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dc.date.accessioned2018-09-21T10:27:11Z
dc.date.available2018-09-21T10:27:11Z
dc.date.issued2018-09-21
dc.identifier.uriurn:nbn:de:hebis:34-2018092156403
dc.identifier.urihttp://hdl.handle.net/123456789/2018092156403
dc.language.isoeng
dc.subjectHydrologyeng
dc.subjectRainfall predictioneng
dc.subjectClimate changeeng
dc.subjectEconomic optimizationeng
dc.subject.ddc620
dc.titleClimate Change Impact Assessment of Basin-scale Water Allocation and Management in the Karkheh River Basin, Iraneng
dc.typeDissertation
dcterms.abstractHydrological research is intended to improve our understanding of hydrologic processes, the role of hydrological systems in providing water for ecosystems and society, and of the water cycle in the functioning of the earth system. The proper understanding of the hydrology of arid and semi-arid regions is becoming increasingly important, due to diminishing projections of water resource availability in response to global environmental changes. The main objective of this thesis study is to investigate the development and management of water in a poorly gauged basin with large spatial and temporal gaps in the climatological parameters’ database, in order to improve the economic return on water use in this sector under different climate change scenarios. The study region is the Karkheh River Basin (KRB), a semiarid region located in southwest Iran, which has seen quite a degradation of its water resources over recent decades. This thesis work is divided into four individual research studies which have been published or being in review and are as follows: 1- Swat-model based identification of watershed components in a semi-arid region with long term gaps in the climatological parameters’ database Data gaps are ubiquitous in hydro-meteorological time series, and filling these values is necessary for any subsequent use of this data in hydrological studies. Two different gap-filling techniques, Inversed Distance Weighting (IDW) and Linear Regression with the Nearest station (LRN), for estimating daily precipitation and temperature series are evaluated. Overall, it can be stated that IDW performed slightly better than LRN. The interpolated data are then used for calibration and subsequent validation of the SWAT hydrological model on observed streamflow. Finally, the impact of the construction of large Karkheh dam (KR) on the downstream river discharge is assessed. The results show that the monthly outflows of the main river before the KR became operational have the same seasonal trends. However, once the dam became operational, notable reductions of the monthly discharge of more than 50% for the two stations Pay-e-Pol and Hamidiyeh downstream of the KR are obtained. 2- SWAT-MODSIM- PSO Optimization of Multi-Crop Planning in the Karkheh River Basin, Iran, under the Impacts of Climate Change A new coupled SWAT-MODSIM-LINGO-PSO (SMLP) model is developed to optimize multi-crop pattern in the downstream sections of the Karkheh dam (KR) under historic and future climate scenarios. To that avail, firstly the SWAT model for estimating river discharge and inflow to the KR is calibrated and validated. Secondly, the projected inflow to the KR is estimated using downscaled climate data for two future climate scenarios (RCP4.5 and RCP8.5) as input to the calibrated SWAT-model. Thirdly, SWAT–estimated KR-inflows are entered into the MODSIM water allocation model to build a reservoir-irrigation system model for the downstream section of the KRB. MODSIM allocates water strictly to the different agricultural regions under the given priority weights. At the same time, the potential crop yields and the associated potential water demands for each crop are determined with SWAT, with these values being further used to optimize the actual crop yield (under deficit irrigation), using LINGO linear programing. Multiplying the latter by the area under cultivation, the total water requirement for this crop is obtained, which is then defined as a demand node value in the MODSIM customization module. The different crop cultivation areas are then the decision variables in the final, iterative MODSIM-PSO process to optimally allocate water among the different cultivation areas, such that the total agricultural economic profits over the planning time horizons become maximal. This SLMP-model is applied to historic and future climate change