Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model perfor- mances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. How- ever, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reser- voir, Bhadra reservoir Tunga-Bhadra River, located in India.

Abstract

Univariate meteorological drought indices are inadequate to represent the complexity of hydrological conditions under the intensification of hydrological cycle due to climate change at catchment scale. In this study, Stand­ ardised Precipitation Actual Evapotranspiration Index (SPAEI) was proposed, which can combine both meteo­ rological and hydrological drought characteristics at catchment scale. The proposed new drought index considers the hydrologically calibrated AET to account for the water use in addition to meteorological effect. The proposed hydrometeorological drought index was potential in identifying meteorological and hydrological drought events accounting for the time-lag effects and comparable with Global Land Evaporation Amsterdam Model (GLEAM) remote sensing AET data-based drought index. The PET based drought index of SPEI, which is based on energy demand, has shown intensified drought characteristics compared to SPAEI, which is based on both energy de­ mand and available moisture supply and can be a promising variable in the drought estimation. The climate change projections of precipitation and temperatures downscaled using statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression were used using three General Circulation Model outputs. Intensified drought characteristics under climate change has been predicted over Krishna River basin, India, in terms of increase of drought areal extent of about 25%-31%, with increase of drought frequency as 5 years per 20 years and durations as 4–5 months based on the proposed hydrometeoro­ logical drought index of SPAEI.

Abstract

Understanding the relevance of potential (PET) and actual evapotranspiration (AET) in the drought characterization over energy and water-limited regions is unexplored. The present study tries to restructure the Standardized Precipitation Evapotranspiration Index (SPEI) with AET to represent the anomalies of actual water availability in addition to precipitation over India. The AET is estimated using the Budyko hypothesis at annual scale which was validated with Global Land Evaporation Amsterdam Model (GLEAM) satellite-based AET data. AET-based drought index was in more agreement with remote sensing-based drought severity index (DSI) for major drought events compared to PET-based drought index for water-limited zone compared to energy-limited. For water-limited zones, the PET-based drought index has overestimated the drought intensities, while for energy-limited zones such effect is not …

Abstract

Machine learning (ML) has been increasingly adopted due to its ability to model complex and non-linearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling …

Abstract

Machine learning (ML) has been increasingly adopted due to its ability to model complex and nonlinearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling framework’s effectiveness is demonstrated with a tropical river system of India, Tunga-Bhadra River, as a case study. The SVR has been noted as the most robust ML model to predict RWT at a monthly time scale compared with daily and seasonal. The study demonstrates how ML methods can be coupled with a global sensitivity algorithm and DA techniques to generate accurate RWT predictions in river water quality modelling.

Abstract

: Reference Evapotranspiration (ET0) is a complex hydrological variable defined by various climatic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Conventionally ET0 is calculated by various empirical methods based on rigorous climatic data. However, there are many places where various climatic data may not available for ET0 estimation. The objective of this study is to evaluate different machine learning (ML) techniques to estimate ET0 with minimal climatic inputs. In this study, FAO56 Penman-Monteith model was considered as the standard model and different ML models based on A Long short-term memory neural networks (LSTM), Gradient Boosting Regressor (GBR), Random Forest (RF) and Support Vector Regression (SVR) were developed to estimate ET0 with climatic variables as input parameters. These models were evaluated in two different climatic regions, Hyderabad in India and Waipara in New Zealand. The results indicated that 99 % accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with fewer data. LSTM model performed better than other ML models with all input combinations at both the stations, followed by SVR and RF. Both LSTM and SVR models have been noted as the most robust ML models for estimating ET0 with minimal climate data. Even though the excellent performance can be achievable when all input variables are used, the study, however, found that even a three-parameter combination (Temperature, Wind Speed and Relative Humidity values) or two-parameter combination (Temperature and Relative Humidity, Temperature and Wind Speed) can also be promising in ET0 estimation. The presented study will help to estimate ET0 for data scare regions, which is vital for agricultural water management in semi-arid climates.

Abstract

Reservoirs are essential infrastructures in human life. It provides water supply, flood control, hydroelectric power supply, navigations, irrigation, recreation, and other functionalities. To provide these services and resources from the reservoir, it’s necessary to know the reservoir system's inflow. The Machine Learning (ML) techniques are widely acknowledged to forecast the inflow into the reservoir system. In this paper, the popular ML technique, Gradient Boosting Regressor (GBR), is used to predict the reservoir system's inflow. This technique has been applied to the Bhadra reservoir of India at a daily time scale. In this study, the effect and complex relationship of climate phenomenon indices with inflow has been considered. The considered climate phenomenon indices are (1) Arctic Oscillation (AO), (2) East Pacific/North Pacific Oscillation (EPO), (3) North Atlantic Oscillation (NAO), (4) Extreme Eastern Tropical Pacific SST (NINO1+2), (5) Eastern Tropical Pacific SST (NINO3), (6) Central Tropical Pacific SST (NINO4), (7) East Central Tropical pacific SST (NINO34), (8) Pacific North American Index (PNA), (9) Southern Oscillation Index (SOI), (10) Western Pacific Index (WP), (11) Seasonality. In this paper, different parameter settings have been discussed on the models’ performances. The analysis of the GBR method for the Bhadra reservoir includes the number of estimators, maximum depth. The results indicate that the GBR model can capture the inflow's peaks and droughts into the reservoir systems. The study demonstrates how ML methods can be used to generate accurate reservoir inflow predictions.

