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Animal ethicsAll experiments were conducted under the UK Home Office licence P88687E07 and with approval from the University of Exeter Ethics Committee.Fish husbandryJuvenile rainbow trout (Oncorhynchus mykiss) (n = 42; body mass: 159.9 ± 5.2 g), were obtained from Houghton Spring Fish Farm (Dorset, UK) and housed in the Aquatic Research Centre at the University of Exeter (UK). Before transfer to individual experimental chambers, all fish were housed across two 400 L tanks (n = 21 per tank) supplied with recirculated fresh water for 14 days. During this 14 day acclimation period, fish were maintained at 15 °C and fed on a 1% ration of commercial trout feed (Aller platinum 4.5 mm (Aller AQUA ©) three times a week. Prior to experimentation, fish were fasted for seven days.Acid buffering dietsDiets were prepared by adding one of three calcium-based salts, CaCO3, Ca3(PO4)2 or CaCl2 (as non-buffering control) with isomolar quantities of calcium to a commercial trout pelleted diet (Skretting 4.5 mm Horizon, Skretting, UK). The quantities of these salts used were designed to mimic the calcium content of the skeletal component of crustacean or bony fish prey.Cameron (1985)50 estimated that the bone of teleost fish represents 16.3% of whole-body mass (and therefore soft tissue represents 83.7%). However, bone is not just calcium phosphate, but includes numerous organic components as well as water content. By comparing titrations of pure calcium phosphate salt and samples of ground-up teleost (rainbow trout) bone, we established that it required 10.25 times less calcium phosphate salt to achieve the same acid-buffering capacity as that of an equal mass of bone. We therefore created a diet that was supplemented with 1.9 g calcium phosphate for every 100 g of trout pellets (i.e. [16.3 g ÷ 10.25] x [100 ÷ 83.7 g] = 1.9 g), in order to match the bone content of calcium phosphate typically found in fish prey as a proportion of the soft tissue mass. This amounted to 18.4 mmoles of calcium phosphate salt (Ca3(PO4)2; M.W. = 310.2) per 100 g of trout pellets. For the two other diets we aimed to maintain the same molar amount of calcium cation added whilst varying the anionic component of the salt added. So, for the unbuffered version of the diet 2.7 g of calcium chloride (CaCl2.2H2O; M.W. = 147.0) was added, whilst for the calcium carbonate (CaCO3; M.W. = 100.0) buffered diet 1.84 g was added, per 100 g of trout pellets.To form each diet, 100 g of Skretting 4.5 mm Horizon trout pellets were ground to a fine powder using a pestle and mortar. Following grinding, 1.9, 1.84 and 2.7 g of Ca3(PO4)2, CaCO3 and CaCl2 were added to the ground pellet and mixed. Then, 70 ml of ultrapure water was added to the dry material to form a paste. This paste was pressed into commercial 4 mm moulds, removed and dried at 70 °C for 24 h. An acid titration test was conducted to ensure that diets remained representative of the buffer capacity of prey and each calcium salt. For this test, 60 ml of ultrapure water were added to 1 g of each experimental diet and titrated down to pH 3.5 using 0.05 mol L−1 HCl. The CaCl2 diet treatment required 4.56 ml of the acid which was only slightly less than the 6.4 ml required to titrate the Ca3(PO4)2 diet. In contrast it took almost double the amount of acid (11 ml) to titrate the CaCO3 diet. In molar terms it took 228, 320 and 550 µmoles of HCl to titrate 1 g of the CaCl2, Ca3(PO4)2 and CaCO3 feeds to pH 3.5, respectively. To calculate the total acid-buffering consumed, the buffer capacity (per g of food) was multiplied by the actual ration ingested for each individual. Based on manufacturer details each diet had a gross energy of 23 kJ per gram of feed.Acid secretion in the stomach and the blood alkaline tideTo investigate the effect of dietary buffer capacity on the blood acid–base chemistry (alkaline tide) and gut secretions, blood and gut samples were taken from fish to determine blood gas and acid–base balance and haematology variables of fish fed each experimental diet. Fish were fasted for 7 days and then fed a 2.5% ration of one of three experimental feeds. Diet was randomly allocated to each individual (n = 6 per diet). At 24 and 48 h following meal ingestion fish were anesthetised using benzocaine (100 mg l−1). Once fish had lost equilibrium and were un-responsive to a tail pinch, fish were transferred to a gill irrigation system dosed with a lower concentration of benzocaine (75 mg l−1). Fish were placed upside down within the irrigation chamber so that the head was fully submerged, and the entire gill basket covered. A micro pump was used to artificially ventilate the gills via a tube placed into the fish mouth. This allowed for the continuous ventilation of fish gills and ensured there was no build-up of CO2 or lactic acid during blood sampling that could unintentionally affect blood acid–base status. Blood was then drawn into a sodium-heparinised syringe via caudal puncture. Fish were then euthanased via pithing and dissected to collect stomach and intestinal contents. Gut samples were centrifuged to isolate gastric and intestinal juices.Blood and gastric pH were measured using an Accumet CP-620-96 MicroProbe (Accumet Engineering Corporation, USA) connected to a Hanna HI 8424 m (Hanna Instruments, Woonsocket, Rhode Island, USA). Whole blood PO2 was measured using a Strathkelvin 1302 electrode, housed within a thermostatted glass chamber (Strathkelvin), and connected to Strathkelvin 781 m (Strathkelvin Instruments Ltd., Scotland)51. Blood was drawn into three micro-haematocrit tubes (Hawksley) via capillary action and anaerobically sealed using Hawksley Critaseal Wax Sealant, then centrifuged (Hawksley microhaematocrit centrifuge, 10,000 rpm for 2 min) and then used to record haematocrit and held on ice before using the plasma. Plasma and intestinal total CO2 was then measured using a Mettler Toledo 965 carbon dioxide analyser and together with blood and intestinal pH measurements was used to calculate plasma and intestinal HCO3− and PCO2 by rearranging the Henderson–Hasselbalch equation and using values for solubility and pKapp from Boutilier et al. (1985)52.Net acid–base fluxes to the external waterThe effect of diet on the net flux of acid–base relevant ions to the external water was measured in a separate subset of juvenile rainbow trout (n = 10, 161.8 ± 6.9 g). Prior to measurements fish were weighed and transferred to individual 25 L chambers supplied with recirculated freshwater maintained at 15 °C. Following a 3-week acclimation period, fish were fed weekly on a 2.5% ration of one of three experimental feeds, with diet order randomised to each individual (See Supplementary Table 4). Initial and final water samples were taken from each chamber over six flux periods each week for three weeks (−23 to 1 (fasted), 0–6, 7–23, 24–47, 48–71 and 72–96 h post feed). Water inflow to each chamber was turned off during each flux period whilst aeration was maintained. Following the final measurement from each flux period, tanks were flushed with dechlorinated freshwater for 60 min so to ensure solid faeces and dissolved waste products (e.g., ammonia) were removed.Total ammonia was measured in triplicate on 200 µL water samples using the colourimetric salicylate-based method adapted from Cooper and Wilson (2008)19 and Verdouw et al. (1978)53 and the Infinite 200 PRO microplate reader (Tecan Trading AG Switzerland ©). Titratable alkalinity was measured in 20 ml water samples using an auto-titrator with autosampler (Metrohm 907 Titrando with 815 Robotic USB Autosampler XL) running double titrations with 0.02 mol l−1 of HCl and 0.005 mol l−1 NaOH. The double titration method calculates titratable alkalinity based on the difference in HCl required to titrate each water sample down to pH 3.9 and the amount of NaOH required to bring the sample back to the starting pH. During the titration, the sample is continuously bubbled or ‘purged’ with the inert gas N2 to remove any CO2. The net fluxes of titratable alkalinity (JTalk) and total ammonia (JTamm) were calculated using the following equation from Cooper and Wilson 2008:$${J}_{mathrm{net}}mathrm{X}=frac{[left(left[{mathrm{X}]}_{i}-{left[mathrm{X}right]}_{mathrm{f}}right) times Vright]}{(M times t)}$$
(1)

