Manoj Gundalia - Academia.edu (original) (raw)

Papers by Manoj Gundalia

International research journal of innovations in engineering and technology, 2024

International Journal of Darshan Institute on Engineering Research & Emerging Technology

The Rainfall is a crucial hydro meteorological variable in arid and semi-arid region due to its y... more The Rainfall is a crucial hydro meteorological variable in arid and semi-arid region due to its yawning impact on agriculture, drinking water and energy sectors. Junagadh (Gujarat-India) region reels under rainfall uncertainties and thereby water resources and crop production suffer a lot. Rainfall is highly complex, nonlinear, and dynamic in nature and affected by many interrelated meteorological parameters. Further the temporal and spatial variability causes more uncertainty in its occurrence. Despite significant contribution of advance computing techniques, the rainfall prediction yet remains a tough challenge. Holt-Winters model is a time series model and it relies on three aspects of the time series: a typical value (average), a slope (trend) over time, and a cyclical repeating pattern (seasonality). Annual rainfall data often exhibits trend and seasonality and hence, the Holt-Winters models could be the best choice for its prediction. This study examined ability of three different types of time series models (Holt-Winters model (HW), Multiplicative Holt-Winters (MHW) and Additive Holt-Winters (AHW)) in predicting annual rainfall for Junagadh (Gujarat-India) region. Performances of the models were evaluated by using refined Willmott's index (dr) and mean absolute error (MAE) evaluation measures. All the three models performed better and are recommended for forecasting annual rainfall of the selected region and the similar hydro-meteorological regions.

In this paper attempt is made to estimate reference evapotranspiration (ET o) from standard meteo... more In this paper attempt is made to estimate reference evapotranspiration (ET o) from standard meteorological observations. The FAO-56 Penman-Monteith method is the most physical, reliable and mostly used as a standard to verify other empirical methods. However, it needs a lot of different input parameters. Hence, in the present study, a model based on most dominant meteorological variables influencing ET o is proposed to estimate ET o in the Middle South Saurashtra region of Gujarat (India). The performance of five different alternative methods and proposed model is compared keeping the FAO-56 Penman-Monteith method as reference. The models are evaluated by using Nash-Sutcliffe efficiency coefficient (E), (R 2), (d r), (RSR) and (MAE) statistical criterions. The results show that the developed model and Hargreaves and Samani (1985) method provide the most reliable results in estimation of (ET o), and it can be recommended for estimating (ET o) in the study region.

Water Practice & Technology

Ghed region is located in the deep western part of Gujarat state, having the cup shape geometry. ... more Ghed region is located in the deep western part of Gujarat state, having the cup shape geometry. The Ozat River begins near the Gir forest's hilly part and moves towards the river mouth near Navi Bandar. The part before the river mouth is called Ghed, near the coastal line. The inundation in this region occurred due to higher coastal line and cup shape geometry with an area of more than 200 km2. This research emphasized early warning of the local community aside from the region during the peak flow condition. The hydrological engineering centre-river analysis system software developed the hydrodynamic model for FEWS (flood early warning system). The model has been validated with observed water depth data at four locations from the river reach area for more precision. In this regard, various statistics have been performed to compare the observed and modelled data. The result depicts the 19 h of leg time available to evacuate the local community. After that, water takes 115 h more...

Disaster Advances

India is an agriculture-based country and the agriculture product is highly influenced by the Sou... more India is an agriculture-based country and the agriculture product is highly influenced by the Southwest monsoon. Forecasting of monsoon is of prime importance for planning to select appropriate Kharif crops and their varieties to minimize crop losses. Many Indian scientists have proposed sciencebased techniques while local traditional farmers have used indigenous methods to forecast weather conditions and predict a likely behaviour of the Southwest monsoon. However, predicting the Southwest monsoon pattern remains the most challenging task till date. In the present study, a methodology is developed to predict the Southwest monsoon for sub-regions of Saurashtra (India) based on the observation of some of the local factors consisting of observation of local weather, type of wind and its direction, heat waves, astronomical parameters and cloud type pattern. The predicted average rainfall was found 860mm which is nearly 20% less (1055mm) for the year 2019.The results show that the metho...

Flooding is an inevitable phenomenon of nature; however, its effect can be reduced via flood asse... more Flooding is an inevitable phenomenon of nature; however, its effect can be reduced via flood assessment. Therefore, flood inundation mapping is vital for flood assessment and mitigation planning in developing countries. But, flood assessment needs massive data sets to perform the flood simulation. Hence, the availability of precious observed data for flood assessment plays a significant role in research methodology to overcome the limitation and barriers for efficient modeling. The present study aims to evaluate the inundated area of Ghed region using 2-dimensional (2D) hydrodynamic analysis. The new HEC-RAS v6 uses an open-source digital elevation model (DEM) for hydraulic analysis to develop flood inundation, velocity, depth, arrival time, and percentage time inundation maps. The results are validated with 2017 and 2021 satellite images, hence the machine-learning algorithm generated in the Google Earth Engine (GEE) cloud platform to visualize the flooded area. In GEE, a flood map...

