Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE=0.11, MAE=0.21, RSME=0.31, and R2=0.92 for the Chattahoochee station, and MSE=0.06, MAE=0.19, RSME=0.23, and R2=0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere.

/pdf/hydrological-drought-forecasting-using-a-deep-transformer-15dc3v0l.pdf

Hydrological Drought Forecasting Using a Deep Transformer Model

Abstract This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive modelling, and spatial modelling of expected risks and vulnerability to drought. Thus, out of 417 articles across all studies on drought, 226 articles published from 2010 to 2022 were analyzed to provide a global overview of the current state of knowledge on multivariate drought modelling using the inclusion criteria. The main objective is to review the recent available scientific evidence regarding multivariate drought modelling based on the joint use of geospatial technologies and artificial intelligence. The analysis focused on the different methods used, the choice of algorithms and the most relevant variables depending on whether they are descriptive or predictive models. Criteria such as the skill score, the given game complexity used, and the nature of validation data were considered to draw the main conclusions. The results highlight the very heterogeneous nature of studies on multivariate modelling of agricultural drought, and the very original nature of studies on multivariate modelling of agricultural drought in the recent literature. For future studies, in addition to scientific advances in prospects, case studies and comparative studies appear necessary for an in-depth analysis of the reproducibility and operational applicability of the different approaches proposed for spatial and temporal modelling of agricultural drought. HIGHLIGHTS The components and fundamentals of multivariate modelling of agricultural drought were discussed. The importance of hybrid artificial intelligence models is widely discussed in improving the performance of traditional machine learning models. Quantum machine learning algorithms are weakly explored in multivariate drought modelling. Therefore, future studies should explore this approach. The major challenge of multivariate modelling of drought frequency is mainly related to the difference in the return periods of the different variables (time-shifted and spatially effects).

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2022.2131471?needAccess=true

Modelling agricultural drought: a review of latest advances in big data technologies

Abstract Hydrological extremes occupy a large spatial extent, with a temporal sequence, both of which can be influenced by a range of climatological and geographical phenomena. Understanding the key information in the spatial and temporal domain is essential to make accurate forecasts. The capabilities of deep learning methods can be applied in such instances due to their enhanced ability in learning complex relationships. Given its success in other domains, this study presents a framework that features a long short-term memory deep learning model for spatio temporal hydrological extreme forecasting in the South Pacific region. The data consists of satellite rainfall estimates and sea surface temperature (SST) anomalies. We use the satellite rainfall estimate to calculate the effective drought index (EDI), an indicator of hydrological extreme events. The framework is developed to forecast monthly EDI using three different approaches: (i) univariate (ii) multivariate with neighbouring spatial points (iii) multivariate with neighbouring spatial points and the eigenvector values of SST. Additionally, better identification of extreme wet events is noted with the inclusion of the eigenvector values of SST. By establishing the framework for the multivariate approach in two forms, it is evident that the model accuracy is contingent on understanding the dominant feature which influences precipitation regimes in the Pacific. The framework can be used to better understand linear and non-linear relationships within multi-dimensional data in other study regions, and provide long-term climate outlooks. 

/pdf/spatio-temporal-hydrological-extreme-forecasting-framework-3t6nnt6p.pdf

Spatio temporal hydrological extreme forecasting framework using LSTM deep learning model

In recent times, India has experienced a significant increase in the frequency and intensity of extreme weather events, particularly in northeast India (NEI), an area known for its rich natural resources. Despite the geographic continuity of NEI and Bangladesh, previous studies have failed to consider their interconnectedness, resulting in an incomplete understanding of the situation. To bridge this gap, a comprehensive study encompassed the entire NEI, including West Bengal and Bangladesh (hereafter referred to as NEIB). This study examined climate extremes in NEIB, utilizing 12 temperature-based and 8 precipitation-based indices developed by the Expert Team on Climate Change Detection and Indices. Analysis was performed on temperature and precipitation data obtained from the India Meteorological Department and Bangladesh Meteorological Department covering the period 1981-2021. Additionally, climate projections from 14 Global Climate Models participating in the CMIP6 were incorporated for the period 2015-2100, considering four different Shared Socioeconomic Pathways (SSPs) scenarios. Findings revealed that under the SSP585 scenario, a substantial rise of 4 °C in maximum temperatures and 5.5 °C in minimum temperatures by the end of the twenty-first century. Warming indices, such as the summer days index, indicated an expected increase of 53 days, while the Warm spell days index was estimated to rise by approximately 2 days. Heavy precipitation days (R20mm) were projected to increase by up to 14 days, with a notable impact in Meghalaya. While the number of rainy days is expected to decrease, the overall magnitude of precipitation is anticipated to remain relatively stable. Notably, the Simple daily intensity index demonstrated a rise of 2.4 mm/day compared to the current baseline of 14.4 mm/day. These projected changes have significant ramifications for water resources, agriculture, health, and infrastructure in the region. 

