Several cases of failure in the prediction of Indian Summer Monsoon Rainfall (ISMR) are the major concern for long-lead prediction. We propose that this is due to the temporal evolution of association/linkage (inherent concept of temporal networks) with various factors and climatic indices across the globe, such as El Niño-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO) etc. Static models establish time-invariant (permanent) connections between such indices (predictors) and predictand (ISMR), whereas we hypothesize that such systems are temporally varying in nature. Considering hydroclimatic teleconnection with two major climate indices, ENSO and EQUINOO, we showed that the temporal persistence of the association is as low as three years. As an application of this concept, a statistical time-varying model is developed and the prediction performance is compared against its static counterpart (time-invariant model). The proposed approach is able to capture the ISMR anomalies and successfully predicts the severe drought years too. Specifically, 64% more accurate performance (in terms of RMSE) is achievable by the recommended time-varying approach as compared to existing time-invariant concepts.

No abstract

Transition-metal dichalcogenides possess high carrier mobility and can be scaled to sub-nanometer dimensions, making them viable alternative to Si electronics. WSe2 is capable of hole and electron carrier transport, making it a key component in CMOS logic circuits. However, since the p-type electrical performance of the WSe2-field effect transistor (FET) is still limited, various approaches are being investigated to circumvent this issue. Here, we formed a heterostructural multilayer WSe2 channel and solution-processed aluminum-doped zinc oxide (AZO) for compositional modification of WSe2 to obtain a device with excellent electrical properties. Supplying oxygen anions from AZO to the WSe2 channel eliminated subgap states through Se-deficiency healing, resulting in improved transport capacity. Se vacancies are known to cause mobility degradation due to scattering, which is mitigated through ionic compensation. Consequently, the hole mobility can reach high values, with a maximum of approximately 100 cm2/V s. Further, the transport behavior of the oxygen-doped WSe2-FET is systematically analyzed using density functional theory simulations and photoexcited charge collection spectroscopy measurements.

This study explores the potential of deep learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of DL algorithm, based on 1-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and skill scores. The accuracy of simulating the correct drought category, among the seven categories, is also high (>70%). Moreover, in general, the skill of any climate model is much higher for the primary meteorological variables as compared with other secondary or tertiary variables/phenomena, like droughts. Thus, the novelty of the proposed DL-based model also lies in the improved assessment of ensuing basin-scale meteorological droughts using the projected meteorological precursors and may lead to new research directions.

Lack of stationarity in most of the hydroclimatic variables is no longer a topic of debate rather a reality. It may be hypothesized that alternative methodologies are needed to deal with such nonstationarity and to improve the skill of hydroclimatic modeling/prediction. We propose the concept of temporal networks in hydroclimatic modeling as a potential solution to this problem. As a typical case, complex association among different hydroclimatic variables and streamflow is considered as an illustrative problem. Evolution of temporal networks over time, obtained through Graphical Modeling (GM), depicts the changes in the model inputs as well as model parameters over time. The proposed concept indicates that the time interval after which the model needs to be updated/recalibrated, referred to as Optimum Recurrence Interval (ORI), is problem specific and is optimized to achieve the best model performance. The proposed concept not only depicts a notable change in the potential predictors for the high and low flow months, but also establishes the different extent of temporal variability for different months, and hence the ORI of model recalibration. As compared to its time‐invariant counterpart, the temporal networks‐based approach shows higher efficacy in capturing the extreme flow events due to its inherent time‐varying characteristics. We recommend the concept of temporal networks to be promising in the context of climate change to capture the time‐varying association. In general, the concept can be applied to other hydroclimatic variables where a time‐varying association is expected due to various reasons including the impacts of climate change.