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Water data from several agencies has been brought together into a map-driven data exploration and visualization tool that allows users to quickly explore regional and local water conditions, focused mostly on surface and near surface supplies. The integrated water portal is especially designed to support drought monitoring and forecasting needs.
Target Audience: Water supply and natural resource managers at municipal, state, basin, and ecosystem scales.
Additionally, summary statistics describing the skill of the forecasts over the period 1991-2010 are available on the forecasts tab. When a basin is selected, a graph and a table will appear beneath the map. The graph indicates the observed and forecasted streamflow values for the particular basin and chosen month for each year in 1991-2010. This is done for each month because the regression equations are determined independently for each month. The graphs also show the forecast’s prediction interval (http://pubs.usgs.gov/twri/twri4a3/pdf/chapter9.pdf) at the 95% confidence level.
Graph showing the observed and forecasted February streamflows for the Tar River at Tarboro, NC streamgauge (02083500) located in the Lower Tar River Basin. The R2 value for February at this site is 0.53. Table of summary statistics for Tar River at Tarboro.Beneath the graph is a table that further summarizes the skill for each year for a particular month’s forecasts. This table indicates whether the forecasted tercile and the observed tercile matched, and colors the cell green if they do. If they do not match, the tercile that the observed flow fell into is shaded blue while the tercile that the forecasted value fell into is shaded yellow. As an example, in the table below, which is also for the Tar River at Tarboro streamgauge (02083500), in 1994 both the observed and forecasted values were in the greater than 67th percentile range. In 1991, however, the observed value was less than the 33rd percentile value while the forecasted value for that month was in the >67th percentile.
How were these basins selected?
The majority of the basins that have streamflow forecasts are HCDN basins. These are unmanaged sites that typically have longterm records. Basins were further selected based on
having more than one forecasted grid point within their boundaries. Following selection of these basins, several more basins were included based on user engagement.
The Forecast tab of the Integrated Water Portal provides monthly/seasonal inflows and storage forecasts for various reservoir systems
throughout the Southeast US. Inflow and storage forecasts can be viewed by selecting the appropriate Forecast Type under the
Reservoir Forecast Menu. Two types of forecasts are available:
All inflow forecasting model used in the portal have three main components: Predictors, Forecasting Model and Predictands. The predictors are inputs used to drive the forecasting model and can include forecasted data such as precipitation forecast (obtained from General Circulation Models) and observed data such as observed streamflow. The forecasting model can be statistical model or physically based hydrological models. The predictands are the output from the model or the variable of interest such as streamflow. Currently, inflow forecasts can be obtained using two methodologies: Principal Component Regression (PCR) and Climatology.
The forecast provided using climatology is simply the long term monthly and seasonal average of the inflow for the reservoir of interest. For climatology every year will have the same results since we are averaging the monthly and seasonal values for all the years to obtain the climatology inflows. The inflows can be viewed at monthly or seasonal time scale as ensembles. The ensembles are displayed using a box plot but can be downloaded in text format from the results page. If a user is viewing the inflows under Retrospective Forecasts tab, all the years should display the same results with the same skills summary since climatology will not change from year to year but only from month to month or season to season. The reason we included climatology inflow is to allow a comparison to forecasted inflows from PCR or any other model. If the model forecasted inflows are better than the climatology it is better to use the model forecasts. If climatology outperforms the model during any month or season, we can conclude that the model does not have good skill during that particular month or season.
Statistical Downscaling of ECHAM4.5 precipitation forecasts
The approach we employ for developing streamflow forecasts is principal components regression, which is one of the commonly employed methodologies for statistical downscaling [Sankarasubramanian et al., 2008]. Given that the precipitation fields obtained from GCMs and SSTs are spatially correlated, application of Principal Components Analysis (PCA) rotates the original GCM fields into orthogonal components with the first mode representing the maximum variance of the original GCM fields. PCA, also known as empirical orthogonal function (EOF) analysis, on the predictors (GCM and SST fields) could also be performed by singular value decomposition on the spatial correlation matrix or covariance matrix of the predictors. Since PCA is scale dependent, loadings (eigenvectors or EOF patterns) obtained from the covariance matrix and the correlation matrix are different. Importance of each EOF pattern is quantified by the fraction of the variance the principal component represents with reference to the original predictor variance. In the case of PCA performed using correlation matrix approach, the sum of all eigenvalues is equal to the total number of elements in the data.