Journal of Engineering Education Transformations

Course Outcomes (CO) assessment is one of the most important components of Outcome Based Educatio... more Course Outcomes (CO) assessment is one of the most important components of Outcome Based Education (OBE) to describe the specific type and level of learning students. The programs must have an effective end results in every course and their systematic assessment with proper documentation and should evaluate continuously to establish certain standards leading to program accreditation. In the calculation of CO attainment, the mean values are usually considered as a target values but it may create ambiguity in decision making. The target value in conventional method is unbounded on the upper and lower ends, which undermines interpretations of student's category associated with their performance. In this paper an attempt has been made with a new approach for calculation of CO attainments based on Lower Confidence Limit (LCL) and Upper Confidence Limit (UCL) and compared against conventional method. This comparison has shown that the proposed method showed a nontrivial improvement ov...

Rainfall is a meagre and crucial hydrological parameter in arid and semi-arid region. Junagadh (G... more Rainfall is a meagre and crucial hydrological parameter in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. The amount of runoff produced and rainfall received determine the development of water resources in any region. Appropriate probability distributions need to be selected and fitted to the historical rainfall time series for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. In this study an attempt was made to find the most appropriate probability distributions for the better prediction of maximum rainfall by fitting the eight different hypothetical probability distributions to the monthly and annual maximum rainfall for...

Modeling Earth Systems and Environment

Parameter estimation of any model remains a very challenging problem since last few decades due t... more Parameter estimation of any model remains a very challenging problem since last few decades due to its non-convexity and ill-conditioning. The non-convexity of the parameter estimation problem usually resolved by using suitable global optimization methods while ill-conditioning and over fitting problems can be reduced with the help of regularization techniques. Nowadays, application of efficient advanced computing techniques and robust optimization algorithm are applied in model calibration, but none of them provide a unique value of parameter. Nash proposed the instantaneous unit hydrograph is often used in flood forecasting and rainfall-runoff. The success of Nash instantaneous unit hydrograph model is highly depended on the accuracy of its parameters 'n' and 'k' estimation. The present study was undertaken to estimate the parameters 'n' and 'k' of Nash conceptual model using moments methods and C programming from excess rainfall hyetograph and dire...

The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to es-t... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to es-timate runoff. It combines watershed parameters and climatic factors in one entity curve number (CN). The CN exhibits an inherent seasonality beyond its spatial variability, which cannot be ac-counted for by the conventional methods. In the present study, an attempt has been made to de-termine the CN for different months of monsoon season with an objective to evaluate the impact of monthly CN on runoff estimation for Ozat catchment (Gujarat State, India). The standard CN and month wise CN were determined by three procedures, viz, the median, geometric mean and stan-dard asymptotic fit using gauged rainfall and runoff. This study shows that the predictive capabil-ity of CN determination methods can be improved by using monthly CN. Refined Willmott’s index (dr) and mean absolute error (MAE) were used to assess and validate the performance of each me-thod. The asymptotic fit CN method with m...

The significance of major meteorological factors, that influence the evaporation were evaluated a... more The significance of major meteorological factors, that influence the evaporation were evaluated at daily timescale for monsoon season using the data from Junagadh station, Gujarat (India). The computed values were compared. The solar radiation and mean air temperature were found to be the significant factors influencing pan evaporation (Ep). The negative correlation was found between relative humidity and (Ep), while wind speed, vapour pressure deficit and bright sunshine hours were found least correlated and no longer remained controlling factors influencing (Ep). The objective of the present study is to compare and evaluate the performance of six different methods based on temperature and radiation to select the most appropriate equations for estimating (Ep). The three quantitative standard statistical performance evaluation measures, coefficient of determination (R 2 ), root mean square of errors-observations standard deviation ratio (RSR) and Nash-Sutcliffe efficiency coefficien...

Modelling the runoff becomes more challenging as runoff generation process is highly complex, non... more Modelling the runoff becomes more challenging as runoff generation process is highly complex, nonlinear, dynamic in nature, and affected by many interrelated physical factors. Furthermore, the temporal and spatial scale of estimating runoff exhibits another complex issue. However, with present technological capabilities, computing techniques and software tools, it is possible to identify, assess and understand the response of the dominant processes rather accurately. Many methods are being used to estimate runoff in literature, however, the Natural Resources Conservation Service Curve Number (NRCS-CN) (formerly called as SCS-CN) method developed by the U. S. Department of Agriculture (USDA) still remain the most popular, fruitful and frequently used method. The major reasons for this popularity may be attributed to ease of use, less number of input parameters, robustness of model results, and acceptability among both researcher and practitioner community. The runoff curve number (CN...

