Sustainability assessment of water management at river basin level: Concept, methodology and application

A practical approach for understanding and monitoring the sustainability of a river basin as a complex socio-hydrological system is to co-develop an indicator-based assessment framework with the help of the major stakeholders. This study defines the concept of Sustainability Assessment (SA) in the context of water management at basin level. A step-by-step methodology is proposed and further applied for developing indicator-based SA framework in the complex and overexploited Mashhad Basin in Iran. The methodology is based on a participatory approach that includes forming an expert panel of basin stakeholders, co-creating goals and objectives, identifying and screening indicators, and shaping the final SA framework. We identify 332 potential indicators from existing literature. Using selection criteria and two-round of fuzzy Delphi method, we adapt 25 fit-for-purpose indicators relevant to sustainable water management in Mashhad Basin. Subsequently, a SA framework is developed by categorizing final indicators into four main components (Technical, Environmental, Economic and Social) and ten subcomponents to provide better links and insights of the basin water management practices between different groups of stakeholders. Finally, using a weighting scheme through the Analytical Hierarchy Process (AHP), a sustainability index is constructed by aggregating the indicators. The results indicate that Mashhad Basin is in a critical unsustainable condition with a sustainability index at 0.34 out of 1. Analysis of the relative importance of the adapted indicators shows that the top-four ranked indicators (including water productivity, access to safe drinking water, renewable groundwater dependency and water pollution) have almost 40% contribution to the basin sustainability index. Such indicator-based SA framework can support identification and analysis of major sustainability trade-offs. Additionally, it can provide an effective tool for achieving water-related targets of the Sustainable Development Goals (SDGs). We therefore highly encourage further development of indicator-based SA frameworks in the context of water management at basin level.

Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran

Expansion of urban areas and alteration of natural land cover exacerbate the local climate change. To find out the effect of land cover changes on the local climate, in this study, the Local Climate Zone (LCZ) concept was utilized to detect urban morphology in Tehran Metropolis. LCZ and Land Surface Temperature (LST) can be identified and classified based on available remote sensing products. Firstly, LCZ maps of Tehran metropolis were extracted using Landsat imagery, and secondly, relationships between LCZ and LST were explored for three years (1990, 2004, and 2018). We found that Tehran urban structure has 13 LCZs based on imagery from Landsat 5 and 14 LCZs based on images for Landsat 7 and 8. Overall accuracy and kappa coefficient were estimated at 62% and 0.60, respectively. Results show that built-up classes including compact high-rise, compact mid-rise, and heavy industrial areas tended to increase the surface temperature, while except for bare land, all other land cover types tended to decrease the surface temperature. The findings also suggest that complementary optical and thermal remote sensing data, such as the combination of OLI with TIRS imageries, were sufficient for supervised LCZ and LST classification in a semi-arid region of Tehran metropolis.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.