Plant rooting depth (Zr) is a key parameter in hydrological and biogeochemical models, yet the global spatial distribution of Zr is largely unknown due to the difficulties in its direct measurement. ...Additionally, Zr observations are usually only representative of a single plant or several plants, which can differ greatly from the effective Zr over a modeling unit (e.g., catchment or grid‐box). Here, we provide a global parameterization of an analytical Zr model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982–2010 average) global Zr map. To test the Zr estimates, we apply the estimated Zr in a highly transparent hydrological model (i.e., the Budyko‐Choudhury‐Porporato (BCP) model) to estimate mean annual actual evapotranspiration (E) across the globe. We then compare the estimated E with both water balance‐based E observations at 32 major catchments and satellite grid‐box retrievals across the globe. Our results show that the BCP model, when implemented with Zr estimated herein, optimally reproduced the spatial pattern of E at both scales (i.e., R2 = 0.94, RMSD = 74 mm yr−1 for catchments, and R2 = 0.90, RMSD = 125 mm yr−1 for grid‐boxes) and provides improved model outputs when compared to BCP model results from two already existing global Zr data sets. These results suggest that our Zr estimates can be effectively used in state‐of‐the‐art hydrological models, and potentially biogeochemical models, where the determination of Zr currently largely relies on biome type‐based look‐up tables.
Key Points:
We estimate the effective plant rooting depth (Zr) using a carbon cost‐benefit model across global terrestrial ecosystems
Both mean climate conditions and climate seasonality are essential in determining Zr
Zr estimated herein is more hydrologically effective than existing global Zr data sets
We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980-2016) and for projected future conditions (2071-2100) under ...climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.
Satellite observations reveal a greening of the globe over recent decades. The role in this greening of the “CO2 fertilization” effect—the enhancement of photosynthesis due to rising CO2 levels—is ...yet to be established. The direct CO2 effect on vegetation should be most clearly expressed in warm, arid environments where water is the dominant limit to vegetation growth. Using gas exchange theory, we predict that the 14% increase in atmospheric CO2 (1982–2010) led to a 5 to 10% increase in green foliage cover in warm, arid environments. Satellite observations, analyzed to remove the effect of variations in precipitation, show that cover across these environments has increased by 11%. Our results confirm that the anticipated CO2 fertilization effect is occurring alongside ongoing anthropogenic perturbations to the carbon cycle and that the fertilization effect is now a significant land surface process.
Key Points
We examine, for a set rainfall, the maximum foliage cover observable by satellite
In warm, dry places, such maximum cover has risen by 11% globally (1982–2010)
We show a physical and quantitative link between this rise and CO2 fertilization
Rates of evaporative demand can be modelled using one of numerous formulations of potential evaporation. Physically, evaporative demand is driven by four key variables – net radiation, vapour ...pressure, wind speed, and air temperature – each of which have been changing across the globe over the past few decades. In this research we examine five formulations of potential evaporation, testing for how well each captures the dynamics in evaporative demand. We generated daily potential evaporation datasets for Australia, spanning 1981–2006, using the: (i) Penman; (ii) Priestley–Taylor; (iii) Morton point; (iv) Morton areal; and (v) Thornthwaite formulations. These represent a range in how many of the key driving variables are incorporated within modelling. The testing of these formulations was done by analysing the annual and seasonal trends in each against changes in precipitation (a proxy for actual evaporation), assuming that they should vary in an approximately inverse manner. The four-variable Penman formulation produced the most reasonable estimation of potential evaporation dynamics. An attribution analysis was performed using the Penman formulation to quantify the contribution of each input variable to overall trends in potential evaporation. Whilst changes in air temperature were found to produce a large increase in Penman potential evaporation rates, changes in the other key variables each reduced rates, resulting in an overall negative trend in Penman potential evaporation. This study highlights the need for spatially and temporally dynamic data describing all drivers of evaporative demand, especially projections of each driving variable when estimating the possible affects of climatic changes on evaporative demand.
MSWEP V2 GLOBAL 3-HOURLY 0.1° PRECIPITATION Beck, Hylke E.; Wood, Eric F.; Pan, Ming ...
Bulletin of the American Meteorological Society,
03/2019, Volume:
100, Issue:
3
Journal Article
Peer reviewed
Open access
We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage ...(all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others, satellites Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT), and reanalyses European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55); iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.
Earlier vegetation greening under climate change raises evapotranspiration and thus lowers spring soil moisture, yet the extent and magnitude of this water deficit persistence into the following ...summer remain elusive. We provide observational evidence that increased foliage cover over the Northern Hemisphere, during 1982-2011, triggers an additional soil moisture deficit that is further carried over into summer. Climate model simulations independently support this and attribute the driving process to be larger increases in evapotranspiration than in precipitation. This extra soil drying is projected to amplify the frequency and intensity of summer heatwaves. Most feedbacks operate locally, except for a notable teleconnection where extra moisture transpired over Europe is transported to central Siberia. Model results illustrate that this teleconnection offsets Siberian soil moisture losses from local spring greening. Our results highlight that climate change adaptation planning must account for the extra summer water and heatwave stress inherited from warming-induced earlier greening.
