Markus Andrews

The Danuri probe will use multiple scientific instruments to probe properties of the Moon.Credit: NASA

By this time next week, South Korea’s first lunar probe will be on its way to the Moon. The probe, Danuri, which means ‘enjoy the Moon’, should arrive at its destination by mid-December and orbit for a year.Researchers are eager for Danuri, which took more than six years to build and cost 237 billion won (US$180 million), to begin revealing insights about aspects of the Moon ranging from its ancient magnetism to ‘fairy castles’ of dust sprinkled across its surface. Researchers also hope that the craft, officially called the Korea Pathfinder Lunar Orbiter, will find hidden sources of water and ice in areas including the permanently cold, dark regions near the poles.Scientists in South Korea say the mission will pave the way for the country’s more ambitious plans to land on the Moon by 2030. Success for Danuri will secure future planetary exploration, says Kyeong-ja Kim, a planetary geoscientist at the Korea Institute of Geoscience and Mineral Resources in Daejeon, and principal investigator for one of Danuri’s instruments, a γ-ray spectrometer. “Everybody is so happy and excited,” says Kim, describing the lines of people who waved goodbye to the orbiter — safely packed in a container — on its way to the airport on 5 July.Danuri was flown from South Korea to the United States, and is now in Cape Canaveral, Florida, preparing to be placed on a Falcon 9 rocket that will take it beyond Earth’s orbit on 2 August.“The spacecraft is ready to launch,” says Eunhyeuk Kim, project scientist for the mission at the Korea Aerospace Research Institute (KARI) in Daejeon, but he still sometimes worries about whether the team is truly ready. “Until the time of the launch, we will be checking all the systems over and over and over.”Within an hour of launching, the 678-kilogram spacecraft will detach from the rocket and KARI will take control of it, extending the craft’s solar panels and deploying its parabolic antenna.“It’s just so cool to see more and more countries sending up their own orbiters and adding to the global understanding of what’s going on on the Moon,” says Rachel Klima, a planetary geologist at the Johns Hopkins University Applied Physics Laboratory in Laurel, Maryland, who is part of the science team.Fairy castlesDanuri will carry five scientific instruments. Among the most exciting is PolCam, which will be the first camera in lunar orbit to map the texture of the Moon’s surface using polarized light. Polarizers are popular for observations of Earth, such as those studying vegetation, but have not been sent to study the Moon, says Klima. By capturing how light reflects off the lunar surface, PolCam will be able to reveal characteristics such as the size and density of grains of dust and rock. This could help researchers to study unusual objects such as the tiny, porous towers of dust called fairy castle structures, says Klima. These structures can’t be reproduced on Earth because of its stronger gravity compared to the Moon, which makes them difficult to study.“It’s a ground-breaking instrument,” says William Farrand, a planetary geologist at the Space Science Institute in Boulder, Colorado, who will be working on PolCam data. Farrand hopes to use the data to study deposits of volcanic ash and improve understanding of the history of explosive eruptions on the Moon.Another widely anticipated instrument is ShadowCam, a highly sensitive camera provided by NASA that will take images of the permanently shadowed regions of the Moon, devoid of sunlight. The camera will need to rely on scattered light such as that from far-off stars to capture images of the surface topography.Since shortly after the Moon formed, volatile materials such as water from comets have been bouncing off its surface and becoming trapped in these very cold regions, says Klima. “We’ve got billions of years of Solar System history locked in the layers of these cold traps.” By giving researchers a view of the terrain in these regions, and identifying brighter regions that might be ice deposits, ShadowCam will be able to inform future landing missions to study that history, she says.MagnetismResearchers hope that data collected by Danuri’s magnetometer (KMAG) will help solve a mystery. The Moon’s surface displays highly magnetic regions; these suggest that for hundreds of millions of years in the Moon’s past, its core generated a magnetic field almost as powerful