Catchment In India Research Articles

Evaluation of augmented infiltration based LIDs for low lying urbanizing coastal catchments: a case study of Chennai city, India

ABSTRACT The study assessed the effectiveness of infiltration-based Low Impact Development (LID) techniques and augmented infiltration LIDs (such as recharge shaft) in a coastal urban catchment in India using a semi-coupled approach between numerical models VS2DI and Storm Water Management Model (SWMM). Site specific VS2DI modelling indicated that the infiltration LIDs resulted in more saturation excess runoff due to the soil’s insufficient hydraulic conductivity and increased lateral flow. Catchment-scale modeling indicated that LIDs significantly improved water retention (p ≪ 0.05), with a 35% reduction in total runoff volume. The recharge shafts were shown to be more efficient in reducing runoff volume and peak runoff, particularly for high runoff rates (more than 22 mm/hr). The study suggests that direct recharge to the aquifer through recharge shafts could be a suitable solution to manage runoff and meet the demand for domestic water supply during times of water scarcity in coastal urban catchments.

Assessment of land use/ land cover change derived catchment hydrologic response: An integrated parsimonious hydrological modeling and alteration analysis based approach

Land use/land cover (LULC) change, often a consequence of natural or anthropogenic drivers, plays a decisive role in governing global catchment dynamics, and subsequent impact on regional hydrology. Insight into the complex relationship between the drivers of LULC change and catchment hydrology is of utmost importance to decision makers. Contemplating the dynamic rainfall-runoff response of the Indian catchments, this study proposes an integrated modeling-based approach to identify the drivers and relative contribution to catchment hydrology. The proposed approach was evaluated in the tropical climate Nagavali River Basin (NRB) (9512 km2) of India. The Soil and Water Assessment Tool (SWAT) hydrological model, which uses daily-scale rainfall, temperature, wind speed, relative humidity, solar radiation, and streamflow information was integrated with the Indicators of Hydrologic Alteration (IHA) technique to characterize the plausible changes in the flow regime of the NRB. Subsequently, the Partial Least Squares Regression (PLSR) based modeling analysis was performed to quantify the relative contribution of individual LULC components on the catchment water balance. The outcomes of the study revealed that forest land has been significantly converted to agricultural land (45–59%) across the NRB resulting in mean annual streamflow increase of 3.57 m3/s during the monsoon season. The affinity between land use class and streamflow revealed that barren land (CN = 83–87) exhibits the maximum positive response to streamflow followed by the built-up land (CN = 89–91) and fallow land (CN = 88–93). The period 1985–1995 experienced an increased ET scenario (911–1050 mm), while the recent period (2005–2020) experienced reduced ET scenario owing to conversion of forest to agricultural land. Certainly, the study endorses adopting the developed methodology for understanding the complex land use and catchment-scale hydrologic interactions across global-scales for early watershed management planning.

Exploring relationships between drought characteristics and environmental flow conditions in Indian catchments

Exploring relationships between drought characteristics and environmental flow conditions in Indian catchments

Effect of climate change on soil erosion rate in a tropical Indian catchment

Abstract This study analyzes the variations in soil erosion rate in a tropical catchment in India using the empirical Revised Universal Soil Loss Equation (RUSLE) model integrated with climate change scenarios from an ensemble of global climate models (GCMs) included in the NEX Global Daily Downscaled Projections Coupled Model Intercomparison Project Phase 6 dataset for Shared Socioeconomic Pathways (SSP) 126 and SSP 585. A set of seven GCMs are initially selected. Based on their ability to simulate the rainfall for the current scenario, the PROMETHEE-II method is used to rank the GCMs and the top four best-performing models are used for further analysis. Soil erosion rates projected for the future climate scenarios are compared with the current scenario. In the near future and in the middle of the century, soil erosion rates under the SSP 126 scenario are projected to be higher than that under the SSP 585 scenario. The ensemble average soil erosion rate is projected to increase by 15.41%–25.94% toward the end of the century for different emission scenarios, and the areas susceptible to high and very high soil erosion rates are projected to increase to 40.3%.

