commonchaffinch @commonchaffinch@qoto.org

#urban_vegetation
#urban_trees
#species_distribution_models

2019 study by Hugh Burley from Macquarie University, Australia and co-authors

Background information

(1) Although global governments are placing great emphasis on using urban trees to improve the adaptability and sustainability of cities under cilmate change, there are little consideration on whether the selected urban tree species will be resilient to climate changes that may happen during their lifespan. Climate change has been demonstrated to shift species taxa for many natural plants, crops, and invasive species, but few such attention has been paid to urban trees.

(2) Several previous studies reported shifts in the growth of urban trees, which were likely due to shifting climate [Lanza & Stone 2016; Nitschke et al. 2017; Pretzsch et al. 2017]. Analysis across climate gradients showed that trees that were adapted to cool environments in the natural setting tended to be planted in warmer cities, and vice versa [Kendal et al. 2018].

(3) The urban heat island may exacerbate heat stress on trees, but irrigation may be available to urban trees [Jenerette et al. 2016; Vogt et al. 2017].

(4) Climate suitability models, i.e. species distribution models, can describe the environmental tolerances of the species, and can be used to identify un-occupied suitable areas, or project climate impacts on species.

Study objective

(1) Assess the climatically suitable habitat for 176 native Australia urban tree species during the baseline (1960-1990), short term (2030) and long term (2070) periods

- which tree species are more likely to experience increases and decreases in the habitat sizes

- which urban areas are likely to see more/fewer tree species that can use it as a suitable habitat

- hypothesis: urban areas in cooler regions will gain in species, and warmer regions lose species; previous findings in the natural setting revealed this to be true [O'Donnell et al. 2012]

Data and methods

(1) The native tree species kept by Australia nurseries were obtained from online resources. The existence of these tree species were further verified by contacting local government authorities, resulting in data for 44 local councils spanning 49 "significant urban areas" (SUAs). The final results were 248 species.

(2) The spatial occurrences of the species were obtained from GBIF and the Atlas of Living Australia (www.ala.org.au, rgbif package, ALA4R package). Due to the relative lack of data for the arid and tropical regions of Australia, the analysis was limited to the temperate Köppen zones in Australia.

(3) The climatic variables for the species distribution modeling were obtained from WorldClim and included 8 bioclimatic variables: 1) annual mean temperature, 2) temperature seasonality, 3) maximum temperature of the warmest month, 4) minimum temperature of the coldest month, 5) annual precipitation, 6) precipitation seasonality, 7) precipitation of the wettest month, 8) precipitation of the driest month.

(4) The species distribution model was Maxent. The performance was assessed using the Area Under the Receiver Operating Characteristic (AUC) and the Truee Skill Statistic (TSS) through five-fold cross-validation. The Multivariate Environmental Similarity Surfaces (MESS) was further used to indicate whether the models were applied on novel states of individual variables, which can cause the model to over-project suitability. Note MESS cannot indicate whether novel combinations of variables occurred. The MESS results further excluded 72 species, due to the insufficient ability of the above 8 variables to characterize their niche. Therefore, 176 species were analyzed in the end.

Results

(1) The climatically suitable habitat will decline from 16,152 km2 in the baseline to 13,043 km2 in 2030, and 12,300 in 2070. Among the 176 species, 18% will lose >50% of their habitat by 2030, 34% will lose >50% by 2070, and 11 species will increase their habitat by >50%. Generally, suitable habitat will shift polewards, so most of the species gained habitat in the south, and lost in the north.

(2) The hotter places (higher mean annual temperature & mean annual maximum temperature) tended to gain fewer species, and lose more species.

(3) At present, each SUA contained suitable climate for 10-139 of the 176 species, and on average 74 species, but by 2070, the average suitable species declined to 63. 21 of the 82 SUAs, mainly in the cooler regions, increased in the number of suitable species between the baseline and 2070.

Discussion

(1) The poleward shift in climatically suitable habitat in urban areas (SUAs) parallels the change in natural areas.

(2) Species and urban areas in cooler regions would fare better than those in warmer regions, which also supports the findings of previous studies [Jenerette et al. 2016; Kendal et al. 2018].