The IHACRES model is being applied in a regionalization approach to develop streamflow prediction... more The IHACRES model is being applied in a regionalization approach to develop streamflow predictions. The IHACRES rainfall-runoff model uses a non-linear loss module to calculate the effective rainfall and a linear routing module to convert effective rainfall into stream flow. A new version of the non-linear module, developed to aid in estimating flows in ungauged basins and for applications where time series estimates of actual evapotranspiration are required. The new module has only 3 parameters and has significantly less correlation between the parameters. Model used one of these regression relationships to represent possible effects of declining forest cover on streamflow, but did not obtain regional models for the parameters of the routing model of IHACRES. Future research should focus on (1) increasing the quality of rainfall estimates as an important way to potentially improve simulation performance; (2) developing joint probability distributions over the full set of IHACRES mo...

International Journal of Advance Engineering and Research Development, 2014

The Soil Conservation Service Curve Number (SCS-CN) is a well-established and widely used loss-ra... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established and widely used loss-rate model to estimate surface runoff. It combines watershed and climatic parameters in one entity curve number (CN). Much of the variability in CN has been attributed to antecedent runoff condition (ARC). The (CN) also exhibits an inherent seasonality beyond its spatial variability, which cannot be accounted for by the conventional methods. In the present study, CN were determined by three different approaches, standard CN, monthly CN and CN based on five day antecedent rainfall-runoff (ARR) data set using standard asymptotic fit and gauged rainfall-runoff data with an objective to evaluate the impact of monthly CN and five days ARR data set on runoff estimation for Ozat watershed (Gujarat State-India). The significant improvement in performance of SCS-CN method is found on application of CN based on five day ARR data set as compare to monthly CN for Ozat watershed. Refined Willmott's index (d r) and mean absolute error (MAE) were used to assess and validate the performance of SCS-CN method. For the study region, the CN determined based on five day ARR data set was judged to be more consistent with d r =0.58 and MAE=0.93 mm for λ=0.05. Keywords-soil conservation service curve number method; curve number; seasonal variation; antecedent rainfall; ozat watershed I. INTRODUCTION The SCS-CN method was first introduced in 1954 and which has been now renamed as Natural Resource Conservation Service (NRCS)-CN method. The primary reason for its wide applicability and acceptability lies in the fact that it accounts for major runoff-generating watershed characteristics, namely, soil type, land use/treatment, surface condition and antecedent moisture condition [1-3]. In contrast, the main weaknesses reported in the literature are that it does not consider the impact of rainfall intensity, seasonal variability and the effects of spatial scale.It is highly sensitive to changes in values of its single parameter CN and ambiguous considering the effect of antecedent moisture conditions [4, 5]. Nevertheless, the model development has made much progress in last three decades; a need of further improvements has always been experienced to satisfy unresolved challenges. After the critical examination of the methodology, the SCS-CN method has gained much attention with respect to its modification and investigation. Many researchers [e.g. 6-9] have examined the accuracy of the CN method and identify specific unrecognized weaknesses and limitations those were rarely noted in textbooks. Inability to account for the temporal variation in rainfall and runoff is its prime limitation. Recent modifications in determination of CN are reported by slope adjustment procedure [10], two-CN system approach [11] and composite CN-generation [12]. The SCS-CN model implementing with these modifications would have a better simulation performance than the existing original SCS-CN. In CN method, parameters those influence the seasonal variation on predicting runoff have not been incorporated and hence, it ignores the impact of seasonal and monthly variation. Although the CN method is well documented and widely used, as [13] pointed out, a need to use the method as a guideline and interpret inputs on a more local and regional level combined with seasonal variation is essential. Runoff simulation with annually consistent parameters has limited application because watershed response varies remarkably from season to season. The seasonal tank model developed by [14] showed better performance compared to the non-seasonal tank model because it can successfully simulate runoff with little error. CN on monthly basis and CN based on five day antecedent rainfall-runoff (ARR) data set, therefore, may also result in more accurate runoff estimation and improve the performance of SCS-CN model. The investigation reported in this paper is motivated by the need toevaluate the impact of monthly CNand CN based on five day ARR data set on runoff estimation for Ozatwatershed. The standard asymptotic fit [15] procedure is employed to calculate standard CN, monthly CN and CN based on ARR data set. The objectives of this study were: (1) to compare standard CN determined by gauged daily rainfall-runoff data set with monthly CN and CN based on ARR data set; and (2) to evaluate the impact of monthly CN and CN based on ARR data set on performance of SCS-CN method for Ozatwatershed.

