|Volume 60(2) 2011
Managing water resources with climate information
by Bruce Stewart*
The need for long-term historical climate information and products is as important as ever. Advances in the science of seasonal climate prediction have created significant potential for this science to contribute to improved water resources management practices. Improved collaboration between the two sectors can only be beneficial.
There is a strong relationship between weather and water resources management that tends to be operational in nature. Dams can be operated on the basis of inflows, which are forecast using predicted rainfall amounts; irrigation systems can be optimized based on weather parameters such as evaporation, wind speed, soil moisture and temperature data.
The old adage of “climate is what you expect, weather is what you get” applies to climate, weather and water resources management. We plan our water resources systems based on climate information and we operate them on the basis of weather information.
Climate data plays a significant role in hydrology, the study of the movement, distribution, and quality of water. In hydrology, a water balance equation can be used to describe the flow of water in and out of a system. Water balance components include precipitation, evapotranspiration, river flow, water supply storage, groundwater storage, water supply releases and transfers, and discharge into the oceans.
Hydrologists are interested in any climatic factor that influences these components. To fully understand water supply capabilities, one must clearly understand the water balance of a region, including river basins or groundwater aquifer systems. Inadequate information for any of these elements will make it more difficult to estimate the resources that are available for development.
Case study: building a dam
Assuming that we want to construct a hydrological structure such as a dam, what are the major climate data information needs and why? We need to know two things, and each is climate related:
Inadequate or poor-quality climate information can result in a dam that does not provide the level of supply for which it has been designed, or a high financial cost of a dam that is bigger than it needed to be to meet the demands for water in a region.
Long-term data needs: Streamflow records are either short or nonexistent at proposed dam sites in most countries. To determine the potential yield of a dam, a long-time series of hydrological data are required. If streamflow data are available, hydrological models are calibrated using climate data inputs such as rainfall and evaporation. The longer the streamflow records, the better the calibration potential; the longer and better the spatial representation of the climate data, the better the modelled hydrological time series will be.
The usual time steps for this data are daily rainfall data and monthly-daily averages of the other climate parameters that have less variability and less influence on the streamflow. In many instances, very simple monthly rainfall-runoff analyses have been used to good effect. In the long term, monthly streamflow data for as long a period as possible are required for storage-yield analyses.
Storage simulation needs: The next stage is to run a storage simulation study for the dam site with basic elements of monthly streamflow (inflow) and monthly evaporation. The analysis takes into account losses from the system due to evaporation and infiltration (see page from the bottom of the storage) and determines the level of withdrawals that can be made (storage yield) over the historical period. Analyses are made for a range of storage sizes and a storage-yield curve is compiled. This curve is used to determine, from a supply perspective, the optimum size of the dam.
There are various techniques used to identify the uncertainty in such analyses, including generating different sequences of inflows, based on either rainfall time series generation or streamflow time series generation. However, these are statistical techniques that do not rely on the underlying climate information.
What characteristics of the climate record are of importance to the hydrologist in this case?
In a changing climate, it will be important to know and understand how these inputs will be altered by climatic change. Will periods of low flow be longer or more frequent?
Estimating floods: Another hydrological requirement in dam design is that of the Probable Maximum Flood (PMF). Dam designs estimate the largest possible flood a spillway will be required to manage. In most instances, modelling and extension of extreme storm events is used as a technique because of the relatively short periods of records available for analysis. This usually requires the estimation of the Possible Maximum Precipitation (PMP). PMP is defined as: “...the greatest depth of precipitation for a given duration meteorologically possible for a given size storm area at a particular location at a particular time of the year, with no allowance made for long-term climatic trends.”
There are many techniques that are used to estimate PMP. They can be in situ studies that look at extreme events in the river basin, or regional studies that look at climatically homogeneous regions so as to increase the base of extreme rainfall events. The analysis examines each extreme event, identifying the highest potential rainfall, given the most extreme conditions to produce precipitable water. Once all events have been analysed and rainfall amount versus time duration can be plotted, an enveloping curve is drawn to estimate the PMP.
There are variations on this approach, but the basic climate information required remains the same: rainfall in at least hourly time steps, storm dew point data, and historical dew point data.