as Earth’s, through a process known as a dynamo, says Ian Garrick-Bethell, a planetary scientist at the University of California, Santa Cruz, who hopes to interpret KMAG data. But scientists are puzzled by how the Moon’s core, which is much smaller and proportionally farther from the surface than Earth’s, could have powered such an intense dynamo, and for so long. KMAG will take precise measurements of the Moon’s magnetic field to help them understand this.Garrick-Bethell hopes that towards the end of its life, the spacecraft will fly closer to the Moon to get even better measurements of the magnetic field. “The most exciting science would come if we flew closer to 20 kilometres.”The KARI team has not yet decided whether it will shrink Danuri’s orbit after the one-year mission is complete and eventually crash-land the craft on the Moon, says Eunhyeuk Kim. Alternatively, he says, the team could send the capsule into a higher orbit that could see it glide on for many more years. More

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Sociodemographic of respondentsA total of 140 households completed surveys with a response rate of 45.0%. The respondents were comprised of 48.6% males (n = 68) and 51.4% females (n = 72) in the general population aged 18 to 64 years, which were differentiated into five age groups: ≤19 (1.4%); 20–29 (22.1%); 30–50 (67.1%); 51–59 (5.0%); ≥60 (4.3%). There was a variation in terms of education levels and employment status; the majority of respondents were Bachelor-degree holders (at least 45%) and working as government servants (60.0%), as tabulated in Table 1. The accounted median monthly household income of Putrajaya is RM 7512 (~USD 1803, mean monthly household income of RM 10401, ~USD 2496), exceeding the national level (RM 4585, ~USD 1100)26. The survey covered household groups: bottom 40% (B40), middle 40% (M40), and top 20% (T20), classified into income groups ≤RM 2999, RM 3000–4999, RM 5000–6999, RM 7000–8999, RM 9000–10999, RM 11000–12999, and ≥RM 13000, where RM 1 approximately equivalent to USD 0.24 in average. On an average, respondents had lived in Putrajaya for seven years.Table 1 Descriptive statistics about risk perception of drinking water supply security with potential EDC contamination.Full size tableHuman morphology and drinking water consumption patternsThe present study involved 140 households with 257 total respondents (n = 257), consisting of infants (n = 4, aged less than 1 year; birth–5; 6–11 months), children (n = 77, aged 1 to 9 years; 1–3; 4–6; 7–9 years), adolescents (n = 37, aged 10 to 19 years; 10–14; 15–19 years), adults (n = 133, aged 20 to 59 years; 20–29; 30–50; 51–59 years) and elderly (n = 6, aged more than 60 years) (Table 2). Age groups were categorized based on previous studies27,28,29,30.Table 2 Age groups and respective mean body weight, body height, body mass index, daily water intake, and daily water intake per body weight.Full size tableThere were no significant differences between males (n = 125) and females (n = 132) in terms of body weight (t(235) = 1.671, p = 0.096), body height (t(225) = 0.804, p = 0.422), body mass index (t(246) = 1.116, p = 0.266), and daily water intake (t(255) = 0.483, p = 0.629). Surprisingly, males consumed more water than females in the United States and Australia19,31. Body weight showed a significant positive correlation to height based on Pearson product-moment correlation test (r = 0.861, p  More

Adjusting GEFS forecasts to local climatologyWhat amount of correction is required for GEFS forecasts to align with CHIRPS local climatology? The amount of correction varies widely across the globe and throughout the year. Figure 1a shows annual mean bias for GEFS reforecast 15-day totals. In this figure, wetter-than-CHIRPS climatology and systematic over-prediction of 15-day totals by GEFS is indicated by positive mean bias values, while the opposite is indicated by negative values. GEFS forecast mean bias was calculated for each month and then averaged across rainy season months, to focus aggregate results on the rainfall seasons, when precipitation forecasts are relevant. Monthly dry masks excluded locations with a monthly average of less than 10 mm, according to CHIRPS climatology. In general, one consistent result from Fig. 1a is a tendency to increase precipitation in many mountainous tropical and subtropical regions. By design, orographic precipitation enhancements in such regions are represented fairly well in CHIRPS, and these are