Water Management Model Research Articles

Robustness of storm water management model parameter sets for dry and wet hydroclimatic conditions

The Storm Water Management Model (SWMM) is an urban watershed model for simulating urban runoff quantity and quality. Although SWMM has been widely used, little to no research has explored the effect of fitting the same SWMM dataset to multiple hydrologic conditions. This paper assesses the impact of best-fit parameter estimates on the hydrologic responses of dry and wet hydroclimatic conditions, evaluates parameter uncertainty, and identifies the physical processes that govern parameter changes during dry and wet conditions. We compared the ability of SWMM to predict flows when independently calibrated to a dry and a wet year, respectively, using an automatic calibration program for SWMM, OSTRICH-SWMM (Optimization Software Toolkit for Research Involving Computational Heuristics). The model developed using SWMM calibrated to a wet year performed better during the model assessment period. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning's roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow depth on surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing the uncertainty of the parameter estimate. However, other parameters, such as Manning's roughness coefficient for streams were the least sensitive and well-defined as the width of their posterior distribution only spanned a relatively narrow range which did not change drastically between the two cases. The robust assessment conducted in this study demonstrated that the hydrologic behavior of SWMM was different between models developed and calibrated for dry and wet hydroclimatic conditions. The different optimal parameter values suggest that although the processes and mechanisms driving hydrologic processes in dry and wet years are similar, their magnitude differs greatly. This study highlights that precipitation patterns affect the selection of optimal parameter values and the water budgets differed markedly between dry and wet years, as expected. The results of this study can assist urban watershed planners in selecting the most appropriate model to use for prediction purposes or when there is a paucity of data available for model calibration. This study recommends changing SWMM model parameters on an annual basis when projecting the effects of new conditions outside of current experience (i.e., land use and climate change) on urban runoff quantity and quality.

Water implications in dialysis therapy, threats and opportunities to reduce water consumption: a call for the planet

Water is a dwindling natural resource, and potable water is wrongly considered an unlimited resource. Dialysis, particularly hemodialysis, is a water-hungry treatment that impacts the environment. The global annual water use of hemodialysis is approximately 265 million m3/yr. In this reference estimate, two-thirds of this water is represented by reverse osmosis reject water discharged into the drain. In this review, we would like to draw attention to the complexity and importance of water saving in hemodialysis. We propose that circular water management may comply with the "3R" concept: reduce (reduce dialysis need, reduce dialysate flow, and optimize reverse osmosis performance), reuse (reuse wastewater as potable water), and recycle (dialysis effluents for agriculture and aquaponic use). Awareness and sustainability should be integrated to create positive behaviors. Effective communication is crucial for water savings because local perspectives may lead to global opportunities. Besides the positive environmental impacts, planet-friendly alternatives may have significant financial returns. Innovative policies based on the transition from linear to circular water management may lead to a paradigm shift and establish a sustainable water management model. This review seeks to support policymakers in making informed decisions about water use, avoiding wasting, and finding solutions that may be planet friendly and patient friendly in dialysis, especially in hemodialysis treatments.

Assessing the Effectiveness of the Use of the InVEST Annual Water Yield Model for the Rivers of Colombia: A Case Study of the Meta River Basin

This paper presents the results of one of the hydrological models, the InVEST “Annual Water Yield” (InVEST–AWY), applied to the Meta River basin in Colombia, which covers an area of 113,981 km². The study evaluates the performance of the model in different subbasins of the Meta River basin. The model’s accuracy was assessed using different statistical measures, including Nash–Sutcliffe Efficiency (NSE) coefficient, Root Mean Square Error (RMSE), correlation coefficients for the calibration (rcal) and validation (rval) periods. The overall performance of the model in the Meta River basin is relatively poor as indicated by the low NSE value of 0.07 and high RMSE value of 1071.61. In addition, the model explains only a 7% of the variance in the observed data. The sensitivity analysis revealed that a 30% reduction in crop coefficient (Kc) values would result in a 10.7% decrease in water yield. The model estimated, for example, the annual average water yield of the river in 2018 as 1.98 × 1011 m3/year or 6273.4 m3/s, which is 1.3% lower than the reported value. The upper Meta River subbasin shows the highest NSE value (0.49), indicating a good result between observed and simulated water discharge. In contrast, the South Cravo River subbasin shows a negative NSE value of −1.29, indicating poor model performance. The Yucao River subbasin and the upper Casanare River subbasin also show lower NSE values compared to the upper Meta River subbasin, indicating less accurate model performance in these subbasins. The correlation coefficients in calibration (rcal) and validation (rval) for the upper Meta River, Yucao River, South Cravo River, and upper Casanare River subbasins were 0.79 and 0.83, 0.4 and 0.22, 0.5 and −0.25, and 0 and 0.18, respectively. These results provide useful insights into the limitations for the proper use of the InVEST–AWY model in Colombia. This study is the first to use the InVEST–AWY model on a large scale in the territory of Colombia, allowing to evaluate its effectiveness in hydrological modeling for water management.

