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Perfect foresight hydroeconomic optimization models are tools to evaluate impacts of water infrastructure investments and policies considering complex system interlinkages. However, when assuming perfect foresight, optimal management decisions are found assuming perfect knowledge of climate and runoff, which might bias the economic evaluation of investments and policies. We investigate the impacts of assuming perfect foresight by using Model Predictive Control (MPC) as an alternative. We apply MPC in WHAT-IF, a hydroeconomic optimization model, for two study cases: a synthetic setup inspired by the Nile River, and a large-scale investment problem on the Zambezi River Basin considering the water–energy–food nexus. We validate the MPC framework against Stochastic Dynamic Programming and observe more realistic modeled reservoir operation compared to perfect foresight, especially regarding anticipation of spills and droughts. We find that the impact of perfect foresight on total system benefits remains small (<2%). However, when evaluating investments and policies using with-without analysis, perfect foresight is found to overestimate or underestimate values of investments by more than 20% in some scenarios. As the importance of different effects varies between scenarios, it is difficult to find general, case-independent guidelines predicting whether perfect foresight is a reasonable assumption. However, we find that the uncertainty linked to climate change in our study cases has more significant impacts than the assumption of perfect foresight. Hence, we recommend MPC to perform the economic evaluation of investments and policies, however, under high uncertainty of future climate, increased computational costs of MPC must be traded off against computational costs of exhaustive scenario exploration.

PR, IFPRI3; CRP2; CRP5; 5 Strengthening Institutions and Governance; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; SA-TIED, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Water, Land and Ecosystems (WLE), Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks.

PR, IFPRI3; CRP2; CRP5; 5 Strengthening Institutions and Governance; 1 Fostering Climate-Resilient and Sustainable Food Supply; 4 Transforming Agricultural and Rural Economies; SA-TIED, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM); CGIAR Research Program on Water, Land and Ecosystems (WLE), This paper uses 7,200 smoothed climate change projections for each of the four emissions scenarios, together with inter-annual variation provided by detrended historical climate data to investigate changes in growing season (wettest 3 months) weather patterns from the 2020s to the 2060s for ten countries of Southern Africa. The analysis is done in 8,888 quarter-degree pixels by month. Temperature unequivocally rises in the region, but it rises relatively less along the coasts, particularly on the eastern side of the region. Precipitation has trended downward for much of the region since 1975, but relatively little change in precipitation is projected between the 2020s and the 2060s. Under the higher emissions “Paris Forever” scenario, we found that by the 2060s, the 1-in-20-year low-rainfall events will occur twice as frequently in most of the region, though it will occur less frequently in northwestern Angola. The 1-in-20-year high-rainfall events will occur 3 to 4 times as often in northeastern South Africa and twice as often in most of Angola.

PR, IFPRI4; CRP2; 1 Fostering Climate-Resilient and Sustainable Food Supply; DCA; SA-TIED, EPTD; PIM, CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Perfect foresight hydroeconomic optimization models are tools to evaluate impacts of water infrastructure investments and policies considering complex system interlinkages. However, when assuming perfect foresight, optimal management decisions are found assuming perfect knowledge of climate and runoff, which might bias the economic evaluation of investments and policies. We investigate the impacts of assuming perfect foresight by using Model Predictive Control (MPC) as an alternative. We apply MPC in WHAT-IF, a hydroeconomic optimization model, for two study cases: a synthetic setup inspired by the Nile River, and a large-scale investment problem on the Zambezi River Basin considering the water–energy–food nexus. We validate the MPC framework against Stochastic Dynamic Programming and observe more realistic modeled reservoir operation compared to perfect foresight, especially regarding anticipation of spills and droughts. We find that the impact of perfect foresight on total system benefits remains small (<2%). However, when evaluating investments and policies using with-without analysis, perfect foresight is found to overestimate or underestimate values of investments by more than 20% in some scenarios. As the importance of different effects varies between scenarios, it is difficult to find general, case-independent guidelines predicting whether perfect foresight is a reasonable assumption. However, we find that the uncertainty linked to climate change in our study cases has more significant impacts than the assumption of perfect foresight. Hence, we recommend MPC to perform the economic evaluation of investments and policies, however, under high uncertainty of future climate, increased computational costs of MPC must be traded off against computational costs of exhaustive scenario exploration.