carried through to CHIRPS-GEFS precipitation forecasts. The CHIRPS-GEFS bias-correction process reduces systematic errors (Fig. 1b), with the overall mean absolute bias error going from 24.1 mm for GEFS to 19.7 mm for CHIRPS-GEFS, an ~18% reduction.Fig. 1Annual mean bias and global error characteristics for GEFS reforecast data compared to CHIRPS, based on 15-day precipitation totals from Day 1, 6, 11, and 16 of each month during 2000–2019. Annual mean bias (a) shows the annual average of differences in GEFS reforecast and CHIRPS monthly means. Annual average error (b) shows the distribution of GEFS reforecast and CHIRPS-GEFS errors (product – CHIRPS). Both panels are based on in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageFigure 1a through Fig. 5 are based on GEFS reforecast, CHIRPS, and CHIRPS-GEFS data for the 5-day or 15-day periods beginning on the 1st, 6th, 11th, and 16th day of the month. All these exclude dry season months. Figure 1b shows the corresponding global distribution of annual average error for the GEFS reforecast and CHIRPS-GEFS, and is discussed later.GEFS has a large annual average positive bias of higher-than 40 mm in some areas of the globe, including in central Mexico, Central America, northern South America, the Andes and Himalayan Mountain ranges, and in southern China, Papua New Guinea, and localized areas of central Africa, the Ethiopian Highlands, and the western montane United States (Fig. 1a). GEFS has positive bias, by more than 5 mm for the annual average 15-day period, across the northern United States including in the Midwest, from Mexico’s northern mountains through most of Central America, in northern South America, the Andes range, eastern Brazil, in parts of central Europe, central and northern Asia, in the area from southern China to Myanmar and Thailand, and in northeastern and western India. GEFS has positive bias in portions of East Africa (Rwanda, Burundi, Tanzania, western Ethiopia), West Africa (Cameroon, Gabon), and Southern Africa (Zambia, central Angola, northern Zimbabwe, eastern South Africa). GEFS has negative bias, by more than 5 mm on average, in parts of central and northern Africa, Senegal, northern Australia, central South America, western India, the Yucatan peninsula, and the United States Gulf Coast.GEFS’ systematic bias changes throughout the year, as shown by the monthly mean bias in January, April, July, and October (Fig. 2). This is unsurprising, given that drivers of weather change too, but higher bias in particular months can be problematic for forecast users. In Ethiopia, for example, GEFS overestimates by large amounts during the Kirempt season (e.g., in July) and in October in the southwest. In central Brazil, the bias changes markedly by season, from a high negative bias in October to an expansive wet bias in April. In the Midwestern and northern United States, GEFS also shows a more expansive wet bias in April than in January, July, or October. In some areas, like in southern China and the Andes mountains, GEFS means are higher than CHIRPS means throughout the year.Fig. 2Monthly mean bias for GEFS reforecast data compared to CHIRPS, based on 15-day precipitation totals from Day 1, 6, 11, and 16 of each month during 2000–2019. Mean bias for January (a), April (b), July (c), and October (d) shows the difference in GEFS reforecast and CHIRPS monthly means. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageThe CHIRPS-GEFS downscaling procedure corrects for systematic errors in GEFS forecasts that vary spatially and temporally. To assess the efficacy of the CHIRPS-GEFS approach, we began by calculating the per-pixel difference between GEFS and CHIRPS, and CHIRPS-GEFS and CHIRPS for 15-day periods. These were calculated for each month, for in-season pixels, and then averaged across the year. We then looked at the histogram of the resulting differences (Fig. 1b), to identify the distribution of annual average errors in these two products. CHIRPS-GEFS errors are shown as gray bars and GEFS errors are overlaid as hollow red bars. A desirable pattern is more small errors (higher bars close to 0 mm) and fewer large magnitude errors (lower bars at larger precipitation values). As shown in Fig. 1b, the bias-correction procedure has this effect, and results in CHIRPS-GEFS having overall lower errors for global rainy seasons compared to GEFS. GEFS 15-day errors more commonly involve over prediction of observed amounts than under prediction, as shown by the higher proportion