Introducing water security indicator model version 3

11 October 2023

Version 3 Highlights

ISciences has transitioned production of our monthly Global Water Monitor & Forecast Watch List reports to use outputs from a new version of our Water Security Indicator Model (WSIM). Version 3 (v3) improves WSIM in the following ways:

Uses temperature and precipitation drivers from ECMWF Reanalysis v5 (ERA5) instead of gridded station data published by NOAA’s Climate Prediction Center. This sharpens spatial resolution from half-degree to quarter-degree and improves fidelity in sparsely instrumented regions due to physics-based data assimilation from multiple sources. Table 1 below documents the evolution of data inputs to WSIM over time.

Changes the baseline period for computing statistical distributions from 1950-2009 (60 years) to 1981-2020 (40 years) to rely more exclusively on data from the satellite era. This reduces the effects of inherent uncertainties in the older data at the expense of a shorter baseline period for calculating return periods.

Rounds calculated depth measurements (soil moisture, runoff, potential - actual evapotranspiration) to the nearest 0.1 mm and volume measurements (total blue water) to the nearest 10,000 m3 before fitting distributions. This reduces the effect of computational artifacts in arid regions where insignificantly small variations in the surface water budget can produce deceptively large return periods..

WSIMv3 maintains the central premise of WSIMv2 and WSIMv1 – that populations structure their activities based upon expected climatic provisions of fresh water, and are able to maintain these activities in response to a certain degree of variation in the amount, frequency, and timing of fresh water provisions. When variation exceeds that experienced in the historical record, populations may be forced to react in unexpected ways. While the relationship between climatic stresses and populations is complex, these reactions could induce transnational water disputes, agricultural shortfalls, electricity shortages, population displacements, infectious disease outbreaks, and perhaps political instability. The premise that societies react to unusual changes in the hydrology of their particular location motivates WSIM to characterize water stresses in terms of anomalies – deviations of actual from expected conditions – expressed as return periods.

As with WSIMv2, WSIMv3 has been published as an open-source toolkit that is available on Docker Hub and GitLab, with full documentation and examples published at wsim.isciences.com. It is primarily written in R with selected components written in C++, Python, and bash.

Comparisons Between Version 2 and Version 3

Figure 1: Global comparison of long-term (12-month integration period) composite surface water anomalies. WSIMv2 results are shown on the left and WSIMv3 results on the right. The top row depicts long-term composite anomalies as of August 2023, and the bottom row depicts the forecast for May 2024. Note the the increased spatial detail of WSIMv3.

Figure 2: Comparison of seasonal (3-month integration period) composite anomalies for the United States. WSIMv2 results are shown on the left and WSIMv3 results on the right. The first row depicts seasonal anomalies as of August 2023 and the subsequent rows depict forecasts for November 2023, February 2024, and May 2024.

Figure 3: Comparison of seasonal (3-month integration period) composite anomalies for the United States during calendar year 2011. The column on the left shows the results of applying the WSIM statistics to the NASA’s Global Land Data Assimilation System V2.0 NOAH results using a 1950-2009 baseline period (publicly available at NASA’s Socioeconomic Data and Applications Center). The middle column shows the results for WSIMv2 (1950-2009 baseline period), and the right columns shows the results for WSIMv3 (2081-2023 baseline period).

DATA INPUTS

Some of the data sets and associated processing that are input to WSIM for production of the Global Water Monitor & Forecast Watch List reports have changed between WSIMv2 and WSIMv3. Table 1 lists WSIM data requirements and changes to the data sources made in WSIMv3.

DATA TABLE
Table 1. WSIM data requirements and data source changes between WSIMv2 and WSIMv3.

