Operations eclipse sequencing in multipurpose dam planning

OPERATIONS ECLIPSE SEQUENCING IN MULTIPURPOSE DAM PLANNING M. Giuliani, W. Arnold, J. Zatarain Salazar, A. Carlino, A. Castelletti GROWING POPULATION INCREASES WATER-ENERGY-FOOD DEMANDS Source: Gerland et al. (2014) probability that world population in 2100 will be between 9.0 and 13.2 billion people. The projections also provide updated answers to longstanding questions about population change. Lutz et al.(14)gavean85%probabilitythatworldpopulation growth would end in the 21st century, but our probabilistic projection indicates that this probability is much lower, at only 30%. Lutz et al.(15) considered a doubling of world population from 1997 to 2100 to be unlikely, with a probability of one-third. We found a similar but slightly lower probability of 25%. The probabilistic intervals were much narrower than those between the traditional high and low scenarios, which seem to overstate uncertainty about future world population. Figure 1B shows the projections of total population for each continent to the end of the century. Asia will probably remain the most populous continent, although its population is likely to peak around the middle of the century and then decline. The main reason for the increase in the projection of the world population is an increase in the projected population of Africa. The continent’s current population of about 1 billion people is projected to rise to between 3.1 and 5.7 billion with probability 95% by the end of the century, with a median projection of 4.2 billion. Although this estimate is large, it does not imply unprecedented population density: Under this projection, Africa’spopulationdensitywouldberoughlyequal to that of China today. The increase in the projected population of Africa is due to persistent high levels of fertility and the recent slowdown in the rate of fertility decline (16). Three-quarters of this anticipated growth is attributable to fertility levels above the replacement level, and the remaining quarter is due to mortality reduction and current youthful age structure (17). Since 1950, fertility has declined rapidly in Asia and Latin America and has also started to decline in Africa. Demographers had projected that fertility in African countries would decline at a rate similar to what has been observed in Asia and Latin America. However, although fertility has been declining in Africa over the past decade, it has been doing so at only about one-quarter of the rate at which it declined in Asia and Latin America in the 1970s, when these regions were at a comparable stage of the fertility transition (16). Indeed, in some African countries, the decline seems to have stalled (18). Bongaarts and Casterline (16) suggest two reasons for the slower fertility decline in subSaharan Africa. First, they note that despite declines in fertility desires in Africa, the most recent levels of ideal family size are still high, with a median of 4.6 children per woman. This is in line with prevailing family norms (19) and the fact that the TFR before fertility started to decline was higher in Africa (6.5) than in the other regions (5.8) (20,21). Second, the unmet need for contraception (the difference between the demand for contraception and its use) has remained substantial at ~25%, with no systematic decline over the past 20 years (22). Astallinthedeclineinthepastdecadeisapparent from the past and projected levels of TFR SCIENCE sciencemag.org 10 OCTOBER 2014 •VOL 346 ISSUE 6206 235 Fig. 1. World and continental population projections. (A) UN 2012 world population projection (solid red line), with 80% PI (dark shaded area), 95% PI (light shaded area), and the traditional UN high and low variants (dashed blue lines). (B)UN2012 population projections by continent. In both panels, the vertical dashed line denotes 2012. Fig. 2. TFR and population projections for Nigeria. UN 2012 projection of (A) TFR and (B)totalpopulation for Nigeria (solid red lines), with 80% PI (dark shaded areas), 95% PI (light shaded areas), and traditional UN high and low variants (dashed blue lines). In both panels, the vertical dashed line denotes 2012. RESEARCH |REPORTS DAMS ARE OFTEN USED TO SUPPORT ECONOMIC DEVELOPMENT Source: Sterl et al. (2022) WHICH DAM TO BUILD? WHEN? AND HOW TO OPERATE THE SYSTEM? Population Time New/Dam New/irrigation New/dam/&/irrigation Baseline Time Food/prone/policy/ Energy/prone/policy/ Compromise/policy/ Transfer/to/a/new/portfolio Adaptation/tipping/point LEGEND Population Time ENERGY DEMAND FOOD DEMAND Dam 1 Dam 2 Dams 1 + 2 CLIMATE CHANGE Figure adapted from Haasnoot et al. (2013) THE ZAMBEZI WATERCOURSE Mozambique Angola Namibia Zambia Zimbabwe Botswana Malawi BG DG MN KA ITT KGU CB KGL Existing Reservoirs Candidate Reservoirs Delta Flow Objective Irrigation Sites ¯ 0 210 420105 Kilometers Z a m b e z i L u a n g w a S h i r e K a f u e HOW TO OPTIMIZE DAM SEQUENCING & OPERATION Two-part optimization of reservoir network expansion sequencing and operations Operating policy selection Candidate reservoir network configurations Sequencing of reservoir networks Objectives:Water, energy, and foodindicators + cost Variables:Construction timing κ1 ι1ι2 κ4 ι3 κ2 κ5 κ7 κ6 κ3 Reservoir network operations