of positive versus negative moderate to large positive errors. Part of this is due to the lower limit of under prediction being zero precipitation, while over prediction can range from marginal precipitation amounts to very high amounts. As shown in Fig. 1b, the CHIRPS-GEFS bias correction particularly reduces GEFS forecast errors for moderate-to-high rainfall amounts, and it results in a global 15-day error distribution that has a higher proportion of small errors, e.g., errors within −10 mm to 10 mm of CHIRPS values (51% for CHIRPS-GEFS and 43% for GEFS). Errors in categories ranging from 10 mm to 40 mm occur less often in CHIRPS-GEFS, globally, with probabilities in those categories reduced by around 15 and 25 percent at 10 mm to 20 mm and 20 mm to 30 mm, respectively, and by around 30 percent to 40 percent for errors that are higher than 40 mm.Next, we show performance of the 5-day and 15-day CHIRPS-GEFS precipitation forecasts by correlations and mean absolute errors for the historical record, compared to CHIRPS data for these periods. As described in Data Records, multiple outlets use forecast amounts for these periods. In the Usage Notes section, probability of detection scores for 15-day CHIRPS-GEFS in Africa are presented while describing an operational application of the CHIRPS-GEFS for seasonal monitoring. In that discussion we also examine the performance of 5-day forecasts during the 2020–2021 season in key regions of Kenya, Angola, Zambia, Zimbabwe, and Madagascar.Pearson correlation coefficients for 5-day and 15-day CHIRPS-GEFS, compared to CHIRPS (Fig. 3), indicate the ability of forecasts to predict deviations from average. It should be noted that correlations are nearly entirely driven by the information coming from the GEFS forecasts. The conversion to CHIRPS-GEFS adjusts the GEFS values to make them more “CHIRPS-like,” while also approximating the historical context of the GEFS forecast. Wet extremes forecasted by GEFS translate into wet extremes in CHIRPS-GEFS. Areas with very low correlations (R  0.7) are the United States, Western Europe, and Eastern Europe, southeastern South America, southern Central Asia, eastern China, parts of East and Southern Africa, and Australia. Globally, correlations are higher in January, April, and October than in July, which indicates generally higher forecast accuracy in those months. Exceptions are in eastern China, southern Brazil, eastern Mexico, northeastern Ethiopia, and central and southern Australia, where July correlations are not substantially lower. 15-day forecasts also have high correlations in some areas, including in the Western and Midwestern United States in January, in central and northern Australia in April, and in eastern Brazil in January and October.Fig. 3CHIRPS-GEFS 5-day and 15-day Pearson correlation coefficients, as compared to CHIRPS, for January, April, July, and October. (Validation data: CHIRPS 5-day and 15-day totals from the 1st, 6th, 11th, and 16th of the month, for 2000 to 2019. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageIn Africa, a region where CHIRPS data is actively used by the Famine Early Warning System Network (FEWS NET) and other organizations for seasonal monitoring and drought early warning, forecast correlations indicate moderate to good 5-day and 15-day forecast performance in areas of East Africa, Southern Africa, and western North Africa during rainy season months. Some of the highest 15-day correlations in Africa are during important rainy season months, for example, in northeastern Ethiopia in July and April, in Kenya in April, in Zimbabwe and southern Mozambique in January, and in the Sudanian zone of West Africa in October. Very low correlations indicate low forecast skill in the Sahel, coastal West Africa, and in Central Africa in the DRC, Republic of the Congo, and Gabon.Mean absolute error of the bias-corrected GEFS forecasts highlight the areas where forecast amounts have historically been less reliable (Fig. 4). These indicate non-systematic errors associated with rains not materializing in the forecast location in the forecast period, which can be from GEFS model deficiencies and the inherent challenges of weather forecasting. Extreme precipitation events and warm season, deep moist convection-driven precipitation are notorious challenges for numerical weather prediction systems48,49, and CHIRPS-GEFS data are not immune to this problem. Remotely sensed data, including CHIRPS, also struggle with estimating extreme high