Atmospheric and Land Drivers of Streamflow Flash Droughts in India

AbstractStreamflow flash droughts (SFDs), characterized by a rapid decline in streamflow over a relatively short period, affect water availability, hydropower generation, and the ecosystem. However, atmospheric and land processes that drive SFDs in the monsoonal climate of India remain unexplored. Using observations, reanalysis data sets, and model simulations, we examined the critical drivers of SFDs in 64 catchments in India during the 1971–2018 period. We identified meteorological flash droughts (MFDs) using precipitation and SFDs using in situ observations and model simulations of streamflow. We show that precipitation deficit and anomalous high temperature, driven mainly by the summer monsoon breaks, lead to the development of MFDs. Antecedent baseflow conditions play a major role in the propagation of MFDs to SFDs. Favorable atmospheric conditions (driven by the monsoon breaks) cause MFDs, which translate to SFDs. High and low baseflow conditions limit the rapid decline in streamflow, which controls the occurrence of streamflow flash drought. On the other hand, favorable atmospheric conditions combined with moderate baseflow can trigger SFDs in India during the summer monsoon season. Moreover, humid catchments are more prone to propagation from MFDs to SFDs during the monsoon season in India. Understanding the crucial role of atmospheric and land drivers can assist in examining the occurrence of streamflow flash drought with implications for water resources planning and management.

Development of pedotransfer functions for predicting saturated hydraulic conductivity in a Himalayan catchment: Sikkim, India

AbstractSaturated hydraulic conductivity (Ks) plays a vital role in irrigation and drainage system design. Generally, Ks is estimated in the laboratory; however, it is expensive and tedious, especially in the Himalayan ranges where soil sampling is challenging due to topographical constraints. Therefore, in this study, pedotransfer functions were generated using multiple linear regression (MLR) models for the predictability of Ks in a Himalayan catchment in India. Fifty soil samples were collected and divided into two groups at a 70:30 ratio. Different soil attributes derived from 70% of samples were used for MLR generation, and attributes of the remaining 30% of samples were used for model validation. Six different MLR models constituting different independent soil attributes were generated and compared statistically. The results indicate that the MLR model comprising soil texture, bulk density, particle density, soil moisture content (MC), organic carbon content and porosity results in the highest adjusted coefficient of determination (R2; 0.93 and 0.89 during model generation and validation, respectively). Additionally, it was found that the weight basis MC ranged from 14% to 29% with a median value of 24%. These results demonstrate that simple MLR models can be used as an alternative to laborious experimental setups for Ks estimation. These findings can be used as guidelines for proper irrigation planning and design in mountainous catchments.

Spatial Variability of Rainfall and Classification of Peninsular Indian Catchments

The strength and success of hydrological analysis depend upon the quantity and quality of observed data. In the recent past, the availability of advanced computing facilities and measurement techniques had a great impact on the field of hydrology, especially in hydrologic analysis and hydrologic modeling. In spite of such growth, the present hydrologic modeling has certain challenges: complexity (involving a large number of parameters), applicability to a specific region (difficult to generalize for other regions), and lack of understanding of the connection between model theories and the actual system. The general solution of simplifying the models in terms of developing a classification framework has been discussed and focused on in the present study. It will greatly help to overcome the hydrologic modeling challenges and provides a better understanding of the hydrologic process. In general, classification is a way of grouping entities which has similar characteristics. The importance of applying nonlinear dynamics and chaos methods for classification has been realized in the recent past; since such studies provide exclusive information on hidden characteristics such as complexity, nonlinearity, dimensionality, etc. Of hydrological processes. The hydrologic processes are complex. In this study, information regarding the complexity is extracted by statistical analysis and linear methods such as Autocorrelation Function, and Average Mutual Information. 367 gridded rainfall stations over Peninsular Indian basins are used to investigate the applicability of different methods used in the study.

Geospatial and AHP technique in assessment of groundwater potential zones—a case study of Boranakanive reservoir catchment in India

Geospatial and AHP technique in assessment of groundwater potential zones—a case study of Boranakanive reservoir catchment in India

Improving the interpretability and predictive power of hydrological models: Applications for daily streamflow in managed and unmanaged catchments

In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions. However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed. The successful application of PIML in different climatic as well as geographical regions shows its generalizability.

An automated approach to establish relationship between total and effective impervious area for urban Indian catchments

ABSTRACT Determination of total impervious area (TIA) or effective impervious area (EIA) is mandatory for hydrological modelling of water quantity and quality in urban areas. In this study, a multilayer deep learning model Convolutional Neural Network (CNN) is implemented for estimating TIA. A more realistic automated method is suggested to determine EIA by integrating the remote sensing data, the digital format of the drainage network, and a digital elevation model (DEM). A graphical user interface (GUI) called EIA estimator is developed for automatic creation of EIA maps. An effort is made to derive a relationship between TIA and EIA. Several power relationships are obtained for easily measurable TIA and hydraulically relevant EIA in urban catchments of India. These relationships would aid planners and decision-makers with quick initial estimates for surface water quantity and quality problems.

