Constraints and potentials of future irrigation water availability on agricultural production under climate change

Elliott, J., D. Deryng, C. Müller, K. Frieler, M. Konzmann, D. Gerten, M. Glotter, M. Flörke, Y. Wada, N. Best, S. Eisner, B.M. Fekete, C. Folberth, I. Foster, S.N. Gosling, I. Haddeland, N. Khabarov, F. Ludwig, Y. Masaki, S. Olin, C. Rosenzweig, A.C. Ruane, Y. Satoh, E. Schmid, T. Stacke, Q. Tang, and D. Wisser, 2013: “Constraints and potentials of future irrigation water availability on agricultural production under climate change.” Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1222474110.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400–1,400 Pcal (8–24% of present-day total) when CO2 fertilization effects are accounted for or 1,400–2,600 Pcal (24–43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20–60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600–2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

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Traits of surface water pollution under climate and land use changes: A remote sensing and hydrological modeling approach

Jordan, Y.C., A. Ghulam, and S. Hartling, 2014: “Traits of surface water pollution under climate and land use changes: A remote sensing and hydrological modeling approach.” Earth-Science Reviews, v. 128, pp. 181-195, doi: 10.1016/j.earscirev.2013.11.005.

In this paper, spatial and temporal trajectories of land cover/land use change (LCLUC) derived from Landsat data record are combined with hydrological modeling to explore the implication of vegetation dynamics on soil erosion and total suspended sediment (TSS) loading to surface rivers. The inter-annual coefficient of variation (CoV) of normalized difference vegetation index (NDVI) is used to screen the LCLUC and climate change. The Soil and Water Assessment Tool (SWAT) is employed to identify the monthly TSS for two times interval (1991 to 2001 and 2001 to 2011) at subbasin levels. SWAT model is calibrated from 1991 to 2001 and validated from 2002 to 2011 at three USGS gauging sites located in the study area. The Spearman’s rank correlation of annual mean TSS is used to assess the temporal trends of TSS dynamics in the subbasins in the two study periods. The spatial correlation among NDVI, LCLUC, climate change and TSS loading rate changes is quantified by using linear regression model and negative/positive trend analysis. Our results showed that higher rainfall yields contribute to higher TSS loading into surface waters. A higher inter-annual accumulated vegetation index and lower inter-annual CoV distributed over the uplands resulted in a lower TSS loading rate, while a relatively low vegetation index with larger CoV observed over lowlands resulted in a higher TSS loading rate. The TSS loading rate at the basin outlet increased with the decrease of annual NDVI due to expanding urban areas in the watershed. The results also suggested nonlinearity between the trends of TSS loading with any of a specific land cover change because of the fact that the contribution of a factor can be influenced by the effects of other factors. However, dominant factors that shape the relationship between the trend of TSS loading and specific land cover changes were detected. The change of forest showed a negative relationship while agriculture and pasture demonstrated positive relationships with TSS loading change. Our results do not show any significant causal relationship between urbanization and the TSS loading change suggesting that further investigation needs to be carried out to understand the mechanism of the impact of urban sprawl on surface water quality.

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Multi-model assessment of water scarcity under climate change

Schewe, J., J. Heinke, D. Gerten, I. Haddeland, N.W. Arnell, D.B. Clarke, R. Dankers, S. Eisner, B.M. Fekete, F.J. Colón-González, S.M. Gosling, H. Kim, X. Liu, Y. Masaki, F.T. Portmann, Y. Satoh, T. Stacke, Q. Tang, Y. Wada, D. Wisser, T. Albrecht, K. Frieler, F. Piontek, L. Warszawski, and P. Kabat, 2013: “Multi-model assessment of water scarcity under climate change.” Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1222460110.

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.

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First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble

Dankers, R., N.W. Arnell, D.B. Clark, P.D. Falloon, B.M. Fekete, S.N. Gosling, J. Heinke, H. Kim, Y. Masaki, Y. Satoh, T. Stacke, Y. Wada, and D. Wisser, 2013: “First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble.” Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1302078110.

Climate change due to anthropogenic greenhouse gas emissions is expected to increase the frequency and intensity of precipitation events, which is likely to affect the probability of flooding into the future. In this paper we use river flow simulations from nine global hydrology and land surface models to explore uncertainties in the potential impacts of climate change on flood hazard at global scale. As an indicator of flood hazard we looked at changes in the 30-y return level of 5-d average peak flows under representative concentration pathway RCP8.5 at the end of this century. Not everywhere does climate change result in an increase in flood hazard: decreases in the magnitude and frequency of the 30-y return level of river flow occur at roughly one-third (20–45%) of the global land grid points, particularly in areas where the hydrograph is dominated by the snowmelt flood peak in spring. In most model experiments, however, an increase in flooding frequency was found in more than half of the grid points. The current 30-y flood peak is projected to occur in more than 1 in 5 y across 5–30% of land grid points. The large-scale patterns of change are remarkably consistent among impact models and even the driving climate models, but at local scale and in individual river basins there can be disagreement even on the sign of change, indicating large modeling uncertainty which needs to be taken into account in local adaptation studies.