rainfall amounts13,50, though since we are comparing CHIRPS-GEFS to CHIRPS, the main source of the large errors shown here would be the GEFS reforecast.Fig. 4CHIRPS-GEFS 5-day and 15-day mean absolute errors, as compared to CHIRPS, for January, April, July, and October. Validation data: CHIRPS 5-day and 15-day totals from the 1st, 6th, 11th, and 16th of the month, for 2000 to 2019. Shown for in-season pixels, which are defined by monthly average CHIRPS  > 10 mm.Full size imageAs shown in Fig. 4, the magnitude of errors follows climatology, with 5-day errors typically under 10 mm for drier rainy season months. In wetter months and locations errors are typically between 10 mm and 20 mm. With higher rainfall magnitude there is greater potential for larger errors. 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Availability per capitaThe APC water stress indicator represents the state of physical water scarcity. The total population under a certain level of water scarcity is called the stressed population. We calculated the water-stressed population and compared it to earlier estimates for validation. We found that the total population percentage (calculated using the ensemble mean discharge) facing acute physical water stress calculated using the APC of 500 m3/capita/year will vary as to ({54.9}_{-1.7}^{+1.1} %;({47.6}_{-2.5}^{+2.1} % )), ({66.6}_{-3.3}^{+2.8} %;({59.8}_{-6.1}^{+5.6} % )), and ({55.6}_{-1.8}^{+4.2} % ;({47.0}_{-2.6}^{+5.7} %)) (+/− values show the maximum variation considering discharge using single GCM to the ensemble mean discharge) at the end of the century (i.e., the year 2099) under the SSP1–RCP2.6, SSP3–RCP7.0, and SSP5–RCP8.5 scenarios, respectively, representing different socioeconomic and climate conditions considering the MY19 (JO16) future population dataset (methods for scenarios and datasets details). By contrast, 44.5% (45.1%) of the global population faced acute physical water stress at the beginning of the century (i.e., the year 2000). The above percentages correspond to 3.5 (3.3), 7.9 (7.5), and 3.9 (3.4) billion populations for the SSP1–RCP2.6, SSP3–RCP7.0, and SSP5–RCP8.5 scenarios and 2.68 (2.75) billion for the historical scenario (i.e., beginning of the century). The historical value is consistent with the value of 2.7 billion previously reported by Hoekstra et al.13 and 2.4 billion mentioned by Oki and Kanae1.APC enhanced with GDP per capita—country-scale assessmentNext, we analysed the relationship between APC and GDP per capita. First, to revisit the findings of Oki et al.19, we conducted country-level analyses for the beginning (i.e., the year 2000) and end (i.e., the year 2099) of the century. To compare the absolute change for a longer period with the constant exchange rate, we used GDP-PPP per capita (USD 2005) due to its availability and defined water stress (physical and economic water scarcity) for both past and future scenarios using the same threshold line (see “Methods” section). The consistency in the results in terms of distribution of countries (Fig. 1 and Supplementary Fig. 1) for both historical (GPWv4 and HYDE3.2) and future (MY19 and JO16) population datasets confirm the similarity in aggregated country-level population data. We did not find any countries below the threshold line at the end of the century, whereas we found Somalia, Western Sahara, Yemen, and Niger below the threshold line at the beginning of the century (Fig. 1, Supplementary Table 1 for base scenario experimental settings, results for other combinations of climate and population dataset are provided as Supplementary Fig. 1). The comparison of per capita water availability (APC) for countries below the threshold line for this study and additional analysis considering various climate forcing data with the same socioeconomic data showed substantial differences. These arid region countries have less runoff and considerable sensitivity towards the metrological data, causing the large difference in availability per capita (APC) of freshwater (Supplementary Table 2 for comparison of values considering different climate forcing data). Additionally, the quality of socioeconomic data contains uncertainty due to political instability23,24,25,26, defying the hypothesis for these countries. We confirmed that although a few countries can contradict, the hypothesis of Oki et al.19 remains valid for various scenarios considered.Fig. 1: Country-level scatter plot for APC vs GDP-PPP per capita and density plot considering the