In recent years, pervious concrete (PC) has gained much attention as one of the strategies for lo... more In recent years, pervious concrete (PC) has gained much attention as one of the strategies for low-impact development (LID) in pavements due to its structural, economic, and road-user benefits. This study sought to review and evaluate changes in the mechanical, hydraulic, and durability performance of PC produced with cement and aggregate replacements. A meta-analysis was conducted to elucidate the feasible range of the replacement percentage and the number of materials that could be used to replace cement and aggregates; single or binary replacements were considered. Results indicated that cement-replacing materials, industrial wastes (IWA), and recycled aggregates (RA) met the minimum requirement for the mechanical, hydraulic, and durability properties of PC. The use of a single cement replacement material provided PC with better performance than when cement was replaced with two or more materials or when cement alone was used. Industrial waste was found to be a better replacement...
<p>In recent years, many researchers indicated that earth-observing satelli... more <p>In recent years, many researchers indicated that earth-observing satellites perform well in measuring or estimating precipitation rates. However, it has been highlighted that the performance of satellite rainfall estimates (SREs) is affected by many factors. In this study, a meta-data analysis was conducted to assess the performance of different SREs for flood and drought monitoring under diverse settings to test the influence of factors related to climate, topography, watershed size, and length of SREs data record. Koppen climate classification was used to classify the different studies into different climatic zone. Mean elevation was used as an indicator of varying topography. Studies were grouped into three different categories depending upon their available data record length. The impact of various factors on the performance of SREs was assessed with three statistical indices: Pearson correlation coefficient, Root Mean Square Error, and Nash-Sutcliffe Efficiency. Results showed that the performance of SREs for drought and flood monitoring is influenced by the climate, length of the data record, interactions between the applied hydrological model and type of SRE, and topography. Microwave-based SREs performed were found to perform better than infrared-based SREs. Low lying landscapes exhibited higher accuracy of SREs in flood and drought monitoring compared to complex mountainous terrain. In most cases, IMERG and CMORPH outperformed other SREs.  IMERG showed the best drought monitoring performance with Pearson correlation values ranging between 0.96-0.99. It was found that the best SREs that can represent the observed streamflow vary depending on the type of hydrological models. Also, the hydrological model performance for flood prediction significantly increases (p<0.05) when using the SREs for model calibration compared to when the model is manually calibrated with historical gauge data. Bias-adjusted SREs performed better than their counterpart. Overall, SREs offer great potential for flood and drought monitoring, but their performance needs to be enhanced for hydrological applications.</p>
In recent years, satellite precipitation products (SPPs) have emerged as an essential source of d... more In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and o...
Flow-vegetation interactions modify the instream roughness and flow characteristics in the river ... more Flow-vegetation interactions modify the instream roughness and flow characteristics in the river and estuaries. This study proposes a new quasi three-dimensional hydrodynamic framework to compute the vertical velocity profile in an open channel having submerged flexible vegetation. A modified form of two-dimensional depth-averaged shallow water equations coupled with vegetal drag forces is derived and applied in the simulation. The explicit second-order accurate TVD McCormack predictor-corrector finite difference method with operator splitting technique is used to solve the governing equations in MATLAB. The TVD approach is robust and gives accurate results free from numerical oscillations. The bending profile of the flexible stems under various flow events is calculated from the cantilever beam theory. The vertical velocity profile in the vegetation layer and the free water layer is estimated from Reynold's stress equation and Shannon's entropy theory. The present model is used to replicate some popular experimental test cases. Results indicate a conservative and robust model performance under different flow conditions and patch density. Quantitative analysis of the predicted results is carried out using two statistical indices and found satisfactory.
The aquatic ecosystems are an integral part of the global environment and play a critical role in... more The aquatic ecosystems are an integral part of the global environment and play a critical role in hydrogeological processes in the rivers. In recent decades, the external stressors on the aquatic species have significantly increased due to hydrologic alterations, human activities, and anthropogenic changes to their natural habitat. Global climate change has led to rivers' hydrological flow regime shifts, leading to unsuitable habitat conditions. It is, therefore, crucial to assess the potential implications of climate change on habitat suitability to ensure the long-term sustainability of freshwater species. In this direction, we investigated the association between endangered Gangetic dolphin populations and climate-driven hydrologic flow regime alterations in the Kulsi river, India. We developed a coupled eco-hydraulic framework comprising hydrological and hydrodynamic modeling to study the impact of past and projected climate change scenarios on the habitat suitability of target species. The framework was tested on a 15-km stretch of the Kulsi River, where the dolphin population has significantly declined in recent years. The temporal changes in the Weighted Usable Area (WUA) were analyzed using flow parameters and habitat suitability curves. Our findings suggest that the dolphin population decline coincided with a decrease in WUA, indicating a strong association between flow regimes and habitat suitability. Under climate change scenarios, multi-model climate projections and hydrological-hydrodynamic simulations show a rising trend in precipitation and streamflow in the basin, with substantial uncertainty. Higher flow depth and velocity would enhance WUA (habitat suitability). Still, the proposed river development projects in upstream regions could pose a serious threat to fragile dolphin communities by changing the seasonal flow patterns. The findings of this study can be included in conservation action plans and flow regulations strategies in upstream projects to ensure the long-term survival of endangered species.
