commonchaffinch @commonchaffinch@qoto.org

#water_stress_index
#water_connectivity
#inter_basin_transfer
#climate_change_impacts

In 2019, Kai Duan from Sun Yat-Sen University and co-authors analyzed the effects of inter-basin transfers ("regional water connectivity") on surface water supply stress in the contiguous US under climate change scenarios. They found climate change would increase the highly water stressed areas from 14% to 18% in the US, along with other results.

Overall, this is a standard analysis of the drivers of historical water stress and future water stress changes. The considered drivers include domestic, thermoelectric, and agriculture uses, and inter-basin transfer.

The methodology was of interest.

The simulation was performed at HUC-8 level for the contiguous US.

The basic water balance equation for each water shed was:

Total flow at the basin outlet = Inflow from upstream basin + Local generated flow + Water consumption* +/- inter-basin transfer

(* note: water consumption = water withdrawal - return flow)

The author noted that there are four types of basins: headwater basins that have no inflow, midstream basins with both inflow and outflow, terminus basins that have no outflow, and isolated basins.

The inter-basin transfer data were pulled from historical sources. Fig. 1b shows that the inter-basin transfer projects have been quite prevalent in the US.

The methods of projecting future streamflow involved designing future scenarios:

(1) domestic uses were calculated as per capita domestic use * future population. The per capita use is assumed to be constant.

(2) thermoelectric use was calculated as per kWh water consumption * future electricity generation. The future electricity generation was taken from the Annual Energy Outlook. The water consumption rates were interpolated from historical values.

(3) irrigation water use was calculated as function of irrigated area and irrigation efficiency. Climate and non-climate factors contribute to changes in irrigation water use.

To evaluate water stress, two indices were used:

(1) Local Water Stress, the ratio of water demand to local generated flow.

(2) Global Water Stress, the ratio of water demand to total flow.

https://www.sciencedirect.com/science/article/pii/S0022169419300290#s0010

#carbon_emissions
#data_products

Spatial coverage: Global

Spatial resolution: country x sector x fuel

Temporal coverage: 1750-most recent year

Temporal resolution: annual

Gap-free: Yes

Year of publication: continually updated

Algorithm: N/A (not examined)

http://www.globalchange.umd.edu/ceds/

related https://www.pnas.org/doi/10.1073/pnas.1909902117

#detection_and_attribution
#aerosols
#large_ensemble_simulations

In 2022, Xuezhi Tan from Sun Yat-sen University and co-authors conducted a detection & attribution study on the 2020-2021 precipitation deficit and drought in Southeastern China (20-28 N, 110-120 E).

Data:

The observed precipitation were from 86 stations. Reanalysis data from ERA5 were also used to calculate the SPEI drought index.

Large ensemble modeled precipitation were from the HadGEM3-GA6 and CESM LENS simulations. The HadGEM3-GA6 operated at 0.56x0.83 degs resolution and participated in the CLIVAR C20C + Detection and Attribution project. The CESM LENS model had single-forcing exclusion experiments. For the aerosol exclusion case, the model considered no industrial aerosols (1920 level throughout 1920-2080), and no aerosolsl from biomass burning in agriculture and wildfires (1920 level throughout 1920-2029).

Methods:

The detection and attribution was conducted using the probability risk ratio (PR) method, which is one of the standard approaches for event-based detection and attribution.

Main findings:

Aerosol emissions from industrial aerosols and biomass burning aerosols have decreasing impacts on precipitation. The impacts are greater than the increasing impacts of than greenhouse gases, resulting in the net decreasing precipitation.

Aerosol emissions impacts on drought events, as defined by the Standardized Precipitation Index, are also detectable, but not greenhouse gases emissions impacts.

https://www.sciencedirect.com/science/article/pii/S0022169422005716#!