number of countries for various socioeconomic and climate scenarios.Each circle corresponds to a country, and the circle’s size corresponds to the country’s population. CHN, ESH, IND, NER, SOM, USA, and YEM represent China, Western Sahara, India, Niger, Somalia, United States of America and Yemen, respectively. Yellow, green, red, and blue colours represent the historical, SSP1–RCP2.6, SSP3–RCP7.0, and SSP5–RCP8.5 scenarios, respectively, and the dashed line represents the threshold value for physical and economic water scarcity. The analysis was performed considering the GPWv4 dataset for the historical, i.e., the year 2000 population and MY19 for the future, i.e., the year 2099 population.Full size imageAPC enhanced with GDP per capita—grid-scale assessmentNext, we proceeded with grid-level analyses. We confirmed the existence of locations in the world facing the challenges of economic and physical water scarcity identified at 0.5° resolution (Fig. 2, results of SSP1–RCP2.6 and SSP5–RCP8.5 are shown in Supplementary Fig. 2 and Supplementary Fig. 3). The total population and spatial distribution facing challenges (i.e., grids below the threshold line defined by Eq. 1) differed in the different scenarios.Fig. 2: Grid-level scatter plots for APC vs GDP-PPP per capita and density plot considering the number of grids.(a) Historical-GPW, (b) Historical-HYDE, (c) Future370-MY19, and (d) Future370-JO16 scenarios. Grid values are represented as circles, and the dashed line represents the threshold line proposed by Oki et al.19. The density plot includes dotted coloured lines (lime and red) for the median and dark shading for the interquartile range (first and third quartiles). The white circle represents the grid size of 20 million population. e Boxplot for the total population facing physical and economic water scarcity (grids below the threshold of Eq. 1) for all considered scenarios. Legend symbols represent the analysis using the discharge considering various GCMs and the ensemble mean of discharge considering all GCMs. *analysis for Historical-GPW and Future-MY scenarios, **analysis for Historical-HYDE and Future-JO scenarios (Supplementary Table 1 for scenarios/ experiment settings, and Supplementary Table 5 for water-scarce population and uncertainty values).Full size imageIt can be observed from the density plots in Fig. 1 and Fig. 2 that there is a rightward shift in the peak and a significant increase in the mean and median values of the GDP-PPP per capita for the future scenarios compared to the historical scenario. The density plot for the APC for the future follows a trend similar to the trend of the past (i.e., a similar frequency distribution of APC at the grid scale), with an increase (decrease) in the median values observed for the future scenarios for MY19 (JO16) at the grid level (Supplementary Table 3 and Supplementary Table 4 for results of all statistical analyses considering future and historical datasets).We calculated the population facing hardship due to both physical and economic water scarcity (i.e., grids below the threshold line defined by Eq. 1). As a result, at the end of the 21st century (i.e., the year 2099), ({0.32}_{+0.00}^{+0.68}) (({234}_{-10}^{+24})) million people were estimated to face hardship under the SSP1–RCP2.6 scenario when using an urban-concentrated, i.e., MY19 (dispersed, i.e., JO16) population dataset. The estimated populations facing hardship under the SSP3–RCP7.0 and SSP5–RCP8.5 scenarios were ({327}_{+35}^{+202}) (({665}_{-67}^{+181})) and ({6.9}_{-1.1}^{+1.2},left({176}_{-3}^{+36}right)) million respectively (+/− values show the maximum variation in the global population facing water scarcity, calculated considering discharge using single GCM and the ensemble mean discharge), compared to 327 (358) million at the beginning of this century (i.e., the year 2000) (Fig. 2e, Supplementary Table 5). Analysis considering MY19 and JO16 population datasets yield three orders of difference in the stressed population at maximum. The total number of water-stressed populations would decrease in the future (except for the SSP3-RCP7.0 with JO16 population distribution i.e., Future370-JO16 experiment) due to an increase in income.Analysis considering various scenarios (Supplementary Table 1 for scenarios) shows that the uncertainty associated with the SSP–RCP scenarios (i.e., maximum, and minimum difference in the population facing scarcity considering any two scenarios among SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5 