(3) Some caveats are that: the study area is limited to the temperate regions of Australia, and the tropical & subtropical & arid area trees may respond differently; only macroclimatic predictors were considered, microclimate, extreme weather, and edaphic conditions need to be considered by future studies; management factors like irrigation may increase the habitat range of some species despite warming.

(4) Heat waves and drought can affect the growth of urban tree species [Nitschke et al. 2017]. Climate change also affects the growth rate [Pretzsch et al. 2017; Jia et al. 2018].

Referenced studies

Jenerette, G.D., Clarke, L.W., Avolio, M.L., Pataki, D.E., Gillespie, T.W., Pincetl, S., Nowak, D.J., Hutyra, L.R., McHale, M., McFadden, J.P., Alonzo, M., 2016. Climate tolerances and trait choices shape continental patterns of urban tree biodiversity. Glob. Ecol. Biogeogr. 25, 1367–1376.

Jia, W., Zhao, S., Liu, S., 2018. Vegetation growth enhancement in urban environments of the conterminous United States. Glob. Chang. Biol. 24, 4084–4094.

Kendal, D., Dobbs, C., Gallagher, R.V., Beaumont, L.J., Baumann, J., Williams, N.S.G., Livesley, S.J., 2018. A global comparison of the climatic niches of urban and native tree populations. Glob. Ecol. Biogeogr. 27, 629–637.

Nitschke, C.R., Nichols, S., Allen, K., Dobbs, C., Livesley, S.J., Baker, P.J., Lynch, Y., 2017. The influence of climate and drought on urban tree growth in Southeast Australia and the implications for future growth under climate change. Landsc. Urban Plan. 167, 275–287.

Pretzsch, H., Biber, P., Uhl, E., Dahlhausen, J., Schütze, G., Perkins, D., Rötzer, T., Caldentey, J., Koike, T., Con, T.V., Chavanne, A., Toit, B.D., Foster, K., Lefer, B., 2017. Climate change accelerates growth of urban trees in metropolises worldwide/631/158/858/704/158/ 2165 article. Sci. Rep. 7, 15403.

https://www.sciencedirect.com/science/article/pii/S0048969719323289?via%3Dihub

#wildfires
#emergent_constraints
#machine_learning

Yan Yu from Peking University and co-authors in 2022.

Background

(1) It is necessary to create constrained future projections of fire emissions

Current earth system models only include incomplete and highly parameterized driving processes of fire, and cannot accurately characterize the human-vegetation-fire-climate feedback.

(2) The conventional emergent constraint approach does not satisfy the need of wildfire constraint, because

- Only 13 of the currently available CMIP6 models provide fire carbon emissions in both historical and future simulations, and they lack diversity in structures and parameters

- The linear relationship estimated by traditional emergent constraint cannot deal with the complex, nonlinear relationship between meteorological, ecological, and socioeconomic factors and fire

- The traditional emergent constraint may be suitable for large-scale averaged quantities, but not for the detailed spatial distributions of wildfire

- The emergent constraint approach, however, is still more suitable than bias-correction or model weighting.

Method

(1) The authors developed a machine learning based emergent constraint method to project future global fire emissions and socioeconomic risks

(2) The machine learning models were trained on the Earth system model-simulated relationships between the historical climate, ecosystem, and socioeconomic variables, and future fire carbon emissions under SSP585. The observed variables were then substituted in. The predictors included fuel abundance (leaf area index, temperature, precipitation), fuel moisture (soil moisture, relative humidity, precipitation, temperature), fire spread conditions (wind, orography), ignition sources (flash rate, land use, population).

Results

(1) The machine learning constraint reduces the biases in fire carbon emission bias. Before and after the emergent constraint, the grid-level biases always ranged from -0.32 to 0.16 kg m-2 yr-1, but the grids that have large errors were much, much more before the constraint than after.