where Xi and Xf are the initial and final ion concentration in each tank (μmol l−1) from each flux period, V is the tank volume (L), M is the animal mass (kg) and t is the flux duration (h).The net acid–base flux was calculated as the difference between the flux of titratable alkalinity (JTalk) and the flux of total ammonia (JTamm).Measuring the SDAIntermittent flow-through respirometry was used to determine the rate of oxygen consumption (MO2) by juvenile rainbow trout fed voluntarily on a 2.5% ration of three experimental feeds. Prior to measurements, juvenile rainbow trout (n = 8, 162.2 ± 7.5 g) were weighed and transferred to individual 25 L chambers supplied with recirculated freshwater at 15 °C for 3 weeks. During this acclimation period, fish were fed weekly on a 2.5% ration of Skretting 4.5 mm Horizon trout pellets (Skretting UK). Following this acclimation period, measurements were conducted after 7 days of fasting. Each fish was fed once per week on all three diets over a 3-week period, with diet order randomised for each individual.During experimentation, fresh water was supplied continuously to two aerated 160 L sumps each fitted with a ballcock valve and overflow. Aerated freshwater was then pumped from the sump to the eight respirometry chambers in a loop for the duration of the testing period. Water within each fish chamber was continuously mixed using a submerged mini-pump (WP300; Tetra Werke, Melle, Germany). During measurements, water inflow to each chamber was shut off and the decline in O2 was recorded by PO2 OxyGuard Mini Probe (OxyGuard ® International, Denmark) connected directly to the mini-pump. Oxygen partial pressure values were logged continuously by Pyro Oxygen Logger software (Pyroscience GmBH, Germany) which interfaced with a respirometry software package (AquaResp3: aquaresp.com, see Svendsen et al. 2016 54) to instantaneously convert PO2 into O2 content and calculate the rate of oxygen consumption (MO2, mg O2 kg−1 body mass h−1) based on the fish body mass in kg (m), chamber water volume in L after discounting the fish body volume (Vresp), and the slope (s) of the decline in oxygen concentration (kPa O2 h−1) versus time using the following equation from Svendsen et al. (2016)54:$${MO}_{2}= {sV}_{Resp}{alpha m}^{-1}$$where:$$s= frac{{O}_{2}, initial- {O}_{2}, final}{time, initial-time, final}$$Following each closed measurement period, the chamber was automatically flushed with freshwater from the aerated sumps by two AquaMedic Ocean Runner pumps (Aqua Medic, Ocean Runner 6500). The length of the flush and measurement periods was controlled by two USB- 4 Cleware switches (Cleware GmbH, Germany) which were also interfaced with the AquaResp software to ensure that the partial pressure of oxygen (PO2) within the respirometry chambers never fell below 90% of the starting value. This meant that the measurement period of 15 min was followed by a flushing period of 2 min and a wait time of 60 s.Prior to feeding a baseline 24 h period of standard metabolic rate (SMR) was recorded. The mean SMR of each individual was calculated using the R package ‘fishMO2’ and the ‘calcSMR’ function. Following Chabot et al. (2016)55, the coefficient of variation (CVmlnd) was used to determine whether the mean of the lowest normal distribution (MLND) or the quantile method (P = 0.2) was used to estimate SMR for each individual. Following the SMR measurement, fish voluntarily fed on a 2.5% ration of experimental feed and MO2 recorded continuously for six days. This procedure was repeated for two more consecutive weeks to measure MO2 in fish fed all three experimental diets. Background oxygen consumption was recorded overnight (18 h) in blank (no fish) chambers. Oxygen consumption was not corrected for background respiration as it was considered negligible ( More

