Drought and Water Cruises: Science, Technology, and Management Issues - Chapter 13 pptx

345 13 A Role for Streamflow Forecasting in Managing Risk Associated with Drought and Other Water Crises SUSAN CUDDY, REBECCA LETCHER, FRANCIS H. S. CHIEW, BLAIR E. NANCARROW, AND TONY JAKEMAN CONTENTS I. Introduction 346 A. Seasonal Forecast and Climate Variability 346 B. Adoption Constraints 347 II. Estimating the Potential 348 A. Case Study Context 348 B. Seasonal Forecast Models 350 C. Forecast Model Results 353 D. Decision-Making Models 355 E. Modeling Results 356 III. Reality Bites 359 IV. Summary 362 V. Future Directions 363 Acknowledgments 364 References 364 DK2949_book.fm Page 345 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group 346 Cuddy et al. I. INTRODUCTION Climatic variability should be a significant factor influencing agricultural production decisions. Historically in Australia, farmers and governments have invested heavily in reducing the influence of this variability on agricultural production. This investment has included construction of large dams on major river systems throughout the country, primarily for irrigation purposes, and allocation and development of groundwater resources. This development policy placed large pressures on ecosystems and has significantly modified river systems. In 1994, the Council of Australian Governments began a period of water reform, entering a new management phase for water resources. These reforms have included assessment of the sustainable yield from aquifer systems, often found to be below current allocation and even extraction levels, as well as allocation of a proportion of flows to the environment. In many catchments these water reforms have not only reduced irrigators’ access to some types of water but have also implicitly increased the effect of climate variability on their decision making by increasing their reliance on pumping variable river flows. These management and allocation pressures are com- pounded by Australian streamflow (and to a lesser extent climate) being much more variable than elsewhere in the world. The interannual variability of river flows in temperate Australia (and southern Africa) is about twice that of river flows elsewhere in the world (Figure 1; Peel et al., 2001). This means that temperate Australia is more vulnerable than other countries to river flow–related droughts and floods. In such a challenging environment, forecasting tools that support improved decision making resulting in efficiencies in water use and reduced risk taking are highly desirable. The development and use of such tools is the focus of considerable research and extension activity in government and industry. A. Seasonal Forecast and Climate Variability Relationships between sea surface temperatures and climate are well documented. The relationship between Australia’s hydroclimate and the El Niño/Southern Oscillation (ENSO) DK2949_book.fm Page 346 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group A Role for Streamflow Forecasting 347 is among the strongest in the world (Chiew and McMahon, 2002). El Niño describes the warm phase of a naturally occur- ring sea surface temperature oscillation in the tropical Pacific Ocean. Southern oscillation refers to a seesaw shift in surface air pressure at Darwin, Australia, and the South Pacific island of Tahiti. Several indices have been derived from this relationship, in particular the Southern Oscillation Index (SOI), which describes the Tahiti minus Darwin sea level pressure and is commonly used as an indicator of ENSO. The strong relationships that exist between climate, streamflow, and ENSO form the scientific basis for forecast tools developed throughout Australia and other parts of the world. In the Australian context, the Bureau of Meteorology routinely provides seasonal climate outlooks (e.g., probability that the total rainfall over the next 3 months will exceed the median) and computer packages such as Rainman Streamflow (Clewett et al., 2003) are heavily promoted. The patchy adoption of these tools, and thus the inability to reap the perceived gains in water use efficiency, is of concern to their promoters and research and development agencies. B. Adoption Constraints A major issue for the designers of decision support tools is the degree of likely uptake by the potential users, and this is no different for seasonal forecasting. The farming community, Figure 1 Interannual variability of Australian streamflow relative to the rest of the world. The L-Cv is used as a measure of interannual runoff variability. It is a measure of relative variability similar to the coefficient of variation (standard deviation divided by the mean). The L-Cv in the plot are for catchments in the Cfb Koppen climate type, which represents a temperate climate. 