AbstractSeasonal drying and wetting highly influence ecosystem diversity in intermittent streams.... more AbstractSeasonal drying and wetting highly influence ecosystem diversity in intermittent streams. Hydrological alterations are responsible for the spawning and reproduction of aquatic habitats. The...
<p>Droughts are considered as one of the most catastrophic natural disasters that affect hu... more <p>Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations. &#160;ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis. &#160;Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models. &#160;All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.&#160; These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.</p>
&lt;p&gt;The success of any civil engineering structure's foundation design depends u... more &lt;p&gt;The success of any civil engineering structure's foundation design depends upon the accuracy of estimation of soil&amp;#8217;s ultimate bearing capacity. Numerous numerical approaches have been proposed to estimate the foundation's bearing capacity value to avoid repetitive and expensive experimental work. All these models have their advantages and disadvantages. In this study, we compiled all the governing equations mentioned in Bureau of Indian standard IS:6403-1981 and modify the equation for Ultimate Bearing Capacity. The equation was modified by considering two new parameters, K1(for general shear) and K2 (for local shear) so that a common governing equation can be used for both general and local shear failure criteria. The program used for running the model was written in MATLAB language code and verified with the observed field data. Results indicate that the proposed model accurately characterized the ultimate, safe, and allowable bearing capacity of a shallow footing at different depths. The correlation coefficients between the observed and model-predicted bearing capacity values for a 2m foundation depth with footing size of 1.5 &amp;#215;1.5, 2.0 &amp;#215; 2.0, and 2.5 &amp;#215; 2.5 m are 0.95, 0.94, and 0.96. A similar result was noted for the other foundation depth and footing size. Findings show that the model can be used as a reliable tool for predicting the bearing capacity of shallow foundations at any given depth. &amp;#160;Moreover, the formulated model can also be used for the transition zone between general and local shear failure conditions.&lt;/p&gt;
Using various GCM (general circulation model), the present study attempted to analyze the impact ... more Using various GCM (general circulation model), the present study attempted to analyze the impact of climate change on the entire stretch of one of the major rivers in South Asia, the Brahmaputra river basin. Initially, we identified a suitable GCM based on some statistical measures of the interpolated and bias-corrected variables. The results of the trend analysis show a significant impact on the climatic variables during future periods. The Brahmaputra basin is likely to experience an increase in rainfall, maximum temperature, and minimum temperature at the rates of 2.5 mm/year, 0.062 °C/year and 0.05 °C/year, respectively, corresponding to representative concentration pathway (RCP) 8.5 scenarios till the end of the current century. Moreover, the climate change impact analysis on streamflow indicates a rise of up to 13.06% in annual discharge at Pandughat, India. The findings of this study will provide a basis for water resource management of the transboundary Brahmaputra basin in the coming decades.
In recent years, pervious concrete (PC) has gained much attention as one of the strategies for lo... more In recent years, pervious concrete (PC) has gained much attention as one of the strategies for low-impact development (LID) in pavements due to its structural, economic, and road-user benefits. This study sought to review and evaluate changes in the mechanical, hydraulic, and durability performance of PC produced with cement and aggregate replacements. A meta-analysis was conducted to elucidate the feasible range of the replacement percentage and the number of materials that could be used to replace cement and aggregates; single or binary replacements were considered. Results indicated that cement-replacing materials, industrial wastes (IWA), and recycled aggregates (RA) met the minimum requirement for the mechanical, hydraulic, and durability properties of PC. The use of a single cement replacement material provided PC with better performance than when cement was replaced with two or more materials or when cement alone was used. Industrial waste was found to be a better replacement...