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Towards improved post-processing of hydrologic forecast ensembles

Madadgar, S., H. Moradkhani, and D. Garen, 2014: “Towards improved post-processing of hydrologic forecast ensembles.” Hydrological Processes, v. 28, pp. 104–122, doi: 10.1002/hyp.9562.

Forecast ensembles of hydrological and hydrometeorologial variables are prone to various uncertainties arising from climatology, model structure and parameters, and initial conditions at the forecast date. Post-processing methods are usually applied to adjust the mean and variance of the ensemble without any knowledge about the uncertainty sources. This study initially addresses the drawbacks of a commonly used statistical technique, quantile mapping (QM), in bias correction of hydrologic forecasts. Then, an auxiliary variable, the failure index (γ), is proposed to estimate the ineffectiveness of the post-processing method based on the agreement of adjusted forecasts with corresponding observations during an analysis period prior to the forecast date. An alternative post-processor based on copula functions is then introduced such that marginal distributions of observations and model simulations are combined to create a multivariate joint distribution. A set of 2500 hypothetical forecast ensembles with parametric marginal distributions of simulated and observed variables are post-processed with both QM and the proposed multivariate post-processor. Deterministic forecast skills show that the proposed copula-based post-processing is more effective than the QM method in improving the forecasts. It is found that the performance of QM is highly correlated with the failure index, unlike the multivariate post-processor. In probabilistic metrics, the proposed multivariate post-processor generally outperforms QM. Further evaluation of techniques is conducted for river flow forecast of Sprague River basin in southern Oregon. Results show that the multivariate post-processor performs better than the QM technique; it reduces the ensemble spread and is a more reliable approach for improving the forecast.

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Reconstructing the duty of water: A study of emergent norms in socio-hydrology

Wescoat, J.L., Jr., 2013: “Reconstructing the duty of water: A study of emergent norms in socio-hydrology.” Hydrology and Earth System Sciences, v. 17, pp. 4759-4768, doi: 10.5194/hess-17-4759-2013.

This paper assesses the changing norms of water use known as the duty of water. It is a case study in historical socio-hydrology, or more precisely the history of socio-hydrologic ideas, a line of research that is useful for interpreting and anticipating changing social values with respect to water. The duty of water is currently defined as the amount of water reasonably required to irrigate a substantial crop with careful management and without waste on a given tract of land. The historical section of the paper traces this concept back to late 18th century analysis of steam engine efficiencies for mine dewatering in Britain. A half-century later, British irrigation engineers fundamentally altered the concept of duty to plan large-scale canal irrigation systems in northern India at an average duty of 218 acres per cubic foot per second (cfs). They justified this extensive irrigation standard (i.e., low water application rate over large areas) with a suite of social values that linked famine prevention with revenue generation and territorial control. The duty of water concept in this context articulated a form of political power, as did related irrigation engineering concepts such as “command” and “regime”. Several decades later irrigation engineers in the western US adapted the duty of water concept to a different socio-hydrologic system and norms, using it to establish minimum standards for private water rights appropriation (e.g., only 40 to 80 acres per cfs). While both concepts of duty addressed socio-economic values associated with irrigation, the western US linked duty with justifications for, and limits of, water ownership. The final sections show that while the duty of water concept has been eclipsed in practice by other measures, standards, and values of water use efficiency, it has continuing relevance for examining ethical duties and for anticipating, if not predicting, emerging social values with respect to water.

Open Access

Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

Sun, Y., Z. Hou, M. Huang, F. Tian, and L.R. Leung, 2013: “Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model.” Hydrology and Earth System Sciences, v. 17, pp. 4995-5011, doi: 10.5194/hess-17-4995-2013.

This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.

Open Access

Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta

Mahat, V., and A. Anderson, 2013: “Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in Southern Alberta.” Hydrology and Earth System Sciences, v. 17, pp. 4941-4956, doi: 10.5194/hess-17-4941-2013.