(2) The constrained results showed weaker trends in fire carbon emissions compared to the un-constrained results, but the socioeconomic exposure became greater, in terms of population, gross domestic production, and agricultural area.

https://www.nature.com/articles/s41467-022-28853-0#MOESM1

#wildfires
#ELM

Jiafu Mao from the Oak Ridge National Laboratory and co-authors in 2021 examined the interannual variability in global burned area using satellite-derived wildfire products and the outputs of the E3SM Land Model v1. The investigation included multiple aspects of the interannual variability, i.e.

(1) the spatial and temporal structures during 1997-2018

(2) contributions from individual biomes

(3) covariations with climate factors

(4) comparison between the ELM and several remote sensing wildfire products.

The data sources of the observations were

(1) The Global Fire Emission Database (GFED): 1997-2016, 0.25 degrees, monthly burned area. This product is derived from several satellite sources (TRMM, ATSR, MODIS).

(2) The FireCCI5.1 burned area: 2001-present, 250m, MODIS image.

Main findings

(1) The highest observed interannual variability is in the boreal area and semi-arid regions, and the lowest in tropical and subtropical regions - especially the African subtropical savannah systems.

(2) The ELM simulations underestimated the interannual variability's magnitudes in the boreal area, and overestimated the variability's magnitudes over Africa. The highest interannual variability in ELM-simulated burned area is still in the boreal forests, but the lowest is in the temperate grassland and shrubland.

(3) The GFED and ELM simulations both showed that the burned area interannual variability were positively correlated with temperature and shortwave radiation, but negative with precipitation. The only disagreement was in North Australia, where GFED had negative correlations with temperature, but ELM had positive.

https://www.sciencedirect.com/science/article/pii/S1674927821000903

#wildfires
#machine_learning
#seasonal_forecasts

Yu Yan from Peking University and co-authors in 2020.

The objectives of this study are to (1) deepen the understanding of the environmental drivers of African fire, (2) improve the capacity to accurately predict seasonal fire activity in Africa. These contribute to better fire management in these vulnerable ecoregions.

1. Background

The Africa subtropical savannah ecosystems emit ~50% of the global fire-related carbon emissions, and occupy ~70% of the global burned area. The interannual variability in atmospheric CO2 concentration is mediated by African fires. African wildfires also affect human health by emitting atmospheric pollutants.

Previous studies indicate that variations in the extent of burned area in Africa are attributable to vegetation composition and distribution, and air & soil controls on fuel drying. Human activities like cropland expansion and population growth also cause decline in African burned area since 1998. However, these factors cannot predict wildfire activities at the seasonal timescale. Previous seasonal climate forecasts resulted in insignificant correlations between the observed and predicted burned areas across most of Africa.

Since ocean sea-surface temperature (SST) and the land surface have longer memory than the atmosphere, the seasonal predictability of African wildfire may be improved if we focus on SST and terrestrial controls, instead of air controls. But past studies mainly focused on ENSO, ignoring the tropical Atlantic SSTs or the interactions between different modes of SST variability.

2. Method

In order to disentangle the vegetation impacts on fire from the fire impacts on vegetation, and the oceanic and land surfaces influences, the Stepwise Generalied Equilibrium Feedback Assessment (SGEFA) method was used. The SGEFA can disentangle the individual linear influences, but machine learning is necessary for building seasonal forecasting models.

The final results show that SST, leaf area index (LAI), and soil moisture are the most important predictors of African fire, and the machine learning model based on these predictors can effectively predict African fire activity 1 month ahead.

Key findings

I. SGEFA results

(1) Northern Africa's fire is sensitive to tropical Atlantic Ocean SST during the dry season (Nov-Mar), North Atlantic Ocean SST during the boreal winter (fire-active season), and tropical Indian Ocean SST during the wet season (Apr-Sep).

(2) Southern Africa's fire is sensitive to South Atlantic Ocean SSTs during the fire-active season (May-Nov/boreal summer).

(3) Soil moisture exerts important controls on the northern Africa fire during the wet season (Apr-Jan), and slightly smaller control on the southern Africa fire than the South Atlantic SSTs during the fire-active season (May-Nov).

(4) LAI is not an important control in Northern Africa. In Southern Africa, the impact of LAI was akin to that of the North Atlantic, and smaller than soil moisture or the South Atlantic.