&amp;lt;p&amp;gt;In recent years, many researchers indicated that earth-observing satelli... more &amp;lt;p&amp;gt;In recent years, many researchers indicated that earth-observing satellites perform well in measuring or estimating precipitation rates. However, it has been highlighted that the performance of satellite rainfall estimates (SREs) is affected by many factors. In this study, a meta-data analysis was conducted to assess the performance of different SREs for flood and drought monitoring under diverse settings to test the influence of factors related to climate, topography, watershed size, and length of SREs data record. Koppen climate classification was used to classify the different studies into different climatic zone. Mean elevation was used as an indicator of varying topography. Studies were grouped into three different categories depending upon their available data record length. The impact of various factors on the performance of SREs was assessed with three statistical indices: Pearson correlation coefficient, Root Mean Square Error, and Nash-Sutcliffe Efficiency. Results showed that the performance of SREs for drought and flood monitoring is influenced by the climate, length of the data record, interactions between the applied hydrological model and type of SRE, and topography. Microwave-based SREs performed were found to perform better than infrared-based SREs. Low lying landscapes exhibited higher accuracy of SREs in flood and drought monitoring compared to complex mountainous terrain. In most cases, IMERG and CMORPH outperformed other SREs. &amp;amp;#160;IMERG showed the best drought monitoring performance with Pearson correlation values ranging between 0.96-0.99. It was found that the best SREs that can represent the observed streamflow vary depending on the type of hydrological models. Also, the hydrological model performance for flood prediction significantly increases (p&amp;lt;0.05) when using the SREs for model calibration compared to when the model is manually calibrated with historical gauge data. Bias-adjusted SREs performed better than their counterpart. Overall, SREs offer great potential for flood and drought monitoring, but their performance needs to be enhanced for hydrological applications.&amp;lt;/p&amp;gt;
In recent years, satellite precipitation products (SPPs) have emerged as an essential source of d... more In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and o...
Flow-vegetation interactions modify the instream roughness and flow characteristics in the river ... more Flow-vegetation interactions modify the instream roughness and flow characteristics in the river and estuaries. This study proposes a new quasi three-dimensional hydrodynamic framework to compute the vertical velocity profile in an open channel having submerged flexible vegetation. A modified form of two-dimensional depth-averaged shallow water equations coupled with vegetal drag forces is derived and applied in the simulation. The explicit second-order accurate TVD McCormack predictor-corrector finite difference method with operator splitting technique is used to solve the governing equations in MATLAB. The TVD approach is robust and gives accurate results free from numerical oscillations. The bending profile of the flexible stems under various flow events is calculated from the cantilever beam theory. The vertical velocity profile in the vegetation layer and the free water layer is estimated from Reynold's stress equation and Shannon's entropy theory. The present model is used to replicate some popular experimental test cases. Results indicate a conservative and robust model performance under different flow conditions and patch density. Quantitative analysis of the predicted results is carried out using two statistical indices and found satisfactory.
The aquatic ecosystems are an integral part of the global environment and play a critical role in... more The aquatic ecosystems are an integral part of the global environment and play a critical role in hydrogeological processes in the rivers. In recent decades, the external stressors on the aquatic species have significantly increased due to hydrologic alterations, human activities, and anthropogenic changes to their natural habitat. Global climate change has led to rivers&#39; hydrological flow regime shifts, leading to unsuitable habitat conditions. It is, therefore, crucial to assess the potential implications of climate change on habitat suitability to ensure the long-term sustainability of freshwater species. In this direction, we investigated the association between endangered Gangetic dolphin populations and climate-driven hydrologic flow regime alterations in the Kulsi river, India. We developed a coupled eco-hydraulic framework comprising hydrological and hydrodynamic modeling to study the impact of past and projected climate change scenarios on the habitat suitability of target species. The framework was tested on a 15-km stretch of the Kulsi River, where the dolphin population has significantly declined in recent years. The temporal changes in the Weighted Usable Area (WUA) were analyzed using flow parameters and habitat suitability curves. Our findings suggest that the dolphin population decline coincided with a decrease in WUA, indicating a strong association between flow regimes and habitat suitability. Under climate change scenarios, multi-model climate projections and hydrological-hydrodynamic simulations show a rising trend in precipitation and streamflow in the basin, with substantial uncertainty. Higher flow depth and velocity would enhance WUA (habitat suitability). Still, the proposed river development projects in upstream regions could pose a serious threat to fragile dolphin communities by changing the seasonal flow patterns. The findings of this study can be included in conservation action plans and flow regulations strategies in upstream projects to ensure the long-term survival of endangered species.