for the ensemble mean discharge) and global climate models (GCMs) (i.e., maximum and minimum difference in the population facing scarcity considering any two GCMs for a particular SSP-RCP scenario) were in the range of 6.58–489 and 0.03–248 million, respectively (Supplementary Table 5).We found that the population distribution uncertainty (i.e., maximum and minimum difference in the population facing scarcity considering MY19 and JO16 gridded population distribution for a particular SSP-RCP scenario) for the end century (i.e., the year 2099) followed a similar trend and was in the range of 169.1–338 million (Supplementary Table 5). At the same time, the uncertainty at the beginning of the century (i.e., the year 2000) was within ~10 %, considering GPWv4 and HYDE3.2 gridded population datasets, confirming the high accuracy in estimation of historical population and their distribution. The maximum range value is brought by SSP3-RCP7.0, which is attributed to the large dispersion of population distribution in the SSP3. The grid-level analyses revealed that the future prediction includes large uncertainty due to the spatial distribution of within-country population along with the SSP–RCP paths of global sustainability (SSP1–RCP2.6), regional rivalry (SSP3–RCP7.0), and economic optimism (SSP5–RCP8.5) taken by the world (Fig. 3). The number of water-scarce grids (i.e., grids below the threshold line) in the future will increase or decrease compared to the past and depend mainly on the spatial distribution of population and GDP compared to freshwater availability.Fig. 3: Physical and economic water-scarce regions.a Historical (2000) considering the GPWv4 dataset, (b) Historical (2000) considering the HYDE3.2 population dataset, (c) Future (2099) considering the MY19 population dataset, and (d) Future (2099) considering the JO16 population dataset scenarios. The future (2099) case shows the possible combinations of scenarios with different colours; the values inside the circular legend show the number of people (in millions) facing scarcity with ranges representing the minimum and maximum values considering scenarios combination.Full size imageFactor decompositionThe spatial distribution of grids below the threshold line of various historical and future scenarios (Fig. 3) showed that there would be an emergence of new water-scarce grids in the future, i.e., new grids facing water scarcity in future scenarios but were not facing water scarcity in the historical scenarios. These grids will face water scarcity either due to the decrease in freshwater availability (climate change) or GDP-PPP (socioeconomic change) or an increase in the population (socioeconomic change) among the considered variables for the analysis. Fig. 4 presents the boxplot distributions of absolute values for freshwater (mm/year), population density (capita/km2), and GDP-PPP (USD/year) for newly identified water-scarce grids (grids facing scarcity in the future but not facing it in the past), comparing the values for the historical and future scenarios. The freshwater availability (mean and median values) does not change significantly over time for the new water-scarce grids, i.e., the difference between the future scenarios (SSP1–RCP2.6, SSP3–RCP7.0, and SSP5–RCP8.5) and the historical scenario is negligible. Compared to freshwater, there is a significant increase in population density for all considered scenarios and a less significant increase (decrease) of GDP-PPP of the grids (regions) for the MY19 (JO16) population datasets (Supplementary Table 6 for statistical analysis), suggesting that the primary reason for the water scarcity in these areas will be population growth.Fig. 4: Comparison of new water-scarce grids (i.e., grids facing physical and economic water scarcity in the future but not in the past).Box plots comparing the absolute values of (a), (d) freshwater availability (mm/year); (b), (e) population density (capita/km2); and (c), (f) GDP-PPP. The analysis for (a), (b), and (c) was performed considering the Future-MY and Historical-GPW Experiment settings, and that of (d), (e), and (f) was performed considering the Future-JO and Historical-HYDE experiment settings (Supplementary Table 1). The error bars show the 100% confidence interval (i.e., 0th and 100th percentile), the bottom and top of the box are the 25th and 75th percentiles, and the line inside the box is the median (50th percentile).Full size imageThe global water scarcity analysis