The link between SDGs and NRRPThe Italian National Recovery and Resilience Plan (NRRP) is part of the Next Generation EU (NGEU) program, the 750-billion-euro package, consisting of about half of grants, agreed by the European Union in response to the pandemic crisis. The main component of the NGEU program is the Recovery and Resilience Facility (RRF), which has a duration of six years, from 2021 to 2026, and a total size of €672.5 billion (€312.5 billion grants, the remaining €360 billion loans at subsidized rates).The Plan is developed around three strategic axes shared at European level: digitalization and innovation, ecological planning and social inclusion.The missions of the NRRP are as follows:

Mission 1: Digitalization, innovation, competitiveness, culture and tourism

Mission 2: Green revolution and ecological transition

Mission 3: Infrastructure for sustainable mobility

Mission 4: Education and research

Mission 5: Cohesion and inclusion

Mission 6: Health.

With the aim of encouraging the debate on the use of sustainability indicators for monitoring the progress of the PNRR, a mapping of the correspondences between the 17 Sustainable Development Goals and the 6 Missions provided for by the NRRP is proposed (Fig. 1). In this way it is possible to identify the SDGs indicators that can be useful tools for achieving the missions of the NRRP.Figure 1Relationships between SDGs indicators and NRRP missions.Full size imageOf particular interest for the purposes of our work is Mission 2 (Green Revolution and Ecological Transition) of NRRP. It provides for investments and reforms for the circular economy and to improve waste management, strengthen separate collection infrastructure and modernize or develop new waste treatment plants. Substantial tax incentives are provided to increase the energy efficiency of buildings, to achieve progressive decarbonization, to increase the use of renewable energy sources. In addition, the Mission devotes resources to enhancing the capacity of electricity grids, their reliability, security, and flexibility (Smart Grid) and water infrastructure. The Mission also includes the issues of territorial security, with prevention and restoration interventions in the face of significant hydrogeological risks, the protection of green areas and biodiversity, and those related to the elimination of water and soil pollution, and the availability of water resources.The main components of this mission are:

M2C1: Circular economy and sustainable agriculture

M2C2: Renewable energy, hydrogen, grid, and sustainable mobility

M2C3: Energy efficiency and upgrading of buildings

M2C4: Protection of land and water resources.

The analysis of Mission 2 (Green Revolution and Ecological Transition) finds ample space in the SDGs creating important interconnections between the different indicators present in the individual Goals and the objectives of the Mission itself.The SDGs indicators to support the NRRPThe SDGs indicators selected for the analysis of Mission 2 (Green Revolution and Ecological Transition) of the NRRP, are descripted in Table 1. We considered 13 indicators, selected from Goals 2, 6, 7, 11, 12 and 15 which may be of significant interest for the achievement of Mission 2. These indicators will then be attributed to the individual components of the mission.Table 1 Goal, indicators, measures e source of SDGs data.Full size tableThe indicators were chosen based on their relevance to the objectives of the mission and on the availability of data on a regional basis. For each main component we can use the following indicators:

M2C1: Circular economy and sustainable agriculture:

– Share of utilized agricultural area invested by organic crops

– Growth rate of organic crops

– Delivery of municipal waste to landfill.

