The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods.

Ground deformation can cause serious environmental issues such as infrastructure damage, ground compaction, and reducing the ground capacity to store water. Mashhad, as one of the largest and most populated cities in the Middle East, has been suffering from extreme subsidence. In the last decade, some researchers have been interested in measuring land subsidence rates in the Mashhad valley by InSAR techniques. However, most of those studies were based on inaccurate measurements introducing uncertainties in the resulting subsidence rates. These researches used a small number of EnviSat data with long perpendicular and inhomogeneous temporal baseline. This paper seeks to determine the subsidence rate in urban areas of Mashhad in recent years, the threat that was neglected by the city managers and decision-makers. For this purpose, the Persistent Scatterer InSAR technique was applied in the study area using two time-series of descending and ascending Sentinel-1A acquisitions between 2014 and 2017. The results demonstrated the maximum line-of-sight deformation rate of 14.6 cm/year and maximum vertical deformation (subsidence) rate about 19.1 cm/year which could have irreversible consequences. The results were assessed and validated using piezometric data, GPS stations, and geotechnical properties. This assessment confirms that the main reason for subsidence in the interested area is groundwater over-extraction. Also, investigation of geotechnical properties shows that thick fine-grained layers in the northwest of the city could strongly affect the results. At the end of this paper, a new simplified method was proposed to estimate specific storage in special cases to predict the subsidence rate.

Los Angeles has experienced ground deformations during the past decades. These ground displacements can be destructive for infrastructure and can reduce the land capacity for groundwater storage. Therefore, this paper seeks to evaluate the existing ground displacement patterns along a new metro tunnel in Los Angeles, known as the Sepulveda Transit Corridor. The goal is to find the most crucial areas suffering from subsidence or uplift and to enhance the previous reports in this metropolitan area. For this purpose, we applied a Persistent Scatterer Interferometric Synthetic Aperture Radar using 29 Sentinel-1A acquisitions from June 2017 to May 2018 to estimate the deformation rate. The assessment procedure demonstrated a high rate of subsidence in the Inglewood field that is near the study area of the Sepulveda Transit Corridor with a maximum deformation rate of 30 mm/yr. Finally, data derived from in situ instruments as groundwater level variations, GPS observations, and soil properties were collected and analyzed to interpret the results. Investigation of geotechnical boreholes indicates layers of fine-grained soils in some parts of the area and this observation confirms the necessity of more detailed geotechnical investigations for future constructions in the region. Results of investigating line-of-sight displacement rates showed asymmetric subsidence along the corridor and hence we proposed a new framework to evaluate the asymmetric subsidence index that can help the designers and decision makers of the project to consider solutions to control the current subsidence.

Rainwater harvesting (RWH) has been recognized as one of the most reliable and efficient methods for water supply, especially in arid and semi-arid regions (ASARs) facing freshwater scarcity. Nevertheless, due to the inherent uncertainty of input data and subjectivity involved in the selection of influential parameters, the identification of RWH potential areas is a challenging procedure. In this study, two approaches for locating potential RWH sites were implemented. In the first approach, a frequently-used method of the multi-criteria decision analysis and geographic information system (MCDA-GIS) was utilized, while, in the second approach, a novel strategy of integrating the soil and water assessment tool (SWAT) model as a hydrology model into an MCDA-GIS method was proposed to evaluate its performance in locating potential RWH sites. The Mashhad Plain Basin (MPB) was selected as a case study area. The developed potential RWH maps of the two approaches indicated similar patterns for potential RWH areas; in addition, the correlation coefficient (CC) between the two obtained maps were relatively high (i.e., CC = 0.914) revealing that integration of SWAT as a comprehensive hydrologic model does not necessarily result in very different outputs from the conventional method of MCDA-GIS for RWH evaluation. The overlap of developed maps of the two approaches indicated that 3394 km² of the study area, mainly located in the northern parts, was identified as high-potential RWH areas. The performed sensitivity analysis indicated that rainfall and slope criteria, with weights of 0.329 and 0.243, respectively, had the greatest sensitivity on the model in the first approach while in the second approach, the criterion of runoff coefficient (with weights of 0.358) had the highest impact. Based on results from the identification of the potential locations for conventional RWH techniques, pond and pan techniques are the most proper options, covering high-potential areas of RWH more effectively than other techniques over MPB.

A novel multiscale postprocessor for ensemble streamflow prediction, MS-EnsPost, is described and comparatively evaluated with the existing postprocessor in the National Weather Service’s Hydrologic Ensemble Forecast Service, EnsPost. MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow, multiscale regression using observed and simulated flows over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For comparative evaluation, 139 basins in eight River Forecast Centers in the United States were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over EnsPost are attributed. The ensemble mean and ensemble prediction results indicate that, compared to EnsPost, MS-EnsPost reduces the root-mean-square error and mean continuous ranked probability score of day-1 to day-7 predictions of mean daily flow by 5%–68% and by 2%–62%, respectively. The deterministic and probabilistic results indicate that for most basins the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the continuous ranked probability skill score results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snowfall and, for non-snow-driven basins, mean annual precipitation.