Rivers in Southern Alberta are vulnerable to climate change because much of the river water originates as snow in the eastern slopes of the Rocky Mountains. Changes in likelihood of forest disturbance (wildfire, insects, logging, etc.) may also have impacts that are compounded by climate change. This study evaluates the impacts of climate and forest changes on streamflow in the upper parts of the Oldman River in Southern Alberta using a conceptual hydrological model, HBV-EC (Hydrologiska Byråns attenbalansavdelning, Environment Canada), in combination with a stochastic weather generator (LARS-WG) driven by GCM (global climate model) output climate data. Three climate change scenarios (A1B, A2 and B1) are selected to cover the range of possible future climate conditions (2020s, 2050s, and 2080s). The GCM projected less than a 10% increase in precipitation in winter and a similar amount of precipitation decrease in summer. These changes in projected precipitation resulted in up to a 200% (9.3 mm) increase in winter streamflow in February and up to a 63% (31.2 mm) decrease in summer flow in June. Flow also decreased in July and August, when irrigation is important; these reduced river flows during this season could impact agriculture production. The amplification in the streamflow is mostly driven by the projected increase in temperature that is predicted to melt winter snow earlier, resulting in lower water availability during the summer. Uncertainty analysis was completed using a guided GLUE (generalized likelihood uncertainty estimation) approach to obtain the best 100 parameter sets and associated ranges of streamflows. The impacts of uncertainty in streamflows were higher in spring and summer than in winter and fall. Forest change compounded the climate change impact by increasing the winter flow; however, it did not reduce the summer flow.

Open Access

Assessing flood risk at the global scale: Model setup, results, and sensitivity

Ward, P.J., B. Jongman, F.S. Weiland, A. Bouwman, R. van Beek, M.F.P. Bierkens, W. Ligtvoet, and H.C. Winsemius, 2013: “Assessing flood risk at the global scale: Model setup, results, and sensitivity.” Environmental Research Letters, v. 8, paper no. 044019, doi: 10.1088/1748-9326/8/4/044019.

Globally, economic losses from flooding exceeded $19 billion in 2012, and are rising rapidly. Hence, there is an increasing need for global-scale flood risk assessments, also within the context of integrated global assessments. We have developed and validated a model cascade for producing global flood risk maps, based on numerous flood return-periods. Validation results indicate that the model simulates interannual fluctuations in flood impacts well. The cascade involves: hydrological and hydraulic modelling; extreme value statistics; inundation modelling; flood impact modelling; and estimating annual expected impacts. The initial results estimate global impacts for several indicators, for example annual expected exposed population (169 million); and annual expected exposed GDP ($1383 billion). These results are relatively insensitive to the extreme value distribution employed to estimate low frequency flood volumes. However, they are extremely sensitive to the assumed flood protection standard; developing a database of such standards should be a research priority. Also, results are sensitive to the use of two different climate forcing datasets. The impact model can easily accommodate new, user-defined, impact indicators. We envisage several applications, for example: identifying risk hotspots; calculating macro-scale risk for the insurance industry and large companies; and assessing potential benefits (and costs) of adaptation measures.

Open Access

Assessing ‘dangerous climate change’: Required reduction of carbon emissions to protect young people, future generations and nature

Hansen, J., P. Kharecha, M. Sato, V. Masson-Delmotte, F. Ackerman, D.J. Beerling, P.J. Hearty, O. Hoegh-Guldberg, S.-L. Hsu, C. Parmesan, J. Rockstrom, E. J. Rohling, J. Sachs, P. Smith, K. Steffen, L. Van Susteren, K. von Schuckmann, and J.C. Zachos, 2013: “Assessing ‘dangerous climate change’: Required reduction of carbon emissions to protect young people, future generations and nature.” PLOS One, doi: 10.1371/journal.pone.0081648.

We assess climate impacts of global warming using ongoing observations and paleoclimate data. We use Earth’s measured energy imbalance, paleoclimate data, and simple representations of the global carbon cycle and temperature to define emission reductions needed to stabilize climate and avoid potentially disastrous impacts on today’s young people, future generations, and nature. A cumulative industrial-era limit of ~500 GtC fossil fuel emissions and 100 GtC storage in the biosphere and soil would keep climate close to the Holocene range to which humanity and other species are adapted. Cumulative emissions of ~1000 GtC, sometimes associated with 2°C global warming, would spur “slow” feedbacks and eventual warming of 3–4°C with disastrous consequences. Rapid emissions reduction is required to restore Earth’s energy balance and avoid ocean heat uptake that would practically guarantee irreversible effects. Continuation of high fossil fuel emissions, given current knowledge of the consequences, would be an act of extraordinary witting intergenerational injustice. Responsible policymaking requires a rising price on carbon emissions that would preclude emissions from most remaining coal and unconventional fossil fuels and phase down emissions from conventional fossil fuels.

Open Access