– Separate waste collection

M2C2: Renewable energy, hydrogen, grid and sustainable mobility:

M2C3: Energy efficiency and upgrading of buildings

M2C4: Protection of land and water resources

– Irregularities in water distribution

– Sealing and soil consumption per capita

– Soil sealing from artificial cover

– Fragmentation of the natural and agricultural territory

– Incidence of urban green areas on the urbanized surface of cities.

The SDGs indicators at the level of territorial distribution in ItalyWe carry out a first analysis by territorial distribution for the different sets of main components of Mission 2.From a first analysis of the M2C1 indicators (Circular Economy and Sustainable Agriculture) it emerges that the share of agricultural area destined for organic crops is greater, especially in the Center and in the South of Italy. In 2019, the extent of organic farming in Italy reached 15.8% of the utilized agricultural area, almost double the EU average. However, the annual growth rate of the areas converted to organic farming or in the process of conversion (+ 1.8%) is the lowest since 2012 and is negative in the South, where for the second consecutive year there is a decrease (− 2.1% in the 2-year period 2017–2019). The dynamics of organic farming is an index of the spread of sustainable agricultural practices, which must be accompanied by measures that also consider the pressure on the environment generated by agriculture (Table 2).Table 2 M2C1 indicators—Circular economy and sustainable agriculture by territorial distribution (year 2019).Full size tableAlso, in the Central and Southern Italy area there is the greatest delivery of waste to landfills. Waste cycle management is crucial for living conditions and global health. The share of municipal waste landfilled is steadily decreasing at national level. In 2019, in fact, the part sent to landfill is equal to 20.9% of the total, down compared to the previous year (21.5%). The separate collection of municipal waste represents a further important step in view of the objective of reducing the amount of waste returned to the environment and, more specifically, of the delivery of waste to landfills. The 18.5 million tons of differentiated RU in 2019 represent 61.3% of national production, a share almost doubled compared to ten years ago and up from last year by 3.1 percentage points. Despite the evident progress, Italy is still marked by a considerable delay compared to the regulatory objectives, having not yet reached, in 2019, the target of 65% of separate collection planned for 2012. Critical issues are also observed in relation to the substantial territorial gaps, which disadvantage the Center and the South compared to the North, despite the distances have been reduced in recent years.
Regarding the M2C2 Mission (Renewable Energy, Hydrogen, Network and Sustainable Mobility), national and international energy policies have been committed for years to the enhancement of renewable energy sources, with the aim of decarbonizing the economy and guaranteeing the commitments made in the field of climate change. In 2019, one year after the expiry of the objectives of the European Union’s Climate-Energy Package, fourteen Member States, including Italy, exceeded the target assigned at national level. In Italy, the overall share of energy from renewable sources in gross final consumption (CFL) of energy, equal to 18.2% (Table 3), a percentage slightly lower than the average of the EU27 (19.7%), is placed for the sixth consecutive year above the 17% target set for our country. However, for Italy to achieve the ambitious programs defined by the 2020 National Integrated Energy and Climate Plan, which set a 30% target for renewables by 2030, a further boost to production from renewable sources is necessary. The resources introduced by the National Recovery and Resilience Plan (NRRP) to achieve the “green revolution and ecological transaction” include significant investments in the energy field, focusing, among other components, on a further strengthening of the Sources from Renewable energy (FER).Table 3 M2C2 indicators—Renewable energy, hydrogen, network and sustainable mobility by territorial distribution (year 2019).Full size tableThe M2C3 Mission (Energy Efficiency and Upgrading of Buildings) devotes resources to enhancing the capacity of electricity grids, their reliability, safety, and flexibility (Smart Grid). Consistent with the objectives of reducing energy consumption pursued by European policies, the Italian figure for 2019 confirms the process of reducing Italian energy intensity, which marks a further contraction of 1.3%, reaching an overall negative balance compared to the last decade of 11.8%, with an average annual rate of change of − 1.2% (Table 4). The reduction in energy intensity is largely attributable to the effect of the measures in favor of energy efficiency, which, between 2011 and 2019, resulted in energy savings of 12 Mtoe/year, equal to 77% of the 2020 target set by the National Action Plan for Energy Efficiency 2017. A further acceleration of energy efficiency is expected, in the coming years, because of the investment plan envisaged by the NRRP, also linked to the redevelopment of the public and private building stock. At the sectoral level, the reduction in energy intensity is driven by improvements in industry, which, despite the slight increase in the last year, in 2019, with 92 toes per million euros, shows a decrease compared to 2009 of 17%, with an average annual rate of change of − 1.8%.Table 4 M2C3 indicators—Energy efficiency and requalification of buildings by territorial distribution (year 2019).Full size tableThe M2C4 Mission (Protection of the territory and water resources) also includes the issues of territorial safety, with prevention and recovery interventions, the protection of green areas and those related to the elimination of water and soil pollution.Italy is among the European countries of the Mediterranean area that use groundwater, springs and wells the most; these represent the most important resource of fresh water for drinking water use on the Italian territory (84.8% of the total withdrawn). The efficiency of municipal drinking water distribution networks has been steadily deteriorating since 2008 for more than half of the regions. The share of families who complain of irregularities in the water supply service in their home is stable (equal to 8.6% in 2019) with more accentuated values in the Center and South of Italy (Table 5).Table 5 M2C4 indicators—Protection of land and water resources by territorial distribution (year 2019).Full size tableLand degradation, understood as loss of ecological functionality, is monitored through the dynamics of land consumption, which Italy has committed to zero by 2030 with the National Strategy for Sustainable Development (2017). The “consumed” soil is that occupied by urbanization and made impermeable by artificial roofing (soil sealing). Excessive fragmentation of open spaces, however, is also a factor of degradation, since the barriers made up of buildings and infrastructures interrupt the continuity of ecosystems, making even unoccupied but not large enough spaces ecologically inert and unproductive. Moreover, in a fragile territory such as Italy, land consumption is also a significant factor of hydrogeological risk and deterioration of the landscape. The index of sealing and land consumption per capita in 2019 increases for the fifth consecutive year, resulting in 357 m2 per inhabitant. The soil sealed by artificial covers is equal to 7.1% of the national territory (8.5% in the North, 6.7% in the Center, 5.9% in the South).According to Ispra estimates, 44.3% of Italy’s natural and agricultural land has a high or very high degree of fragmentation. A joint representation of the variations in fragmentation and soil sealing over the last two years summarizes recent trends in land consumption and their impact on the environment and landscape.A further objective for 2030 is to provide universal access to safe, inclusive, and accessible public green spaces, for women and children, the elderly, and people with disabilities. In 2019 the incidence of urban green areas on the urbanized surface of cities is equal to 8.5% in Italy with slightly higher values in the North and less elevated in the South. More

