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

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

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

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

The following corrections should be made to the original paper: In the “Eflow Estimation Based on Hydrological and Hydraulic Factors” subsection of the “Results and Discussion” section: • In the sentence beginning with “Hydrological Eflow recommendations for maintaining good health over the Yamuna River were estimated to lie between 986.4 and 2,959.3 m3=s,” “986.4 and 2,959.3 m3=s” should be replaced with “27.9 m3=s (986.4 ft3=s) and 83.8 m3=s (2,959.3 ft3=s).” • In the sentence published as, “The minimum Eflow to be maintained in the river during the dry season is 3,945.7 m3=s for the Yamuna and 5.8 m3=s for the Bhadra,” “3,945.7 m3=s” should be replaced with “111.7 m3=s (3,945.7 ft3=s).” In the Supplemental Data: • In Table S3, the values in the columns under “Eflow of Yamuna River (m3=s)” are updated. The published paper contained values in ft3=s, which are retained in brackets. The values of Eflow are converted to m3=s as shown in the revised Table S3. • In Table S5, the “Yamuna” column under “Recommended Eflow (m3=s)” contains values in ft3=s in the published paper. The values of flow are converted in m3=s as shown in the revised Table S5. The values in ft3=s are retained in brackets. • In Fig. S2(a), the y-axis label of “Discharge (cumecs)” should be replaced with “Discharge (cusecs).” The corrected Fig. S2 is provided in the Supplemental Data. In the subsection “Yamuna River (Delhi Segment)” under the section “Description of Study Regions,” “Uttaranchal” should become “Uttarakhand.” In Fig. 2(a), the labels for Drains 15 and 16 should be interchanged. The revised figure is provided herein.

Abstract

n this study, a 3D seismic response of soil deposit, soil-ash deposit and soil-ash-foundation system was investigated. Homogeneous sand deposit of 80m × 9m × 20m was initially analyzed. A pond ash layer is on top of the sand deposit with varying thicknesses and the efficiency of the pond ash layer on the sand deposit was evaluated for its best suitability. The optimum sand-ash deposit overlain by a shallow foundation has been analysed under the excitation of the Nepal (Mw:7.8) and North East India earthquake (Mw:7.5). A seismic response analysis was performed using finite element software PLAXIS3D. The finite element model adopted for the present study has been validated using 1D nonlinear ground response analysis programs. e.g. DEEPSOIL and Cyclic1D. Results of the response analysis have been determined in terms of acceleration, displacement, excess pore pressure, and excess pore pressure ratio. It was observed that, the sand-pond ash-foundation system experienced liquefaction when excited under the Nepal earthquake motion whereas it is safe against the North East India earthquake.

Abstract

Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector …

Abstract

In the present study, it was attempted to investigate the cyclic resistance of equi-proportionate silt–sand range pond ash (with 50% fines) at relatively high shear strains using the strain-controlled cyclic triaxial test. The cyclic triaxial tests have been performed considering the effect of relative compaction (97–99%), cyclic shear strain (0.6–1.35%), frequency of loading (0.3–1 Hz) and effective confining pressure (50–100 kPa) on cyclic resistance of pond ash. Dynamic characteristics such as dynamic shear modulus and damping ratio of equi-proportionate silt–sand range pond ash was evaluated for all the parameters considered at high shear strain. The maximum value of the dynamic shear modulus and damping ratio of pond ash observed in this study was 6534.8 kPa and 23.64%, respectively. The dynamic shear modulus and damping ratio of pond ash was decreased from 6534.8 to 5023.87 kPa and 23.64 to 14.17 …

Abstract

We thank the respected discussers for appreciating the relevance of the present work, critically analyzing it, and bringing forth the possible constraints in its applicability. This discussion provides us an opportunity to elaborate the point related to the scope and applicability of original work, especially with focus on the hydrological community that otherwise would possibly remain unclarified. Because there are two discussions of our paper, we have addressed them in two different sections.