#data_products
#evapotranspiration

Name: The Global LAnd Surface Satellite evapotranspiration product version 5.0 (GLASS ET)

Spatial coverage: Global

Spatial resolution: 1 km

Temporal coverage: 2001-2015

Temporal resolution: 8 day

Gap-free: Yes

Year of publication: 2022

Algorithm: machine learning, integrating five satellite-derived ET products

https://www.sciencedirect.com/science/article/pii/S0022169422005650

#snow_droughts

In 2022, Alexander R. Gottlieb and Justin S. Mankin from Dartmouth College published a commentary/data paper on the emerging topic of snow droughts. The snow droughts happen because there is decreasing snow pack, especially in the western US, under climate warming. The definitions, quantification methods, and warm-season implications of snow droughts are still unclear. The authors therefore compiled an ensemble of in situ, satellite, and reanalysis datasets in an attempt to address some of these issues.

https://journals.ametsoc.org/view/journals/bams/103/4/BAMS-D-20-0243.1.xml

#carbon_sequestration
#nature_based_solutions

In 2022,

Pete Smith, University of Aberdeen,

Zhangcai Qin, Sun Yat-sen University,

Catherine E. Lovelock, University of Queensland,

Carlos A. Joly, University of Campinas,

Zahra Kalantari (KTH Royal Institute of Technology) and Georgia Destouni (Stockholm University)

Lalisa Duguma, CIFOR-ICRAF

jointly published a Voice piece that discusses Nature-based Solutions to the decarbonization problem.

Smith argued that nature-based solutions provide a beneficial long-term aid to the short-term mitigation measures that are needed to meet the near-term carbon goals.

Qin argued that nature-based solutions is cost-effective and must be implemented with urgency and inputs from multiple sectors.

In terms of specific nature-based solutions, the following angles are described:

- Coastal carbon sequestration

- Urban carbon sequestration

- Restoration of the Brazilian forests

- Restoration of the African ecosystems

https://www.sciencedirect.com/science/article/pii/S2590332222002160

Some additional useful papers

These two shows #aerosols_fast_response in precipitation

https://journals.ametsoc.org/view/journals/clim/29/2/jcli-d-15-0174.1.xml

https://acp.copernicus.org/articles/20/8381/2020/#section2

This one shows #aerosols_total_response with regional aerosol perturbations

https://acp.copernicus.org/articles/18/12461/2018/

#regional_aerosols
#aerosols_fast_response
#aerosols_slow_response
#precipitation
#temperature
#apparent_hydrologic_sensitivity
#energy_budget
#atmosphere_dynamics
#atmosphere_only_simulations
#atmosphere_ocean_coupled_simulations
#perturbed_simulations
#idealized_simulations

The PDRMIP uses idealized experiments that involve large increases in GHGs and aerosols to investigate the fast and slow responses of precipitation to aerosols.

Some standard facts in the aerosol response literature:

(1) Aerosols effects are complicated because (i) aerosols have short life span and therefore creates larger loadings when and where they are emitted, rather than being globally uniformly distributed like the greenhouse gases, (ii) aerosols create both direct and indirect effects, the latter of which is especially complicated and diverse, (iii) different species of aerosols do not have the same effects (e.g. sulfate cools, black carbon warms)

(2) Aerosols induce fast (i.e. within a few years) and slow (i.e. after several decades) responses

(2.1) Fast responses stem from atmospheric and land surface interactions

(2.2) Fast responses scale with global mean atmospheric absorption

(2.3) Fast responses can be investigated using atmosphere-only simulations

(2.4) Slow responses stem from atmosphere-ocean interactions

(2.5) Slow responses scale with global mean surface temperature

(2.6) Slow responses must be investigated with coupled simulations

Simulation experiments analyzed in this study

(1) SULASIA - 10 times present-day sulfate concentrations over Asia

(2) SULEUR - 10 times present-day sulfate concentrations over Europe

(3) BCASIA - 10 times present-day black carbon concentrations over Asia

(4) Control - all aerosol concentrations remain at present-day levels

(5) SO4x5 - 5 times global sulfate concentrations

(6) BCx10 - 10 times global black carbon concentrations

Seven GCMs participated. The perturbed concentrations were introduced as step changes and then kept constant over time. For each GCM, there was a fixed-SST (fSST; i.e. atmosphere-only experiment for assessing the fast response) run of 15 years, and a fully coupled run of 100 years. The last 10 years of the fSST and the last 50 years of the fully coupled runs were used, with the first few years being treated as spin-up. (side note: all models include direct effects of BC and sulfate, and semidirect effects of BC; but only some of the models include the full aerosol indirect effects on cloudes)

Metrics:

(1) Apparent hydrologic sensitivity (AHS): total precipitation change per unit global surface temperature change, in the fully coupled simulations

(2) Precipitation changes: $\Delta$P_{fast} is calculated from the fSST simulations, $\Delta$P_{total} from the coupled simulations, and $\Delta$P_{slow} = $\Delta$P_{total} - $\Delta$P_{fast}

(3) Forcing changes: effective radiative forcing at the top-of-atmosphere (RF_{TOA}) and the surface (RF_{surf}) was calculated from fSST; Net atmospheric absorption (AA) = RF_{TOA} - RF_{surf}

(4) L_c * $\Delta$P = $\Delta$Q + $\Delta$H, latent heat of concensation of water * precipitation = column-integrated diabatic cooling + column-integrated dry static energy flux divergence ($\Delta$Q = $\Delta$LW + $\Delta$SW - $\Delta$SH; H = L_c*P - Q)

Selected Results (the global energy budget and dynamics analysis not included):

(1) Total precipitation responses map: SULASIA results in very clear precipitation declines in the Asian monsoon region and southward displacement of the ITCZ; SULEUR induces precipitation declines in Mediterranean and Sahel-central Africa, and slight southward displacement of the ITCZ; BCASIA causes wetting of the Himalayas, north-wetting and south-drying of China, and northward shift of the ITCZ that result in drying of the tropical Indian and Southeast Asia waters. Strength-wise, the effect of SULEUR is weaker than SULFASIA due to the much lower sulfate loading in Europe than in Asia; the per-unit efficacy is in fact slightly stronger. The effect of BCASIA is much weaker than SULASIA, which is due to lower black carbon mass in the atmosphere than sulfate; the per-unit efficacy of black carbon is in fact stronger. The inter-model uncertainty in the response to black carbon is larger than to sulfate.

(2) Total temperature responses map: SULASIA and SULEUR generates temperature decreases that are especially strong in Asia and Europe, respectively; BCASIA generates temperature increases.

(3) In global total, the sensitivity of temperature to sulfate is negative, to black carbon is positive. The sensitivities of precipitation to sulfate and black carbon are both negative.

(4) The global $\Delta$P_{fast} scales linearly with global AA in the three regional perturbation experiments, and $\Delta$P_{slow} of the global changes scales with global $\Delta$T, which are consistent with past global experiment results. The scaling relationships between the regional $\Delta$P_{fast} and $\Delta$P_{slow} in Asia and Europe and the global forcings have more variability (i.e. scales less well).

(5) The global $\Delta$P_{total} scales linearly with regional RF_{TOA}. The regional responses v.s. regional forcings have slightly more uncertainty but follows a line better than (4).

https://journals.ametsoc.org/view/journals/clim/31/11/jcli-d-17-0439.1.xml

#aerosols

Two major projects that investigate the climate system's response to natural and anthropogenic aerosols forcings are:

1. the PDRMIP - Precipitation Driver and Response Model Intercomparison Project

2. the AerChemMIP - the Aerosol Chemistry Model Intercomparison Project, which is part of CMIP6

The key papers are:

1. https://journals.ametsoc.org/view/journals/bams/98/6/bams-d-16-0019.1.xml

2. https://acp.copernicus.org/articles/special_issue1057.html

In 2022, Apoorva Nisal & Urmila Diwekar, both from the University of Illinois, and others presented the main generalized global sustainability model (CGSM) for modeling food-energy-water nexus to study global sustainability. The model is compartmentalized, using differential equations to describe resource depletion, and involves a macroeconomic model.

#integrated_assessment_modeling

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267403#sec002

In 2014, Qianlai Zhang from Indiana University and others studied the aerosol direct effect (radiative effect) on the surface energy fluxes of global terrestrial ecosystems during 2003-2010.

Background: the effects of aerosols can be divided into

(1) Direct radiative effects

(1.1) Aerosol reduces the total downward solar radiation, but increases the diffuse radiation. The former is bad for vegetation, while the latter is good.

(1.2) Observational studies on the direct radiative effects at site, regional, and global levels are abundant

(1.3) Modeling studies on the direct radiative effects are also abundant at site and regional levels, but few at global levels

(2) Indirect effects

(2.1) Such effects happen through aerosol-cloud interactions, which are not considered in this study.