All the results were reported relative to the control, unless specifically stated to the contrary or for clarity.Growth of cucumber plants in response to different biofertilizersSoilThere was no significant difference in cucumber growth before microbial fertilizer was applied. However, some microbial fertilizers significantly increased cucumber height and stem diameter when they were applied within 4 weeks from when the seedlings were planted (Fig. 1a,b,e,f). In the second week, SHZ and SMF increased plant height by 11.2 and 9.5%, respectively. In the third week, S267, SBS, SBH, SM and SHZ increased plant height by 12.0, 13.8, 15.0, 20.5 and 26.9%, respectively (Fig. 1a). In the fourth and fifth weeks, some treatments significantly increased cucumber height. In the second and third weeks, S267 significantly increased stem diameter by 21.2 and 16.8% (Fig. 1b).Figure 1Effect of different biofertilizer treatments on the growth of cucumber seedlings produced in soil or substrate in a greenhouse. S267 = Trichoderma Strain 267 added to soil; SBH = Bacillus subtilis and T. harzianum biofertilizers added to soil; SBS = B. subtilis biofertilizer added to the soil; SM = Compound biofertilizer added to soil; SHZ = T. harzianum biofertilizer added to soil; SCK = Untreated soil. US267 = T.267 biofertilizer added to substrate; USBH = B. subtilis and T. harzianum biofertilizers added to substrate; USBS = B. subtilis biofertilizer added to substrate; USM = Compound biofertilizer added to substrate; USHZ = T. harzianum biofertilizer added to substrate; USCK = Untreated substrate.Full size imageOver the subsequent 5 weeks, some microbial fertilizer treatments decreased cucumber height and stem diameter (Fig. 1g,h).SubstrateThere were no significant differences in cucumber growth before microbial fertilizer microbial fertilizer was applied (Fig. 1c,d,g,h). However, within 4 weeks of applying the microbial fertilizer, each biofertilizer treatment applied significantly increased cucumber height (Fig. 1c). US267 and USHZ significantly increased cucumber height by 39.8–75.4% and 56.1–86.1%, respectively. US267, USM and USHZ significantly increased the stem diameter by 76.8–108.9%, 71.1–97.6% and 80.4–122.4%, respectively (Fig. 1d).Over the subsequent 5 weeks, US267, USM and USHZ treatments continued to significantly increase cucumber height and stem diameter (Fig. 1g,h).Changes in the taxonomic composition of soil-borne fungal pathogensSoilBiofertilizers application significantly reduced the taxonomic composition of soil-borne fungal pathogens at different times during the cucumber growth period (Tables 1 and 2). Fusarium spp. were significantly reduced (T, 63.8% reduction, P  More