(5) If one focuses on specific oceanic modes (ENSO, Atlantic Nino), the response to both of these modes are the highest during the fire-active season (boreal winter in Northern Africa, and boreal summer in Southern Africa). The fire-incuding SST anomalies are generally conducitve to warm and/or dry conditions.

(6) Soil moisture generally supresses fire through low-level cooling and elevated precipitation. LAI enhances fire in Northern Africa and the grasslands in Southern Africa, but supresses fire through surface cooling and reduced wind speed in most of Southern Africa.

II. Seasonal predictability

Fig. 3 shows the predictability of African wildfires by leading time. In both Northern and Southern Africa, the predictability at 1-month lead is significantly enhanced if one uses oceanic + terrestrial predictors, instead of atmospheric + socioeconomic predictors. Using all predictors resulted in only very slight improvements compared to using oceanic + terrestrial predictors.

Also, using season-specific predicting models resulted in much better performances than annual models.

3. Implications of the work

The work will aid the development of fire models, which currently can capture the spatial distribution of observed global fire, but not the seasonal or interannual variations.

The work also has some limitation caused by observational data availability (e.g. lightening, agricultural practice).

The results are good, outperforming past prediction models.

4. Data sources

The data sources are in their Supplemental Table 1. The fire carbon emissions and burned area fractions are from the Global Fire Emissions Database (GFED), which covers 1997-2016 at 0.25 degrees resolution. This is an observation-based analysis.

https://www.nature.com/articles/s41467-020-16692-w

#tbc

Landsat data analysis https://emapr.github.io/LT-GEE/introduction.html

#factual_information
#land_use_and_land_cover_change

In 2021, Karina Winkler from Wageningen University & Research (WUR) and co-authors presented maps of global land use changes between 1960 and 2019.

Fig. 1 shows the total net and gross changes for all the land use categories.

Fig. 2 shows the changes in three individual land use types: forests, crops, and pasture/rangeland.

https://www.nature.com/articles/s41467-021-22702-2

#terrestrial_water_storage
#GRACE

In 2019, Bin He from Beijing Normal University and co-authors analyzed the response time of terrestrial water storage (TWS) to precipitation (P) over 168 global river basins.

The study period was 2003-2014. The trend metric was Mann-Kendall. The trends were all calculated at the basin level for the 168 global basins.

Key findings

(1) In low- and mid-latitude basins, TWS is correlated with P with a shorter lag (1-2 months) than in high-latitude basins (6-9 months).

(2) Three of the individual components of TWS - surface, ground, soil - have the same correlation lags pattern with P as TWS. The other two components - canopy and snow - have different correlation lags, i.e. 0 months and 3-8 months.

(3) Groundwater and soil moisture contributions are generally the largest. In high-latitude basins, snow contribution is greater, which can explain the longer lag in high latitude basins. Interestingly, snow contribution is also large in northern India. Soil moisture contribution is not negligible in very wet regions (e.g. Mackenzie in Canada, Amazon region, Yangtze, Yellow River, Yenisei & Lena in Russia) and a few arid basins (Niger and Nile).

https://journals.ametsoc.org/view/journals/hydr/20/9/jhm-d-18-0253_1.xml

#terrestrial_water_storage
#GRACE
#global_hydrological_models
#land_surface_models

2018 study designed by Bridget R. Scanlon from University of Texas, Austin and Zizhan Zhang from Wuhan University, China.

This is a basin-level analysis for the globe of TWS trends during 2002-2014.

The concept of terrestrial/land terrestrial water storage (TWS), which does not include glaciers.

TWS = SnowWS + CanopyWS + SoilWS + SurfaceWS + GroundWS (WS = water storage)

Caveats of different data sources:

(1) Land surface models have greater emphasis on fluxes, whereas global hydrological models have more emphasis on water storage and human water use.

(2) Many land surface models only simulate the snow and soil moisture storage components, while most global hydrological models simulate all except glaciers.