Data	Source WSIMv1	Source WSIMv2 (CPC)	Source WSIMv3 (ERA5)
Temperature (monthly, observed)	Global Historical Climatology Network (GHCN) + Climate Anomaly Monitoring System (CAMS) [1]	No change	ERA5 [2]
Precipitation (monthly, observed), Number of wet days	NOAA’s PRECipitation REConstruction over Land (PREC/L) [3]	No change	ERA5
Precipitation (daily, observed)	NOAA>NOAA/CPC Unified Gauge-Based Analysis of Global Daily Precipitation [4]	No change	ERA5
Precipitation (monthly, forecast)	NOAA’s Climate Forecast System Version 2 (CFSv2) [5]	No change	No change
Temperature (monthly, forecast)	NOAA’s Climate Forecast System Version 2 (CFSv2)	No change	No change
Soil water holding capacity	Harmonized World Soil Database v1.1 [6] ISRIC WISE v3.1 soil profile database [7]	ISRIC WISE Derived Soil Properties [8]	No change
Basin Delineation	Global Drainage Basin Database (GDBD) [9]	HydroBASINS [10]	No change
Flow Direction Grids	ISLSCP II Simulated Topological Network (STN-30P) [11]	No change	University of Montana DRT [14]
Terrain Elevation	SRTM30 global enhanced elevation [12]	GMTED2010 [13]	No change

[1] Fan, Y. & van den Dool, H. (2008). A global monthly land surface air temperature analysis for 1948-present. Journal of Geophysical Research, 113. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007JD008470

[2] European Centre for Medium-Range Weather Forecasts. ECMWF Reanalysis V5. https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5.

[3] Chen, M., Xie, P., Janowiak, J.E. , & Arkin, P.A. (2002). Global Land Precipitation: A 50-yr monthly analysis based on gauge observations. Journal of Hydrometeorology, 3, 249-266. https://doi.org/10.1175/1525-7541(2002)003%3C0249:GLPAYM%3E2.0.CO;2, & https://psl.noaa.gov/data/gridded/data.precl.html.

[4] CPC Global Unified Gauge-Based Analysis of Daily Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html

[5] Saha, S., et. al (2014). The NCEP climate forecast system version 2. Journal of Climate, 27, 2185-2208. https://doi.org/10.1175/JCLI-D-12-00823.1

[6] FAO, IIASA, ISRIC, ISS-CAS, JRC. (2009). Harmonized World Soil Database (version 1.1). FAO: Rome, Italy and IIASA: Laxenburg, Austria. https://www.fao.org/documents/card/es/c/2fa14e5e-ae97-516e-9dd2-24bc7abbc823/

[7] Batjes, N.H. (2008). ISRIC-WISE harmonized global soil profile dataset (Ver. 3.1). (2008). ISRIC – World Soil Information Center: Wageningen, The Netherlands. https://data.isric.org/geonetwork/srv/api/records/a351682c-330a-4995-a5a1-57ad160e621c

[8] Batjes, N.H. (2016). Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma. 269(1): 61-68. https://doi.org/10.1016/j.geoderma.2016.01.034

[9] Masutomi, Y., Inui, Y., Takahashi, K., & Matsuoka, Y. (2009). Development of highly accurate global polygonal drainage basin data. Hydrological Processes. 23, 572–584. https://doi.org/10.1002/hyp.7186

[10] Lehner, B. & Grill, G. (2013). Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15), 2171–2186. https://doi.org/10.1002/hyp.9740

[11] Vorosmarty, C.J., B.M. Fekete, F.G. Hall, G.J. Collatz, B.W. Meeson, S.O. Los, E.Brown De Colstoun, and D.R. Landis. 2011. ISLSCP II River Routing Data (STN-30p). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1005

[12] ISciences, LLC. (2003). TerraViva! SRTM30 global enhanced: elevation, slope, aspect. ISciences, Ann Arbor, MI.

[13] US Geological Survey, Earth Resources Observation and Science (EROS) Center (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010). https://doi.org/10.3133/ofr20111073 & https://www.usgs.gov/coastal-changes-and-impacts/gmted2010

[14] Wu, H., J.S. Kimball, H. Li, M. Huang, L.R. Leung, and R.F. Adler, 2012. A new global river network database for macroscale hydrologic modeling. Water Resources Research, 48, W09701. & “Dominant River Tracing.” https://www.umt.edu/numerical-terradynamic-simulation-group/project/drt.php

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