Water Management Applications Research Articles

Advanced Technologies for Sustainable Water Management: A Comprehensive Review

This review paper provides a comprehensive analysis of the latest advancements in water management technology, focusing on the integration of Internet of Things (IoT), machine learning, blockchain, and other emerging technologies. The study examines 19 key research papers, exploring various methodologies, key outcomes, and the challenges associated with these technologies. While significant progress has been made in improving water resource management through real-time monitoring, predictive analytics, and decentralized data management, several challenges persist, including high implementation costs, integration complexities, and the need for standardized protocols. The review identifies opportunities for future research, particularly in developing cost-effective solutions, enhancing interoperability, and advancing artificial intelligence applications in water management. Additionally, the paper highlights the importance of evolving regulatory frameworks and increasing public awareness to ensure the successful adoption of these technologies. The findings of this review contribute to the ongoing efforts to create more sustainable and efficient water management systems capable of addressing the growing global water demand.

Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province.

This study presents a comparative analysis of various Machine Learning (ML) techniques for predicting water consumption using a comprehensive dataset from Kocaeli Province, Turkey. Accurate prediction of water consumption is crucial for effective water resource management and planning, especially considering the significant impact of the COVID-19 pandemic on water usage patterns. A total of four ML models, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), were evaluated. Additionally, optimization techniques such as Particle Swarm Optimization (PSO) and the Second-Order Optimization (SOO) Levenberg-Marquardt (LM) algorithm were employed to enhance the performance of the ML models. These models incorporate historical data from previous months to enhance model accuracy and generalizability, allowing for robust predictions that account for both short-term fluctuations and long-term trends. The performance of each model was assessed using cross-validation. The R2 and correlation values obtained in this study for the best-performing models are highlighted in the results section. For instance, the GBM model achieved an R2 value of 0.881, indicating a strong capability in capturing the underlying patterns in the data. This study is one of the first to conduct a comprehensive analysis of water consumption prediction using machine learning algorithms on a large-scale dataset of 5000 subscribers, including the unique conditions imposed by the COVID-19 pandemic. The results highlight the strengths and limitations of each technique, providing insights into their applicability for water consumption prediction. This study aims to enhance the understanding of ML applications in water management and offers practical recommendations for future research and implementation.

Modeling the spatial distribution of soil physical properties in a semiarid tropical region

In semiarid regions, sustainably managing the limited available water resources is critical for food, water, and economic security. An important aspect of water management is monitoring, analyzing, and predicting soil moisture and hydrologic fluxes, especially under the threats of climate change. Unfortunately, since monitoring of soil moisture and water fluxes is often sparse in these regions, one needs to rely on hydrological modeling. The latter requires accurate knowledge of soil properties (e.g., texture, porosity, hydraulic conductivity), which in semiarid regions is also complicated by high level of spatial heterogeneity that is not captured by the low density of soil sampling efforts as well as by globally gridded soil databases. Using 300 monitoring sites across the Semiarid region of NE Brazil and a soil moisture model, this study aims to estimate soil properties at the sites through inverse-modeling and then spatially interpolation these properties via geostatistics. Inverse modeling with the Levenberg–Marquardt algorithm accurately captured soil properties across the sites, while clear spatial patterns in these properties allowed us to confidently interpolation them across the region. Comparing our results with a global soil dataset shows that the latter does not capture spatial variability in the region, underlining the need to leverage small scale information. The derived soil property datasets may then particularly be valuable for regional water and ecosystem management applications.

Expert-based prior uncertainty analysis of gridded water balance components: Application to the irrigated Hindon River Basin, India

Study regionHindon River Basin, North India. Study focusAccurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the regionGrid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R2 above 0.8) than ET fluxes (R2 less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

Design and implementation of a BigQuery dataset and application programmer interface (API) for the U.S. National Water Model

We introduce an open-source web-based Application Programming Interface (API) developed within a representational state transfer (REST) architecture framework that provides access to the operational streamflow forecasts from the U.S. National Water Model (NWM). We built this API within the Google Cloud infrastructure, taking advantage of Google's API Gateway, BigQuery, and the Google Cloud Run architecture. We ran a data transformation from netCDF to tabular formation then ingested data into BigQuery using Apache Beam parallel processing technology. The API activates functions deployed to Cloud Run that executes SQL queries within the BigQuery data warehouse. The API gives users granular control to specify queries based on forecast type, reference datetime, stream segment specifics, and forecast ensemble members. This API greatly simplifies access and use of current and historic NWM forecasts, an otherwise arduous task due to the large volume of data and unwieldy storage methods. Retrieved forecast data can be used for many critical water management applications providing actionable intelligence for, e.g., flood management, safety, recreation, agriculture, and related uses.