(3) GRACE data estimated by mascons can have leakage, i.e., the decreasing TWS trends in melting glaciers can cause decreasing trends in the adjacent pixels

The assessments were conducted at large-basin levels (<100 across the globe). The units of basin-level TWS were km^3/year, but larger trends in km^3/year generally corresponded to larger trends in mm/year.

Major findings:

(1) All the models generally under-estimate the magnitudes of trends, which may be increasing or decreasing trends. The agreements based on regression analysis were also pretty poor.

(2) The more (less) irrigated a basin is, the more negative (positive) is the TWS trends in GRACE. The non-irrigated basins are mainly in the humid regions. Nonetheless, human intervention is not the greatest driver of global trends in water storage, because the land surface models, which do not simulate human interventions, produced more negative global trends than

(3) Larger (smaller) basins have smaller (larger) GRACE measurement and leakage uncertainties. Examples of leakage existed in the Yukon Basin (from the Alaskan glaciers), the Ganges (from the Asian High Mountain Glaciers), and the Salado basins in South America (glacker & Chile 2010 Maule earthquake).

(4) Land TWS affects global mean sea level rise by taking water from or putting water into the sea. GRACE suggests the global land is increasing in TWS, but the land surface and hydrological models all suggests negative global land average TWS trends.

(5) Sources of discrepancy between GRACE and the model outputs are discussed and analyzed in detail.

#TBC
#methods_to_learn
#closure_techniques
#data_products

https://www.sciencedirect.com/science/article/pii/S0034425720305642

https://journals.ametsoc.org/view/journals/clim/33/5/jcli-d-19-0036.1.xml

https://journals.ametsoc.org/view/journals/hydr/21/5/jhm-d-19-0255.1.xml

https://hess.copernicus.org/articles/22/241/2018/

#data_products
#terrestrial_water_storage

Name: GRACE-REC

Spatial coverage: global

Spatial resolution: 0.5 degrees

Temporal coverage: MSWEP-based reconstruction 1979-2016, ERA5-based 1979-present, GSWP3-based 1901-2014

Temporal resolution: Monthly (100 ensemble members for each combination of the three meteorological datasets and two GRACE satellite mascons), daily (ensemble mean only)

Gap-free: Yes

Year of publication: 2019

Algorithm: (1) linear water storage model, driven by temperature and precipitation, calibrated against de-seasonalized, de-trendedd GRACE satellite data (2002-2017), (2) spatial autoregressive model was used to generate an ensemble of spatially autocorrelated residuals, to facilitate uncertainty propagation from grid-level to regional or global averages. This method to propagate uncertainty is noteworthy.

Pros: outperforms hydrological and land surface models when evaluated against de-seasonalized, de-trended GRACE terrestrial water data, sea-level budget, basin-scale water balance, and streamflow.

Cons: The trends in GRACE-REC is purely driven by precipitation changes, therefore missing a ton of relevant factors (evapotranspiration change, dams, human water withdrawal, ice melt...). The seasonality is constant and not particularly reliable, either.

https://essd.copernicus.org/articles/11/1153/2019/

https://www.pnas.org/doi/full/10.1073/pnas.0403491101

https://link.springer.com/article/10.1007/s11104-017-3231-z#Sec12

#remote_sensing
#vegetation_index
#ndvi
#evi
#review

2022. Dalei Hao from Pacific Northwest National Lab, and coauthors.

Satellite-measured spectral reflectance is co-determined by leaf reflectance, background soil reflectance, canopy structure, and sensor geometry.

The below are broad-band vegetation indices for measuring vegetation structure:

* NDVI is simple and requires only the NIR and Red band, but sensitive to the soil background variations and insensitive to dense vegetation.
* EVI requires the blue band as well as the NIR and Red band, but minimizes both soil and atmospheric effects and is more sensitive to dense vegetation than NDVI. However, EVI is sensitive to the BRDF (bidirectional reflectance distribution function, related to the sun-target-sensor geometry) effect.
** The blue band helps remove atmospheric effects. With the advancement in atmospheric correction algorithms, EVI2 is developed and requires only the red and NIR bands.

