Traditional approaches to hydrologic forecasting have relied on historical or antecedent observations of hydrologic conditions, typically without consideration of climate predictors. The following section describes some of these models and methods of integrating basic climate information.
1The Cooperative Program for Operational Meteorology, Education and Training (COMET Program) of the US National Weather Service and the University Corporation for Atmospheric Research offer a wide range of helpful teaching modules including climate and hydrology topics. To access these free online courses, visit http://www.meted.ucar.edu/.
Hydrologic persistence
In many locations, observations of antecedent or current watershed conditions can provide useful information for predicting future conditions. The persistence of streamflow (i.e. tendency of high flows to follow high flows, and low flows to follow low flows) is therefore often a useful predictor for lead times of up to 1–3 months or more, depending on the size of river system2(as well as the nature of the hydroclimate system). An illustration is provided in Figure 4.1 for the Chagres River in Panama.
The linear regression shown in Figure 4.1 represents a simple statistical model that might be used to predict the monthly flow in August based on the flow observed in July. Since the data do not perfectly follow the regression line, there is uncertainty in this simple forecast of August flow given the observed flows in July. As discussed in Chapter 3, seasonal forecasts are probabilistic and should address and communicate this uncertainty. In this example, the difference between the observed values and the regression line (the error) can be used to estimate the probability for a range of flows or likelihood of exceeding a particular flow.
Ensemble streamflow prediction
Another approach to seasonal streamflow forecasting that utilizes only observations as input is called the Ensemble Streamflow Prediction (ESP) method, originally developed at the United States National Weather Service (Day, 1985). ESP generates probabilistic forecasts by computing multiple streamflow traces (or scenarios) using a physically based watershed model. The procedure begins with Figure 4.1 Relationship between July and August flows on the Chagres River, Panama.
The linear regression illustrates an example of a simple forecast method. Also note that the area of this watershed is relatively small (approximately 1025 km2), and thus consistent with highly variable runoff and streamflow.Data source:USACE (2000).
2Persistence is often stronger for larger river systems because flows typically change much more slowly than in smaller rivers.
a calibrated and verified watershed model, which is updated to represent current watershed conditions (e.g. soil moisture, groundwater levels). A set of historical climate precipitation and temperature time series is then input to the model to generate an ensemble (or set) of streamflow traces. For example, if historical climate data is available for the period 1951–2000 (50 years) and it is desired to make a forecast for the April-May-June period starting from observed conditions in the month of March in a given year, then the 50 individual years of precipitation and temperature data will be input to the watershed model to produce 50 traces, or possible outcomes, of streamflow. Figure 4.2 shows an example of such an ESP forecast.
In this method each climate scenario is considered equally likely. Thus, each streamflow trace is also considered equally likely. The observed range in climate conditions over 50 years provides a measure of the possible range in streamflows for the season being forecast. However, there is no information included in the model to indicate what past conditions (e.g. unusually wet or dry) are more likely to occur during the forecast period. Thus, while the ESP approach described implicitly accounts for hydrologic persistence and historical variability of climate, it does not explicitly consider forecasted climate information (such as information based on ENSO) nor account for nonstationarity in the system.
Figure 4.2 Ensemble streamflow and interpretation of a forecast.
Panel (a) shows an Ensemble Streamflow Prediction (ESP) forecast for the Chagres River, Panama. Each line represents a simulated streamflow projection, or trace. Panel (b) provides a guide for how to interpret an ESP forecast. Source: Data for (a), USACE (2000); (b) COMET®Website.
Climate predictability and forecasts 43
Conditional ensemble streamflow prediction
The ESP method can be further modified by considering only those past years that had climate conditions deemed similar to those in progress when the forecast is made. In other words each year of this subset of similar past years represents an analogto the current year. A classic example of determining analog years is to consider the state of ENSO, as indicated by an index of SST in the tropical Pacific. The teleconnections described in Chapter 3 suggest that ENSO conditions can affect seasonal rainfall and, thus, streamflow in many regions across the globe. The strength of these associations can often be quantified using historical data3.
If a streamflow forecast is being made for a region which is known to be affected by ENSO, then one can select analog years from only those past years when an El Niño or La Niña event occurred. This can be used as a simple ensemble of seasonal
“forecasts”. These climate conditions are then used as inputs to the watershed model. In this method, the resulting streamflows simply represent a sample (i.e. a sub-set) from the full range of streamflows determined when using all past years in the unconditional ESP approach. A danger in the use of analog years is that there may be only a very few cases (e.g. less than 10) that can be considered reasonably good analogs, making the resulting streamflow forecasts very sensitive to sampling error. Nonetheless, the analog method represents a simple conditional ESP approach to seasonal streamflow forecasting. An example of such a forecast is shown below in Figure 4.3 for the Chagres River.
Figure 4.3 Example of combining the ESP and analog approaches to make forecasts for the Chagres River flow during El Niño events.
Each line represents an analog streamflow projection, or trace, based on similar ENSO conditions (e.g. all El Niño events).Source:Chagres River data, USACE (2000); ENSO data accessed from NOAA Climate Prediction Center at http://www.cpc.noaa.gov/products/ analysis_monitoring/ensostuff/ensoyears.shtml.
3The International Research Institute for Climate and Society provides several resources for exploring ENSO-related impacts. See http://iri.columbia.edu/climate/ENSO/globalimpact/temp_precip/.
Section 2: Further climate-based approaches to seasonal