Methods

(1) Two models are coupled

(1.1) Atmospheric radiative transfer model (uses MODIS-based global aerosol properties, estimates direct and diffuse radiations)

(1.1.1) The aerosol loading data shows highest levels in northern India, China, and the Sahel.

(1.2) Terrestrial ecosystem model (the integrated Terrestrial ecosystem model, iTem; run at 1 deg resolution)

(2) Two experiments are conducted

(2.1) S0: Aerosol effect (uses transient solar radiation data estimated by the atmospheric radiative transfer model)

(2.2) S1: No aerosol effect (uses the solar radiation data estimated by the atmospheric radiative model without considering the aerosol loadings)

Findings

(1) Global mean

(1.1) Aerosol loadings decrease the mean latent heat flux by 2.4 Wm-2, from 46.00 Wm-2 to 43.60 Wm-2

(1.2) Aerosol loadings decrease the sensible heat flux by 16 Wm-2, from 95.26 Wm-2 to 79.57 Wm-2

(1.3) Global mean surface soil moisture increases by 0.5%. This is because (i) surface water evapotranspiration is inhibited by the cooler land surface, (ii) aerosol-induced sensible and latent heat decreases are positively correlated with leaf area index (LAI) decreases, further reducing transpiration.

(1.4) Global mean water evaporative fraction increases by 4%.

(2) Spatial distributions

(2.1) Aerosol direct effects on the sensible heat flux are universally negative or zero across the globe. The highest reductions coincide with where the highest aerosol loadings occur, but are larger in spatial extents - i.e. Central Africa, the Indian subcontinent, and China. Also, the Amazon basin is a hotspot.

(2.2) Aerosol direct effects on the latent heat flux are also universally negative or zero across the globe. But compared to sensible heat, the hotspot in Central Africa is much muted, whereas the hotspot in the Amazon basin is strengthened. This is because in high-LAI systems (e.g. forests), the effect of reduced transpiration will be especially apparent.

(2.3) Aerosol direct effects on the surface soil moisture are universally positive or zero across the globe. The hotspots of increases are mainly the region below Sahel in Central Africa, India, and China. The Amazon region shows slight increases, but hardly as much as the previously mentioned two hotspots.

(3) Seasonal-latitudinal distributions

(3.1) For sensible heat, the strongest effect is in summer in each hemisphere, and is the strongest between 20S and 20N. The latitudinal pattern should be related to the aerosol emission patterns.

(3.2) For latent heat, the strongest effect coincide with latitudes of high LAI, but not always with the season where vegetation productivity is the highest. [Perhaps this is because leaf area changes, percentage-wise, are the most apparent in the northern mid-latitudes during the transition seasons when leaves are growing out or senescing.]

#aerosols_direct_effect
#land_atmosphere_coupled_simulations
#land_energy_fluxes
#soil_moisture
#evaporative_fraction

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014GL061640

Siao Sun & Qiuhong Tang et al. from the Chinese Academy of Sciences used multi-regional input-output (MRIO) method to track the depletion of groundwater from locations of production to end consumers. This is an advancement since previous studies have not investigated the virtual transfer of groundwater.

The paper defines consumption-based groundwater withdrawal as "the groundwater that is virtually embedded in products throughout the supply chain and consumed by end consumers."

Past studies of virtual water transfer or trade often uses the terms blue water and green water. Groundwater falls in the scope of blue water, which is surface water and groundwater sources, and not green water, which is soil moisture sources (Hoekstra et al. 2011; Dalin et al. 2014). Also, the study considers water withdrawal as opposed to consumption, because withdrawn groundwater is unlikely to be recharged back underground and is likely to have degraded quality.

Consumers who receive groundwater depletion intensive products should be aware of the risk from groundwater depletion upstream in their supply chain.

The authors gave a detailed description of a noteworthy methodology that merges MRIO and groundwater simulation techniques. Yearbook statistics only provide provincial water withdrawals at the level of agricultural, industrial, and tertiary sectors. The authors disaggregated the provincial water withdrawals to grid level, and then used the grid-level water withdrawal to drive model simulations that further divided the water withdrawal into from surface water, groundwater, and other sources. The authors then calculated grid-level groundwater depletion as "groundwater withdrawal - natural discharge". The grid-level groundwater withdrawals and depletions are re-aggregated to sectoral and provincial levels that are compatible with the MRIO table.