UTrack atmospheric moisture tracking modelThe UTrack atmospheric moisture tracking model is a novel Lagrangian model that tracks parcels of moisture forward in three-dimensional space9. UTrack is the first moisture tracking model to employ ERA5 reanalysis data8. The basic principle of the model is that for each mm of evaporation, a certain number of “moisture parcels” is released and subsequently tracked through time and space. At each time step, the moisture budget of the parcels is updated based on evaporation and precipitation at the respective time and location, meaning that for each location of evaporation, a detailed image of the “footprint” of evaporation can be created. All types of evapotranspiration are included, and here is simply called evaporation.For each mm of evaporation, 100 parcels are released 50 hPa above the surface height at random spatial locations within each 0.25° grid cell of input evaporation data. The trajectories of the parcels are based on interpolated three-dimensional ERA5 wind speed and wind direction data, which also have a horizontal resolution of 0.25° and consist of 25 pressure layers in the atmospheric column. The spatial coordinates of each parcel are updated at each time step of 0.1 h. Also, at each time step, there is a certain probability that a parcel is redistributed randomly along the atmospheric column such that, on average, every parcel is redistributed every 24 h (see methods section Moisture recycling dataset: validation and uncertainties below for further details). The relative probability of the new position in the atmospheric column is scaled with the vertical moisture profile. Parcels are tracked for 30 days or until 99% of their moisture has precipitated.To allocate a certain fraction of any moisture parcel to precipitation events at the current time and location, ERA5 hourly total precipitation (P) and total precipitable water (TPW) are interpolated to the simulation time step of 0.1 h. The amount of moisture that precipitates at a certain time step equals the amount of precipitation at that time step over the total precipitable water in the atmospheric water column (P/TPW). Specifically, precipitation A in mm per time step at location x, y at time t that originated as evaporation from a particular source is described as:$${A}_{x,y,t}={P}_{x,y,t}frac{{W}_{{{{{{{{rm{parcel,t}}}}}}}}}{E}_{{{{{{{{rm{source,t}}}}}}}}}}{{{{{{mathrm{TP}}}}}}{{{{{{mathrm{W}}}}}}}_{x,y,t}}$$
(1)
with P being precipitation in mm at time step t, Wparcel,t (mm) the amount of moisture in the parcel of interest, Esource,t the fraction of moisture present in the parcel at time t that has evaporated from the source, and TPW (mm) the precipitable water in the atmospheric water column. The moisture content of parcels is updated each time step using evaporation and precipitation at its current location:$${W}_{{{{{{{{rm{parcel,t}}}}}}}}}={W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}+({E}_{{{{{{{{rm{x,y,t}}}}}}}}}-{P}_{{{{{{{{rm{x,y,t}}}}}}}}})frac{{W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}}{{{{{{mathrm{TP}}}}}}{{{{{{mathrm{W}}}}}}}_{{{{{{{{rm{x,y,t}}}}}}}}}}$$
(2)
The moisture (fraction) that has evaporated from the source is updated as follows:$${E}_{{{{{{{{rm{source,t}}}}}}}}}=frac{{E}_{{{{{{{{rm{source,t-1}}}}}}}}}{W}_{{{{{{{{rm{parcel,t-1}}}}}}}}}{A}_{x,y,t}}{{W}_{{{{{{{{rm{parcel,t}}}}}}}}}}$$
(3)
The moisture flow mij from evaporation in cell i to precipitation in cell j is aggregated on a monthly basis (mm/month), where [x, y] ∈ j becomes:$${m}_{ij}=mathop{sum }limits_{t=0}^{{{{{{{{rm{month}}}}}}}}}{A}_{j,t}frac{{E}_{i,t}}{{W}_{i,t}}$$
(4)
with Wi,t being the tracked amount of moisture from the source cell i at time t. These simulations were performed for all evaporation on Earth during 2008–2017. The results were then aggregated on a mean-monthly basis to produce monthly means, and stored at 0.5 degree resolution. This dataset can be downloaded from ref. 53. For details on how to process the data, we refer to the accompanying paper by ref. 3.Moisture recycling dataset: validation and uncertaintiesAs with all moisture recycling simulations, the ones used in this study rely on a number of assumptions that may affect the moisture recycling rates. All offline moisture recycling models use atmospheric model output to simulate the path of evaporation through the atmosphere to the location where it precipitates. Therefore, there are two sources of uncertainty that affect the moisture recycling estimates: (1) the quality of the atmospheric forcing data and (2) the assumptions in the moisture tracking model.Tuinenburg and Staal (2020)9 explored these sources of uncertainty for a number of locations globally. The effects of a decrease in the quality of the atmospheric forcing data were most important in the vertical resolution of the atmospheric data: the forcing data should have enough vertical levels to resolve any vertical shear in atmospheric moisture transport. If the forcing data has a low vertical resolution, the moisture tracking model is forced with the mean atmospheric flow over a number of layers. In many regions, there are surface moisture flows that are in a different direction than the moisture flow aloft, resulting in a very small vertically integrated transport, which would distort the simulation of atmospheric moisture transport. Compared to the vertical resolution of the forcing data, the horizontal and temporal resolutions were less important in order to keep errors as small as possible. Because of the importance of this high vertical resolution, it was recommended9 to use the ERA5 dataset8 as its forcing dataset, as this currently is the atmospheric reanalysis dataset with the highest vertical resolution.In addition, the change of ERA-interim to ERA5 resulted in a much better land-surface scheme with monthly varying vegetation and better bare soil evaporation. Also, many more observations are assimilated, which results in a better precipitation product compared to ERA-interim. Following this, the tracking of atmospheric moisture using ERA5 allows for a better quality of the atmospheric moisture cycle than before. But, of course, also the already high horizontal resolution of 0.5∘ × 0. 