The rainfall data have a dual use. If streamflow data are available for the dam site, a flood model can be calibrated using the available rainfall and streamflow data. Then the PMP can be input to the flood model and the PMF determined. If data are not available at a specific dam site, regional approaches are an option. They depend on the availability of the same types of information within the region, or in a hydrologically similar region.
The hydrologist undertaking these studies would want to know if climate change will result in conditions conducive to creating more extreme storms.
Financial impact of climate data
Again, the longer the period of record, the larger the number of extreme events available for analysis, and the better the spatial distribution of rainfall measurement sites, the more accurate the actual catchment rainfall will be.
Inadequate or poor rainfall data can result in a spillway design that cannot handle the PMP and which will pose a higher risk of failure and dam break. “Over design” can also occur with poor data, bringing a potentially high financial cost. There have been a number of instances of dam break due to inadequate spillway design. There have also been cases of expensive major upgrades to spillways following extreme events in a region that exceeded PMP estimates.
Seasonal climate outlooks for decision-makers
An area of climate studies of growing interest to water resources managers is that of seasonal climate outlooks or predictions. These are often related to extended hydrological predictions spanning several weeks to a year, which can enable proactive planning and adaptive responses, such as to seasonal water supply shortages.
Seasonal predictions can inform the range of decisions extending from environmental watering strategies to operating a diversified water supply scheme.
Urban areas: Water resources in cities are under increasing pressure from growing populations and high per capita water consumption. Managing urban water demand through the right mix of restrictions, pricing and efficiency is essential for ensuring healthy, safe and reliable water supplies in times of low water availability. Urban water authorities prepare a range of plans to implement sustainable water strategies and government policies, including plans for corporate action, drought response, and permanent water saving.
Streamflow predictions also inform demand management programmes. An increase in river flow forecast in the middle of summer can dramatically affect water consumption. Mid- to medium-term forecasts (three to six to eight months) are important to the urban water industry, both for planning restrictions and for decisions on introducing new water sources. Forecasts are used for urban water management. A particularly useful product would be one that provides water utility companies with a way to predict where their storages will be at the end of the filling season.
Rural areas: Rural water authorities are responsible for supplying water for non-urban water uses, particularly irrigation and livestock and domestic supply. They also manage public reservoirs and supply water to urban water authorities. Extreme weather events can have a serious impact on rural water authorities. Advance warning would provide opportunities for improved allocation and use of water supplies.
Irrigation is a widely used practice to supplement low rainfall levels for assisting in the production of crops or pasture. Common crops produced using irrigation include rice, cotton, canola, sugar, various fruits, vegetables, and other tree crops, as well as pasture, hay and grain for beef and dairy production.
The main concerns for farmers are soil moisture, followed by how much water is already allocated through bores or rivers, and then the seasonal forecast. Seasonal information becomes more important as irrigated crops grow.
Dryland farming involves cultivating land that receives little rainfall. Key elements of dryland farming include: capture and conservation of available moisture, effective use of available moisture, soil conservation and control of input costs. Dryland farmers use seasonal forecast information in their planning to manage risk. There is a significant preparatory investment on large farms when the outcome of the season is in doubt. Growers will not plant as much if it is looking like a bad season. However, if the outlook is good they may decide to increase the growing area.
There are two main sources of predictability in streamflows. One is strong serial correlations in streamflows, due to soil and groundwater storages extending the time between incidence of rainfall and any resulting streamflow and future rainfall. The other is climatic conditions that influence future streamflows. Many indices of large-scale climate anomalies, such as the Southern Oscillation Index and Indian Ocean Dipole Mode Index, show significant concurrent and lagged correlations with rainfall and streamflows.
When the skill of seasonal streamflow forecasts produced using only antecedent flow predictors is high, climate predictors tend to add little skill. When the flow predictors are low, climate predictors tend to increase the skill of the forecasts, in some cases substantially.
In terms of a changing climate, the hydrologist will need to know if the current climate drivers will continue to influence seasonal climate outlooks. If not, what new drivers are likely to emerge and what will their impacts be?
These examples show the strong connection between the water and climate sectors. Mechanisms to improve cooperation and collaboration between the two sectors can only be beneficial.