IV International Symposium on Irrigation of Horticultural Crops, 2004

Open Journal of Modern Hydrology, 2014

The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to esti... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to estimate runoff. It combines watershed parameters and climatic factors in one entity curve number (CN). The CN exhibits an inherent seasonality beyond its spatial variability, which cannot be accounted for by the conventional methods. In the present study, an attempt has been made to determine the CN for different months of monsoon season with an objective to evaluate the impact of monthly CN on runoff estimation for Ozat catchment (Gujarat State, India). The standard CN and month wise CN were determined by three procedures, viz, the median, geometric mean and standard asymptotic fit using gauged rainfall and runoff. This study shows that the predictive capability of CN determination methods can be improved by using monthly CN. Refined Willmott's index (dr) and mean absolute error (MAE) were used to assess and validate the performance of each method. The asymptotic fit CN method with monthly CN resulting d r from 0.46 to 0.49 and MAE from 1.13 mm to 1.18 mm was judged to be more consistent with the existing commonly used CN methods in terms of runoff estimation for the study area.

Meteorology and Atmospheric Physics, 2012

In achieving water resource management goals such as irrigation scheduling, an accurate estimate ... more In achieving water resource management goals such as irrigation scheduling, an accurate estimate of reference evapotranspiration (ET 0) is critical. Support vector regression (SVR) was applied to the modeling of daily ET 0 at three meteorological stations in Iran subject to different climates: Isfahan (arid), Urmia (semi-arid), and Yazd (hyper-arid). Different pre-processing approaches [relief (RL), random forests (RF), principal component analysis (PCA), and Pearson's correlation (COR)] served to determine the SVR's optimal input combinations. While these approaches introduced different inputs to the SVR models, those drawn upon by the RF approach (i.e., RF-SVR) generated better results than other approaches. Models performance was evaluated using the root mean square error (RMSE), normalized RMSE (NRMSE), mean absolute error (MAE), coefficient of determination (R 2), and the Nash-Sutcliffe efficiency (E). A novel hybrid model, coupling SVR with a whale optimization algorithm (WOA), was also developed and applied to daily ET 0 modeling. The hybrid models outperformed the SVR-only models, with the hybrid RF-SVR-WOA model having the best performance. ET 0. In modeling monthly mean ET 0 of 275 stations in Turkey, Citakoglu et al. (2014) found an adaptive neuro-fuzzy inference system (ANFIS) outperformed an artificial neural network (ANN). Similarly, Kisi et al. (2015), employing data-driven methods including ANN, GEP, ANFIS-grid partitioning (ANFIS-GP), and ANFIS-subtractive clustering (ANFIS-SC) to model mean monthly ET 0 at 50 stations in Iran, found the ANFIS-GP to have performed best. Comparing the performance of ANN and GEP for modeling daily ET 0 at 19 stations in Saudi Arabia, Yassin et al. (2016) found ANN models to provide greater accuracy than GEP models. Likewise, Patil and Deka (2016), estimating weekly ET 0 at Jodhpur and Pali, India, compared the performance of ANN, least square support vector machine (LSSVM), and extreme learning machine (ELM) approaches. The ELM provided better ET 0 estimates than either

International Journal of Hydrology, 2018

Infiltration refers to the downward movement of water into the soil from the surface, driven by f... more Infiltration refers to the downward movement of water into the soil from the surface, driven by force of gravity and capillary attraction. Infiltration of water in unsaturated soils has long been an important impact in soil science, hydrology, and geotechnical engineering. Fieldmeasurement of infiltration is very tedious and time consumingand, hence, it is often estimated from different conceptual models. Thus, in the present study, a model based on complementary error function peak (ERFC) is proposed to determine infiltration rate for the Ozat Watershed of Gujarat (India). The two quantitative standard statistical performance evaluation measures, refined Willmott's index (d r) and mean absolute error (MAE) are employed in comparingand evaluating the performance of theproposed model with existing Horton's, Modified Kostiakov, and Green-Ampt infiltration models. The results of the models are validated with the Double-ring infiltrometer field data of different soil types. The proposed ERFC based model was judged to be more consistent with d r =(0.87 to 0.90) and MAE=(0.47 to 0.27cm/h r). The results presented in this work are quite encouraging. Further the findings indicate that the use of ERFC based proposed model appears to be the most suitable and appropriate for estimation of infiltration rate in Ozat watershed.