Calculation of the consumption-based water withdrawal, groundwater withdrawal, and groundwater depletion follows conventional MRIO techinques with environmental extension coefficients.

Assignment of the provincial consumption-based numbers to grid-level was proportional to population.

In the results, the authors had three parallel perspectives: (1) production vs consumption-based accounting of groundwater depletion, (2) inter-provincial transfers of groundwater depletion, (3) exported groundwater depletion.

In broad picture, the total amount of virtually transferred groundwater in China was 38 billion m3 yr-1, with 21 billion m3 yr-1 being groundwater depletion. Embedded groundwater depletion accounts for about 70% of total groundwater depletion. The toal amount of virtually exported groundwater was ~8.6 billion m3 yr-1, with 4.8 billion m3 yr-1 being depletion.

Regarding spatial distribution, groundwater withdrawal is the heaviest in northern China (North China Plain, Northeast provinces, scattered grids in the Norhtwest; these reach 50-100 mm yr-1). A few provinces in the south also have withdrawals, but these are at lower rates. Groundwater depletion driven by local water uses is concentrated in the Hebei province. Smaller clusters of hotspots exist throughout North China. In contrast, ground depletion embedded in consumption is spread out spatially, and major cities are hotspots.

Regarding virtual transfer pathways, the major inter-provincial transfer routes of groundwater withdrawal are from north to south. In some provinces, the virtual groundwater depletion are driven mainly by local production, whereas in others, the virtual groundwater depletion are driven mainly by imported products. Net groundwater-delivering and -receiving provinces are not the same as net water-delivering & -receiving provinces.

Regarding sectoral patterns, the major products in which the virtual water is embdeeded are industrial (84%) and tertiary (11%) products, not agricultural. But agricultural products are often upstream of these products and are the source of the groundwater depletion (53%).

The proportion of groundwater in China's exported virtual water is lower than the global average, but the proportion is higher in the regions where groundwater is most severely depleted (e.g. Hebei, Shanxi, Henan, Beijing).

A noteworthy figure is Fig. 5. It displays the pathway of export of the embedded groundwater by showing the major province-sectors from which the depletion stems, and the major province-sectors where the export actually happened.

#input_output
#groundwater_depletion
#virtual_water
#China
#groundwater_modeling

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021WR030695

Satellites can now take videos, which contain more reflectance info than traditional images. Ye (Wuhan University) et al. examined the accuracy of classifying urban objects based on videos from the Jilin-1 agile video satellite constructed by Chang Guang Satellite Technology Co., Ltd. in Jinlin, China, using machine learning. Higher number of viewing angles in the training data led to greater classification accuracy when using a robust machine learning method like ensemble learning. The findings proved the value of satellite videos.

https://www.mdpi.com/2072-4292/14/10/2324/htm

#remote_sensing
#satellite_videos
#method_advancements
#pilot_scale

Xie et al. 2022 used optimal fingerprinting to detect and attribute summer soil moisture changes for the globe and six other regions.

They investigated multiple forcings (GHG, AA, LU) using single-forcing simulations of CMIP6, as well as future changes under different RCP scenarios.

One interesting perspective here is that the selected regions focus on those with strong land-atmosphere interactions (mainly semi-arid regions). In these regions, evapotranspiration is limited by soil moisture, and yet is not so low that the impact on the overlaying atmosphere is negligible.

They demonstrated that summer soil moisture is drying for the majority of the globe, but wetting consistently in Sahel and parts of mid-latitudes of Asia.

The best detected & attributed effect is GHG. AA is detected, but not for the same depths for the two used reanalysis datasets (ERA5 and GLDAS NOAH).

As GHG emissions continues to become larger, the GHG-induced wetting and drying trends are projected to accelerate.

The surface soil also dries faster than the deeper soil, where the increasing atmospheric demand for evapotranspiration may be mitigated by CO2 fertilization effect and increasing water use efficiency of vegetation.

https://www.frontiersin.org/articles/10.3389/feart.2021.745185/full

#detection_and_attribution
#soil_moisture
#land_atmosphere_interactions