Global monthly water stress: 2. Water demand and severity of water stress

Abstract

[1] This paper assesses global water stress at a finer temporal scale compared to conventional assessments. To calculate time series of global water stress at a monthly time scale, global water availability, as obtained from simulations of monthly river discharge from the companion paper, is confronted with global monthly water demand. Water demand is defined here as the volume of water required by users to satisfy their needs. Water demand is calculated for the benchmark year of 2000 and contrasted against blue water availability, reflecting climatic variability over the period . Despite the use of the single benchmark year with monthly variations in water demand, simulated water stress agrees well with long-term records of observed water shortage in temperate, (sub)tropical, and (semi)arid countries, indicating that on shorter (i.e., decadal) time scales, climatic variability is often the main determinant of water stress. With the monthly resolution the number of people experiencing water scarcity increases by more than 40% compared to conventional annual assessments that do not account for seasonality and interannual variability. The results show that blue water stress is often intense and frequent in densely populated regions (e.g., India, United States, Spain, and northeastern China). By this method, regions vulnerable to infrequent but detrimental water stress could be equally identified (e.g., southeastern United Kingdom and northwestern Russia).

One-way coupling of an integrated assessment model and a water resources model: evaluation and implications of future changes over the US Midwest

“…Unlike the work of Wada et al (2011) who used a crop growth model to estimate monthly irrigation water requirements, crop water requirements in GCAM are computed using a simplified methodology that utilizes estimated coefficients of water requirement per crop type and AEZ from crop growth models to efficiently compute irrigation water on an annual basis (see Chaturvedi et al, 2013). This reduced form is essential to the computational feasibility of iterating food demands and prices hundreds of iterations in each GCAM time period without resorting to running a crop growth model that many times.…”

“…Wada et al (2011) suggested an R of 0.1 based on their assessments in Spain and Japan. However, this term is found to be closer to around 1.0 in the US, based on four cities that lie within four climate zones (See Fig.…”

“…Domestic water demand is temporally downscaled using the algorithm developed by Wada et al (2011). The equation is…”

“…This section presents the disaggregation to the monthly timescale. Wada et al (2011) devised a set of simple methods to map non-irrigation sectors from annual to monthly time step. We adopted their approaches for domestic, mining, livestock and manufacturing, extended the electricity generation technique, and simplified the irrigation one.…”

“…For the temporal downscaling of water demand in mining, livestock, and manufacturing sectors, a uniform distribution (1/12) is applied following the work of Wada et al (2011).…”