Objectives:Water, energy, and food indicators Variables:Operating policies Operating policies for each candidate reservoir network configuration (section 4.1) Pathways of reservoir network expansion with adaptive operations (section 4.3) (EMODPS) (MOEA) Part 1 Part 2 Sensitivity of sequencing to operating policy selection (section 4.4) Op. Policy 1-a Op. Policy 1-b Op. Policy 1-c Robustness evaluation of selected solutions (section 4.5) 450-member synthetic streamflow ensemble Sensitivity Testing Op. Policy 1 Op. Policy 2 HOW TO OPTIMIZE DAM SEQUENCING & OPERATION Two-part optimization of reservoir network expansion sequencing and operations Operating policy selection Candidate reservoir network configurations Sequencing of reservoir networks Objectives:Water, energy, and foodindicators + cost Variables:Construction timing κ1 ι1ι2 κ4 ι3 κ2 κ5 κ7 κ6 κ3 Reservoir network operations Objectives:Water, energy, and food indicators Variables:Operating policies Operating policies for each candidate reservoir network configuration (section 4.1) Pathways of reservoir network expansion with adaptive operations (section 4.3) (EMODPS) (MOEA) Part 1 Part 2 Sensitivity of sequencing to operating policy selection (section 4.4) Op. Policy 1-a Op. Policy 1-b Op. Policy 1-c Robustness evaluation of selected solutions (section 4.5) 450-member synthetic streamflow ensemble Sensitivity Testing Op. Policy 1 Op. Policy 2 Pareto optimal policies for the coordinate operations of the 5 existing reservoirs HOW TO OPTIMIZE DAM SEQUENCING & OPERATION Two-part optimization of reservoir network expansion sequencing and operations Operating policy selection Candidate reservoir network configurations Sequencing of reservoir networks Objectives:Water, energy, and foodindicators + cost Variables:Construction timing κ1 ι1ι2 κ4 ι3 κ2 κ5 κ7 κ6 κ3 Reservoir network operations Objectives:Water, energy, and food indicators Variables:Operating policies Operating policies for each candidate reservoir network configuration (section 4.1) Pathways of reservoir network expansion with adaptive operations (section 4.3) (EMODPS) (MOEA) Part 1 Part 2 Sensitivity of sequencing to operating policy selection (section 4.4) Op. Policy 1-a Op. Policy 1-b Op. Policy 1-c Robustness evaluation of selected solutions (section 4.5) 450-member synthetic streamflow ensemble Sensitivity Testing Op. Policy 1 Op. Policy 2 Pareto optimal policies for the coordinate operations of 5+1 reservoirs HOW TO OPTIMIZE DAM SEQUENCING & OPERATION Two-part optimization of reservoir network expansion sequencing and operations Operating policy selection Candidate reservoir network configurations Sequencing of reservoir networks Objectives:Water, energy, and foodindicators + cost Variables:Construction timing κ1 ι1ι2 κ4 ι3 κ2 κ5 κ7 κ6 κ3 Reservoir network operations Objectives:Water, energy, and food indicators Variables:Operating policies Operating policies for each candidate reservoir network configuration (section 4.1) Pathways of reservoir network expansion with adaptive operations (section 4.3) (EMODPS) (MOEA) Part 1 Part 2 Sensitivity of sequencing to operating policy selection (section 4.4) Op. Policy 1-a Op. Policy 1-b Op. Policy 1-c Robustness evaluation of selected solutions (section 4.5) 450-member synthetic streamflow ensemble Sensitivity Testing Op. Policy 1 Op. Policy 2 Pareto optimal policies for the coordinate operations of 5+2 and 5+3 reservoirs ROBUSTNESS VIA COMPROMISE OPERATIONS ROBUSTNESS VIA COMPROMISE OPERATIONS Shaded bands show the full range of performance re-evaluated under a 450member synthetic hydrology ensemble TAKEAWAYS TAKEAWAYS •Operating policies eclipse reservoir sequencing in balancing conflicting objectives TAKEAWAYS •Operating policies eclipse reservoir sequencing in balancing conflicting objectives •System performance is more sensitive to operational tradeoffs than climate change TAKEAWAYS •Operating policies eclipse reservoir sequencing in balancing conflicting objectives •System performance is more sensitive to operational tradeoffs than climate change •Integrating operations into dam planning becomes crucial for addressing multisector tradeoffs TAKEAWAYS •Operating policies eclipse reservoir sequencing in balancing conflicting objectives •System performance is more sensitive to operational tradeoffs than climate change •Integrating operations into dam planning becomes crucial for addressing multisector tradeoffs References: Arnold, W., Salazar, J. Z., Carlino, A., Giuliani, M., & Castelletti, A. (2023). Operations eclipse sequencing in multipurpose dam planning. Earth’s Future, 11, e2022EF003186 DEPT. of ELECTRONICS, INFORMATION, and BIOENGINEERING POLITECNICO DI MILANO Matteo Giuliani [email protected] | @MxgTeo www.ei.deib.polimi.it HP PRODUCTION TARGETS X - 14 : Figure S1. Projected hydropower production targets for existing and candidate ZRB reservoirs under the nominal future scenario. January 25, 2023, 10:27am Osemosys TEMBA model for SAPP using projected energy demand (based on population) PROJECTED IRRIGATION DEMANDS :X - 15 Figure S2. Projected irrigation demands for all irrigation districts under the nominal future scenario. January 25, 2023, 10:27am AQUACROP simulation under RCP45 and considering planned irrigation expansions