Non-PR, IFPRI5; CRP2; SA-TIED, EPTD; PIM, 24 pages, CGIAR Research Programs on Policies, Institutions, and Markets (PIM), This study presents results from a large ensemble of projected changes in seasonal precipitation and near-surface air temperature changes for the nation of South Africa. The ensemble is based on a combination of pattern-change responses derived from the Coupled Model Intercomparison Project Phase 5 (CMIP-5) climate models along with the Massachusetts Institute of Technology Integrated Global Systems Model (MIT-IGSM), an intermediate complexity earth-system model coupled to a global economic model that evaluates uncertainty in socio-economic growth, anthropogenic emissions, and global environmental response. Numerical experimentation with the MIT-IGSM considered four scenarios of future climate and socio-economic development to span a range of possible global actions to abate greenhouse gas emissions through the 21st century. We evaluate distributions of surface-air temperature and precipitation change over three regions across South Africa: western (WSoAfr), central (CSoAfr), and eastern (ESoAfr) South Africa. In all regions, by mid-century, we find a strong likelihood (greater than 50%) that temperatures will rise considerably higher than the current climate’s range of variability (a threefold increase over the current climate’s two-standard deviation range of variability). In addition, scenarios that consider more aggressive global climate targets (e.g. 2C and 15C scenarios) all but eliminate the risk of these acutely salient temperature increases. For precipitation, there is a preponderance of risk toward decreased precipitation (3 to 4 times higher than increased) for western and central parts of South Africa. There is a clear benefit seen within the evolving hydroclimatic risks as a result of strong climate targets, such as limiting the global climate warming to 1.5˚C by 2100. We find that the risk of precipitation changes in the 15C scenario toward the end of this century (2065-2074) is nearly identical to that seen in the REF scenario during the 2030s. Thus

Non-PR, IFPRI1; GRP38; DCA, EPTD, This series of IFPRI Research Briefs is based on research supported by the Federal Ministry for Economic Cooperation and Development, Germany, under the project "Food and Water Security under Global Change: Developing Adaptive Capacity with a Focus on Rural Africa," which forms part of the CGIAR Challenge Program on Water and Food. Through collaboration with the Center for Environmental Economics and Policy in Africa, the Ethiopian Development Research Institute, the Ethiopian Economics Association, and the University of Hamburg, the project aims to provide policymakers and stakeholders in Ethiopia and South Africa with tools to better understand and analyze the consequences of global change-in particular climate change-and to form policy decisions that facilitate adaptation in these countries and bey." -- from text

We assess the ability of global water systems, resolved at 282 large river basins or Assessment Sub Regions (ASRs), to the meet water requirements over the coming decades under integrated projections of socioeconomic growth and climate change. We employ a Water Resource System (WRS) component embedded within the MIT Integrated Global System Model (IGSM) framework in a suite of simulations that consider a range of climate policies and regional hydroclimatic changes through the middle of this century. We find that for many developing nations water-demand increases due to population growth and economic activity have a much stronger effect on water stress than climate change. By 2050, economic growth and population change alone can lead to an additional 1.8 billion people living in regions with at least moderate water stress. Of this additional 1.8 billion people, 80% are found in developing countries. Uncertain regional climate change can play a secondary role to either exacerbate or dampen the increase in water stress due to socioeconomic growth. The strongest climate impacts on relative changes in water stress are seen over many areas in Africa, but strong impacts also occur over Europe, Southeast Asia and North America. The combined effects of socioeconomic growth and uncertain climate change lead to a 1.0 to 1.3 billion increase of the world's 2050 projected population living in regions with overly exploited water conditions— where total potential water requirements will consistently exceed surface-water supply. Under the context of the WRS model framework, this would imply that adaptive measures would be taken to meet these surface-water shortfalls and would include: water-use efficiency, reduced and/or redirected consumption, recurrent periods of water emergencies or curtailments, groundwater depletion, additional inter-basin transfers, and overdraw from flow intended to maintain environmental requirements.

Non-PR, IFPRI2; ESSP; CRP2, DSGD; PIM; EPTD, CGIAR Research Program on Policies, Institutions, and Markets (PIM), Ongoing debate over water resource management and land degradation suggests a need for efficient sustainable land management mechanisms to improve agricultural output in the Blue Nile basin in Ethiopia. Numerous econometric and hydrological models have been developed to assess the effects of sustainable land and watershed management (SLWM) investments. However, these models fail to address the trade-offs faced by rural farmers in maintaining such structures. This study combines household survey data that evaluates the economic determinants of program sustainability with a detailed hydrological model that explores location specific effects of SLWM structures. Simulations suggest that more comprehensive investments (such as SLWM with increased fertilizer application) may reap more economically significant increases in household income. Cost benefit analysis suggests that a packaged investment approach is needed in order to outweigh the opportunity costs (foregone labor, particularly) of investing in SLWM infrastructure at farm level.