AquaCrop Model Performance in Yield, Biomass, and Water Requirement Simulations of Common Bean Grown under Different Irrigation Treatments and Sowing Periods

The application of crop growth simulation and water management models will become increasingly important in the future. They can be used to predict yield reductions due to water scarcity and allocate water to ensure profitable crop production. The objective of this research was to calibrate the AquaCrop model for common bean (Faseolus vulgaris L.) grown in temperate climates and to test whether the model can be used for different irrigation strategies to achieve high yield productivity. The model was calibrated using data obtained from two years of experimental research in the Serbian territory of the Syrmia region. There were three sowing periods/plots: I—mid April, II—end of May/beginning of June, and III—third decade of June/beginning of July; and three levels of irrigation/subplots: full irrigation (F) providing 100% of crop evapotranspiration (ETc), mild deficit irrigation (R) at 80% of ETc, and moderate deficit irrigation (S) at 60% of ETc. The results show that the AquaCrop model accurately predicts common bean yield, biomass, canopy cover, and water requirements. The statistical indices of the calibrated dataset, coefficient of determination (R2), normalized root mean square error (NRMSE), mean bias error (MBE), and Willmott agreement index (d) for yield and biomass were: 0.91, 0.99; 6.9%, 11.4%; −0.046, 1.186 and 0.9, 0.89, respectively. When testing three irrigation strategies, the model accurately predicted irrigation requirements for the full and two deficit irrigation strategies, with only 29 mm, 32 mm, and 34 mm more water than was applied for the Fs, Rs, and Ss irrigation strategy, respectively. The AquaCrop model performed well in predicting irrigated yield and can be used to estimate the yield of common bean for different sowing periods and irrigation strategies.

An improvement in drainage flow equation of EPA SWMM green roof module.

Green roofs are among the important green infrastructures which decrease the negative impacts of urbanization. In this study, the green roof modeling capability of U.S. Environmental Protection Agency Storm Water Management Model (EPA SWMM) is evaluated and the modeling methods used in EPA SWMM, which need improvements, are determined. In particular, the drainage flow, which is originally calculated with Manning's equation in SWMM, is improved by introducing the drainage mat void thickness and adapting a new value for the exponential coefficient of depth in the equation. In addition, the capability of the percolation equation is further investigated, i.e., the value of the coefficient of the exponential function of soil moisture content that considers the percolation through the soil layer is determined for different substrates using the existing unsaturated hydraulic conductivity and soil moisture content data from steady-state tests. A comparison of the improved equation of drainage flow results with the existing experimental data shows that the improved equation represents the drainage flow more accurately than the one used in EPA SWMM.

An Event-Based Stochastic Parametric Rainfall Simulator (ESPRS) for Urban Stormwater Simulation and Performance in a Sponge City

The temporal heterogeneity of rainfall is substantial in urban catchments, and it often has huge impacts on stormwater simulation and management. Using a design storm with a fixed pattern may cause uncertainties in hydrological modeling. Here, we propose an event-based stochastic parametric rainfall simulator (ESPRS) for stormwater simulation in a sponge city with green roofs, permeable pavements, and bioretention cells. In the ESPRS, we used five distributions to fit the measured rainfall events and evaluated their performance using Akaike’s Information Criterion, Anderson—Darling goodness-of-fit test, and p-values. The vast rainfall time series data generated using the ESPRS were used to run the storm water management model for outflow simulations in the catchment, thus revealing the influence of temporal rainfall characteristics on the hydrological responses. The results showed the following: (1) The ESPRS outperforms the Chicago method in predicting extreme precipitation events, and its control factors are the rainfall peak period, rainfall peak fraction, and cumulative rainfall fraction at the peak period. (2) The best-fit functions for the rainfall depth in each period have different distributions, mostly being in lognormal, gamma, and generalized extreme value distributions. (3) Rear-type precipitation events with high peak fractions are the most negative pattern for outflow control. The developed ESPRS can suitably reproduce rainfall time series for urban stormwater management.