5∘ has the limitation that very localized moisture recycling features like orography and locally varying land use cannot be resolved. Out of these reasons, the uncertainty in the evaporation estimates is a lot larger than that in the precipitation estimates, because of the lack of global evaporation measurements and the difficulty in measuring evaporation in general54,55.There are also uncertainties due to the assumptions in the moisture tracking model that can be split into a category of simulation assumptions and physical assumptions. The simulation assumptions include model formulation (Eulerian vs. Lagrangian model set-ups), time step lengths, number of parcels released, and types of interpolation. Of these simulation assumptions, the most important aspects were the model formulation, with Lagrangian models better able to resolve complex terrain and atmospheric flows. For the other model assumptions (see methods section UTrack atmospheric moisture tracking model), it was chosen to simulate with the highest level of precision before any more information (e.g., more parcels) would no longer affect evaporation footprints and moisture recycling statistics (see ref. 9 for further details). Even though the ERA5 dataset is known to have some precipitation biases in the tropics, the results of UTrack (forced by ERA5) have recently been validated across the tropics by independent measurements of deuterium excess, a measure of a stable isotope that depends on terrestrial precipitation recycling56. UTrack estimates and isotope-based estimates of terrestrial moisture recycling corresponded, especially in tropical rainforests (Kendall’s (overline{tau }=0.52)56), which are found to be moisture recycling hubs on a global scale.Network constructionMotivated by the network-like structure of the data, we here employ a network perspective to study moisture flows. Hence, nodes in such a network are grid cells on a regular spherical grid and edges represent the moisture transported. However, interpreting the dataset directly as a weighted network is neither computationally feasible nor does a weighted network allow for identifying motifs, the building blocks of complex networks17. We, therefore, aim for an approach utilizing an unweighted network.As shown in Fig. S1, moisture recycling strengths are heterogeneously distributed over multiple powers of magnitude. Thus, it is not appropriate to just withdraw the moisture transport volume and include all moisture transport connections within the dataset as equal and unweighted links. Instead, we attempt to highlight the strongest moisture pathways and, thus, the backbone of the Earth’s moisture recycling network. To, on the one hand, include as much moisture volume as possible but also keep the absolute volume of moisture transport represented per edge as similar as possible, we decided to include edges in a data-adaptive way: we step-wise include links starting from the strongest and stop this procedure as the total moisture transport volume exceeds the variable threshold ρ. The resulting edges then represent the backbone of the global moisture recycling network. In the main text, we have shown the results for a network where all edges together represent ρ = 25% of the total moisture transport. Here and in the SI figures, we add a sensitivity analysis for ρ = 20% and ρ = 30% and find that the results are stable for this broader range of total moisture volume thresholds.Network measures and motifsThe topology of an unweighted network is typically encoded in an adjacency matrix A with elements aij indicating if there exists an edge from node i to node j (aij = 1) or not (aij = 0). The degree k of a node i describes the number of adjacent edges pointing towards or away from node i. Hence, the in-degree is defined by25$${k}_{{{{{{mathrm{in}}}}}}}^{i}=mathop{sum }limits_{i=1}^{N}{a}_{ji}$$
(5)
and out-degree is defined by25$${k}_{{{{{{mathrm{out}}}}}}}^{i}=mathop{sum }limits_{i=1}^{N}{a}_{ij}.$$
(6)
To further analyze the topology of a network and, in particular, the local connectivity patterns, we study the presence of three motifs—the feed-forward loop, the neighboring loop, and the zero loop.The feed-forward loop (FFL) consists of three nodes, A, B, and C, where nodes A and C are directly connected via a detour over node B (intermediary node). Therefore, we have two different pathways that focus on node C. Hence, this motif can be referred to as a directed lens, due to its focused flow from two nodes on one singular and its purely directed linkage. This network motif has been studied in the context of tipping elements and has been proven to facilitate tipping cascades by lowering critical thresholds19. The zero loop (ZL) is made up of a bidirectional connection of two nodes. In contrast to the FFL, where node A does not receive feedback from node C, here, both nodes are dependent on each other without a preferred direction of network flow. This facilitates tipping to a much lesser degree than the FFL motif19. The neighboring loop (NBr) is an extension of the ZL. In this case, there is an additional node connected to one of the nodes of a zero loop. Hence, there is a two-step directionality in the motif, but in contrast to the FFL, this motif is characterized by reciprocity.We count the number of motifs a certain node is involved in the network. The number of FFLs is counted as the number that a certain node is a so-called “target” node. The target node is the node, on which the triangular structure of the motif is