Abstract

Surface Temperature (ST) is important in terms of surface energy and terrestrial water balances affecting urban ecosystems. In this study, to process the nonlinear changes of climatological variables by leveraging the distinct advantages of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM), we propose an LSTM-BiLSTM hybrid deep learning model which extracts multi-dimension features of inputs, i.e., backward (future to past) or forward (past to future) to predict ST. This study assessed the climatological variables, i.e., wind speed, wind direction, relative humidity, dew point temperature, and atmospheric pressure impact on ST using five major coastal cities of India: Chennai, Mangalore, Visakhapatnam, Cuddalore, and Cochin. The Recurrent Neural Networks (RNN) and hybrid LSTM-BiLSTM models have effectively predicted ST and outperformed the standalone Artificial Neural Networks (ANN), LSTM, and BiLSTM models. The RNN and LSTM-BiLSTM models have performed better in predicting ST for Mangalore (Nash-Sutcliffe efficiency (NSE)¼0.91), followed by Cochin (NSE¼0.89), Chennai (NSE¼0.88), Cuddalore (NSE¼0.88), and Vishakhapatnam (NSE¼0.81). The hybrid data-driven modeling framework indicated that coupling the LSTM and BiLSTM models were proven effective in predicting the ST of coastal cities

Abstract

Droughts are recognized as a natural disaster that is caused by extreme and continuous shortage of precipitation. Drought indices assist in a number of tasks, including its early warning and monitoring by computing severity levels and proclaiming the start and end of drought. Various drought indices were formulated for the forecasting and prediction of spatiotemporal drought characteristics using various hydrological variables, such as precipitation, evapotranspiration, runoff, soil moisture content, etc. Due to anthropogenic global warming and increase of temperature, evapotranspiration based drought indices have become interest in recent years in the drought assessment. This paper attempt to provide more information on drought indices which incorporates Evapotranspiration. The study used Standardized Precipitation Evapotranspiration index (SPEI), based on evapotranspiration to understand the drought variability at various time scales. This study adopted Hargreaves model to calculate Potential Evapotranspiration. SPEI index can also be used to study the wet and dry periods including Evapotranspiration along with precipitation. The study used SPEI to understand the dry and wet years over an urban semi-arid region, Hyderabad, capital and biggest city of the southern Indian state of Telangana for the year 1965 to 2015. The years 1965, 1966, 1972, 1973, 1985, 1993 and 2012 were noted as dry years with SPEI values as -1.35,-1.06,-1.43,-1.31,-1.05, -1.22 and -1.51 sequentially and year 2006 as severe wet year with SPEI value as +1.65. The characterization of dry and wet years as demonstrated in the present study will enhance the better urban water resources management.

Abstract

Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods Penman-Monteith, Hargreaves, Turc, and Priestley-Taylor were used to estimate ET0 at a daily time scale. Dataset consists of daily meteorological data over a period of 51 years (1965–2015) for Hyderabad, the largest city of the Indian state, Telangana, with semi-arid climate. The input variables for the ANN model consist of maximum and minimum air temperatures, relative humidity, solar radiation, and wind speed. The Penman-Monteith method was considered as the standard method to compare the ANN and various empirical models of ET0. ANN model was trained and tested with climate variables as input variables and various empirical models as reference models. The most influencing climate variables on ET0 were found in the order of temperature, solar radiation, wind speed, and relative humidity based on correlation coefficients. These variables have formed as the basis to choose different datasets to train over ANN model. Validation has been carried out using the coefficient of determination (R2) which is obtained for the training (1965–2000) and testing period (2001–2015) period as 0.97 and 0.96 respectively. Temperature and radiation-based models of Turc and Priestley-Taylor methods can be used to estimate ET0 when all other climate variables are not available as they also correlate well with the Penman-Monteith method. Advancement towards artificial intelligence techniques in water resources engineering has motivated to simulate reference ET0 using limited meteorological variables to produce accurate results. Such data-driven algorithms developed based on standard empirical models can be implemented for prediction with limited climate data.