scenarios to determine optimal cropping pattern for the historic and near-future period (2038– 2060) under two RCP-climate scenarios. The results show that the total annual benefits will be significantly reduced from the present-day (baseline period) value of 94 million US$ to 88 and 72 Mill US$ for RCP 4.5/2038– 2060 and RCP 8.5/2038–2060 scenarios/period, respectively, i.e. a drop of about 20% for the latter. This is the price agriculture in the KRB has to pay for climate change in the near future. Interestingly, the results indicate also that even for the historic period the application of the SLMP-method results in optimized annual agricultural benefits which are about 2 million US$ higher than the actually projected ones (94 versus 92 million US$). 3- Predicting rainfall and runoff through satellite soil moisture data and SWAT modelling for a poorly gauged basin in Iran The objective of this work is twofold. Firstly, the accuracy of rainfall estimates obtained from SM2RAIN is evaluated. For this purpose, the AMSR-E soil moisture (SM) product is used as input into the SM2RAIN algorithm to estimate rainfall, called SM2R-AMSRE from now on, at 10 ground-based stations of the KRB. In order to apply the SM2RAIN algorithm, the parameters are estimated in a calibration period (1st January 2003 to 31th December 2005) and then the performances are validated in a subsequent independent period (1st January 2006 to 30th September 2006), by comparison with ground-based rainfall observations. Moreover, the quality of the SM2RAIN- rainfall estimations is evaluated further by comparing them with ground-based rainfall observations and TRMM-satellite- predicted rainfall of two versions of the 7 TRMM-TMPA-products, daily near-real-time (3B42RT) and research-grade (3B42). Secondly, the suitability of SM2R-AMSRE as input to the SWAT model for monthly streamflow simulation at 6 gauging stations of the KRB is assessed. In the preliminary step, the SWAT model parameters are calibrated with SWAT-CUP in the period 1985-1999, using ground observed climate data as input. The calibrated SWAT model is then run again for a different time period (1st January 2003 to 31th September 2006) by considering SM2R-AMSRE and ground observed rainfall datasets as input. Similarly, the two TMPA- products are employed in the calibrated-SWAT model as well to predict the monthly streamflow at the same gauging stations. The results indicate that the SWAT-predicted runoff driven by SM2R-AMSRE rainfall is in good agreement with the observations, with R2- values between 0.72 and 0.87 which is slightly less than the range obtained with the SWAT model using ground-based rainfall as input (R2 ~ 0.83-0.89). 4- Rainfall prediction with AMSR-E soil moisture products using SM2RAIN and nonlinear autoregressive networks with exogenous input (NARX) for poorly gauged basins: Application to the Karkheh river basin, Iran In this chapter a new application of the nonlinear autoregressive networks with exogenous input (NARX) neural network to better predict continuous rainfall series across the KRB is described. To this avail, changes of relative AMSR-E satellite soil moisture and measured temperature data are considered as input data in NARX to estimate the rainfall. These estimates are then compared with the ground-based observations as well as with those obtained by using the SM2RAIN approach of the previous chapter. The new NARX neural network developed here is able to approximate the daily rainfall data at 5 KRB stations in an acceptable manner, wherefore the R - values range between 0.42 and 0.76 for the testing period. From the time series of the biases obtained with the NARX and SM2RAIN prediction methods, it can be inferred that although SM2RAIN underestimates daily rainfall in many cases, this physically-based method works somewhat better than NARX, as the former produces at all stations lower biases and RMSEs than the latter.eng
dcterms.accessRightsopen access
dcterms.creatorFereidoon, Majid
dc.contributor.corporatenameKassel, Universität Kassel, Fachbereich Bauingenieur- und Umweltingenieurwesen
dc.contributor.refereeKoch, Manfred (Prof. Dr.)
dc.contributor.refereeWeltzien, Cornelia (Prof. Dr.)
dc.subject.swdHydrologieger
dc.subject.swdWasserreserveger
dc.subject.swdHydrologische Vorhersageger
dc.subject.swdKlimaänderungger
dc.subject.swdIranger
dc.date.examination2018-08-28


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