AbstractSeasonal drying and wetting highly influence ecosystem diversity in intermittent streams.... more AbstractSeasonal drying and wetting highly influence ecosystem diversity in intermittent streams. Hydrological alterations are responsible for the spawning and reproduction of aquatic habitats. The...
<p>Droughts are considered as one of the most catastrophic natural disasters that affect hu... more <p>Droughts are considered as one of the most catastrophic natural disasters that affect humans and their surroundings at a larger spatial scale compared to other disasters. Rajasthan, one of India's semiarid states, is drought inclined and has experienced many drought events in the past. In this study, we evaluated different preprocessing and Machine Learning (ML) approaches for drought predictions in Rajasthan for a lead-time of up to 6 months. The Standardized Precipitation Index (SPI) was used as the drought quantifying measure to identify the drought events. SPI was calculated for 3, 6, and 12-month timescales over the last 115-year using monthly rainfall data at 119 grid stations. &#160;ML techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Linear Regression (LR), were used to evaluate their accuracy in drought forecasting over different lead times. Furthermore, two data processing methods, namely the Wavelet Packet Transform (WPT) and Discrete Wavelet Transform (DWT), have also been used to enhance the aforementioned ML models' predictability. At the outset, the preprocessed SPI data from both the methods were used as inputs for LR, SVR, and ANN to form a hybrid model. The hybrid models' drought predictability for a different lead-time was evaluated and compared with the standalone ML models. The forecasting performance of all the models for all 119 grid points was assessed with three statistical indices: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency (NSE). RMSE was used to select the optimal model parameters, such as the number of hidden neurons and the number of inputs in ANN, and the level of decomposition and mother wavelet in wavelet analysis. &#160;Based on these measures, the coupled model showed better forecasting performance than the standalone ML models. The coupled WPT-ANN model shows superior predictability for most of the grid points than other coupled models and standalone models. &#160;All models' performance improved as the timescale increased from 3 to 12 months for all the lead times. However, the model performance decreased as the lead time increased.&#160; These findings indicate the necessity of processing the data before the application of any machine learning technique. The hybrid model's prediction performance also shows that it can be used for drought early warning systems in the state.</p>
&lt;p&gt;The success of any civil engineering structure's foundation design depends u... more &lt;p&gt;The success of any civil engineering structure's foundation design depends upon the accuracy of estimation of soil&amp;#8217;s ultimate bearing capacity. Numerous numerical approaches have been proposed to estimate the foundation's bearing capacity value to avoid repetitive and expensive experimental work. All these models have their advantages and disadvantages. In this study, we compiled all the governing equations mentioned in Bureau of Indian standard IS:6403-1981 and modify the equation for Ultimate Bearing Capacity. The equation was modified by considering two new parameters, K1(for general shear) and K2 (for local shear) so that a common governing equation can be used for both general and local shear failure criteria. The program used for running the model was written in MATLAB language code and verified with the observed field data. Results indicate that the proposed model accurately characterized the ultimate, safe, and allowable bearing capacity of a shallow footing at different depths. The correlation coefficients between the observed and model-predicted bearing capacity values for a 2m foundation depth with footing size of 1.5 &amp;#215;1.5, 2.0 &amp;#215; 2.0, and 2.5 &amp;#215; 2.5 m are 0.95, 0.94, and 0.96. A similar result was noted for the other foundation depth and footing size. Findings show that the model can be used as a reliable tool for predicting the bearing capacity of shallow foundations at any given depth. &amp;#160;Moreover, the formulated model can also be used for the transition zone between general and local shear failure conditions.&lt;/p&gt;
Using various GCM (general circulation model), the present study attempted to analyze the impact ... more Using various GCM (general circulation model), the present study attempted to analyze the impact of climate change on the entire stretch of one of the major rivers in South Asia, the Brahmaputra river basin. Initially, we identified a suitable GCM based on some statistical measures of the interpolated and bias-corrected variables. The results of the trend analysis show a significant impact on the climatic variables during future periods. The Brahmaputra basin is likely to experience an increase in rainfall, maximum temperature, and minimum temperature at the rates of 2.5 mm/year, 0.062 °C/year and 0.05 °C/year, respectively, corresponding to representative concentration pathway (RCP) 8.5 scenarios till the end of the current century. Moreover, the climate change impact analysis on streamflow indicates a rise of up to 13.06% in annual discharge at Pandughat, India. The findings of this study will provide a basis for water resource management of the transboundary Brahmaputra basin in the coming decades.
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