0 0.1 0.2 0.3 0.4 0.5 0.6 10 100 1000 10000 Mean annual runoff (mm) L-Cv of annual runoff Australia World DK2949_book.fm Page 347 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group 348 Cuddy et al. which is traditionally conservative when it comes to changing well-entrenched behaviors, is particularly reticent to adopt such tools. Many factors play a part in users’ decisions to adopt these tools and the information they provide. Knowledge, awareness, and understanding of the potential outcomes available through the use of the tools vary. Confidence in the outcomes is often lacking, especially when the tools may be replacing well-tried and comfortable practices. These practices may be seen to be adequate for the decisions they are assisting, and hence users do not perceive a need for new technologies. Previous experiences associated with the technologies being used by the tools will also be a factor. These may be first-hand experiences or purely word of mouth in the community. Local opinion will frequently be more powerful than information from “outsiders.” Naturally, if past experiences have resulted in negative consequences, the uptake of the new technology will be even less likely. Confidence in the new technology and trust in the provider of the technology are therefore likely to be highly influential. In fact, the “human factor” frequently can be less certain than the technologies themselves. II. ESTIMATING THE POTENTIAL Most investigations of the potential of forecast tools compare their predictions against “no knowledge.” This section describes the coupling of forecast models to models that simulate a range of water management behaviors within a con- strained problem definition. Quantification of the net financial return to irrigators of adopting climate forecasts as part of their decision-making process would provide a strong measure of the benefit of these forecasts. This is tempered by an analysis of the potential market, which reveals that a significant improvement in reliability and relevance is required before widespread adoption can be considered. A. Case Study Context To consider the potential benefits to agricultural production of seasonal forecasts, we investigated their potential impact DK2949_book.fm Page 348 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group A Role for Streamflow Forecasting 349 on farm-level decisions and returns in an irrigated cropping system. We premised that the potential benefit of seasonal forecasts was probably greatest in a farming system subject to significant uncertainty. For this reason, the farming system represented in the decision-making models is that of an irrigated cotton producer operating on an unregulated river system, relying on pumping variable river flows for irrigation purposes during the season. This type of farm is typical in unregulated areas of the Namoi basin in the northern Mur- ray-Darling basin, particularly the Cox’s Creek area (Figure 2). However, for this analysis, the modeling should be considered to represent a theoretical or model farm rather than a farm from a particular system; the value of forecasts was tested on this farm using forecasts and flows from many Figure 2 Map of the case study area highlighting the Cox’s Creek region of the Namoi basin within the Murray-Darling basin system of eastern Australia. Namoi Basin Coxs Creek 418025 412080 421636 412082 410033 410061 410047 Murray-Darling Basin DK2949_book.fm Page 349 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group 350 Cuddy et al. different river systems in eastern New South Wales. We did this to test the sensitivity of the results and recommendations to the hydrology and climate of the river system. Given that the model farm is assumed to be pumping from the river for irrigation supply, production and water availability are limited by the number of days on which the farm can pump flows from the river. To mimic the types of flow rules on these unregulated systems and to test the sensitivity of results to these rules, two pumping thresholds were considered—the 20th and 50th percentile of flow (i.e., flow that is exceeded 20% or 50% of the time). The forecast provided for each year is the number of days that are above these pumping thresholds (i.e., the number of days on which pumping is allowed). The model farmer factors this forecast and the total volume of water allowed to be pumped on each such day (the daily extraction limit, defined by policy as a fixed volume of water) into the planting decision. Climate forecasts were constructed over an 86-year period for seven catchments and the two pumping threshold regimes using three forecast methods. Farmer decisions were then simulated using these three forecast methods as the basis of the decision, as well as using three decision alterna- tives for comparison. This section describes the catchments considered in the analysis and the climate forecasting results for each. The decision models used in the analysis of these forecasts are then described before results are presented. These results should be considered to be indicative of the potential benefits of seasonal forecasting in eastern Australia. The complexity of different production systems and many of the influences on real-life decisions have not been considered for this preliminary analysis. However, this analysis does provide an interesting insight into the potential for forecasting methods to help farmers adjust away from the impacts of climate variability. B. Seasonal Forecast Models The relationship between streamflow and ENSO and the serial correlation in streamflow can be exploited to forecast streamflow several months ahead. These relationships are DK2949_book.fm Page 350 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group A Role for Streamflow Forecasting 351 well described in Chiew and McMahon (2003) and demon- strate the statistical significance of the lag correlation of the linear relationship between 3-month