International research journal of innovations in engineering and technology, 2024

International Journal of Darshan Institute on Engineering Research & Emerging Technology

The Rainfall is a crucial hydro meteorological variable in arid and semi-arid region due to its y... more The Rainfall is a crucial hydro meteorological variable in arid and semi-arid region due to its yawning impact on agriculture, drinking water and energy sectors. Junagadh (Gujarat-India) region reels under rainfall uncertainties and thereby water resources and crop production suffer a lot. Rainfall is highly complex, nonlinear, and dynamic in nature and affected by many interrelated meteorological parameters. Further the temporal and spatial variability causes more uncertainty in its occurrence. Despite significant contribution of advance computing techniques, the rainfall prediction yet remains a tough challenge. Holt-Winters model is a time series model and it relies on three aspects of the time series: a typical value (average), a slope (trend) over time, and a cyclical repeating pattern (seasonality). Annual rainfall data often exhibits trend and seasonality and hence, the Holt-Winters models could be the best choice for its prediction. This study examined ability of three different types of time series models (Holt-Winters model (HW), Multiplicative Holt-Winters (MHW) and Additive Holt-Winters (AHW)) in predicting annual rainfall for Junagadh (Gujarat-India) region. Performances of the models were evaluated by using refined Willmott's index (dr) and mean absolute error (MAE) evaluation measures. All the three models performed better and are recommended for forecasting annual rainfall of the selected region and the similar hydro-meteorological regions.

In this paper attempt is made to estimate reference evapotranspiration (ET o) from standard meteo... more In this paper attempt is made to estimate reference evapotranspiration (ET o) from standard meteorological observations. The FAO-56 Penman-Monteith method is the most physical, reliable and mostly used as a standard to verify other empirical methods. However, it needs a lot of different input parameters. Hence, in the present study, a model based on most dominant meteorological variables influencing ET o is proposed to estimate ET o in the Middle South Saurashtra region of Gujarat (India). The performance of five different alternative methods and proposed model is compared keeping the FAO-56 Penman-Monteith method as reference. The models are evaluated by using Nash-Sutcliffe efficiency coefficient (E), (R 2), (d r), (RSR) and (MAE) statistical criterions. The results show that the developed model and Hargreaves and Samani (1985) method provide the most reliable results in estimation of (ET o), and it can be recommended for estimating (ET o) in the study region.

Water Practice & Technology

Ghed region is located in the deep western part of Gujarat state, having the cup shape geometry. ... more Ghed region is located in the deep western part of Gujarat state, having the cup shape geometry. The Ozat River begins near the Gir forest's hilly part and moves towards the river mouth near Navi Bandar. The part before the river mouth is called Ghed, near the coastal line. The inundation in this region occurred due to higher coastal line and cup shape geometry with an area of more than 200 km2. This research emphasized early warning of the local community aside from the region during the peak flow condition. The hydrological engineering centre-river analysis system software developed the hydrodynamic model for FEWS (flood early warning system). The model has been validated with observed water depth data at four locations from the river reach area for more precision. In this regard, various statistics have been performed to compare the observed and modelled data. The result depicts the 19 h of leg time available to evacuate the local community. After that, water takes 115 h more...

Disaster Advances

India is an agriculture-based country and the agriculture product is highly influenced by the Sou... more India is an agriculture-based country and the agriculture product is highly influenced by the Southwest monsoon. Forecasting of monsoon is of prime importance for planning to select appropriate Kharif crops and their varieties to minimize crop losses. Many Indian scientists have proposed sciencebased techniques while local traditional farmers have used indigenous methods to forecast weather conditions and predict a likely behaviour of the Southwest monsoon. However, predicting the Southwest monsoon pattern remains the most challenging task till date. In the present study, a methodology is developed to predict the Southwest monsoon for sub-regions of Saurashtra (India) based on the observation of some of the local factors consisting of observation of local weather, type of wind and its direction, heat waves, astronomical parameters and cloud type pattern. The predicted average rainfall was found 860mm which is nearly 20% less (1055mm) for the year 2019.The results show that the metho...

Flooding is an inevitable phenomenon of nature; however, its effect can be reduced via flood asse... more Flooding is an inevitable phenomenon of nature; however, its effect can be reduced via flood assessment. Therefore, flood inundation mapping is vital for flood assessment and mitigation planning in developing countries. But, flood assessment needs massive data sets to perform the flood simulation. Hence, the availability of precious observed data for flood assessment plays a significant role in research methodology to overcome the limitation and barriers for efficient modeling. The present study aims to evaluate the inundated area of Ghed region using 2-dimensional (2D) hydrodynamic analysis. The new HEC-RAS v6 uses an open-source digital elevation model (DEM) for hydraulic analysis to develop flood inundation, velocity, depth, arrival time, and percentage time inundation maps. The results are validated with 2017 and 2021 satellite images, hence the machine-learning algorithm generated in the Google Earth Engine (GEE) cloud platform to visualize the flooded area. In GEE, a flood map...

