Stochastic generation of daily streamflow data incorporating land use and/or climate change effects

PhD ThesisIn the stochastic hydrology literature, suitable time series modelling approaches have been developed for modelling daily streamflow. However, problems arise with this approach if changes are occurring to the precipitation regime generating the historic streamflow data, or if land-use changes are occurring within the catchment which may alter the water balance and the streamflow regime. Traditional time series modelling approaches employ historic streamflow data only and will generate synthetic data which are representative only of the historic conditions. It is not possible to predict how the model parameters should be changed to reflect changes in the climate (precipitation) and catchment response regimes. Developing a methodology to deal with the stochastic generation of daily streamflow that reflects changes to the catchment system and climatic inputs (rainfall and potential evapotranspiration) and then applying the corresponding methodology to a study catchment (upper Thames) in England is the focus of this study. To study the water resources impacts of land-use change on the daily streamflow regime of a catchment, a daily rainfall-runoff model is needed which can accommodate various land cover characteristics and provide separate estimates of potential and actual evapotranspiration in its evapotranspiration component for each land cover type. Given a model with this capability, the impacts of various land-use scenarios on daily streamflow can be investigated. In the case of climate change, since GCMs do not provide useable results on a short time scale such as a day and on a spatial scale such as a catchment of about 1000 km2, a methodology is required to predict the changes which may occur in the climate inputs of a catchment, and the resulting impacts on water resources. The approach developed here for water resources impact studies of land-use change and climate change has three main elements: (I) Two stochastic models, one for rainfall (Neyman-Scott Rectangular Pulses, NSRP, model) and the other for potential evapotranspiration (PET), are employed to generate daily rainfall and daily PET sequencesr,e spectively. Thesem odels have been validated using historic records for the study catchment. ABSTRACT ii (II) The ARNO model has been calibrated and validated using daily streamflow data for the study catchment. The evapotranspiration component of the model has been modified to obtain a satisfactory water balance. The model is then extended to include the explicit calculation of interception for different land cover types within the catchment. The runoff from these areas is then routed to the catchment outlet. The rainfall and PET models are used to generate synthetic daily input series to the modified ARNO model for present catchment land-use conditions, and overall procedure is validated using the historic streamflow record. This is then worked out using the extended model and referred to as the constructed` control' scenariow hich is used as a benchmarkf or assessingla nd-usec hange impacts on water resources for two different land-use scenarios. (III) The transient GCM climate scenarios are used as the starting point for assessing climate change impacts. Regression relationships are derived between atmospheric circulation variables and rainfall statistics used in fitting the NSRP model for present climate conditions and then used to predict the rainfall statistics for future conditions using GCM outputs. That is, the scenarios of a climate model are downscaled by a regression technique to a resolution sufficient to represent daily rainfall at the catchment scale. To generate potential evapotranspiration (PET) scenarios, an empirical equation is used to estimate PET daily values as a function of temperature, thus enabling future scenarios to be generated as a function of GCM temperature predictions. Generated rainfall and PET scenarios are used as inputs to the adapted ARNO catchment response model to generate daily streamflow data. Impact assessments using both land-use change and climate change scenarios are then carried out using a range of water resources assessment measures such as flow duration curves, cumulative run sums and storage/yield relationships, and the practical implications discussed.Iran's Ministry of Culture and Higher Education Iran University of Science and Technolog

Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran

Competition for water between agriculture and the environment is a growing problem in irrigated regions across the globe, especially in endorheic basins with downstream freshwater lakes impacted by upstream irrigation withdrawals. This study presents and applies a novel simulation-optimization (SO) approach for identifying water management strategies in such settings. Our approach combines three key features for increased exploration of strategies. First, minimum environmental flow requirements are treated as a decision variable in the optimization model, yielding more flexibility than existing approaches that either treat it as a precomputed constraint or as an objective to be maximized. Second, conjunctive use is included as a management option by using dynamically coupled surface water (WEAP) and groundwater (MODFLOW) simulation models. Third, multi-objective optimization is used to yield entire Pareto sets of water management strategies that trade off between meeting environmental and agricultural water demand. The methodology is applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Results identify multiple strategies, i.e., combinations of minimum environmental flow requirements, deficit irrigation, and crop selection, that simultaneously increase environmental flow (up to 16 %) and agricultural profit (up to 24 %) compared to historical conditions. Results further show that significant temporary drops in agricultural profit occur during droughts when long-term profit is maximized, but that this can be avoided by increasing groundwater pumping capacity and temporarily reducing the lake's minimum environmental flow requirements. Such a strategy is feasible during moderate droughts when resulting declines in groundwater and lake water levels fully recover after each drought. Overall, these results demonstrate the usefulness and flexibility of the methodology in identifying a range of potential water management strategies in complex irrigated endorheic basins like the Lake Urmia basin.</p