Vegetation change is a critical factor that profoundly affects the terrestrial water cycle. Here we derive an analytical solution for the impact of vegetation changes on hydrological partitioning ...within the Budyko framework. This is achieved by deriving an analytical expression between leaf area index (LAI) change and the Budyko land surface parameter (n) change, through the combination of a steady state ecohydrological model with an analytical carbon cost‐benefit model for plant rooting depth. Using China where vegetation coverage has experienced dramatic changes over the past two decades as a study case, we quantify the impact of LAI changes on the hydrological partitioning during 1982–2010 and predict the future influence of these changes for the 21st century using climate model projections. Results show that LAI change exhibits an increasing importance on altering hydrological partitioning as climate becomes drier. In semiarid and arid China, increased LAI has led to substantial streamflow reductions over the past three decades (on average −8.5% in 1990s and −11.7% in 2000s compared to the 1980s baseline), and this decreasing trend in streamflow is projected to continue toward the end of this century due to predicted LAI increases. Our result calls for caution regarding the large‐scale revegetation activities currently being implemented in arid and semiarid China, which may result in serious future water scarcity issues here. The analytical model developed here is physically based and suitable for simultaneously assessing both vegetation changes and climate change induced changes to streamflow globally.
Key Points
We derive an analytical solution for the impact of LAI changes on hydrological partitioning within the Budyko framework
Sensitivity of hydrological partitioning to changes in LAI increases with the increase of climate aridity
Impacts of past and future vegetation and climate changes on streamflow across China were assessed
► Penman’s potential evapotranspiration (Ep) is a fully-physically based model of Ep. ► Attributed changes in Penman’s Ep due to changes in four key meteorological variables. ► Fitted Choudhury’s ...parameter (n) using the precipitation (P), Ep and streamflow (Q). ► Analytical assessed the sensitivity of Q to changes in P, Ep and n. ► Explained reasons for Q changes using Budyko framework in a large water-limited basin.
Potential evaporation (Ep) reflects the combined effects of four key meteorological variables: (i) net radiation (Rn); (ii) wind speed (u); (iii) relative humidity (rh); and (iv) air temperature (Ta). Here, attribution analysis was conducted to investigate the contribution of the four key meteorological variables to changes of a physically-based Ep in a large water-limited basin, the Yellow River Basin (YRB), China. Then the influences of these changes, and precipitation (P) changes, on streamflow (Q) were explored analytically. Results show that: (i) Ep presented different temporal trends for the water yielding region (WYR) and water consuming region (WCR) with a overall changes of +0.16mma−2 and −0.66mma−2 during 1961–2010, respectively; (ii) trend analysis of Ep and the four key meteorological variables at the basin scale showed that increasing trend in Ta increased Ep during 1961–2010, while changes in Rn and u increased the 1961–1979 Ep rate and reduced it during 1980–1994 and 1995–2010; (iii) revealed by attribution analysis, Ep increased by changes in Ta and rh and reduced by changes of Rn and u in both WYR and WCR, in all, Ep rate presented positive and negative trends in the WYR and WCR, respectively; (iv) the changes of Q and actual evaporation (E) are much more sensitive to changes in P than the changes in Ep; and (v) of critical importance for water resource management of the YRB changes in Q are mainly attributed to changes in catchment-specific parameter (n) and P, while Ep reduced Q in WYR and increased Q in WCR. These results indicated that the causes of trend of Ep rates, influenced by combined effects of radiative and aerodynamic variables should be explicitly explained using fully physically based Ep formulations. Additionally, in the water-limited YRB, changes of Q are primarily controlled by the changes in catchment conditions, and secondarily by hydroclimatic factors where the available water (P) rather than energy condition (Ep) is more important. Better understanding all of these relationships and how they have varied will help water resource management in a changing climate.
Accurate quantification of terrestrial evapotranspiration (ET) is essential to understand the Earth's energy and water budgets under climate change. However, despite water and carbon cycle coupling, ...there are few diagnostic global evapotranspiration models that have complete carbon constraint on water flux run at a high spatial resolution. Here we estimate 8-day global ET and gross primary production (GPP) at 500 m resolution from July 2002 to December 2017 using a coupled diagnostic biophysical model (called PML-V2) that, built using Google Earth Engine, takes MODIS data (leaf area index, albedo, and emissivity) together with GLDAS meteorological forcing data as model inputs. PML-V2 is well calibrated against 8-day measurements at 95 widely-distributed flux towers for 10 plant functional types, indicated by Root Mean Square Error (RMSE) and Bias being 0.69 mm d−1 and −1.8% for ET respectively, and being 1.99 g C m−2 d−1 and 4.2% for GPP. Compared to that performance, the cross-validation results are slightly degraded, with RMSE and Bias being 0.73 mm d−1 and −3% for ET, and 2.13 g C m−2 d−1 and 3.3% for GPP, which indicates robust model performance. The PML-V2 products are noticeably better than most GPP and ET products that have a similar spatial resolution, and suitable for assessing the influence of carbon-induced impacts on ET. Our estimates show that global ET and GPP both significantly (p < 0.05) increased over the past 15 years. Our results demonstrate it is very promising to use the coupled PML-V2 model to improve estimates of GPP, ET and water use efficiency, and its uncertainty can be further reduced by improving model inputs, model structure and parameterisation schemes.
•PML-V2 performs well for both 8-day ET and GPP at 95 flux tower sites.•PML-V2 is similar or better that the state-of-the-art ET and GPP products.•Global coupled estimates of ET and GPP at 500 m resolution in the period of 2002–2017•Global ET and GPP are both significantly increased over the past 15 years.
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