Journal of Engineering Education Transformations

Course Outcomes (CO) assessment is one of the most important components of Outcome Based Educatio... more Course Outcomes (CO) assessment is one of the most important components of Outcome Based Education (OBE) to describe the specific type and level of learning students. The programs must have an effective end results in every course and their systematic assessment with proper documentation and should evaluate continuously to establish certain standards leading to program accreditation. In the calculation of CO attainment, the mean values are usually considered as a target values but it may create ambiguity in decision making. The target value in conventional method is unbounded on the upper and lower ends, which undermines interpretations of student's category associated with their performance. In this paper an attempt has been made with a new approach for calculation of CO attainments based on Lower Confidence Limit (LCL) and Upper Confidence Limit (UCL) and compared against conventional method. This comparison has shown that the proposed method showed a nontrivial improvement ov...

Rainfall is a meagre and crucial hydrological parameter in arid and semi-arid region. Junagadh (G... more Rainfall is a meagre and crucial hydrological parameter in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. The amount of runoff produced and rainfall received determine the development of water resources in any region. Appropriate probability distributions need to be selected and fitted to the historical rainfall time series for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. In this study an attempt was made to find the most appropriate probability distributions for the better prediction of maximum rainfall by fitting the eight different hypothetical probability distributions to the monthly and annual maximum rainfall for...

Modeling Earth Systems and Environment

Parameter estimation of any model remains a very challenging problem since last few decades due t... more Parameter estimation of any model remains a very challenging problem since last few decades due to its non-convexity and ill-conditioning. The non-convexity of the parameter estimation problem usually resolved by using suitable global optimization methods while ill-conditioning and over fitting problems can be reduced with the help of regularization techniques. Nowadays, application of efficient advanced computing techniques and robust optimization algorithm are applied in model calibration, but none of them provide a unique value of parameter. Nash proposed the instantaneous unit hydrograph is often used in flood forecasting and rainfall-runoff. The success of Nash instantaneous unit hydrograph model is highly depended on the accuracy of its parameters 'n' and 'k' estimation. The present study was undertaken to estimate the parameters 'n' and 'k' of Nash conceptual model using moments methods and C programming from excess rainfall hyetograph and dire...

The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to es-t... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to es-timate runoff. It combines watershed parameters and climatic factors in one entity curve number (CN). The CN exhibits an inherent seasonality beyond its spatial variability, which cannot be ac-counted for by the conventional methods. In the present study, an attempt has been made to de-termine the CN for different months of monsoon season with an objective to evaluate the impact of monthly CN on runoff estimation for Ozat catchment (Gujarat State, India). The standard CN and month wise CN were determined by three procedures, viz, the median, geometric mean and stan-dard asymptotic fit using gauged rainfall and runoff. This study shows that the predictive capabil-ity of CN determination methods can be improved by using monthly CN. Refined Willmott’s index (dr) and mean absolute error (MAE) were used to assess and validate the performance of each me-thod. The asymptotic fit CN method with m...

The significance of major meteorological factors, that influence the evaporation were evaluated a... more The significance of major meteorological factors, that influence the evaporation were evaluated at daily timescale for monsoon season using the data from Junagadh station, Gujarat (India). The computed values were compared. The solar radiation and mean air temperature were found to be the significant factors influencing pan evaporation (Ep). The negative correlation was found between relative humidity and (Ep), while wind speed, vapour pressure deficit and bright sunshine hours were found least correlated and no longer remained controlling factors influencing (Ep). The objective of the present study is to compare and evaluate the performance of six different methods based on temperature and radiation to select the most appropriate equations for estimating (Ep). The three quantitative standard statistical performance evaluation measures, coefficient of determination (R 2 ), root mean square of errors-observations standard deviation ratio (RSR) and Nash-Sutcliffe efficiency coefficien...

Modelling the runoff becomes more challenging as runoff generation process is highly complex, non... more Modelling the runoff becomes more challenging as runoff generation process is highly complex, nonlinear, dynamic in nature, and affected by many interrelated physical factors. Furthermore, the temporal and spatial scale of estimating runoff exhibits another complex issue. However, with present technological capabilities, computing techniques and software tools, it is possible to identify, assess and understand the response of the dominant processes rather accurately. Many methods are being used to estimate runoff in literature, however, the Natural Resources Conservation Service Curve Number (NRCS-CN) (formerly called as SCS-CN) method developed by the U. S. Department of Agriculture (USDA) still remain the most popular, fruitful and frequently used method. The major reasons for this popularity may be attributed to ease of use, less number of input parameters, robustness of model results, and acceptability among both researcher and practitioner community. The runoff curve number (CN...