converging to, i.e., the node that has been referred to as node C above. The ZL is a symmetric motif for the two involved nodes. Therefore, the number of ZLs of a certain node in the network is counted directly as the number of bidirectional interactions of the inspected node. Lastly, the number of NBrs of a certain node is the number of being in the center of a neighboring loop. With this procedure, each node is characterized by its number of FFLs, ZLs, and NBrs (cf. ref. 19).Motif strength and their spatially aggregated differenceTo assess the presence of motifs and, in particular, their relative frequency, we first determine the numbers of FFLs, ZLs, and NBrs per node. Subsequently, we normalize these counts by the respective maximum to obtain the motif strength, which is shown for each network motif in Fig. S5. In Fig. S5a–c, we display the motifs for the global network, and in Fig. S5d–f for the land-to-land network.To specifically characterize the focus regions by means of the network topology, we evaluate which motifs dominate in which region. Consequently, we compute the difference of the motif strengths shown in Fig. S5 and reveal the patterns shown in Fig. 2. For spatially aggregated motif strength differences (Fig. 2c, d), we then compute the average of the respective values inside the highlighted boxes.Sensitivity to link threshold ρ
The network analysis featured in the main text uses those moisture recycling edges that together represent ρ = 25% of all atmospheric moisture recycling on Earth. As we aimed to focus on the strongest moisture flows, we chose a threshold of ρ = 25% aggregating the strongest moisture transport pathways. This allows us to reveal the regions of strongest moisture connections, which are located in and close to the tropics, as we expected. Overall, the aim of this thresholding procedure is to utilize a network approach with unweighted edges but also take into account the large spread of moisture recycling strengths. To test the robustness of the results to the threshold value, we here show the same figures as above in the main text but with different thresholds ρ. Note that the error bars in Fig. 2 are based on the analysis featured in this part (the resulting differences using thresholds of ρ = 20% and ρ = 30%).Figures S6 and S7 show the in- and out-degree of the all-to-all and land-to-land network using a threshold of ρ = 20% (Fig. S6) and ρ = 30% (Fig. S7). Note that the color bar has been adjusted as the number of links differs substantially between the networks. The main difference between Figs. S6 and S7 is the greater emphasis on moisture recycling in the mid-latitudes in Fig. S7. This is a direct consequence of considering more, and thus also some weaker, links. Acknowledging this difference, we stress that especially the land-to-land patterns (Figs. S6c, d, S7c, d) are consistent. In particular, the four focus regions, as defined in the main text, stand out as the main global land-to-land moisture recycling hubs. To support this visual analysis of the in- and out-degree pattern, we furthermore compute the motif strengths for both network configurations for quantitative validation of the results.In line with the main text, we compare the FFL and ZL strength (see Fig. 2a–d). Not only the spatial patterns in our sensitivity analysis agree remarkably well with the results in the main text above, but also the focus regions remain basically the same (cf. Fig. S8 for ρ = 20% and Fig. S9 for ρ = 30% with Fig. 2). The only slight change is the shift towards a directed lens (spatially aggregated FFL and ZL strength difference) for the Amazon basin in the all-to-all network for increasing ρ (cf. Fig. S8c vs Fig. S9c vs Fig. 2c). We attribute the overproportional increase of the number of FFLs to those that include at least one oceanic grid cell to this noticeable shift. This underscores our characterization of the Amazon basin as a directed lens.The spatially aggregated FFL and NBr difference (Figs. S10, S11) is structurally the same as above, where we computed the FFL and ZL difference (see Figs. S8, S9). The spatial patterns and the aggregated values are robust against shifts of ρ. However, for the Amazon basin (AB), the number of FFLs increases overproportionally in the all-to-all network when we include more links in our analysis. In other words, the spatially aggregated FFL-strength for AB increases for higher thresholds ρ (cf. Figs. S10c, S11c and Fig. 2g).Sensitivity to the size of the focus regionsAnother aspect affecting the results is the spatial extent chosen as a focus region (i.e., the rectangles in Fig. 2). Varying the size of these rectangles affects the spatially aggregated measures. For all focus regions besides the Amazon Basin (AB), the values are not significantly affected by changing the rectangle size, as the values close to the focus regions are either coherently negative, as for the Congo Rainforest (CR) and the Indonesian Archipelago (IA), or close to zero (South Asia: SA). The AB is characterized by positive values (tendency to lensing), whereas the more southern parts along the Andes are marked by more negative (corridor/washing machine) values.Hence, we assess the stability of the results by using the spatial region covered by the Amazon rainforest (the extent of the Amazon rainforest is based on ref. 6) and compare them to the ones obtained by using the rectangle. The results featured in Fig. S12 indicate that only considering the rainforest-covered parts of the AB leads to similar or even more positive (lensing) values, confirming our conclusions that the Amazon rainforest region functions differently from the other focus regions.Notes on mapsThis paper makes use of perceptually uniform color maps developed by ref. 57. The underlying world maps have been created by cartopy58. More

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