Assessing Water Management Strategies in Data-Scarce Mountain Regions under Uncertain Climate and Socio-Economic Changes

Water management in mountainous regions faces significant challenges due to deep uncertainties arising from data scarcity, knowledge gaps, and the complex interplay of climate and socio-economic changes. While existing approaches focused on uncertainty reduction and water system optimization contribute to managing uncertainties, they often require probability distributions that can be difficult to obtain in data-scarce mountain regions. To address these challenges, we demonstrate the effectiveness of Exploratory Modeling and Analysis (EMA) in assessing water management strategies and identifying operational ranges that avoid future water scarcity. Through a case study in the complex and data-scarce Peruvian Andes, we employed EMA to run 12,000 simulations by 2050, incorporating deep uncertainties from climate and socio-economic scenarios, and hydrological modeling parameters. This analysis identified specific policy combinations demonstrating greater robustness across diverse scenarios and uncertainties. EMA explicitly identifies operational ranges of policies to avoid water scarcity but also highlights the conditions that might trigger policy failure. We also delve into the roles of the different factors used in EMA and their significance in water management applications. Our research illustrates that an exploratory hydrological modeling approach based on robust decision-making can foster a more informed decision-making process for long-term water adaptation in rapidly changing mountain regions under data scarcity and deep uncertainties.

Multifunctional Piezoelectric Yarns and Meshes for Efficient Fog Water Collection, Energy Harvesting, and Sensing

Given global water scarcity and the quest for sustainable energy, there's a pressing need for integrated approaches addressing water‐energy interdependence worldwide. A practical approach for this challenge involves the implementation of fog water collectors. Herein, a polyvinylidene fluoride (PVDF) multifunctional device capable of harvesting water and electricity from wind is developed and tested, collecting up to 365 mg cm−2 h−1 of fog water. Due to the piezoelectric nature of electrospun PVDF, these yarns and meshes not only serve as piezoelectric sensors, enabling the detection of incoming fog flow and determination of its speed and, bust also harvest electricity by charging a capacitor, making it a green and renewable power source. In this study, promising insights are offered into developing efficient fog water collection methods and utilizing piezoelectric fiber‐based yarns and meshes for multifunctional applications in sustainable water management, energy harvesting, and sensing in a single device.

Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions

Surface soil moisture (SSM) information could have important applications in agricultural and regional water management. Remote sensing, particularly synthetic aperture radar (SAR), is an important technology for the estimation of spatial–temporal SSM over larger areas. Using Sentinel-1 and Sentinel-2 data, this research developed a general spatial–temporal constrained machine-learning-based method for surface soil moisture mapping over agricultural regions. Central to this method is the construction of spatial and temporal constraints and their implementation in machine-learning models. We first defined the spatial and temporal constraints for SSM estimation by investigating the spatial division of cultivated crop types and the temporal division of cumulative precipitation. Second, under the presumption that the SSM and associated variables are smoothly changing, we extracted the temporal difference variables from the multi-temporal remote sensing data. Finally, we incorporated two constraints as categorical features and temporal differences into a CatBoost-based model to improve surface soil moisture mapping. We verified the proposed model in a Spain study area with multi-temporal remote sensing observations. The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R2 = 0.7328, RMSE = 0.0451 vol, and MAE = 0.0351 vol This study also concluded that using multiple polarization in the machine-learning-based method could reliably and accurately estimate surface soil moisture.

Blockchain Opportunities for Water Resources Management: A Comprehensive Review

Blockchain technology has been used for the digitalization of physical asset management and data management processes in many areas in the industry and academia, including the water domain. Its potential as an immutable data storage system and smart contract integration has provided a plethora of use cases and utility in the domain of hydrology and water resources. This systematic review critically examines the application of blockchain technology in the field of water resources and hydrology. By analyzing 104 academic publications and 37 non-academic studies from 2017 to 15 October 2022, this paper identifies the current state of blockchain applications in water management, delineates their potential use cases, and assesses their practical utility and scalability. Despite the theoretical promise of blockchain for enhancing water governance, data security, and stakeholder trust, the review reveals a noticeable gap between theoretical potential and the existence of workable, real-time applications specifically in water resources management. The findings indicate that while blockchain technology has been effectively implemented in various sectors, its adoption in hydrological domain is still emerging, with limited empirical evidence to support full-scale deployment. The paper concludes with a call for more empirical research to validate theoretical benefits, address scalability and interoperability challenges, and integrate blockchain technology with real-time data networks for sustainable water management practices.