The IHACRES model is being applied in a regionalization approach to develop streamflow prediction... more The IHACRES model is being applied in a regionalization approach to develop streamflow predictions. The IHACRES rainfall-runoff model uses a non-linear loss module to calculate the effective rainfall and a linear routing module to convert effective rainfall into stream flow. A new version of the non-linear module, developed to aid in estimating flows in ungauged basins and for applications where time series estimates of actual evapotranspiration are required. The new module has only 3 parameters and has significantly less correlation between the parameters. Model used one of these regression relationships to represent possible effects of declining forest cover on streamflow, but did not obtain regional models for the parameters of the routing model of IHACRES. Future research should focus on (1) increasing the quality of rainfall estimates as an important way to potentially improve simulation performance; (2) developing joint probability distributions over the full set of IHACRES mo...

International Journal of Advance Engineering and Research Development, 2014

The Soil Conservation Service Curve Number (SCS-CN) is a well-established and widely used loss-ra... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established and widely used loss-rate model to estimate surface runoff. It combines watershed and climatic parameters in one entity curve number (CN). Much of the variability in CN has been attributed to antecedent runoff condition (ARC). The (CN) also exhibits an inherent seasonality beyond its spatial variability, which cannot be accounted for by the conventional methods. In the present study, CN were determined by three different approaches, standard CN, monthly CN and CN based on five day antecedent rainfall-runoff (ARR) data set using standard asymptotic fit and gauged rainfall-runoff data with an objective to evaluate the impact of monthly CN and five days ARR data set on runoff estimation for Ozat watershed (Gujarat State-India). The significant improvement in performance of SCS-CN method is found on application of CN based on five day ARR data set as compare to monthly CN for Ozat watershed. Refined Willmott's index (d r) and mean absolute error (MAE) were used to assess and validate the performance of SCS-CN method. For the study region, the CN determined based on five day ARR data set was judged to be more consistent with d r =0.58 and MAE=0.93 mm for λ=0.05. Keywords-soil conservation service curve number method; curve number; seasonal variation; antecedent rainfall; ozat watershed I. INTRODUCTION The SCS-CN method was first introduced in 1954 and which has been now renamed as Natural Resource Conservation Service (NRCS)-CN method. The primary reason for its wide applicability and acceptability lies in the fact that it accounts for major runoff-generating watershed characteristics, namely, soil type, land use/treatment, surface condition and antecedent moisture condition [1-3]. In contrast, the main weaknesses reported in the literature are that it does not consider the impact of rainfall intensity, seasonal variability and the effects of spatial scale.It is highly sensitive to changes in values of its single parameter CN and ambiguous considering the effect of antecedent moisture conditions [4, 5]. Nevertheless, the model development has made much progress in last three decades; a need of further improvements has always been experienced to satisfy unresolved challenges. After the critical examination of the methodology, the SCS-CN method has gained much attention with respect to its modification and investigation. Many researchers [e.g. 6-9] have examined the accuracy of the CN method and identify specific unrecognized weaknesses and limitations those were rarely noted in textbooks. Inability to account for the temporal variation in rainfall and runoff is its prime limitation. Recent modifications in determination of CN are reported by slope adjustment procedure [10], two-CN system approach [11] and composite CN-generation [12]. The SCS-CN model implementing with these modifications would have a better simulation performance than the existing original SCS-CN. In CN method, parameters those influence the seasonal variation on predicting runoff have not been incorporated and hence, it ignores the impact of seasonal and monthly variation. Although the CN method is well documented and widely used, as [13] pointed out, a need to use the method as a guideline and interpret inputs on a more local and regional level combined with seasonal variation is essential. Runoff simulation with annually consistent parameters has limited application because watershed response varies remarkably from season to season. The seasonal tank model developed by [14] showed better performance compared to the non-seasonal tank model because it can successfully simulate runoff with little error. CN on monthly basis and CN based on five day antecedent rainfall-runoff (ARR) data set, therefore, may also result in more accurate runoff estimation and improve the performance of SCS-CN model. The investigation reported in this paper is motivated by the need toevaluate the impact of monthly CNand CN based on five day ARR data set on runoff estimation for Ozatwatershed. The standard asymptotic fit [15] procedure is employed to calculate standard CN, monthly CN and CN based on ARR data set. The objectives of this study were: (1) to compare standard CN determined by gauged daily rainfall-runoff data set with monthly CN and CN based on ARR data set; and (2) to evaluate the impact of monthly CN and CN based on ARR data set on performance of SCS-CN method for Ozatwatershed.

