Multimodel projections and uncertainties of irrigation water demand under climate change

Wada, Yoshihide; Wisser, Dominik; Eisner, Stephanie; Flörke, Martina; Gerten, Dieter; Haddeland, Ingjerd; Hanasaki, Naota; Masaki, Yoshimitsu; Portmann, Felix T.; Stacke, Tobias; Tessler, Z. D.; Schewe, Jacob

doi:10.1002/grl.50686

Cited by 337 publications

(295 citation statements)

References 34 publications

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“…In 2055, there was no expressive IWR, and the production will probably be little affected by variations in the rainfall regime. However, according to Wada et al (2013), future water demands for irrigation (IWR) are subject to great uncertainties owing to anticipation of climate and changes in precipitation variability.…”

Section: Future Irrigation Water Requirements (Iwrs)mentioning

confidence: 99%

Future irrigation water requirements in the Ijuí River basin, RS

Silva

Siqueira

Adam

et al. 2018

Rev. bras. eng. agríc. ambient.

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A B S T R A C TChanges in temperature and precipitation intensity and frequency have influenced the water demand for irrigation. Regions that have agriculture-based economies, as in the Ijuí River basin, are often affected by periods of drought or excessive rainfall, which is harmful for agricultural productivity. This study aimed to evaluate future irrigation water demands of four crops in this basin (bean, corn, wheat and soybean), comparing them with a baseline period. Meteorological data forecasts were obtained from the regional climate model ETA 40 CTRL for the climatic scenario A1B, for the baseline and future (2011-2100) periods. The one-dimensional SWAP model was used to estimate the water demand for irrigation. The results showed that, in the future, irrigation water requirements will be smaller for all crops. In the short term , water demands were similar to those for the baseline period, but from the middle of the century onwards (2041-2100), greater reductions were observed.Demandas futuras de água para irrigação na bacia hidrográfica do rio Ijuí, RS R E S U M O A alteração de temperatura e da intensidade e frequência dos índices pluviométricos tem influenciado a demanda de água para irrigação. Regiões que possuem sua economia voltada para a agricultura, como na bacia hidrográfica do rio Ijuí, são frequentemente afetadas por períodos de estiagem ou de excesso de chuva que prejudicam a produtividade agrícola. O presente trabalho teve como objetivo estimar as demandas futuras de água para irrigação em quatro culturas nesta bacia (feijão, milho, trigo e soja), comparando com um cenário atual. Os dados das projeções meteorológicas foram obtidos por meio do modelo climático regional ETA 40 CTRL, em um cenário futuro A1B, referentes ao período atual (1961-1990) e futuro (2011-2100). O modelo unidimensional SWAP foi utilizado para simular as demandas de água para irrigação. Os resultados mostraram que no futuro a necessidade de irrigação será menor para todas as culturas analisadas. Em curto prazo , as demandas se mantiveram próximo ao período atual. Contudo, da metade do século em diante (2041-2100) houve maiores reduções.

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Section: Future Irrigation Water Requirements (Iwrs)mentioning

confidence: 99%

Future irrigation water requirements in the Ijuí River basin, RS

Silva

Siqueira

Adam

et al. 2018

Rev. bras. eng. agríc. ambient.

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“…Neglecting the reaction of crops to increased CO 2 may lead to an overestimation of future irrigation water demand Elliott et al 2014;Gerten et al 2014). In the multi-model study of Wada et al (2013), the only model that considered CO 2 effects on crop photosynthesis and transpiration shows a decreasing trend in future irrigation water demand (about 10 % by the end of this century) and increasing yields, while model runs without CO 2 effect indicated pronounced increases in future irrigation water demand ([20 % by the end of this century).…”

Section: Quantification Of the Role Of Active Vegetation Under Changimentioning

confidence: 99%

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

et al. 2015

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Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) series of water availability and use are needed to provide good indicators of water scarcity.(6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

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“…In rain-fed cropland or irrigation-free seasons, evapotranspiration has a complex dependence on meteorological conditions (Wang and Dickinson, 2012) because E act also depends on the soil moisture content (Eq. 3).…”

Section: Sensitivity Experiments With Hypothetical Bias In Humiditymentioning

confidence: 99%

“…Recently, studies on irrigation water published as ISI-MIP Fast Track results have reported future changes in its seasonality (Wada et al, 2013) and the possibility of reduced water availability in river basins due to increasing demand for irrigation water . In both papers, the authors reported that there are large differences in the future projections of hydrological elements among the GHMs.…”

Section: Caveats On Different Sensitivities Of Evapotranspiration To mentioning

confidence: 99%

“…In Asia, a larger proportion of abstracted water is consumed through evapotranspiration (52 % of the total human withdrawal and 59 % of the total agricultural withdrawal) than the global average. Moreover, the volume of irrigation water is expected to increase in the future because of an increase in evapotranspiration from cropland under warmer climates (e.g., Wada et al, 2013) and the expansion of irrigated cropland to meet the increasing demand for food owing to the increase in the world population (e.g., Bruinsma, 2011;Elliott et al, 2014). Precise estimation of the amount of irrigation water abstraction is crucial for the sustainable use of available water in the future.…”

Section: Introductionmentioning

confidence: 99%

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Propagation of biases in humidity in the estimation of global irrigation water

et al. 2015

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Abstract. Future projections on irrigation water under a changing climate are highly dependent on meteorological data derived from general circulation models (GCMs). Since climate projections include biases, bias correction is widely used to adjust meteorological elements, such as the atmospheric temperature and precipitation, but less attention has been paid to biases in humidity. Hence, in many cases, uncorrected humidity data have been directly used to analyze the impact of future climate change. In this study, we examined how the biases remaining in the humidity data of five GCMs propagate into the estimation of irrigation water demand and consumption from rivers using the global hydrological model (GHM) H08. First, to determine the effects of humidity bias across GCMs, we ran H08 with GCM-based meteorological forcing data sets distributed by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). A state-of-the-art bias correction method was applied to the data sets without correcting biases in humidity. Differences in the monthly relative humidity amounted to 11.7 to 20.4 % RH (percentage relative humidity) across the GCMs and propagated into differences in the estimated irrigation water demand, resulting in a range between 1152.6 and 1435.5 km 3 yr −1 for 1971-2000. Differences in humidity also propagated into future projections. Second, sensitivity analysis with hypothetical humidity biases of ±5 % RH added homogeneously worldwide revealed the large negative sensitivity of irrigation water abstraction in India and East China, which are heavily irrigated. Third, we performed another set of simulations with bias-corrected humidity data to examine whether bias correction of the humidity can reduce uncertainties in irrigation water across the GCMs. The results showed that bias correction, even with a primitive methodology that only adjusts the monthly climatological relative humidity, helped reduce uncertainties across the GCMs: by using bias-corrected humidity data, the uncertainty ranges of irrigation water demand across the five GCMs were successfully reduced from 282.9 to 167.0 km 3 yr −1 for the present period, and from 381.1 to 214.8 km 3 yr −1 for the future period (RCP8.5, 2070-2099). Although different GHMs have different sensitivities to atmospheric humidity because different types of potential evapotranspiration formulae are implemented in them, bias correction of the humidity should be applied to forcing data, particularly for the evaluation of evapotranspiration and irrigation water.

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Multimodel projections and uncertainties of irrigation water demand under climate change

Cited by 337 publications

References 34 publications

Future irrigation water requirements in the Ijuí River basin, RS

Future irrigation water requirements in the Ijuí River basin, RS

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Propagation of biases in humidity in the estimation of global irrigation water

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