Assessment of Existing Fate and Transport Models for Predicting Antibiotic Degradation and Transport in the Aquatic Environment: A Review

In recent years, the use of antibiotics for human medicine, animal husbandry, agriculture, aquaculture, and product preservation has become a common practice. The use and application of antibiotics leave significant residues in different forms, with the aquatic environment becoming the critical sink for accumulating antibiotic residues. Numerous studies have been conducted to understand antibiotic removal and persistence in the aquatic environment. Nevertheless, there is still a huge knowledge gap on their complex interactions in the natural environment, their removal mechanism, and the monitoring of their fate in the environment. Water quality models are practical tools for simulating the fate and transport of pollutant mass in the aquatic environment. This paper reports an overview of the physical, chemical, and biological elimination mechanisms responsible for the degradation of antibiotics in natural surface water systems. It provides an in-depth review of commonly used quantitative fate models. An effort has been made to provide a compressive review of the modeling philosophy, mathematical nature, environmental applicability, parameter estimation, prediction efficiency, strength, and limitation of commonly used environmental antibiotic fate models. The study provides information linking paradigms of elimination kinetics and their simulation in the antibiotic fate models aiming at critical issues regarding current model development and future perspectives and to help users select appropriate models for practical water quality assessment and management.

Calibration of hydrological models for ungauged catchments by automatic clustering using a differential evolution algorithm: The Gorganrood river basin case study

Abstract The hydrological model calibration is a challenging task, especially in ungauged catchments. The regionalization calibration methods can be used to estimate the parameters of the model in ungauged sub-catchments. In this article, the model of ungauged sub-catchments is calibrated by a regionalization approach based on automatic clustering. Under the clustering procedure, gauged and ungauged sub-catchments are grouped based on their physical characteristics and similarity. The optimal number of clusters is determined using an automatic differential evolution algorithm-based clustering. Considering obtained five clusters, the value of the silhouette measure is equal to 0.56, which is an acceptable value for goodness of clustering. The calibration process is conducted according to minimizing errors in simulated peak flow and total flow volume. The Storm Water Management Model is applied to calibrate a set of 53 sub-catchments in the Gorganrood river basin. Comparing graphically and statistically simulated and observed runoff values and also calculating the value of the silhouette coefficient demonstrate that the proposed methodology is a promising approach for hydrological model calibration in ungauged catchments.

Quantifying and improving flood resilience of urban drainage systems based on socio-ecological criteria

In this paper, a new framework is developed for evaluating the resilience of urban drainage systems (UDSs) under floods by proposing and quantifying some technical and socio-ecological (SE) criteria. The proposed criteria are used to quantify the seven principles of building resilience in socio-ecological systems. The criteria mainly focus on preserving diversity and multiplicity in a UDS, managing variables that gradually change over time (slow variables), improving structural and functional connectivity, maintaining system adaptability, encouraging learning, broadening participation, and promoting polycentric governance systems. For evaluating the efficiency of the proposed framework, it is applied to a real-world case study of improving resilience of the UDS in the eastern part of Tehran metropolitan area. Three scenarios for flood management are proposed based on the Low Impact Development (LID) practices which are simulated using the Storm Water Management Model (SWMM). The Entropy method is used to consider the uncertainty in the relative importance of different criteria in estimating the flood resilience. The estimated values for the proposed criteria regarding the current drainage system in the study area show its undesirable condition in many sub-catchments. The results also show that using around 2.3 km2 of LID practices in this urban watershed can significantly improve the resilience in many sub-catchments (nearly, 30%) and reduce the total volume of the overflow (about 50%). The results also show that using the flood management scenarios, improving connectivity is the most influential factor that enhances the general resilience of the system.

