ALBERT

Description: 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.

Description: This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

Description: The present study aims to evaluate the capability of the Tropical Rainfall Measurement Mission (TRMM), Multi-satellite Precipitation Analysis (TMPA), version 7 (TRMM-3B42-V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus Basin (UIB) by analyzing the dependency of the estimates’ accuracies on the time scale. To that avail, various statistical analyses and comparison of Multisatellite Precipitation Analysis (TMPA) products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the aggregation time scale is analyzed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA Tropical Rainfall Measurement Mission (TRMM) precipitation estimates for the UIB, as well as high numbers of false alarms and miss ratios. The correlation of the TMPA estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e., time scale. There is a predominant trend of underestimation of the TRMM product across the UIB at most of the gauge stations, i.e., TRMM-estimated rainfall is generally lower than the gauge-measured rainfall. For the seasonal aggregates, the bias is mostly positive for the summer but predominantly negative for the winter season, thereby showing a slight overestimation of the precipitation in summer and underestimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA estimates and gauge data, the use of the former in hydrological watershed modeling undertaken by the authors may be a valuable alternative in data-scarce regions like the UIB, but still must be taken with a grain of salt.

Description: 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

Description: Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR–E) generated by implementing the standard National Aeronautics and Space Administration (NASA) algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in south-west Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into training (1 January 2003 to 31 September 2005) and testing (1 September 2005 to 30 September 2006) stages. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR–E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the coefficient of determination R2, the RMSE and the statistical bias. For the SM2RAIN method, R2 ranges between 0.32 and 0.79 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. Moreover, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN–CDR) is employed to evaluate its potential for predicting the ground-based observed station rainfall, but it is found to work poorly. In conclusion, the results of the present study show that with the use of AMSR–E soil moisture products in the physically based SM2RAIN algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be predicted satisfactorily.

Description: The current study applied a new approach for the interpolation and regionalization of observed precipitation series to a smaller spatial scale (0.125° by 0.125° grid) across the Upper Indus Basin (UIB), with appropriate adjustments for the orographic effect and changes in glacier storage. The approach is evaluated and validated through reverse hydrology, and is guided by observed flows and the available knowledge base. More specifically, the generated corrected precipitation data is validated by means of SWAT-modelled responses of the observed flows to the different input precipitation series (original and corrected ones). The results show that the SWAT-simulated flows using the corrected, regionalized precipitation series as input are much more in line with the observed flows than those using the uncorrected observed precipitation input for which significant underestimations are obtained.

Description: Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² 〉 0.71 and NSE 〉 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 〈 R2 〈 0.72 and−0.06 〈 NSE 〈 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

Description: 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.