streamflow (in Oct–Nov–Dec and in Jan–Feb–Mar) and the SOI value in the previous 3 months in catchments throughout Australia. Using this relationship, we can forecast summer streamflow throughout most of eastern Australia from spring indicators of ENSO. Serial correlation in streamflow must also be considered when forecasting streamflow because it is generally stronger than the streamflow–ENSO relationship and is per- sistent throughout the year. To make risk-based management decisions, we must express forecasts as exceedance probabilities (e.g., probability of getting at least 10 pumping days). In this study, exceedance probability forecasts are derived at tributary scale for seven unimpaired catchments in the Murray-Darling basin. The derivation of the forecasts is based on categorization and consequent nonparametric modeling of streamflow distribu- tions and their antecedent conditions (e.g., discrete SOI cat- egories) (see, for example, Sharma, 2000, and Piechota et al., 2001). Catchments were selected because of their relative proximity to the Namoi basin (all within the Murray-Darling basin in New South Wales) and to reflect a range of rainfall–runoff conditions and forecast skills. Proximity to the Namoi basin is to support coupling with the decision-making models that have been developed by Letcher (2002) within the water management regulatory framework in the Namoi basin, although they simulate representative farmer behavior. Daily streamflow data from the period 1912–1997 are used. The data include extended streamflow estimates using a conceptual daily rainfall–runoff model (Chiew et al., 2002). The catchment locations and long-term average rainfall–runoff characteristics are summarized in Table 1. Forecasts are made for the number of days in Octo- ber–February that the daily flow exceeds the two pumping thresholds under consideration. The thresholds are calculated based on flow days only, defined as days when the daily flow exceeds 0.1 mm. The forecast is derived by relating the number of days in October–February that the daily flow exceeds a threshold to explanatory variables available at the end of DK2949_book.fm Page 351 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group 352 Cuddy et al. T ABLE 1 Summary of Characteristics for Catchments Used in the Analysis Catchment Catchment and Rainfall–Runoff Characteristics Lat. Long. Area (km 2 ) Rainfall (mm) Runoff (mm) Runoff Coef. (%) % Days Flow >0.1 mm Percentile Flows (mm) 20% 50% 410033 Murrumbidgee R @ Mittagang Crossing 36.17 149.09 1891 882 134 10–15 71 0.55 0.28 410047 Tarcutta Ck @ Old Borambola 35.15 147.66 1660 818 110 10–15 50 0.68 0.31 410061 Adelong Ck @ Batlow Road 35.33 148.07 155 1138 256 >20 89 0.97 0.44 412080 Flyers Creek @ Beneree 33.50 149.04 98 915 106 10–15 50 0.65 0.29 412082 Phils Creek @ Fullerton 34.23 149.55 106 821 124 10–15 62 0.58 0.27 418025 Halls Creek @ Bingara 29.91 150.58 156 755 44 6 24 0.22 0.14 421036 Duckmaloi River @ Below Dam Site 33.77 149.94 112 967 244 >20 80 0.95 0.40 DK2949_book.fm Page 352 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group A Role for Streamflow Forecasting 353 September. The explanatory variables used are the SOI value averaged over August and September and the total flow volume in August and September. We derive the forecast using the nonparametric seasonal forecast model described in Piechota et al. (2001) and express it as exceedance probabilities. Such forecasts closely approximate low-risk decision- making behavior and can be used as a direct input into the decision-making models. Three forecast models are used: 1. FLOW: Forecast derived from flow volume in August–September 2. SOI: Forecast derived from SOI value in August–Sep- tember 3. FLOW+SOI: Forecast derived from flow volume and SOI value in August–September C. Forecast Model Results All models exhibit significant skill in the forecast, summarized in Table 2. Two measures of forecast skill are used—Nash-Sutcliffe E and LEPS scores. The Nash-Sutcliffe E (Nash and Sutcliffe, 1970) provides a measure of the agreement between the “mean” forecast (close to the 50% exceedance probability forecast) and the actual number of days in October–February that the daily flow exceeds a threshold. A higher E value indicates a better agreement between the forecast and actual values, with an E value of 1.0 indicating that all the “mean” forecasts for all years are exactly the same as actual values. The LEPS score (Piechota et al., 2001) attempts to compare the distribution of forecast (forecast for various exceedance probabilities) with the number of days in October–February that the daily flow exceeds a threshold. A LEPS score of 10% generally indicates that the forecast skill is statistically significant. A forecast based solely on climatol- ogy (same forecast for every year based on the historical data) has a LEPS score of 0. DK2949_book.fm Page 353 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group 354 Cuddy et al. T ABLE 2 Summary of Forecast Skills for Catchments Used in the Analysis Catchment Forecast Skill Case FLOW SOI FLOW+SOI E LEPS E LEPS E LEPS 410033 Murrumbidgee R @ Mittagang Crossing Days >20% 0.35 27.1 0.23 11.6 0.58 41.7 Days >50% 0.36 23.1 0.19 12.2 0.60 39.7 410047 Tarcutta Ck @ Old Borambola Days >20% 0.41 32.8 0.23 17.6 0.57 46.4 Days >50 0.39 26.2 0.18 11.2 0.50 36.0 410061 Adelong Ck @ Batlow Road Days >10% 0.54 41.4 0.16 12.0 0.64 49.5 Days >20% 0.63 42.0 0.17 11.1 0.71 50.4 412080 Flyers Creek @ Beneree Days >20% 0.34 25.8 0.22 10.2 0.54 37.6 Days >50% 0.42 28.8 0.22 10.9 0.56 40.0 412082 Phils Creek @ Fullerton Days >20% 0.40 19.2 0.22 12.3 0.59 32.1 Days >50% 0.54 30.0 0.22 12.2 0.64 39.7 418025 Halls Creek @ Bingara Days >20% 0.13 12.4 0.16 11.7 0.29 26.3 Days >50% 0.26 15.3 0.16 13.0 0.44 31.5 421036 Duckmaloi River @ Below Dam Site Da ys >20% 0.16 12.3 0.24 13.5 0.45 28.1 Days >50% 0.24 16.7 0.27 17.7 0.51 34.0 DK2949_book.fm Page 354 Friday, February 11, 2005 11:25 AM Copyright 2005 by Taylor & Francis Group [...]