considering various future scenarios (SSP1–RCP2.6, SSP3–RCP7.0, and SSP5–RCP8.5) identify various possible water stress regions (grids below the threshold line) of the world affecting the different number of populations. The common water-scarce grids recognised in all these scenarios (grids showing water scarcity for the SSP1–RCP2.6, SSP3–RCP7.0, and SSP5-RCP8.5 scenarios simultaneously) have the highest possibility (certainty) of facing water scarcity in the future. We compared the sensitivity analyses (methods for the approach adopted and Supplementary Table 7 for sensitive analysis experiment settings) results with the base scenario (Supplementary Table 1) values to know the major factor causing water stress among the considered variables for the grids with the highest possibility of water scarcity. The water-scarce population, which can be simultaneously identified in all future scenarios, will be in the range of 0.46–1.82 (156–393) million (range shows the minimum and maximum population affected considering all three future scenarios), considering the historically available freshwater for future scenarios, i.e., Historical-MY (Historical-JO) experiments. Similarly, the population affected considering the historical population for the future scenarios, i.e., Future-GPW (Future-HYDE) experiments, was determined to be 13 (10–16) million; considering the historical GDP-PPP for the future scenarios, i.e., Future-MY-TG, (Future-JO-TG) experiments, the result was 1514–2928 (1466–3132) million (Supplementary Fig. 4 and Supplementary Fig. 5, Supplementary Table 7 for experiment settings). The comparison of all sensitive analysis scenarios values with the base scenario value of 0.0 (110–269) million (Fig. 3c, d) showed that the effects of the different variables were in the order of GDP  > population > climate for the regions with the highest chances of facing water scarcity in future.Even though the overall water availability on the globe per capita are 6525.16 (6434.99) m3/capita/year for historical (i.e., the year 2000) and 6960.63 (6375.98) m3/capita/year, and 3894.64 (3671.39) m3/capita/year, 6821.34 (6459.90) m3/capita/year for future (i.e. the year 2099) considering SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5 scenarios respectively (values in bracket consider HYDE3.2 and JO16 population datasets), more than 70% of world population faces the physical water scarcity defined using a threshold value of 1700 m3/capita/year of APC15 for all the scenarios (Supplementary Table 8, and Supplementary Note 1). Estimation of population facing severe water stress considering physical aspect only (i.e., APC of 500 m3/capita/year) is 2.7 billion for historical scenarios and 3.9–7.9 (3.3–7.5) billion for the future scenario, whereas considering both physical and economic aspects (i.e., threshold line defined by Oki et al.19) is 301 (333) million for the historical scenarios and 0.33–325 (176–665) million for the future scenarios (Supplementary Fig. 6 and Supplementary Fig. 7). These values show a substantial difference in the water-stressed population when considering only physical aspects and accounting for both physical and economic factors. It also indicates that a few rich (i.e., grids with high GDP-PPP per capita) physical water-scarce regions (water-stressed regions identified using APC) can ease water scarcity by water management and technological measures.The overall analysis revealed the possibility of underestimation (or overestimation) of the population facing scarcity in the future due to large differences associated with the population and GDP data distribution within the country for the SSP scenarios. The spatial distribution of the future population and GDP within and outside a country can be affected by many factors, such as water availability27,28, job opportunities, disaster adaptation and mitigation capability of a location, migration of people29, and different policies, which can be directly and indirectly associated with climatic29,30 and socioeconomic factors27,29. Hence, it would be preferable for the projection of population and GDP to consider the feedback from the hydrological and hydrodynamic models to increase their reliability based on various climate phenomena, such as water availability, floods, and droughts, in addition to simple approaches such as the statistical model limited to roads and other infrastructure for auxiliary variables by Murakami and Yamagata21 and the gravity-based model by Jones and O’Neill22. More