Our interest is in estimating NPP from cropland, as opposed to natural forests or vegetation. To do so, we make use of a new and unique land cover data set developed by the European Space Agency’s (ESA) Climate Change Initiative. This data set provides information on 37 land cover classes based on the United Nations Land Cover Classification System at a 300m resolution. The data rely on state‐of‐the‐art reprocessing of four different satellite missions (MERIS, SPOT‐VGT, AVHRR, and PROBA‐V).<sup>4</sup> We use a cross‐walking table from Poulter _et al_ . (2015) that <mark>characterizes plant functional types across the ESA classes to guide the grouping of classes into a single cropland category.</mark><sup>5</sup> <mark>Our final data measure changes in NPP for each 0.1‐degree gridcell (approximately 11x11 km at the equator) which contains a minimum level of cropland in the year 2000 (the first year of our data set). There are trade‐offs when choosing the minimum level of cropland for a gridcell to contain to be included the analysis. Too low of a threshold and the analysis will include gridcells which are dominated by natural vegetation or urban land, and thus of no interest to the study, potentially biasing the results. Too high of a threshold and the analysis becomes too restrictive, where only agriculture situated in large farming communities is included. For this reason, we test a range of thresholds, with the main analysis using a 30% cropland minimum threshold to be included in the analysis, and robustness checks using 75% and 90% thresholds.</mark>

# 2.3 Irrigation Data

Gridcells included in this analysis are restricted to those where it is believed that agriculture is irrigated. This ensures that the EC measured in the surface water is like the EC in the water which is feeding the crops. In India, data on irrigation by district were obtained from the Ministry of Agriculture and the International Crops Research Institute for the Semi‐Arid Tropics. Only districts for which greater than 50 percent of agricultural land is irrigated by surface irrigation are included in the analysis (see Figure 2).

For the Mekong River basin analysis, as well as the global analysis, data on irrigation were obtained from Food and Agriculture Organization’s Global Map of Irrigated Areas (GMIA) version 5.0.<sup>6</sup> GMIA gives a global raster of area equipped for irrigation for the year 2005 at a resolution of 0.083 degrees. These data were aggregated up to the 0.1‐degree gridcell described in the previous section, and gridcells are only included if some land area is equipped for irrigation according to GMIA. GMIA does not distinguish between groundwater irrigation and surface water irrigation. For the Mekong analysis, this is not a concern, as surface water irrigation comprises 91 percent of total irrigation in Thailand, 99 percent in Lao People’s Democratic Republic and Vietnam, and 100 percent in Cambodia (FAO 2016). Thus, we can be reasonably confident that nearly all the NPP included in the analysis is irrigated using surface waters.

For the wider, global study, there is unfortunately no way to distinguish between ground and surface irrigated crops. However, i <mark>t is well‐known that ground and surface‐water bodies interact with each other. Various studies in the hydrological literature have begun to show that groundwater can be a major and potentially long‐term contributor to contamination of surface water (e.g. Yu et al. 2018; Delsman et al., 2015; De Louw et al., 2010; Holman et al., 2008). The interactions between river water and</mark>

> 4 https://www.esa‐landcover‐cci.org/.

> 5 <mark>We</mark> aggregate land cover categories 10 through 30 to a single cropland class. This is also consistent with the <mark>widely‐ used global vegetation classification scheme of the</mark> International Geosphere Biosphere Programme (IGBP). 6 The surface irrigation data set is available here:

> http://www.fao.org/nr/water/aquastat/irrigationmap/index10.stm.

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