Investigating impact of CORDEX-based predicted climatic and LCM-based LULC scenarios on hydrologic response of a semi-gauged Indian catchment.

The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration's Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva's Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12-46% higher than the baseline period of 1982-2017. Conversely, while surface runoff is expected to decrease by 4-28% in lower parts of the basin, it will increase by 2-39% in the rest of it, depending on the subtle alterations in land use and climatic variability.

Urbanicity and rates of untreated psychotic disorders in three diverse settings in the Global South.

Extensive evidence indicates that rates of psychotic disorder are elevated in more urban compared with less urban areas, but this evidence largely originates from Northern Europe. It is unclear whether the same association holds globally. This study examined the association between urban residence and rates of psychotic disorder in catchment areas in India (Kancheepuram, Tamil Nadu), Nigeria (Ibadan, Oyo), and Northern Trinidad. Comprehensive case detection systems were developed based on extensive pilot work to identify individuals aged 18-64 with previously untreated psychotic disorders residing in each catchment area (May 2018-April/May/July 2020). Area of residence and basic demographic details were collected for eligible cases. We compared rates of psychotic disorder in the more v. less urban administrative areas within each catchment area, based on all cases detected, and repeated these analyses while restricting to recent onset cases (<2 years/<5 years). We found evidence of higher overall rates of psychosis in more urban areas within the Trinidadian catchment area (IRR: 3.24, 95% CI 2.68-3.91), an inverse association in the Nigerian catchment area (IRR: 0.68, 95% CI 0.51-0.91) and no association in the Indian catchment area (IRR: 1.18, 95% CI 0.93-1.52). When restricting to recent onset cases, we found a modest positive association in the Indian catchment area. This study suggests that urbanicity is associated with higher rates of psychotic disorder in some but not all contexts outside of Northern Europe. Future studies should test candidate mechanisms that may underlie the associations observed, such as exposure to violence.

Assessment of enhanced Kohonen self-organizing map, quantile mapping and copula-based bias-correction approaches for constructing basin-scale rainfall forecasts

ABSTRACT This study evaluates the performance of an enhanced Kohonen self-organizing map (eKSOM)-based bias-correction technique and compares the results with a copula-based bias-correction approach and the traditional quantile mapping (QM) to correct the daily multi-model ensemble short- to medium-range rainfall forecast products of the India Meteorological Department (IMD-MME) in the Hirakud Reservoir catchment in India. The copula and eKSOM outperformed the raw IMD-MME and QM rainfall across all lead times. While both the copula and eKSOM-based bias-corrected forecasts could satisfactorily provide the temporal pattern of the observed rainfall, the corresponding eKSOM-based forecasts presented better skills in capturing seasonality in the observed rainfall. The streamflow at one- to five-day lead times are simulated by forcing a suitable hydrological model, MIKE11 NAM (Nedbør Afstrømnings Model)-HD (Hydrodynamic) with the raw and bias-corrected rainfall inputs. The overall performance evaluation reveals significant improvement in both the rainfall and streamflow forecasts by copula and eKSOM, with the latter performing most accurately at higher lead times.

Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes

The impact of climate change on the oxygen saturation content of the world’s surface waters is a significant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change affects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct effect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.

Water Quality Index Process Using Artificial Neural Networks

This Study intends to explore the association among the water quality record (WQI) for water framework drives four free environment factors. Our logical examination was driven on the Euphrates River inside Karbala city, Iraq over the period between 2008 to 2021.The nonlinear backslide perfect was gotten to base the WQI since the coefficient of affirmation and least mix-up regard stayed improved than persons gained by the ANN. The outcomes got in this exhibit that the LM strategy is more proficient and viable in catching the non-direct and complex spillover measure in a huge Indian catchment.

On the Evaluation of Both Spatial and Temporal Performance of Distributed Hydrological Models Using Remote Sensing Products

Evaluating the spatial and temporal model performance of distributed hydrological models is necessary to ensure that the simulated spatial and temporal patterns are meaningful. In recent years, spatial and temporal remote sensing data have been increasingly used for model performance evaluation. Previous studies, however, have focused on either the temporal or spatial model performance evaluation. In addition, temporal (or spatial) model performance evaluation is often conducted in a spatially (or temporally) lumped approach. Here, we evaluated (1) the temporal model performance evaluation in a spatially distributed approach (spatiotemporal) and (2) the spatial model performance in a temporally distributed approach (temporospatial). We further demonstrated that both spatiotemporal and temporospatial model performance evaluations are necessary since they provide different aspects of the model performance. For this, a case study was developed using the Soil and Water Assessment Tool (SWAT) for the Upper Baitarani catchment in India, and the spatiotemporal and temporospatial model performance was evaluated against three different remotely based actual evapotranspiration (ETa) products (MOD16 A2, SSEBop, and TerraClimate). The results showed that an increase in the spatiotemporal model performance would not necessarily lead to an increase in the temporospatial model performance and vice versa, depending on the evaluation statistics. Overall, this study has highlighted the necessity of a joint spatiotemporal and temporospatial model performance evaluation to understand/improve spatial and temporal model behavior/performance.

Investigating the impact of calibration timescales on streamflow simulation, parameter sensitivity and model performance for Indian catchments

ABSTRACT Hydrological model calibration is a quintessential step in model development, and the time scale of calibration depends on the application. However, the implications of choice of time scale of calibration have not been explored extensively. Here, we evaluate the effect of the time scale of calibration on model sensitivity, best parameter ranges, and predictive uncertainty for three river basins using the Soil and Water Assessment Tool (SWAT) model. Multiple models were set up for three different catchments from southern India. Our results showed that the sensitivity of the parameters, best parameter ranges, and model performance are conditioned on the time scale of calibration. The models calibrated at coarser time scales marginally outperformed the models calibrated at fine time scale in terms of Nash-Sutcliffe efficiency and percentage bias. Transfer of parameters across scales (both from coarse to fine and from fine to coarse) have a general tendency to worsen the model performance in all three catchments, with few exceptions.

Evaluation of approaches to estimate discharge indices based on mean sediment load for suspended sediment transport in South Indian catchments

ABSTRACT Discharge indices based on mean sediment load such as fraction-load (f-load) discharge and functional-equivalent discharge represent long-term sediment transfer through river networks. These discharge indices can be used as design flows for the restoration of river channels and in geomorphic and eco-hydrologic studies. In this paper, lognormal distribution-based and Gamma distribution-based Magnitude-Frequency Analysis (MFA) approaches are evaluated to determine reliable estimates of discharge indices for suspended sediment transport in South Indian catchments. The discharge indices are estimated by considering (i) total suspended sediment load and (ii) fine, medium, and coarse suspended sediment loads, separately, as various fractions of suspended sediments have different hydraulic characteristics, transport behaviour, and effect on the ecological system. Results indicate that the lognormal distribution being heavy-tailed tends to assign considerable frequency densities to higher discharges which results in unreliable and magnified estimates of mean sediment load and subsequent discharge indices for most of the catchments. Overall, the Gamma distribution-based MFA approach is found to yield reliable estimates of the f-load discharge and functional-equivalent discharge in comparison to the lognormal distribution-based MFA approach.

Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

AbstractWith the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis were also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas there was a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.

Household food insecurity after the early monsoon flash flood of 2017 among wetland (Haor) communities of northeastern Bangladesh: a cross‐sectional study

AbstractThe livelihoods of wetland (Haor) communities living in northeastern region of Bangladesh are largely dependent on agriculture. Unseasonably heavy rainfall in Haor basin and upstream Indian catchments triggered a devastating early monsoon flash flood in Haor basin at the beginning of April in 2017, which destroyed the nearly ready‐to‐harvest annual rice crop and resulted in significant income loss of Haor dwellers. The present study aims at assessing post‐flood household food insecurity and the factors associated with it in the aftermath of this flash flood. A cross‐sectional study design was adopted, and a survey covering 1845 flood‐affected households was conducted to measure the post‐flood household food insecurity. A set of questionnaires was adopted from Household Food Insecurity Access Scale (HFIAS) to assess the level of household food insecurity, and a modified poisson regression model was used to identify the risk factors of household food insecurity. A staggering 62% of the surveyed households were found food insecure following the flash flood. Being ultra‐poor, loss of livestock in flood, household head's occupation being natural resource‐based, and household reliance on market purchase of food were identified as significant risk factors of post‐flood household food insecurity. Household being headed by relatively younger, educated people, and household owning agricultural land were found to be protective against post‐flood food insecurity. Therefore, post‐flood emergency programs, especially the food assistance programs should be designed prioritizing these most food insecure groups instead of considering household poverty as the only selection criteria. As a long‐term plan, a multi‐sectoral approach focusing on disaster risk reduction by taking into account the risk of disaster‐induced food insecurity is essential to reduce the vulnerability of wetland communities.