of April-July and water year runoff, which are then disaggregated to monthly volumes using historical relationships and forecaster judgment. An alternative approach is to use hydrologic forecasting systems, such as the West-Wide Seasonal Hydrologic Forecast System, developed by the University of Washington, to provide probabilistic forecasts in the form of ensemble streamflow predictions (ESP). Whether or not ESP forecasts are conditioned by seasonal climate forecasts, the approach places the natural variability of hydrometeorologic forcing (e.g. precipitation, temperature, snow extent') as the primary source of forecast uncertainty. This presentation will attempt to evaluate the effect of model uncertainty on the uncertainty statements issued by probabilistic forecasts generated from the California Hydrologic Forecast System (CaliForecast). CaliForecast, which is a regional implementation by the University of California, Irvine, of the west-wide forecasting system, will be used to issue ESP forecasts that account for uncertainty in model parameters. Within the system, parameter uncertainties will be assessed using the Bayesian-based Particle Filtering technique in order to obtain posteriori distributions of key model parameters for the Variable Infiltration Capacity Model (VIC-3L). The posterior distributions of model parameters, in conjunction with traditional ESP will allow the propagation of parameter uncertainty into the probabilistic streamflow forecasts. Comparison between probabilistic forecasts issued with and without parameter uncertainties will be conducted to assess the impact of parameter uncertainty for a sub-basin within the Feather River in Northern California.