Future projection of climate extremes across contiguous northeast India and Bangladesh

Hybrid deep learning approach for multi-step-ahead prediction for daily maximum temperature and heatwaves

\n This study explores the potential of the 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 the DL algorithm, based on a one-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.

/pdf/potential-of-deep-learning-in-drought-assessment-by-4apajdm096.pdf

Potential of Deep Learning in drought assessment by extracting information from hydrometeorological precursors

Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change

This study presents an analysis to identify precipitation-based climate change hotspots and key-vulnerable cities using multi-model, multi-scenario, high-resolution (0.25°×0.25°), bias-corrected precipitation dataset across India from 14 state-of-the-art General Circulation Models (GCMs), participating in Coupled Model Intercomparison Project version 6 (CMIP6). Preliminary analysis indicates an overall wetter future across India with 290±150 mm to 530±260 mm increase in mean annual precipitation towards end of this century under various climate change scenarios. Apart from the mean precipitation, the extremes are also found to be increasing by alarmingly higher rates. However, the spatio-temporal variations of such increments are notably diverse over different seasons in a year and across different Homogeneous Precipitation Zones (HPZs) in the country. Therefore, a new and more inclusive index, named as Precipitation-based Hotspot Index (PHI), is developed to identify the ‘precipitation-based hotspots’, i.e. the places with most pronounced changes in precipitation characteristics. The PHI considers the changes in various aspects of precipitation, such as mean, variability, and characteristics of extremes (magnitude, frequency and intensity). Based on the PHI values in the far-future period (2061-2100) and for the worst climate change scenario, the hotspot regions are identified mostly in the northwest, west-central, west coast, and northeast parts of India. Further, considering two important socio-economic vulnerability factors (population density and human development index) along with PHI, the key-vulnerable tier-I cities are identified across India. The analysis reveals four out of ten tier-I cities will be highly vulnerable towards end of this century. The findings of this analysis (hotspot maps) and the data products (high-resolution, bias-corrected precipitation dataset) are expected to be highly beneficial for impact assessments, hydrologic modelling, and formulating suitable adaptation and mitigation strategies for India over future. 

Precipitation-based climate change hotspots across India through a Multi-model assessment from CMIP6

Temperature rise may exhibit time-varying characteristics due to its inherent non-stationarity. Whereas most of downscaling models inherently assume stationarity, this study establishes the benefit of Time-Varying Downscaling Model (TVDM) over time-invariant approaches, such as Statistical Downscaling Model (SDSM) and Bias Corrected Spatial Disaggregation (BCSD) model. Daily maximum (Tmax) and minimum temperature (Tmin) at some of the metropolitan cities in India, namely Delhi, Kolkata, Hyderabad, Mumbai, Chennai, and Bengaluru, are considered for demonstration. During the baseline period, the correspondence between observed and downscaled temperature is much better in case of TVDM as compared to BCSD and SDSM, particularly for Tmax. Next, the future temperature is downscaled for two Representative Concentration Pathways (RCPs), viz., RCP4.5 (medium–low-type) and RCP8.5 (high-emission) scenarios. The future period is divided into three epochs (30 years each) and results are compared with the baseline period. In general, the time-varying approach (TVDM) reveals more increase as compared to time-invariant approach (SDSM) during the future period. The drastic (~ 3 °C) change in Tmin is noticed by the end of twenty-first century (epoch-3) during post summer months (June through January) as per TVDM. Overall, the time-varying (TVDM) approach shows more rise during the future period as compared to time-invariant approaches and the results from the former are more reliable, since its performance during baseline period is much better than the latter.

Benefit of time-varying downscaling model for the assessment of urban temperature rise

Climate change may cause oasification or desertification both: an analysis based on the spatio-temporal change in aridity across India

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