For the InstructorThese student materials complement the Water Science and Society Instructor Materials. If you would like your students to have access to the student materials, we suggest you either point them at the Student Version which omits the framing pages with information designed for faculty (and this box). Or you can download these pages in several formats that you can include in your course website or local Learning Managment System. Learn more about using, modifying, and sharing InTeGrate teaching materials.
Forecasting and Predictions
Meteorologists have made excellent progress in the past few decades to improve our abilities to forecast when rain events might occur over the next week or so, which facilitates the short-term forecasting of floods and droughts discussed in the paragraph above. However, the complex nature of atmospheric dynamics suggests that we may never have the ability to forecast whether it is going to rain or snow on a given date more than a few weeks in advance. Nevertheless, we need to have some basis for making decisions about development, infrastructure and agriculture related to how much rain or snow we might expect over different time periods (e.g., How big should we build a culvert under a road? What size detention basin is needed next to a new housing development? Which agricultural fields are likely to require artificial drainage to remove water from the landscape and which require irrigation?). For these longer-term predictions we can use statistics to determine how likely it is that a given location will experience, for example, more than 10 cm of rain in a day, or less than 5 cm of rain during a given month, etc.. These are some of the critical predictions hydrologists make. Many million- and billion-dollar decisions about development and infrastructure are based on such predictions.
To make these predictions, hydrologists synthesize historical data and use a probabilistic approach to determine the likelihood that any given event might occur. While Figure 1 highlights the 'messiness' of precipitation events over time, a simple reorganization of the data starts to provide useful information. For example, Figure 2 shows a histogram of the precipitation data presented in Figure 1. A histogram is a plot showing the number of events that fall within particular bins (shown on the x axis). From these data you can quickly determine that Kingston, NY experiences no rain about 2 out of every 3 days (731 out of the total 1096 days in this record). Only 10 days in the record had rainfall that exceeded 6 cm, so from these data alone you would expect such large rainfall events to happen 10 days out of 1096, or about 1% of the time. On 210 days during this time period the amount of rainfall was between the minimum measurable (typically 0.025 cm or 0.01 inch) and 1 cm (0.4 inches).
Hydrologists tend to use the term 'forecast' when referring to a future projection for which we have a lot of information (and therefore relatively high certainty of when an event might occur and what magnitude it might be). In contrast, hydrologists use the term 'prediction' for future projections for which less information is available, and therefore uncertainty is greater.