Dimension Reduction and Predictor Selection
For developing streamflow forecasts for various basins, we consider two candidate predictors for statistical downscaling: (a) Precipitation forecasts from ECHAM4.5 forced with constructed analogue SST forecasts over the Southeast US (23-40N;92-73W) (b) observed monthly streamflow at the site. The grid points of precipitation forecasts that correlate well with the observed streamflow are identified based on Spearman rank correlation (Table 1). These grid points of precipitation forecasts together with observed streamflow provide the predictor set for predicting streamflow for each basin. Since these predictors are correlated, it is better to employ principal components regression than multiple linear regression.
Point | Latitude | Longitude |
1 | 32.09194 N | 84.375 W |
2 | 32.09194 N | 81.562 W |
3 | 32.09194 N | 78.750 W |
4 | 32.09194 N | 75.938 W |
Along with inflow forecasts, the user can also view storage forecast for a particular reservoir. The inflows forecast obtained by using the models in Section 2 are used to obtain the storage forecast. The storage forecasts are developed using a water balance model as represented by the following equation:
Current monthly storage (St) is obtained based on previous month storage (St-1). The net inflows (qt) are added to the storage while the evaporation (Et) and releases (Rt) are subtracted. The net inflows (qt) are obtained from the models in Section 2 as ensembles. The user may select observed releases or provide releases for multiple users (represented as i in the equation) of a reservoir system.
The performance of the forecast is also summarized by comparing the conditional mean of the forecast with the observed flows using three different measures: (a) Correlation (b) Relative root mean square error (R-RMSE) (c) Mean Square Skill Score (MSSS).
Correlation:
The correlation is computed as Pearson correlation between the observed and the predicted flows (conditional mean) from the PCR. A good forecast is expected to
have a correlation around 1. However, for the correlation to be statistically significant, the computed correlation should be greater than 1.96/(n-3)0.5 where 'n'
denotes the number of years of data used for computing correlation.
Relative - Root Mean Square Error:
Relative-RMSE denotes the average error in the conditional mean of the forecasts compared to the observed flows. A good forecast is expected to have R-RMSE closer
to zero. Another way to summarize the performance of the mean of the forecast is using MSSS.
Mean Squared Error Skill Score:
MSSS is similar to R-RMSE but it compares the mean square error of the candidate forecast with the mean square error of the climatological forecast (which is just
the mean monthly/seasonal streamflow). For a good forecast, we would expect MSSS should be closer to one. A forecast is considered to be poorer than climatology
if MSSS is lesser than zero.
Rank Probabilistic Skill Score:
RPSS computes the cumulative squared error between the categorical forecast probabilities and the observed category in relevance to a reference forecast (Wilks, 1995).
Low RPS indicates high skill and vice versa. Similarly, if RPSS is positive, then the forecast skill exceeds that of the climatological probabilities. For a good forecast,
we would like the RPSS to be closer to one. RPSS could give an overly pessimistic view of the performance of the forecasts and it is a tough metric for evaluating probabilistic
forecasts. For a detailed example on how to compute RPS and RPSS for given forecast, see Goddard et al., [2003].
Goddard, L., A.G. Barnston, and S.J.Mason, Evaluation of the IRI's "net assessment" seasonal climate forecasts 1997-2001. Bulletin of the American Meteorological Society 2003;84(12):1761-+. (.pdf)
Sankarasubramanian, A., U.Lall, and S.Espuneva, Role of Retrospective Forecasts of GCM Forced with Persisted SST anomalies in Operational Streamflow Forecasts Development, Journal of Hydrometeorology,9(2), 212-227, 2008.
Wilks, D.S., Statistical Methods in the Atmospheric Sciences, Academic Press, 1995.
This page is designed to assist users in finding sites that record data for a select parameter. For example, say you are interested in obtaining a list of sites having
100% of 7-day Average Streamflow observations between October 1, 2015 and December 31, 2015.
The observed data page allows user to view current and historical station observation in addition to other useful information, such as gridded drought indices and the US drought monitor. Some key
elements of this page are listed below and shown in the image on the right.
Monthly and seasonal forecasts for a variety of basins have been developed using output from NASA LIS. Additionally, gridded monthly forecasts of soil moisture percentiles have been generated using NASA LIS. These can be viewed under the "Gridded Data" menu. To view a time series of forecasted output:
Under the "Reservoir Forecast Menu" (not shown) are seasonal and monthly inflow and storage forecasts for two reservoirs: Falls Lake and Jordan Lake. More detailed information about these forecasts can be found under the "Methodology: Reservoir Forecasts" tab.