Reducing greenhouse gas emissions from rice cultivation applied with melaleuca and rice husk biochar in the Vietnamese Mekong Delta

ABSTRACT Biochar application is considered a potential approach to mitigating greenhouse gas emissions (GHGs) from the agricultural sector and improving soil physicochemical properties. Thus, we conducted a field experiment to examine the efficacy of biochar application rates in combination with water management practices on soil methane (CH4) and nitrous oxide (N2O) emissions and carbon increment in the Vietnamese Mekong Delta (VMD). Rice husk biochar (RB) and Melaleuca biochar (MB) were incorporated in the topsoil (0–20 cm) at 5 and 10 Mg ha−1, followed by the water management regimes of continuous flooding (CF) and an alternative wetting and drying (AWD). The results identified that biochar application rates and water management measures were major factors in decreasing CH4 and N2O emissions. Compared with the conventional practice, the RB and MB amendment in the rice fields under CF practices reduced 12.9–21.3% of total GHGs (tGHGs), while biochar incorporation under the AWD management practice reduced from 22.3% to 30.5% tGHGs. Compared to CF treatments, biochar amendment concerning the AWD regime lowered from 25.4% to 31.6% tGHGs. Increasing biochar from 5 to 10 Mg ha−1 did not reduce tGHGs considerably. We identified that biochar application significantly increased grain yields from 12.8% to 20.6% under the CF management practice, while the percentage varied from 2.5% to 3.8% for the AWD practice. Similarly, higher grain yields were achieved from 4.9% to 13.7% by applying AWD compared to the CF treatments. Our findings showed that although higher biochar application rates could reduce the tGHGs, the difference in biochar types and water management applications in connection with the tGHGs was neglectable. Incorporating biochar enhanced soil bulk density and porosity under AWD and CF practices. Going forward in further studies, we recommend a long-term biochar application in the VMD’s low-lying paddy fields with a broader range of biochar rates under different soil types.

Harmony in Hydroinformatics: Integrating AI and IEC for sustainable groundwater conservation in Solapur

This research investigates the synergistic potential of artificial intelligence (AI) and Information Education and Communication (IEC) in the context of groundwater conservation for Solapur, a region participating in the Atal Bhujal Yojana. The primary objective is to assess the effectiveness of integrating AI technologies with community-centric education strategies to enhance water management practices. The methodology involves a comprehensive review of the Atal Bhujal Yojana, exploration of AI applications in global water management, and the formulation of strategies for AI-IEC integration. Key findings highlight the pivotal role of community engagement, the diverse applications of AI in water management, and the significance of IEC in shaping sustainable behaviors. Challenges and solutions, case studies, and future prospects are examined to provide a comprehensive overview. The implications of this research extend to the development of resilient water ecosystems, emphasizing the importance of collaborative efforts and forward-thinking solutions. This interdisciplinary approach positions Solapur as a model for effective groundwater conservation, leveraging technological advancements and community participation for a water-secure future.

Electropolymerization of Polydopamine at Electrode-Supported Insulating Mesoporous Films.

Bioinspired, stimuli-responsive, polymer-functionalized mesoporous films are promising platforms for precisely regulating nanopore transport toward applications in water management, iontronics, catalysis, sensing, drug delivery, or energy conversion. Nanopore technologies still require new, facile, and effective nanopore functionalization with multi- and stimuli-responsive polymers to reach these complicated application targets. In recent years, zwitterionic and multifunctional polydopamine (PDA) films deposited on planar surfaces by electropolymerization have helped surfaces respond to various external stimuli such as light, temperature, moisture, and pH. However, PDA has not been used to functionalize nanoporous films, where the PDA-coating could locally regulate the ionic nanopore transport. This study investigates the electropolymerization of homogeneous thin PDA films to functionalize nanopores of mesoporous silica films. We investigate the effect of different mesoporous film structures and the number of electropolymerization cycles on the presence of PDA at mesopores and mesoporous film surfaces. Our spectroscopic, microscopic, and electrochemical analysis reveals that the amount and location (pores and surface) of deposited PDA at mesoporous films is related to the combination of the number of electropolymerization cycles and the mesoporous film thickness and pore size. In view of the application of the proposed PDA-functionalized mesoporous films in areas requiring ion transport control, we studied the ion nanopore transport of the films by cyclic voltammetry. We realized that the amount of PDA in the nanopores helps to limit the overall ionic transport, while the pH-dependent transport mechanism of pristine silica films remains unchanged. It was found that (i) the pH-dependent deprotonation of PDA and silica walls and (ii) the insulation of the indium-tin oxide (ITO) surface by increasing the amount of PDA within the mesoporous silica film affect the ionic nanopore transport.

