Analysis of the influence of changes in land use on the sediment yield from watersheds provides crucial inputs that aid the development of appropriate strategies for the sustainable management of consequent land degradation. Event-based hydrologic models can be used for performing such analysis, as long-term hydrological datasets are not available for most of the rivers in developing countries like India. In this study, an event-based Two-Dimensional Runoff, Erosion, and Export (TREX) model was used to simulate the soil erosion process in Moozhy, an ungauged watershed in the Karamana River basin in Thiruvananthapuram District, Kerala State, India. Rainfall, streamflow, and sediment concentration data corresponding to three isolated storm events were used to calibrate and validate the model. The performance of the model was assessed using four statistical measures, namely, Percent bias (PBIAS), Nash-Sutcliffe Efficiency Coefficient (NSEC), Coefficient of determination (R2) and RMSE-observations standard deviation ratio (RSR). The values of PBIAS varied between 46% and 49%, indicating satisfactory performance of the model. Also, from the values of NSEC and RSR, it can be concluded that the TREX model yields acceptable results. For a rainfall event that occurred on the 17 September 2017, the simulated values showed that about 1054 t of suspended sediments are transported from the watershed to the stream channels, and about 1020 t is carried out of the Moozhy watershed. Only a very small amount of sediment is left in the channel at the end of the simulation. About 34 t of sediment is deposited in the channel bed, about 0.04 t remains in suspension, and the balance 0.04 t remains in suspension as a suspended load. About 3% of the total sediment entering the reach of the stream considered in this study is deposited on the riverbed. The major component of the settled sediments is silt; about 12% of the total silt size fraction entering the stream settles on the river bed. Future scenarios of land use in the watershed were modelled using a hybrid Artificial Neural Network (ANN) and Cellular Automata (CA) model. These were used for simulating sediment discharge using the TREX erosion model. The predicted land use scenarios were used to investigate both short-term (2025 and 2029) and long-term (2037, 2045, and 2053) variations in sediment discharge. The sediment yield for the storm event that occurred on 17 September 2017 was used to benchmark the variation in sediment yield under the predicted land use scenarios. Results indicate that the peak runoff and the runoff volume are expected to increase by about 29% and 22% respectively in the period from 2005 to 2053. The expected increase in the volume of sediments in this period is about 50%; the peak sediment concentration is likely to increase by about 56%. The study highlights the threats of likely increase in soil erosion and consequent land degradation posed by unscientific changes in land use caused by urbanisation and calls for proper management interventions to address the problem.

The word "inhomogeneity" in a time-series analysis is defined as those changes that occur in the time-series datasets due to non-climatic factors. Rainfall datasets without the presence of inhomogeneity are needed for conducting accurate hydro-climatological studies. The homogeneity of long-term rainfall (exceeding 100 years) datasets encompassing Kerala's entire state has not been checked. This research article bridges the gap by conducting homogeneity tests on every calendar month, monthly, seasonal, and annual scale from 1901 to 2013. The classical absolute homogeneity tests, such as the Standard normal homogeneity test (SNHT), Pettitt test, Buishand range (BR) test, and Von Neumann ratio (VNR) test at a 95% confidence level were applied for this purpose. Based on the results obtained, the rainfall datasets are further classified as "useful", "doubtful", and "suspect". The time-series datasets were generated by arranging the monthly rainfall data at each calendar month, seasonal and annual scale. The results indicated that more than 80% of the time-series datasets were homogeneous and accepted the null hypothesis; SNHT (88.50%), Pettit's test (88.12%), BR test (87.35%) and VNR test (82.15%). The research article presents two major findings. Firstly, it identifies cases where a data series deemed homogeneous by one test is considered non-homogeneous by other tests, emphasizing the importance of using multiple tests for homogeneity analysis. Secondly, the study suggests that researchers and practitioners should carefully consider the homogeneity test type and temporal scale/arrangement of the rainfall data while selecting and analyzing it for various applications.

Comparison of the performance of different rainfall-runoff models helps to identify an appropriate model for use in a specific situation at a reasonable cost. An event-based hydrologic model gives an idea about the response of a drainage basin to a single rainfall event and is useful for locations which do not have data for a long time period. Rainfall and discharge data for long periods of time are required for continuous rainfall-runoff modelling and its availability is a major challenge as most rivers and streams in India are either ungauged or poorly gauged. In this work, the rainfall-runoff process in a small tropical watershed is simulated using three event-based models-the fully distributed TREX model, the semi-distributed HEC-HMS model, and the GIUH model, which is a lumped empirical model. The study area selected is an ungauged watershed located in Thiruvananthapuram District of Kerala State, India, namely, the Moozhy sub-basin of the Karamana River. Automatic rain gauges were installed at three locations in the watershed for collecting rainfall data. Runoff from the watershed was estimated using the stage measured with a staff gauge installed at the outlet of the watershed and the velocity measured using a flow prob. SRTM DEM analysed using various GIS-based tools is a major input to all the three models along with land use and soil data. Data pertaining to three rainfall events were used to calibrate these models. The models were further validated using another set of three observed rainfall and runoff events. Results of the statistical analyses performed indicated that the performance of the TREX model was reasonably good when compared to those of the HEC-HMS and GIUH models.

Satellite-based flood monitoring is a powerful tool to map inundated areas and distinguish water, vegetation, and urban settlements. In this study, Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite were used for this purpose as images are available round the clock. Also, being a passive microwave sensing radar, the images obtained are not affected by cloud cover. The satellites Sentinel-1A and 1B combined have a 6-day revisit frequency which can be used to create a near-real-time flood inundation map. The study area is the areal extent of the Nilambur municipality in the Chaliyar river basin in Kerala, India. This area was very badly affected during the floods of 2018 and 2019. Google Earth Engine (GEE) was used to process the Sentinel-1A and 1B images. Manual selection of threshold backscattering coefficient values was used to determine open water (−16 dB) and submerged urban areas with water (−5 dB). The extent of flooding was analyzed by plotting the time series of the number of flooded pixels in the images of the area during the major flood events of 2018 and 2019. Flood maps were generated for pre and post-flood events.

Development of a generalized representation of a water network for a building at minimal cost, that follows water conservation practices is an explicit requirement for a sustainable infrastructure design. A fast, integrated, easily sharable, sophisticated and parametric integrated building modelling and analysis tool is required to attain this objective. Currently, 2D CAD-based manual modelling and analysis is carried out in different application platform to meet the local regulations. This technique may cause inherent errors due to its 2D representation of elements, resulting in monetary loss and wastage of time. Building Information Modelling (BIM) is a promising technology that minimizes such errors due to its enhanced dimensional capabilities. BIM provides a single realistic virtual representation of an infrastructure project, supported by an object-oriented relational database management system. BIM has broad applications in infrastructure development and management in the urban water sector. It includes water supply and drainage network planning, modelling, analysis and flood management. The capabilities of BIM can be enhanced further by integrating it with Geographic Information System (GIS) where the analysis and modelling can be extended to the regional level. However, awareness of technology among the stakeholders and standardization of the technique is essential for further progress.

Agricultural water management plays a significant role in the sustainable development of the watershed. Three different water management techniques based on the water harvesting scenario, demand reduction scenario, and soil management scenario are simulated. In the first scenario, the runoff potential and suitable measures to harvest the surface runoff in the watershed are considered. In the second scenario, initially, double-cropped areas in the watershed are identified. Then, to reduce agricultural water consumption, the double-cropped regions that are suitable for single cropping with vegetation are identified. The third management measure is the addition of soil organic carbon in the watershed to retain the soil moisture. The hydrological modeling is carried out with modified scenarios to compute the hydrological responses. The scenarios are evaluated for their environmental and economic effectiveness. All the scenarios indicated improvement in the water yield and the soil moisture storage in the watershed. The response analysis technique using spatial inputs provides a good insight into the watershed conditions for drought management.

In this article, the performance evaluation of four univariate time-series forecasting techniques, namely Hyndman Khandakar-Seasonal Autoregressive Integrated Moving Average (HK-SARIMA), Non-Stationary Thomas-Fiering (NSTF), Yeo-Johnson Transformed Non-Stationary Thomas-Fiering (YJNSTF) and Seasonal Naive (SN) method, is carried out. The techniques are applied to forecast the rainfall time series of the stations located in Kerala. It enables an assessment of the significant difference in the rainfall characteristics at various locations that influence the relative forecasting accuracies of the models. Along with this, the effectiveness of Yeo-Johnson transformation (YJT) in improving the forecast accuracy of the models is assessed. Rainfall time series of 18 stations in Kerala, India, starting from 1981 and ending in 2013, is used. A classification system based on root mean square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe model efficiency coefficient (NSE) is proposed and applied to find the best forecasting model. The models HK-SARIMA and YJNSTF performed well in the Western lowlands and Eastern highlands. In Central midlands, out of 12 stations, the performance indices of 8 stations are in favour of the HK-SARIMA model. It can be concluded that HK-SARIMA models are more reliable for forecasting the monthly rainfall of the stations located in all geographic regions in the state of Kerala.

The impact of future land-use and climate changes on the surface hydrology of a watershed was studied using an event-based hydrological model. Artificial Neural Network (ANN) and Monte-Carlo Cellular Automata (CA) based model was used to generate future land-use scenarios of an urbanizing watershed. Simulations were thereafter performed using a hydrological model to assess the expected changes in runoff. The spatial data inputs for the hydrological model were generated using satellite data, field observations, and data obtained from government agencies. Sensitivity analysis, calibration, and validation of the model were carried out using the observed storm and flow hydrographs, and the model performance was assessed using various statistical performance metrics. Historical land-use data for 2005, 2013, and 2017 was prepared using Landsat satellite imageries and Google Earth images. The land-use scenario generated for the year 2021 exhibited good agreement with the actual land-use map, with a Kappa value above 0.8. Future land-use scenarios were generated to assess the short-term (2025 and 2029) and long-term (2037, 2045, and 2053) changes in the characteristics of the flow hydrographs. The expected changes in the hydrographs due to urbanization were analyzed by applying a selected storm event and considering 2005 as the baseline year using the Two-Dimensional Runoff Erosion and Export (TREX) model. Results indicate that the peak discharge is likely to increase by 29%, and the time to peak is likely to decrease by 6% in 2053 when only land use change is considered. The corresponding values in the worst case considering the combined effect of climate and land use changes are 34.63% and 8.28%, respectively.

Rainfall forecasting models developed using the seasonal autoregressive integrated moving average (SARIMA) technique for spatially distributed rain gauge stations in the state of Kerala are presented in this paper. Monthly rainfall data for 113 years were considered to build models for 29 meteorological stations. As the time-series data span 11 decades, there is a high chance of non-stationarity, owing to the presence of trend and seasonality. The non-stationarities in the time-series datasets are assessed by applying the seasonal and trend decomposition using the Loess technique (STL) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. Prior to applying this procedure, preliminary analyses on the datasets are carried out to detect and fill the missing values, examining the properties of the time-series datasets, including the tests for detecting trend and seasonality. Out of 29 stations, the results of the Mann–Kendall trend test indicated that marginal trends were present only in the rainfall datasets belonging to two stations. The partial autocorrelation function (PACF) and autocorrelation function (ACF) plots of most of the stations showed strong seasonal autocorrelation. The developed SARIMA models also indicated that the influence of the seasonal components is dominant compared to non-seasonal components for most of the stations.

Quick and accurate building-level infectious disease transmission evaluation is a key concern that gains traction nowadays. This chapter summarizes Building Information Modeling (BIM) applications for COVID-19 Spread Assessment due to the Organization of Building Artifacts (CSAOBA). Development of BIM-based inbuilt and add-in tools for CSAOBA offer a faster approach for data gathering and sharing. A Geographic Information System (GIS) and BIM-integrated CSAOBA at the provincial level information system is a suitable platform for retrieving and processing huge data automatically and accurately. BIM delivers project-level information and GIS stores and manipulates the regional-level information for district-level assessments. Future BIM standards are to incorporate CSAOBA-related modeling rules and regulations, which may ease handling emergency situations. BIM-based reliable CSAOBA tools require efficient ontologies and related algorithms to increase the accuracy and industrial deployment.

Optimal planning of utility supply station location is an integral part of infrastructural projects. In general, this is a multi-objective optimization process by considering engineering, financial and geographical constraints. A shift from conventional 2D-CAD, manual quantification and design application-based approach to Building Information Modelling (BIM)-Geographic Information System (GIS) integrated approach is found to be suitable for minimizing optimal planning time, cost and increasing automation. In this paper, an Autodesk Revit add-in tool is proposed aimed at integrating BIM and GIS for Genetic Algorithm (GA) based utility supply station location optimization and to assess the possibilities of this integration. From the case study it is observed that up to 90% of cost saving can be accomplished by this proposed approach. It is found that compared to the traditional multi-software approach with manual data transfer, this integration can be utilized for multi-stage optimization and is suitable for automating heterogenous data integration with increased accuracy. The platform in which the add-in tool is developed for the utility network can be at either BIM or GIS and this selection is influenced by the availability and ease of data retrieval from the respective semantic information system and the level of automation that is to be accomplished. Standardised BIM-based modelling combined with concepts like artificial intelligence and image processing techniques can be promising for attaining desired results in industrial applications.

Surface soil moisture is an essential parameter for the hydrological modelling of the watershed. Field techniques adopted for soil moisture measurements are laborious and time-consuming. In a watershed, the soil moisture varies both spatially and temporally. Making continuous on-field observations simultaneously at several locations is practically impossible for large watersheds. Soil moisture estimation using remote sensing techniques is considered a viable alternative. In this study, it is proposed to apply a modified water cloud model for surface soil moisture (SSM) retrieval over partially vegetated regions. This method combines the microwave data obtained from Sentinel-I and optical data obtained from Landsat Operational Land Image (OLI) for SSM estimation. It is necessary to remove the effect of vegetation moisture content for better SSM estimation. Therefore, the index derived from the Landsat OLI spectral bands is applied to build a model for the vegetation water content estimation. A modified water cloud model (MWCM) is developed by integrating the vegetation index with the original water cloud model. In this study, the backscatter coefficients measured using the C-band (5.405 GHz) synthetic aperture radar (SAR) sensor onboard the Sentinel-IA satellite has been used. The developed MWCM has been used to prepare multi-temporal SSM maps.

Climate change is recognised as a serious phenomenon affecting socio-economic agricultural development activities around the globe. Regional climate models (RCMs) are found to be more reliable to study the impact of climate change at a regional scale. However, the RCMs are not free from bias errors. Therefore, it is necessary to apply a bias correction before the application of models for water resources research. Linear scaling (LS), power transformation (PT), local intensity scaling (LOCI), delta change correction (DC) and distribution mapping (DM) are the methods commonly applied for the bias correction of the precipitation data. On the other hand, temperature datasets are corrected by using linear scaling (LS), variance scaling (VS), delta change correction (DC) and distribution mapping (DM) techniques. In the proposed study, various bias correction techniques are evaluated to determine a suitable method for correcting the meteorological datasets. The delta change method showed better performance for both climate variables with good statistical metrics (frequency and time-series-based). The corrected datasets were used as a forcing input to the SWAT hydrological model to project streamflow for three future time periods in the twenty-first century. Simulations were carried out for the initial period (2021–2040), mid-period (2041–2070) and end-period (2071–2100) under RCP 4.5 and 8.5 scenarios. Predicted streamflow shows significant changes in all time intervals, with the highest increase during the end of this century. The result indicates that mean annual streamflow is likely to increase by about 7.4% and 23.1% under RCP 4.5 and RCP 8.5 scenarios, respectively.

In the current research, missing value analysis and tests for the presence of homogeneity were applied to the temperature records obtained from seven meteorological observatories spread throughout the state of Kerala. The monthly mean maximum and mean minimum temperature datasets of observatories managed by the Indian Meteorological Department (IMD) for the period 1969–2015 were considered. During analysis, every observatory was studied independently and those observatories which are having missing values for five continuous years, and more were rejected. The missing records were estimated using the Expectation Maximization Algorithm (EMA). The infilled dataset needs to be hydrologically as well as statistically stable for later hydrological and meteorological assessments. The reliability of datasets was tested using eight statistical absolute homogeneity tests. Before applying the homogeneity tests on the datasets, their assumptions must be fulfilled; one predominant assumption is regarding the normal distribution of the dataset. Thereby, the datasets are checked for normality behaviour by four statistical tests, namely Skewness z-ratio, Kurtosis z-ratio, Kolmogorov–Smirnov (KS) and Shapiro–Wilk (SW) test. Out of 14 datasets consisting of seven mean monthly maximum and seven mean monthly minimum temperature, ten datasets were found to be normal at 95% level of confidence (LOC) and the rest four were highly skewed and kurtotic. Following the normality tests, eight statistical absolute homogeneity tests were applied individually on the annual scale by which non-homogeneous stations were detected and eliminated. The datasets which resembled normal behaviour were tested using parametric homogeneity tests such as Linear Regression, Student's t-test, Cumulative Deviation and Worsley Likelihood Ratio test. Out of normally distributed datasets, only three datasets were statistically homogenous at 99% LOC, and seven datasets failed to clear the homogeneity test. Remaining four non-normally distributed datasets was checked for homogeneity by applying non-parametric tests, such as Distribution-Free CUSUM, Rank-Sum, Median Crossing and Turning Points test. Out of four datasets, three were homogenous at 99% LOC and one failed homogeneity test.

The present study was conducted to evaluate the performance of five wheat varieties (HD-2967, WH-542, DPW-621-50, HD-943 and WH-1105) under different spacing geometry (3×3, 6×1.5 and 17×1×1 m (paired row)) of already established Eucalyptus tereticornis based agroforestry system during 2013-14 and 2014-15. Significantly higher growth of E. tereticornis was recorded in 3×3 m spacing. Reduction in growth as well as yield attributes of wheat varieties was recorded under different spacing. Among different spacings with respect to different wheat varieties, the maximum grain yield (2.30 and 2.10 t ha -1 ) was recorded in var. HD-2967 which was closely followed by var. DPW-621-50 (1.70 and 1.50 t ha -1 ) and WH-542 (1.50 and 1.40 t ha -1 ) in paired row planting (17×1×1 m) of Eucalyptus during study period (2013-14 and 2014-15). On an average, highest per cent decrease in grain yield over control was recorded in var. WH-1105 (90.7 and 91.1%) under 3×3 m spacing and it followed the order: HD-943 (84.6 and 85.3%) > WH-542 (82.9 and 83.2%) > DPW-621-50 (76.7 and 75.6%) > HD-2967 (60.1 and 61.3%) during study period (2013-14 and 2014-15). The grain yield of wheat reduced significantly with the increase in age of E. tereticornis trees.

The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) finding and filling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal differencing and one first-order differencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-fit SARIMA models are developed for each of the fourteen variables. The study revealed that $$\text{SARIMA}(2,1,1)(1,1,1)_{12}$$ as the ideal forecasting model for eight out of the fourteen time-series datasets.

In semi-arid watersheds, hydrological drought is manifested by reasonably low streamflow conditions. This makes streamflow forecasting as an inevitable component for implementing drought management practices. Data-driven modelling techniques are often applied for simulating the streamflow forecasts. In this study, a comparison between conventional feedforward neural network (FFNN) model and wavelet enabled artificial neural network (WANN) model is carried out to analyse their effectiveness in streamflow forecasting. The input data used to develop and simulate the models are monthly precipitation, and monthly river stage of twenty-five years (January 1991 to December 2015). Data pre-processing is carried out using correlation analysis prior to neural network modelling for selecting appropriate input combinations. The preprocessed data is directly given as input for FFNN; whereas for WANN, the preprocessed time series datasets are decomposed into several sub-series and are used as the inputs. Analysis on three different transfer functions that are commonly used in ANN models is carried out to identify the best transfer function. Hyperbolic tangent sigmoid transfer function is found to be best suitable for modelling streamflow forecasts. The result also shows that there is a significant improvement in streamflow forecasting ability for WANN models compared to FFNN. Drought forecasting is carried out by developing a standardized streamflow index from the forecasted streamflow. The drought forecasting technique discussed here will help planners to make informed decisions on watershed management and drought mitigation measures.

: Wireless technology is developing very quickly. Most researchers work in the field of wireless communication. VANET is an evolving technology in the field of wireless communication and as it progresses it will contribute more to the intelligent transport system in the coming days. VANET offers a communication framework that has improved traffic service and helped reduce road accidents. The exchange of data in this system is urgent and requires a fast and vigorous network connection. VANET fulfils these purposes, but there are some problems and challenges such as efficient management of fast transfers for video streaming applications. Therefore, in this document we have examined and discussed several studies related to routing protocols to judge which is the best for VANET applications. Furthermore, after studying several systems developed by the researchers, a survey is presented on efficient routing algorithms for ad hoc vehicle networks.

Drought is a natural hazard whose consequences are strongly affected by various components of the hydrological cycle. Among the various hydrological variables, soil moisture component plays a major role in determining drought since it is controlled by various other subprocesses. In spite of its importance in agriculture and drought monitoring, soil moisture information is not widely available on watershed basis. This chapter gives an idea of different drought types and importance of agricultural drought estimation through hydrological models. As a case study, an open source distributed hydrologic model Soil and Water Assessment Tool is used to develop soil moisture records of a semiarid watershed in southern part of India. The soil moisture is then used to derive the standardized soil moisture index. The study promises a drought monitoring system based on soil moisture. The proposed methodology is also suitable for an extended use with any other hydrological quantities.