Biovalley analysis as a LID control to delay surface runoff in the city of Recife/PE

Urban growth brought with it the formation of large impermeable areas in cities, causing a reduction in the infiltration of rainwater into the soil and an increase in the volume of surface runoff, causing setbacks on days of extreme precipitation events. In this context, the present research aimed to analyze the use of a biotaver as a Low Impact Development (LID) for delaying surface runoff in areas prone to flooding in the city of Recife/PE. As a methodology, the QGIS model was used to extract data from raster images, and the Storm Water Management Model (SWMM) for scenario simulation. Three scenarios were simulated with rainfall of 20.00mm, 39.00mm and 60.00mm. The results showed that the implementation of the bioditches provided a reduction in the total flow of the study area by 0.50% and in the maximum flow of the SB1 sub-basin for scenarios 2 and 3 by 77.96% and 41.23% respectively. In addition, there was a reduction in surface runoff of 11.15% on average and an increase in green area of 4.00%. The bioretention system showed infiltration ranging from 29.83mm to 89.35mm between the three scenarios. Thus, through the SWMM model, it was possible to analyze that the implantation of bioditches as a control for delaying surface runoff and increasing infiltration into the soil is valid, and the literature recommended that various types of sustainable devices be implemented and integrated into the existing drainage network and urban space so that more expressive results are possible.

Improving detention ponds for effective stormwater management and water quality enhancement under future climate change: a simulation study using the PCSWMM model

Urban surfaces are often covered by impermeable materials such as concrete and asphalt which intensify urban runoff and pollutant concentration during storm events, and lead to the deterioration of the quality of surrounding water bodies. Detention ponds are used in urban stormwater management, providing two-fold benefits: flood risk reduction and pollution load minimization. This paper investigates the performance of nine proposed detention ponds (across the city of Renton, Washington, USA) under different climate change scenarios. First, a statistical model was developed to estimate the pollutant load for the current and future periods and to understand the effects of increased rainfall on stormwater runoff and pollutant loads. The Personal Computer Storm Water Management Model (PCSWMM) platform is employed to calibrate an urban drainage model for quantifying stormwater runoff and corresponding pollutant loads. The calibrated model was used to investigate the performance of the proposed nine (9) detention ponds under future climate scenarios of 100-year design storms, leading to identifying if they are likely to reduce stormwater discharge and pollutant loads. Results indicated significant increases in stormwater pollutants due to increases in rainfall from 2023 to 2050 compared to the historical period 2000–2014. We found that the performance of the proposed detention ponds in reducing stormwater pollutants varied depending on the size and location of the detention ponds. Simulations for the future indicated that the selected detention ponds are likely to reduce the concentrations (loads) of different water quality constituents such as ammonia (NH3), nitrogen dioxide (NO2), nitrate (NO3), total phosphate (TP), and suspended solids (SS) ranging from 18 to 86%, 35–70%, 36–65%, 26–91%, and 34–81%, respectively. The study concluded that detention ponds can be used as a reliable solution for reducing stormwater flows and pollutant loads under a warmer future climate and an effective adaptation option to combat climate change related challenges in urban stormwater management.

Identification and Quantification of Surface Depressions on Grassy Land Surfaces of Different Topographic Attributes Using High-Resolution Terrestrial Laser Scanning Point Cloud and Triangulated Irregular Network

The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.

Continuous Hydrologic Modeling of a Parking Lot and Related Best Management Practices with PCSWMM

AbstractPermeable pavements are a green infrastructure stormwater management practice that can serve as a functional component of the site design. However, previous field studies suggest high uncertainty in the parameters used for performing hydrologic calculations for permeable pavements. The Environmental Protection Agency (EPA) Storm Water Management Model (SWMM) within the PCSWMM software package was used to simulate the hydrologic dynamics of a parking lot that is 25% covered with permeable interlocking concrete pavers in Auburn, AL. The model was calibrated to field observations of water level at two points where the pavement system outflows to a bioretention basin and rainfall data from a nearby weather station. The use of the Curve Number (CN) method within SWMM resulted in good prediction of pavement outflow by the calibrated model, with R2 and Nash‐Sutcliffe model efficiency both greater than 0.8, except where issues with precipitation data coverage occurred. This demonstrates that permeable pavements can be modeled as a land cover type rather than as detention storage. The calibrated value of the runoff CN for permeable pavement was 60, much lower than what is recommended in many design guidelines for the underlying soil type at the research site, which is hydrologic soil group B. Based on evaluation of alternative model scenarios, the permeable pavement reduced runoff by 11‐38% across contrasting rain events.

