Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region, Iran

Arid and semi-arid regions are particularly vulnerable to global environmental change because of their fragile climatic conditions. The rapid development of land use is expected to affect aquatic ecosystems in these regions. In this study, we focused on how land use change affects the stream flow and inflow to Urmia Lake in the Mordagh Chay basin, Iran. This case-study exemplifies dynamics found across a much larger region. We mapped changes in land use between 1993–2015 using satellite imagery and modeled future changes using the Dyna-CLUE model. We projected future land use change until 2030 under four scenarios: continuing of the current trend of water use, 40% water withdrawal reduction, and two other scenarios with 40% water withdrawal reduction and improvements of irrigation efficiency up to 50% and 85%. Between 1993–2015, 21% of the study area changed to orchard and arable land mostly at the cost of rangeland. However, upon reduction of water withdrawal our analyses showed that garden must decrease between 27% and 40%. Rainfed cropland is projected to experience a major increase in all scenarios, especially in the case of reduced water withdrawal, where it will increase by 217%. In order to achieve sustainable water resources management land use plays a major role and leads to different land use futures in this type of semi-arid regions

Impacts of future climate and land use change on water yield in a semi‐arid basin in Iran

Studying the interaction between hydrology, land use and climate change is necessary to support sustainable water resources management. It is unknown how land management interventions in dry climate conditions can benefit water yield in the context of climate and land use change interactions. In this study, we assessed the effects of both land use and climate change on the Mordagh Chay basin water yield using the Integrated Valuation Ecosystem Service and Tradeoffs model (InVEST). First, we modelled the current water yield, followed by developing six combined climate‐land use scenarios until 2030 based on the CCSM4 climate model for the RCP4.5 and RCP8.5 scenarios. We used three future land use scenarios simulated by the Dyna‐CLUE model. The trend scenario of land use change, which does not include any improvements in irrigation efficiency, significantly affected basin water yield under both climate scenarios. Water yield decreases by 19.8% and 31.8% for the RCP4.5 and RCP8.5, respectively. Under all land use scenarios that included improvements in irrigation efficiency the water yield responded positively. For the RCP4.5 scenario, the water yield was projected to increase between 16.6 and 18% depending on the land use scenario. The increase in water yield under the RCP8.5 climate scenario was much lower than for the RCP4.5 scenario (about one third). Overall, the results showed that by adopting appropriate irrigation efficiency, it is possible to achieve a better balance between environmental needs, regional economic and agricultural development. The results provide insight into possible sustainable development options and also provide guidance for managing the other Urmia Lake sub‐basins while the approach of integrated assessment of climate, land use change and land management options is also applicable in other conditions to help inform sustainable management

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteoro- logical drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R2) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet

How Do Different Land Uses/Covers Contribute to Land Surface Temperature and Albedo?

Land surface temperature (LST) and land surface albedo (LSA) are the two key regional and global climate-controlling parameters; assessing their behavior would likely result in a better understanding of the appropriate adaptation strategies to mitigate the consequences of climate change. This study was conducted to explore the spatiotemporal variability in LST and LSA across different land use/cover (LULC) classes in northwest Iran. To do so, we first applied an object-oriented algorithm to the 10 m resolution Sentinel-2 images of summer 2019 to generate a LULC map of a 3284 km2 region in northwest Iran. Then, we computed the LST and LSA of each LULC class using the SEBAL algorithm, which was applied to the Landsat-8 images from the summer of 2019 and winter of 2020. The results showed that during the summer season, the maximum and minimum LSA values were associated with barren land (0.33) and water bodies (0.11), respectively; during the winter season, the maximum LSA value was observed for farmland and snow cover, and the minimum value was observed in forest areas (0.21). The maximum and minimum LST values in summer were acquired from rangeland (37 °C) and water bodies (24 °C), respectively; the maximum and minimum values of winter values were detected in forests (4.14 °C) and snow cover (−21.36 °C), respectively. Our results revealed that barren land and residential areas, having the maximum LSA in summer, were able to reduce the heating effects to some extent. Forest areas, due to their low LSA and high LST, particularly in winter, had a greater effect on regional warming compared with other LULC classes. Our study suggests that forests might not always mitigate the effects of global warming as much as we expect.Validerad;2023;Nivå 2;2023-01-01 (joosat);Licens full text: CC BY license</p