Humans directly change the dynamics of the water cycle through dams constructed for water storage, and through water withdrawals for industrial, agricultural, or domestic purposes. Climate change is expected to additionally affect water supply and demand. Here, analyses of climate change and direct human impacts on the terrestrial water cycle are presented and compared using a multimodel approach. Seven global hydrological models have been forced with multiple climate projections, and with and without taking into account impacts of human interventions such as dams and water withdrawals on the hydrological cycle. Model results are analyzed for different levels of global warming, allowing for analyses in line with temperature targets for climate change mitigation. The results indicate that direct human impacts on the water cycle in some regions, e.g., parts of Asia and in the western United States, are of the same order of magnitude, or even exceed impacts to be expected for moderate levels of global warming (+2 K). Despite some spread in model projections, irrigation water consumption is generally projected to increase with higher global mean temperatures. Irrigation water scarcity is particularly large in parts of southern and eastern Asia, and is expected to become even larger in the future. ISI-MIP | WaterMIPT errestrial water fluxes are affected by both climate and direct human interventions, e.g., dam operations and water withdrawals. Climate change is expected to alter the water cycle and will subsequently impact water availability and demand. Several hydrologic modeling studies have focused on climate change impacts on discharge in large river basins or global terrestrial areas under naturalized conditions using a single hydrologic model forced with multiple climate projections (1, 2). Recently, hydrological projections from eight global hydrological models (GHMs) were compared (3). In many areas, there was a large spread in projected runoff changes within the climate-hydrology modeling chain. However, at high latitudes there was a clear increase in runoff, whereas some midlatitude regions showed a robust signal of reduced runoff. The study also concluded that the choice of GHM adds to the uncertainty for hydrological change caused by the choice of atmosphere-ocean general circulation models (hereafter called GCMs) (3). Expected runoff increases in the north and decreases in parts of the middle latitudes have been found also when analyzing runoff from 23 GCMs (4).These studies focused on the naturalized hydrological cycle, i.e., the effects of direct human interventions were not taken into account. However, in many river basins humans substantially alter the hydrological cycle by constructing dams and through water withdrawals. Reservoir operations alter the timing of discharge, although mean annual discharge does not necessarily change much. A study with the water balance model (WBM) showed that the impact of human disturbances, i.e., dams and water consumption, in some river basins is equal to or greater...
Peer reviewed eScholarship.orgPowered by the California Digital Library University of California largest absolute correlations with the label. However, he or she verifies the correlations (with the label) on the holdout set and uses only those variables whose correlation agrees in sign with the correlation on the training set and for which both correlations are larger than some threshold in absolute value. The analyst then creates a simple linear threshold classifier on the selected variables using only the signs of the correlations of the selected variables. A final test evaluates the classification accuracy of the classifier on the holdout set. Full details of the analyst's algorithm can be found in section 3 of (17). In our first experiment, each attribute is drawn independently from the normal distribution N(0,1), and we choose the class label y ∈ f−1; 1g uniformly at random so that there is no correlation between the data point and its label. We chose n = 10,000 and d = 10,000 and varied the number of selected variables k. In this scenario no classifier can achieve true accuracy better than 50%. Nevertheless, reusing a standard holdout results in reported accuracy of >63 ± 0.4% for k = 500 on both the training set and the holdout set. The average and standard deviation of results obtained from 100 independent executions of the experiment are plotted in Fig. 1A, which also includes the accuracy of the classifier on another fresh data set of size n drawn from the same distribution. We then executed the same algorithm with our reusable holdout. The algorithm Thresholdout was invoked with T = 0.04 and t = 0.01, which explains why the accuracy of the classifier reported by Thresholdout is off by up to 0.04 whenever the accuracy on the holdout set is within 0.04 of the accuracy on the training set. Thresholdout prevents the algorithm from overfitting to the holdout set and gives a valid estimate of classifier accuracy. In Fig. 1B, we plot the accuracy of the classifier as reported by Thresholdout. In addition, in fig. S2 we include a plot of the actual accuracy of the produced classifier on the holdout set.In our second experiment, the class labels are correlated with some of the variables. As before, the label is randomly chosen from {-1,1} and each of the attributes is drawn from N(0,1), aside from 20 attributes drawn from N(y·0.06,1), where y is the class label. We execute the same algorithm on this data with both the standard holdout and Thresholdout and plot the results in Fig. 2. Our experiment shows that when using the reusable holdout, the algorithm still finds a good classifier while preventing overfitting.Overfitting to the standard holdout set arises in our experiment because the analyst reuses the holdout after using it to measure the correlation of single attributes. We first note that neither cross-validation nor bootstrap resolve this issue. If we used either of these methods to validate the correlations, overfitting would still arise as a result of using the same data for training and validation (...
[1] Crop irrigation is responsible for 70% of humanity's water demand. Since the late 1990s, the expansion of irrigated areas has been tapering off, and this trend is expected to continue in the future. Future irrigation water demand (IWD) is, however, subject to large uncertainties due to anticipated climate change. Here, we use a set of seven global hydrological models (GHMs) to quantify the impact of projected global climate change on IWD on currently irrigated areas by the end of this century, and to assess the resulting uncertainties arising from both the GHMs and climate projections. The resulting ensemble projections generally show an increasing trend in future IWD, but the increase varies substantially depending on the degree of global warming and associated regional precipitation changes. Under the highest greenhouse gas emission scenario (RCP8.5), IWD will considerably increase during the summer in the Northern Hemisphere (>20% by 2100), and the present peak IWD is projected to shift one month or more over regions where ≥80% of the global irrigated areas exist and 4 billion people currently live. Uncertainties arising from GHMs and global climate models (GCMs) are large, with GHM uncertainty dominating throughout the century and with GCM uncertainty substantially increasing from the midcentury, indicating the choice of GHM outweighing by far the uncertainty arising from the choice of GCM and associated emission scenario. Citation: Wada, Y., et al. (2013), Multimodel projections and uncertainties of irrigation water demand under climate change, Geophys. Res. Lett., 40,[4626][4627][4628][4629][4630][4631][4632]
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