Teaching Systems Thinking and System Dynamics in Engineering, Ecology and Environmental Sciences: A Concise Course Based on the Water Management and Population Dynamics Models

Teaching Systems Thinking and System Dynamics in Engineering, Ecology and Environmental Sciences: A Concise Course Based on the Water Management and Population Dynamics Models

Water Sensitive Urban Design (WSUD) Spatial Prioritisation through Global Sensitivity Analysis for Effective Urban Pluvial Flood Mitigation

Water Sensitive Urban Design (WSUD) has attracted growing attention as a sustainable approach for mitigating pluvial flooding (also known as flash flooding), which is expected to increase in frequency and intensity under the impacts of climate change and urbanisation. However, spatial planning of WSUD is not an easy task, not only due to the complex urban environment, but also the fact that not all locations in the catchment are equally effective for flood mitigation. In this study, we developed a new WSUD spatial prioritisation framework that applies global sensitivity analysis (GSA) to identify priority subcatchments where WSUD implementation will be most effective for flood mitigation. For the first time, the complex impact of WSUD locations on catchment flood volume can be assessed, and the GSA in hydrological modelling is adopted for applications in WSUD spatial planning. The framework uses a spatial WSUD planning model, the Urban Biophysical Environments and Technologies Simulator (UrbanBEATS), to generate a grid-based spatial representation of catchment, and an urban drainage model, the U.S. EPA Storm Water Management Model (SWMM), to simulate catchment flooding. The effective imperviousness of all subcatchments was varied simultaneously in the GSA to mimic the effect of WSUD implementation and future developments. Priority subcatchments were identified based on their influence on catchment flooding computed through the GSA. The method was tested for an urbanised catchment in Sydney, Australia. We found that high priority subcatchments were clustering in the upstream and midstream of the main drainage network, with a few distributed close to the catchment outlets. Rainfall frequency, subcatchment characteristics, and pipe network configuration were found to be important factors determining the influence of changes in different subcatchments on catchment flooding. The effectiveness of the framework in identifying influential subcatchments was validated by comparing the effect of removing 6% of the Sydney catchment's effective impervious area under four WSUD spatial distribution scenarios. Our results showed that WSUD implementation in high priority subcatchments consistently achieved the largest flood volume reduction (3.5-31.3% for 1% AEP to 50% AEP storms), followed by medium priority subcatchments (3.1-21.3%) and catchment-wide implementation (2.9-22.1%) under most design storms. Overall, we have demonstrated that the proposed method can be useful for maximising WSUD flood mitigation potential through identifying and targeting the most effective locations.

Teaching Systems Thinking and System Dynamics in Engineering, Ecology and Environmental Sciences: A Concise Course Based on the Water Management and Population Dynamics Models

Teaching Systems Thinking and System Dynamics in Engineering, Ecology and Environmental Sciences: A Concise Course Based on the Water Management and Population Dynamics Models

Multicriteria land cover design via coupled hydrologic and multi-sector water management models

We investigate how hydrologic-land feedbacks and a hydrologic-water management linkage impact land cover arrangements optimized within a multiobjective land cover design framework. The framework integrates a spatially-distributed and physically-based hydrologic model, for simulating surface and subsurface flow and land processes, with a network-based multi-sector water resources management and allocation model. Both models used (Parflow, Pywr) are open-source. The framework is applied to a hillslope problem to identify land cover patterns that optimize trade-offs between water, food, energy and environment objectives. Results show trade-offs depend on land cover composition and the spatial arrangement of land covers within the catchment. Total runoff volume and peak flow of runoff was found to change 3 and 2-fold, respectively, between optimized solutions as land cover composition and spatial patterns were altered to satisfy different combinations of objectives. At the same time, up to a 15% reduction in the total runoff volume and an 8% reduction in the peak flow of runoff were observed within optimized land cover patterns having equal composition but different spatial arrangements. This emphasizes the impact on hydrologic behavior of the spatial location of land covers within a catchment. The emergence of patterns in land cover distribution for different trade-offs between objectives is driven by feedback mechanisms between subsurface hydrology and land processes, which are implicitly linked to the properties of each land cover and the interactions between neighboring land covers through lateral groundwater flow. The study demonstrates the added benefits of coupling distributed hydrologic models with water management simulation for multisector multicriteria land cover planning.