A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts

This study develops and applies the first coupled surface water-groundwater (SW-GW) flow model for the irrigated Miyandoab plain located in the Urmia basin, in the northwest of Iran. The model is implemented using a dynamic coupling between MODFLOW and WEAP and consists of spatially distributed monthly water balances for the aquifer, root-zone, rivers, canals, and reservoirs. Multi-objective calibration of the model using river discharge and GW level data yields accurate simulation of historical conditions, and results in better constrained parameters compared to using either data source alone. Model simulations show that crop water demand cannot be met during droughts due to limited GW pumping capacity, and that increased GW pumping has a relatively strong impact on GW levels due to the small specific yield of the aquifer. The SW-GW model provides a unique tool for exploring management options that sustain agricultural production and downstream flow to the shrinking Urmia Lake

Simulation–optimization modeling for sustainable conjunctive water management in irrigated agriculture: WEAP-MODFLOW application in the Miyandoab plain, Urmia basin, Iran

Water Resource

Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran

Competition for water between agriculture and the environment is a growing problem in irrigated regions across the globe, especially in endorheic basins with downstream freshwater lakes impacted by upstream irrigation withdrawals. This study presents and applies a novel simulation-optimization (SO) approach for identifying water management strategies in such settings. Our approach combines three key features for increased exploration of strategies. First, minimum environmental flow requirements are treated as a decision variable in the optimization model, yielding more flexibility than existing approaches that either treat it as a precomputed constraint or as an objective to be maximized. Second, conjunctive use is included as a management option by using dynamically coupled surface water (WEAP) and groundwater (MODFLOW) simulation models. Third, multi-objective optimization is used to yield entire Pareto sets of water management strategies that trade off between meeting environmental and agricultural water demand. The methodology is applied to the irrigated Miyandoab Plain, located upstream of endorheic Lake Urmia in Northwestern Iran. Results identify multiple strategies, i.e., combinations of minimum environmental flow requirements, deficit irrigation, and crop selection, that simultaneously increase environmental flow (up to 16 %) and agricultural profit (up to 24 %) compared to historical conditions. Results further show that significant temporary drops in agricultural profit occur during droughts when long-term profit is maximized, but that this can be avoided by increasing groundwater pumping capacity and temporarily reducing the lake's minimum environmental flow requirements. Such a strategy is feasible during moderate droughts when resulting declines in groundwater and lake water levels fully recover after each drought. Overall, these results demonstrate the usefulness and flexibility of the methodology in identifying a range of potential water management strategies in complex irrigated endorheic basins like the Lake Urmia basin.Accepted Author ManuscriptWater Resource

A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts

This study develops and applies the first coupled surface water-groundwater (SW-GW) flow model for the irrigated Miyandoab plain located in the Urmia basin, in the northwest of Iran. The model is implemented using a dynamic coupling between MODFLOW and WEAP and consists of spatially distributed monthly water balances for the aquifer, root-zone, rivers, canals, and reservoirs. Multi-objective calibration of the model using river discharge and GW level data yields accurate simulation of historical conditions, and results in better constrained parameters compared to using either data source alone. Model simulations show that crop water demand cannot be met during droughts due to limited GW pumping capacity, and that increased GW pumping has a relatively strong impact on GW levels due to the small specific yield of the aquifer. The SW-GW model provides a unique tool for exploring management options that sustain agricultural production and downstream flow to the shrinking Urmia Lake.Accepted author manuscriptWater Resource

Spatio-Temporal Assessment of Global Gridded Evapotranspiration Datasets across Iran

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ETwb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ETERA5 provides higher base average values and lower maximum annual average values than ETGLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ETERA5 has a higher correlation with available energy than available water, while ETGLEAM has higher correlation with available water, and ETGLDAS does not correlate with none of these drivers. Based on the comparison of ETERA5 and ETGLEAM with ETwb, both have similar errors in spatial distribution, while ETGLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ETERA5 and ETGLEAM). All three datasets provide better ET estimates (values closer to ETWB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran