Hydrological modeling for streamflow and sediment yield simulation using the SWAT model in a forest-dominated watershed of north-eastern Himalayas of Kashmir Valley, India

In this study, the Soil and Water Assessment Tool (SWAT) model was used to examine the spatial variability of sediment yield, quantify runoff, and soil loss at the sub-basin level and prioritize sub-basins in the Sindh watershed due to its computational efficiency in complex watersheds. The Sequential Uncertainty Fitting-2 approach was used to determine the sensitivity and uncertainty of model parameters. The parameter sensitivity analysis showed that Soil Conservation Services Curve Number II is the most sensitive model parameter for streamflow simulation, whereas linear parameters for sediment re-entrainment is the most significant parameter for sediment yield simulation. This study used daily runoff and sediment event data from 2003 to 2013; data from 2003 to 2008 were utilized for calibration and data from 2009 to 2013 were used for validation. In general, the model performance statistics showed good agreement between observed and simulated values of streamflow and sediment yield for both calibration and validation periods. The noticed insights of this research show the ability of the SWAT model in simulating the hydrology of the Sindh watershed and its reliability to be utilized as a decision-making tool by decision-makers and researchers to influence strategies in the management of watershed processes.

Evaluation of hydrodynamic behavior of biosand filters using computational fluid dynamics combined with the design of experiments

Biosand filters (BSFs) are widely used in rural and urban areas where access to drinking water is limited or non-existent. This study applies computational fluid dynamics in the assessment of hydrodynamic characteristics considering changes in the design of two BSF models to make construction options available to communities, without losing hydrodynamic efficiency. The commercial code ANSYS-CFX 20.1 together with a central composite design of experiments methodology to simulate the flow was used under different combinations of porosities, permeabilities, pipe diameters, and filter diameters and heights. These parameters were combined statistically from Statistica 13.3. Our results have shown that combining greater filter depths with smaller pipe diameters has played a key role in the BSF best performance, and the CAWST V10 model has performed better than HydrAid, with lower velocities and longer hydraulic retention times.

Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer.

Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

Reliability analysis of complex water distribution systems: the role of the network connectivity and tanks

A reliable water distribution network (WDN) can provide an adequate supply service to customers under both normal and abnormal working conditions. The WDN reliability analysis, therefore, is a keystone to improve the supply service efficiency. Strategies for reliability analysis are usually proved on small WDNs, which do not compare with large real complex systems in terms of number of water tanks, pressure reduction valves, variable speed pumps, controlled devices and possible alternative water supply schemes. The topological changes due to pipeline interruptions impact on emptying–filling of water tanks and network pressure status. This work proposes a two-level procedure for mechanical reliability assessment, suited for large real WDNs. It leverages a path/connectivity-based approach to set up reliability indicators for global-level analysis and local screening of the most critical scenarios. The employed advanced hydraulic model includes the automatic detection of topological changes and the robust modelling of water level in tanks using the generalized global gradient algorithm. The extended period simulation enables the reliability assessment of alternative water supply schemes and the sensitivity of tanks and controlled devices to single failure events. The procedure is demonstrated on a real complex network, being consistent with the ongoing digital transition in the WDN management sector.

Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

Foreign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance.

Comparing methods to place adaptive local RTC actuators for spill volume reduction from multiple CSOs

The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation.

Dissolved oxygen determination in sewers using flow hydraulic parameters as part of a physical simulation model

This paper aims to develop a model for calculating the hydraulic and water quality parameters of wastewater within sewers. Information from the wastewater collection network and the transmission line in Birjand were used to verify the model performance. The parameters used for modelling quality changes include the yield constant for biomass (YH), the maximum specific growth rate (μH), the saturation constant for dissolved oxygen (KOG) and the saturation constant for readily biodegradable substrate within a biofilm (KSF), as well as the Gauckler–Manning–Strickler coefficient (n). They were selected from references and were verified at the calibration stage comparing measurements with the modelling values. Inputs of the created model are the average concentrations of dissolved oxygen and chemical oxygen demand of the incoming wastewater, the flow rate of wastewater at the exit point of the network, physical characteristics of the pipes and the height of drops within the sewer network. The amount of dissolved oxygen at different positions of the sewer network was calculated. The acceptable calculated sum of squares of errors and the correlation coefficient (R2) of the calibrated model for dissolved oxygen were 1.6872 and 0.77, respectively.

Investigation of satellite rainfall-driven rainfall–runoff model using deep learning approaches in two different catchments in India

Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's Eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in NSC values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.

Evaluation of ECMWF's forecasting system for probabilistic urban flood prediction: a case study in Mexico City

As the world continues urbanizing, including efforts to forge a new framework of urban development is necessary. Recent studies related to flood prediction and mitigation have shown that Ensemble Prediction Systems (EPSs) constitute a valuable and essential tool for an Early Warning System. However, the use of EPS for flood forecasting in urban zones has yet to be understood. This work has the objective to investigate the potential use of the Operational EPS, issued by the European Centre for Medium-Range Weather Forecasts (ECMWF), for probabilistic urban flood prediction. In this research, a precipitation forecast verification was carried out in two study zones: (1) Mexico Valley Basin and (2) Mexico City, where for the latter, forecasts were compared against real-time observed data. The results showed good forecast reliability for a rain threshold of up to 20 mm in 24-hourly accumulations, with the first 36 h of the forecast horizon being the most reliable. The EPS has sufficient resolution and precision for flood prediction in Mexico City, which represents a further step toward developing a flood warning system at the local level based on ensemble forecasts.