... within Land and Water Australia, the government agency responsible for land and water resources research and development in Australia Such integration demonstrates the gains that can be made by adopting an interdisciplinary approach to developing practical tools to support sound environmental management REFERENCES Chiew, FHS; McMahon, TA Global ENSO-streamflow teleconnection, streamflow forecasting and interannual... decision-making behaviors, and current resistance must be clearly articulated These niche markets need to be identified This case study gives some strong leads—for example, irrigation versus dryland and high-equity versus mortgage participants Significant benefit from research and development in climate risk management can only be realized if it produces tools that match users’ needs and expectations and. .. Forecasting 363 An aggressive water reform agenda, underpinned by an acknowledgment of the finite size of the water resource and recognition of the legitimacy of the environment as a water user, is driving research in and development of tools that can fine-tune critical decisions about water allocation V FUTURE DIRECTIONS In spite of recent improvements, adoption of climate variability management tools such... directly related to degree of exposure and risk management behavior A consequence of the current water reforms—as timely access to instream water is no longer guaranteed and on-farm water storage is increasingly regulated—is an increase in risk exposure This may force users to invest in tools that provide marginal gains To identify where these marginal gains are, and the different levels of benefit that... the SOI model, the E and LEPS for the FLOW+SOI model are generally greater than 0.5 and 40%, respectively (compared to 0.35 and 25% in the FLOW model) In the two catchments where the FLOW model and SOI model have similar skill, the E and LEPS for the FLOW+SOI model are generally greater than 0.3 and 25% (compared to less than 0.25 and 20% in the FLOW or SOI model alone) D Decision-Making Models All decisions... regimes—irrigated cotton with winter wheat rotation, dryland sorghum and winter wheat rotation, and dryland cotton and winter wheat rotation Production costs are incurred on crop planting, so areas planted for which insufficient water is available over the year generate a loss For such crops, it is assumed that the area irrigated is cut back and a dryland yield is achieved on the remaining area planted E... better result in terms of water use efficiency and net gains in profit Yet adoption is slow, perhaps reflecting the general conservative nature of farmers, at least in Australia, and the need for them to see real and sustained benefit before they will consider incorporating such tools into their decision making The social analysis confirmed that knowledge and understanding of the term and the forms of seasonal... FHS; McMahon, TA Australian rainfall and streamflow and El Niño/Southern Oscillation Australian Journal of Water Resources 6:115–129, 2003 Chiew, FHS; Peel, MC; Western, AW Application and testing of the simple rainfall–runoff model SIMHYD In: VP Singh, DK Frevert, eds Mathematical Models of Small Watershed Hydrology and Applications (pp 335–367) Littleton, CO: Water Resources Publication, 2002 Clewett,... improved water supply management: Part 3—A nonparametric probabilistic forecast model Journal of Hydrology 239:249–259, 2000 URS Australia Defining researching opportunities for improved applications of seasonal forecasting in south-eastern Australia with particular reference to the southern NSW and Victorian grain regions Report to the Climate Variability in Agriculture Program, Land and Water Australia,... very important, and until this could be achieved to help farmers in decision making, uptake of the technology would be limited And memories can be very long Indigo Jones was a long-range forecaster a while back, and he was considered to be very good In 1974 he predicted it would be wet and we had some of the biggest floods in history However, in 1975 he predicted it would be wetter still, and we had one . rotation, dryland sorghum and winter wheat rotation, and dryland cotton and winter wheat rotation. Production costs are incurred on crop planting, so areas planted for which insufficient water is. forecasting is directly related to degree of exposure and risk management behavior. A consequence of the current water reforms—as timely access to instream water is no longer guaranteed and on-farm water. decision making. The social analysis confirmed that knowledge and understanding of the term and the forms of seasonal forecasting were highly variable. There seemed to be a misunderstanding of

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