Abstract:Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), firstly future daily temperatures and precipitation are predicted using two statistical downscaling methods: the classical statistical downscaling model (SDSM), augmented by a trend-preserving bias correction, and a two-step updated quantile mapping (QM) method. The general circulation models (GCM) input to SDSM are climate predictors of the Canadian Earth System Model (CanESM2) GCM under the representative concentration pathway (RCP) emission scenarios, RCP45 and RCP85, whereas that to the QM is provided by the most suitable of several Climate Model Intercomparison Project Phase 5 (CMIP5) GCMs under RCP60, in addition. The performances of the two downscaling methods are compared to each other for a past "future" period (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) and the QM is found to be better and so is selected in the subsequent ZR streamflow simulations by means of the Soil and Water Assessment Tool (SWAT) hydrological model, calibrated and validated for the reference period . The impacts of climate change on the hydrologic response of the river basin, specifically the inflow to the Boukan Reservoir, the reservoir-dependable water release (DWR), are then compared for the three RCPs in the near-(2020-2038), middle-(2050-2068) and far-(2080-2098) future periods assuming (1) the "current" consumptive demand to be continued in the future, and (2) a more conservative "recommended" demand. A systematic future shortage of the available water is obtained for case (1) which can be mitigated somewhat for (2). Finally, the SWAT-predicted ZRB outflow is compared with the Montana-based estimated environmental flow of the ZR. The latter can successfully be sustained at good and fair levels for the near-and middle-future periods, but not so for the summer months of the far-future period, particularly, for RCP85.

For water-stressed regions/countries, like Iran, improving the management of agricultural water-use in the wake of climate change and increasingly unsustainable demands is of utmost importance. One step further is then the maximization of the agricultural economic benefits, by properly adjusting the irrigated crop area pattern to optimally use the limited amount of water available. To that avail, a sequential hydro-economic model has been developed and applied to the agriculturally intensively used Zarrine River Basin (ZRB), Iran. In the first step, the surface and groundwater resources, especially, the inflow to the Boukan Dam, as well as the potential crop yields are simulated using the Soil Water Assessment Tool (SWAT) hydrological model, driven by GCM/QM-downscaled climate predictions for three future 21th-century periods under three climate RCPs. While in all nine combinations consistently higher temperatures are predicted, the precipitation pattern are much more versatile, leading to corresponding changes in the future water yields. Using the basin-wide water management tool MODSIM, the SWAT-simulated water available is then optimally distributed across the different irrigation plots in the ZRB, while adhering to various environmental/demand priority constraints. MODSIM is subsequently coupled with CSPSO to optimize (maximize) the agro-economic water productivity (AEWP) of the various crops and, subsequently, the net economic benefit (NEB), using crop areas as decision variables, while respecting various crop cultivation constraints. Adhering to political food security recommendations for the country, three variants of cereal cultivation area constraints are investigated. The results indicate considerably-augmented AEWPs, resulting in a future increase of the annual NEB of ~16% to 37.4 Million USD for the 65%-cereal acreage variant, while, at the same time, the irrigation water required is reduced by ~38%. This NEB-rise is achieved by augmenting the total future crop area in the ZRB by about 47%—indicating some deficit irrigation—wherefore most of this extension will be cultivated by the high AEWP-yielding crops wheat and barley, at the expense of a tremendous reduction of alfalfa acreage. Though presently making up only small base acreages, depending on the future period/RCP, tomato- and, less so, potato- and sugar beet-cultivation areas will also be increased significantly.

The impacts of climate change on the water availability of Zarrine River Basin (ZRB), the headwater of Lake Urmia, in western Iran, with the Boukan Dam, are simulated under various climate scenarios up to year 2029, using the SWAT hydrological model. The latter is driven by meteorological variables predicted from MPI-ESM-LR-GCM (precipitation) and CanESM2-GCM (temperature) GCM models with RCP 2.6, RCP 4.5 and RCP 8.5 climate scenarios, and downscaled with Quantile Mapping (QM) bias-correction and SDSM, respectively. From two variants of QM employed, the Empirical-CDF-QM model decreased the biases of raw GCM- precipitation predictors particularly strongly. SWAT was then calibrated and validated with historical (1981–2011) ZR-streamflow, using the SWAT-CUP model. The subsequent SWAT-simulations for the future period 2012–2029 indicate that the predicted climate change for all RCPs will lead to a reduction of the inflow to Boukan Dam as well as of the overall water yield of ZRB, mainly due to a 23–35% future precipitation reduction, with a concomitant reduction of the groundwater baseflow to the main channel. Nevertheless, the future runoff-coefficient shows a 3%, 2% and 1% increase, as the −2% to −26% decrease of the surface runoff is overcompensated by the named precipitation decrease. In summary, based on these predictions, together with the expecting increase of demands due to the agricultural and other developments, the ZRB is likely to face a water shortage in the near future as the water yield will decrease by −17% to −39%, unless some adaptation plans are implemented for a better management of water resources.

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The present study aimed to quantify the future sustainability of a water supply system using dynamically-downscaled regional climate models (RCMs), produced in the South Asia Coordinated Regional Downscaling Experiment (CORDEX) framework. The case study is the Boukan dam, located on the Zarrine River (ZR) of Urmia’s drying lake basin, Iran. Different CORDEX- models were evaluated for model performance in predicting the temperatures and precipitation in the ZR basin (ZRB). The climate output of the most suitable climate model under the RCP45 and RCP85 scenarios was then bias-corrected for three 19-year-long future periods (2030, 2050, and 2080), and employed as input to the Soil and Water Assessment Tool (SWAT) river basin hydrologic model to simulate future Boukan reservoir inflows. Subsequently, the reservoir operation/water demands in the ZRB were modeled using the MODSIM water management tool for two water demand scenarios, i.e., WDcurrent and WDrecom, which represent the current and the more sustainable water demand scenarios, respectively. The reliability of the dam’s water supply for different water uses in the study area was then investigated by computing the supply/demand ratio (SDR). The results showed that, although the SDRs for the WDrecom were generally higher than that of the WDcurrent, the SDRs were all <1, i.e., future water deficits still prevailed. Finally, the performance of the water supply system was evaluated by means of risk, reliability, resiliency, vulnerability, and maximum deficit indices, and the combination of the indices to estimate the Sustainability Group Index (SGI). The findings indicated that, compared to the historical period for both the water demand scenarios, WDcurrent and WDrecom, the average SGI of each RCP would be decreased significantly, particularly, for the more extreme RCP85 scenario. However, as expected, the SGI decrease for the WDrecom was less than that of the WDcurrent, indicating the advantage of implementing this more sustainable water demand scenario.