Extensibility of a Machine Learning Model for Stormwater Basin Design and Retrofit Optimization Through a User-Friendly Web Application

To sequester particulate matter (PM) and chemical loads, stormwater basins are a common infrastructure component of transportation land use systems in a planning/design or a retrofit phase. Basin design relies on residence time (RT), capacity–inflow ratio (CIR), and continuous stirred-tank reactor (CSTR) concepts to provide presumptive guidance for load reduction. For example, 14-day (or 21-day) RT is presumed to provide load reduction for total suspended solids (TSS) (e.g., commonly 80%), and for total phosphorus (TP) (e.g., commonly 60%). Despite such guidance, most existing basins are impaired—not meeting presumptive guidance, whether for TSS, nutrients, or chemicals (e.g., metals). RT guidance results in high initial basin design costs (land and construction). For impaired basin retrofit, RT guidance generates a significantly higher cost (area/volume increase), if such a retrofit is even feasible considering proximate infrastructure constraints. This study presents a computational fluid dynamics (CFD)-machine learning (ML) augmented web application, DeepXtorm, for cost–benefit optimization of basin design and retrofit. DeepXtorm is examined against basin costs from as-built data, RT, CIR, and CSTR (deployed in the Storm Water Management Model [SWMM]) over a range of load reduction levels of TSS (60%–90%) and TP (40%–60%). For a given load reduction requirement, DeepXtorm demonstrates up to an order of magnitude (or more) lower basin cost compared with RT, CIR, and CSTR models, and greater than an order of magnitude compared with as-built cost data. DeepXtorm represents a more robust and cost-effective tool for stormwater basin design and retrofit.

A VBA-Based Field Water Balance Model for Efficient Irrigation Water Management of Corn (Zea mays L.)

A field water balance model for efficient irrigation water management of corn was developed using Excel VBA. The model consists of five sub-components or modules, namely, (1) a plant subcomponent, (2) an effective rainfall subcomponent, (3) an evapotranspiration subcomponent, (4) a soil water dynamics subcomponent for the modeling of water flow into and within the soil layers, and (5) an irrigation subcomponent for the estimation of the required amount and timing of irrigation. The model was calibrated and validated using observed data from field experiments and the results showed a reasonably good agreement between the observed and simulated soil moisture values (MAE = 5.76 mm to 12.00 mm, RMSE = 6.83 mm to 13.12 mm, NRMSE = 0.102 to 0.196, and NSE = 0.37 to 0.90). The simulations emphasized that a significant amount of water savings can be achieved when rainfall is properly accounted for in managing water in the field, and that the frequency of rainfall occurrences is as important as the magnitude of rainfall received by the crops. The wide-ranging user-friendliness and simplicity of the model developed in this study can pave the way to eliminating the barriers which cause farmers to resist advancements in their farming practices as the model can easily be used not only by researchers and scientists but also by farmers, especially those with basic knowledge of spreadsheets.

Identification of Indicators for Developing an Integrated Study on Urban Water Supply System, Planning, and Management

The increasing population growth, urbanization, climate change, mismanagement of water resources, and poorly planned water distribution systems with negligible integration with spatial planning are increasing water stress in many countries across the globe. Even though optimization of water distribution systems and integrated water management practices are being implemented in many cities and regions, there is a lack of an integrated study considering the urban water supply system, planning, and management. This paper identifies the various parameters and indicators to be considered along with the relevant tools and techniques for optimizing water resources, optimum water resource allocation, and integrated water supply and management. During the research, 116 indicators were identified through a literature review, followed by a three-round Delphi survey to attain consensus from 30 experts. At the end of the third round, 72 indicators achieved maximum consensus among the experts (W=0.741). The identified indicators can be used for developing an integrated model for urban water supply systems, planning, and management.