* PPI (Plant Phenology Index) uses red and NIR bands, and is nearly linearly correlated with green LAI. But PPI is moderately sensitive to soil brightness.
** Still, the original paper (https://www.sciencedirect.com/science/article/abs/pii/S0034425714002557?via%3Dihub) seems to suggest it is more useful for GPP than LAI.

* NIRv is not sensitive to soil background variations, but sensitive to the BRDF effect.

Narrow-band vegetation indices can measure (1) biochemical properties, (2) physiological properties (e.g. photosynthetic light-use efficiency, or environmental stress). These indices make use of measurements made at specific wavelengths. The former includes indices for chlorophyll content, carotenoid content, normalized difference water index (vegetation water condition), land surface water index (vegetation water condition), normalized difference lignin index, etc. The latter include the PRI (photochemical reflectance index) and CCI (chlorophyll/carotenoid index), etc.

Artefacts in vegetation indices arise from (1) differences between sensors, (2) satellite product versions, (3) atmospheric and directional corrections, (3) compositing algorithms, (4) application of different levels of quality assurance and quality control flags. Some notable known facts are:
(1) AVHRR and MODIS NDVI time series can show trends in opposite directions.
(2) NDVI values are substantially different between AVHRR, MODIS, and VIIRS. The level of the differences varies across land cover types
(3) Products from AVHRR and MODIS have instrumental drifts. AVHRR products, especially, are from a series of satellites. The NDVI values from AVHRR aboard NOAA-11 were higher than the values measured by other AVHRR sensors. The trends in the NIRv values are not sufficiently trustworthy to measure the global CO2 fertilization effect. The MODIS Collection 5 and Collection 6 products have inconsistent greening trends. The MODIS Terra satellite suffers from sensor degradation, more so in the blue band than in the longer-wavelength bands, resulting in artificial negative drifts in NDVI and EVI values in Collection 5. The Collection 6 products remedied the sensor degradation problem.
(4) MODIS-based NDVI exhibited increasing trend during 2001-2016, but GIMMS-based NDVI showed the opposite trend, especially after 2012. This highlights uncertainty in global greening.
(5) VIP3 and LTDR4 NDVI, and the GIMMS-3g NDVI in the more humid areas, have orbital drift effects.
(6) SPOT-VGT NDVI time series have an abrupt jump at the shift from VGT-1 to VGT-2.
(7) As a rule of thumb, ratio-based indices (e.g. NDVI) have smaller biases than straight indices (e.g. NIRv = NDVI*NIR), because of the cancellation between denominator and numerator. Therefore, it is especially important to check for instrumental drifts in the latter.
(8) BRDF effect does not influence the long-term trends or interannual variations, since the sun returns its orbit every year. But it affects phenology. Single-season analysis should be rigorously corrected for BRDF [refs 64, 122, 123, 124]. Landsat's viewing angle is relatively restricted (+/-7.5 degrees from nadir), but other sensors like AVHRR and MODIS must be corrected for this effect.
(9) GIMMS-3g datasets used relatively old compositing algorithm (maximum value compositing). The MOD13A1 and MYD13A1 used a newer one. The MCD43A4 C6 product used a even newer one and removed the view-angle effects; the angle was always set at the local solar noon zenith angle. GIMMS-3g also had only limited atmospheric correction.

Soil artefacts are worse in sparsely vegetated regions (<50% cover, or LAI ~= 1). Snow and ice lead to discrepancies in temperate regions. Topograph also create artefacts, but decreases with spatial averaging.

Finally, the pixels can be mismatched with the actual point on the ground. MODIS has accurate geolocation, but the offset can still be as much as 0.5 pixel between scenes. This is a problem for single-site study. Hyper-resolution (<3m) remote sensing products have their own problems because there can be shadows.

There is need to more extensively measure reflected radiation from vegetation to verify the remote sensing products. There is also much uncertainty in greening.

https://www.nature.com/articles/s43017-022-00298-5

#coding_pitfalls

Let me start this chain and see how it goes.

`xarray` decode_times = True recognizes any variable with a time unit (e.g. "days") as a datetime variable. Clearly, this can cause problem when there are variables that are intended to measure length of time instead of date.

#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