DRAM technology has developed rapidly in recent years. Several industrial solutions offer 3D packaging of DRAM and some are envisioning the integration of CPU and DRAM on the same die. These solutions allow higher density and better performance and also lower power consumption in DRAM designs. However, accurate simulation tools have not kept up with DRAM technology, especially for the modeling of 3D DRAMs. In this work we present a cycle-accurate, validated DRAM simulator, DRAMsim3, it offers the best simulation performance and feature sets among existing cycle-accurate DRAM simulators. DRAMsim3 is also the first DRAM simulator to offer runtime thermal modeling alongside with performance modeling.

With the limited availability of meteorological variables in many remote areas, estimation of evapotranspiration (ET) at different spatiotemporal scales for efficient irrigation water management and hydrometeorological studies is becoming a challenging task. Hence, in this study, indirect ET estimation methods, such as moderate resolution imaging spectroradiometer (MODIS) satellite-based remote-sensing techniques and the water-budget approach built into the semidistributed variable infiltration capacity (VIC-3L) land-surface model are evaluated using the Penman-Monteith (PM) equation approach suggested in the literature together with a crop coefficient approach. To answer the research question of whether regional or local controls of a river basin with tropical monsoon-type climatology affect the accuracy of the VIC and MODIS-based ET estimates, these methodologies are applied in the Kangsabati River Basin in eastern India at $25\times 25\mathrm{km}$ resolutions attributed with dominant paddy land uses. The results reveal that the VIC-estimated ET values are reasonably matched with the PM-based ET estimates with the Nash-Sutcliffe efficiency (NSE) of 54.14–71.94%; however, the corresponding MODIS-ET values are highly underestimated with a periodic shift that may be attributed to the cloud cover and leaf shadowing effects. To enhance the field applicability of the satellite-based MODIS-ET products, these estimates are standardized by using a genetic-algorithm-based transformation that improves the NSE from $-390.83$ to 99.57%. Hence, this study reveals that there is the need of a regional-scale standardization of the MODIS-ET products using the PM or lysimeter data or possible modification of the MOD16A2 algorithm built-into the MODIS for generalization. Conversely, the satisfactory grid-scale ET estimates by the VIC model show that this model could be reliably used for the world’s river basins; however, at smaller temporal scales, the estimates could be slightly inconsistent.

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r² = 0.73 whereas the NDVI showed satisfactory performance with r² = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r² = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.