#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.

#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/