IV International Symposium on Irrigation of Horticultural Crops, 2004

Open Journal of Modern Hydrology, 2014

The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to esti... more The Soil Conservation Service Curve Number (SCS-CN) is a well-established loss-rate model to estimate runoff. It combines watershed parameters and climatic factors in one entity curve number (CN). The CN exhibits an inherent seasonality beyond its spatial variability, which cannot be accounted for by the conventional methods. In the present study, an attempt has been made to determine the CN for different months of monsoon season with an objective to evaluate the impact of monthly CN on runoff estimation for Ozat catchment (Gujarat State, India). The standard CN and month wise CN were determined by three procedures, viz, the median, geometric mean and standard asymptotic fit using gauged rainfall and runoff. This study shows that the predictive capability of CN determination methods can be improved by using monthly CN. Refined Willmott's index (dr) and mean absolute error (MAE) were used to assess and validate the performance of each method. The asymptotic fit CN method with monthly CN resulting d r from 0.46 to 0.49 and MAE from 1.13 mm to 1.18 mm was judged to be more consistent with the existing commonly used CN methods in terms of runoff estimation for the study area.

Meteorology and Atmospheric Physics, 2012

In achieving water resource management goals such as irrigation scheduling, an accurate estimate ... more In achieving water resource management goals such as irrigation scheduling, an accurate estimate of reference evapotranspiration (ET 0) is critical. Support vector regression (SVR) was applied to the modeling of daily ET 0 at three meteorological stations in Iran subject to different climates: Isfahan (arid), Urmia (semi-arid), and Yazd (hyper-arid). Different pre-processing approaches [relief (RL), random forests (RF), principal component analysis (PCA), and Pearson's correlation (COR)] served to determine the SVR's optimal input combinations. While these approaches introduced different inputs to the SVR models, those drawn upon by the RF approach (i.e., RF-SVR) generated better results than other approaches. Models performance was evaluated using the root mean square error (RMSE), normalized RMSE (NRMSE), mean absolute error (MAE), coefficient of determination (R 2), and the Nash-Sutcliffe efficiency (E). A novel hybrid model, coupling SVR with a whale optimization algorithm (WOA), was also developed and applied to daily ET 0 modeling. The hybrid models outperformed the SVR-only models, with the hybrid RF-SVR-WOA model having the best performance. ET 0. In modeling monthly mean ET 0 of 275 stations in Turkey, Citakoglu et al. (2014) found an adaptive neuro-fuzzy inference system (ANFIS) outperformed an artificial neural network (ANN). Similarly, Kisi et al. (2015), employing data-driven methods including ANN, GEP, ANFIS-grid partitioning (ANFIS-GP), and ANFIS-subtractive clustering (ANFIS-SC) to model mean monthly ET 0 at 50 stations in Iran, found the ANFIS-GP to have performed best. Comparing the performance of ANN and GEP for modeling daily ET 0 at 19 stations in Saudi Arabia, Yassin et al. (2016) found ANN models to provide greater accuracy than GEP models. Likewise, Patil and Deka (2016), estimating weekly ET 0 at Jodhpur and Pali, India, compared the performance of ANN, least square support vector machine (LSSVM), and extreme learning machine (ELM) approaches. The ELM provided better ET 0 estimates than either

International Journal of Hydrology, 2018

Infiltration refers to the downward movement of water into the soil from the surface, driven by f... more Infiltration refers to the downward movement of water into the soil from the surface, driven by force of gravity and capillary attraction. Infiltration of water in unsaturated soils has long been an important impact in soil science, hydrology, and geotechnical engineering. Fieldmeasurement of infiltration is very tedious and time consumingand, hence, it is often estimated from different conceptual models. Thus, in the present study, a model based on complementary error function peak (ERFC) is proposed to determine infiltration rate for the Ozat Watershed of Gujarat (India). The two quantitative standard statistical performance evaluation measures, refined Willmott's index (d r) and mean absolute error (MAE) are employed in comparingand evaluating the performance of theproposed model with existing Horton's, Modified Kostiakov, and Green-Ampt infiltration models. The results of the models are validated with the Double-ring infiltrometer field data of different soil types. The proposed ERFC based model was judged to be more consistent with d r =(0.87 to 0.90) and MAE=(0.47 to 0.27cm/h r). The results presented in this work are quite encouraging. Further the findings indicate that the use of ERFC based proposed model appears to be the most suitable and appropriate for estimation of infiltration rate in Ozat watershed.