# 2.3.Sensitivity analysis

The investment support scenarios described above are implemented under central assumptions derived from the Shared Socioeconomic Pathway (SSP) 2: Middle of the Road (Fricko et al., 2017). In SSP2 the global population reaches 9.2 billion people (KC and Lutz, 2017) and global average GDP per capita doubles to around $16,000 (Dellink et al., 2017). We estimate crop and livestock productivity over the time period using an econometric estimate of the relationship between crop yields and GDP per capita assumptions of SSP2 (Fricko et al., 2017; Herrero et al., 2014).

The main scenarios of investment are complemented by some sensitivity analysis scenarios where some key assumptions and drivers from the SSP2 baseline are varied, to test their influence on the irrigation policy impacts. Table 3 provides an overview of the types of modeling assumptions we varied in the sensitivity analysis and a description of the alternative assumptions used. Section 5 provides the more detailed examination of the impacts of alternative assumptions for the sensitivity analysis on irrigation investments and impact of irrigation investments on the agriculture development, food security and land use change. First, we test the impact of irrigation investments under completely different socioeconomic pathways (SSPs). Our analysis considers SSP1, “Sustainability”, and SSP3, “Regional Rivalry” where the development of population and economic growth, speed of technological change in agriculture, dietary preferences and food waste management, as well as degree of globalization of agricultural commodity markets differ from the baseline (SSP2). We pay particular attention to the irrigation development storylines and the projections for water demand from domestic and industrial uses developed for the SSPs from Water Futures and Solutions Initiative (Wada et al., 2016) and Hanasaki et al. (2013).

The socioeconomic pathway SSP1 provides an alternative dietary preference scenario which moderates overconsumption and aims to reduce of the share of red meat in the diets compared to the diet preferences in the baseline (SSP2). Two additional diet scenarios are also modeled– healthy diet and healthy sustainable diet. The Healthy Diet scenario modifies current diets to set them strictly in line with the World Health Organization dietary recommendations and the Healthy Sustainable Diet scenario considers the land and water resource requirements of food items.

We evaluate the impacts of climate change using spatially explicit impacts on crop yields, irrigation water requirements which were calculated by the crop growth model EPIC (Balkovič et al., 2013) under the representative concentration pathway 2.6 and 8.5 for five global climate models (GCMs) and coupled to GLOBIOM. Additionally, the water available for irrigation is determined by the globally gridded monthly surface water availability and environmental flows is modeled by LPJmL (Palazzo et al., 2018, 2017, Pastor et al., 2016, 2014).

Over the time period, our baseline assumption is that the efficiency of irrigation systems (water application efficiency) moderately increases. We model two alternative specifications: a high water application efficiency for irrigation systems scenario with rapidly increasing irrigation water application efficiency through substitution of the inefficient systems by sprinkler and drip, and a low water application efficiency scenario with stagnating water use efficiency.

Finally, we analyze the effects of more integrated international markets (open trade) and more regionalized markets (restricted trade) and their role in irrigation investment needs.

Additionally, we combined various parameters and assumptions of the uncertainty scenarios described above to form more extreme scenarios. The goal of these “extreme scenarios” is to identify the smallest and largest expansion of irrigated areas as well as the least and greatest irrigation 11