Blockchain for environmental peacebuilding: application in water management

PurposeThe purpose of this paper is to spur further exploration of blockchain technologies for environmental peacebuilding, specifically through water management. Although the environmental peacebuilding field acknowledges the potentially transformative nature of frontier technologies, most existing studies do not address how such technologies can contribute to peacebuilding through improved natural resource governance. Using a theory synthesis research design, this conceptual paper connects these studies to discuss how blockchain technologies in water management may contribute to environmental peacebuilding through the efficient and transparent management of natural resources.Design/methodology/approachThe authors use a conceptual approach and a theory synthesis research design to present potential mechanisms through which blockchain technology can potentially contribute to environmental peacebuilding.FindingsThe authors discuss the limitations in the literature on environmental peacebuilding, blockchain and water management, concluding that the third generation of studies should focus on the role of frontier technologies. This approach should especially address the negative consequences of technology for peacebuilding objectives. The authors argue that blockchain applications in water management can potentially contribute to environmental peacebuilding objectives in three ways: (i) creating a mechanism for confidence-building in low-trust contexts through automated and transparent water transactions, (ii) facilitating postconflict economic development through attracting capital and increasing the efficiency of water management and (iii) improving governance through greater transparency and local participation in natural resource management.Originality/valueTo the best of the authors’ knowledge, this study is among the first to conceptually explore the use of blockchain technology for water management in the context of environmental peacebuilding. The insights from this study can guide policymakers of conflict sides that focus on resolving issues such as lack of governance and low state agency.

Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates of numerical weather prediction (NWP) models that show improved forecast skill. This circumstance leads to the recurrent delivery of revised reanalysis databases that provide weather estimates for several decades back in time by combining the latest NWP models with observations. Since climate studies and agriculture water management applications require the availability of accurate and reliable weather data, assessing the performance of reanalysis products contributes to informed choices of potential weather proxy of ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is the latest regional reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), in August 2022. CERRA is forced by the global ERA5 reanalysis, and it provides weather data with resolution of 5.5 km for the pan-European territory from 1984. For the first time in literature, this study explores the performance of CERRA data at 38 ground-based weather stations located in Sicily, an Italian region with Mediterranean climate, during the irrigation seasons 2003–2022. The objective of the study lies in evaluating CERRA performance with respect to air temperature, actual vapor pressure, wind speed and solar radiation that are input variables for assessing reference evapotranspiration, ETO, which is a key variable for quantifying irrigation volumes needed for water resources studies. The accuracy of ETO estimates depends on those input variables through the equation provided by the Food and Agriculture Organization of the United Nations (FAO), i.e., the FAO Penman-Monteith equation. Here, it is also evaluated the performance of ETO estimates by using CERRA weather inputs in the FAO Penman-Monteith equation. The results show that the performances of CERRA weather data are excellent, especially for air temperature, and this determines that CERRA ETO estimates present high accuracy and reliability with a mean PBIAS and NRMSE equal to 5.6% and 13%, respectively, over the region. Those outcomes lead to the conclusion that CERRA dataset represents a valid alternative to ground-based agrometeorological measurements and their spatial interpolation for water resource regional studies.

Synthesis of a polyacrylamide hydrogel modified with a reactive carbamate surfactant: Characterization, swelling behavior, and mathematical models

Hydrogels are polymeric materials that can absorb and retain large amounts of water. They have various applications in agriculture, horticulture, oil recovery, and water management. However, conventional hydrogels have limited swelling (Q), equilibrium swelling (Q∞), and water retention capacities (DQ) under different environmental conditions. In this study, we synthesized a series of hydrophobically associated hydrogels by the aqueous polymerization of acrylamide, acrylic acid, 2-acrylamide-2-methylpropanesulfonic acid, and a novel amphiphilic hydrophobic monomer (RCS-10). Further, we investigated the effects of the RCS-10 content on Q, the mechanical properties, and water retention capacity (DQ) of the hydrogels. We also examined the influence of temperature, pH, salt type and concentration, and urea concentration on Q, finding that an increase in the RCS-10 content decreased Q and increased the DQ of the hydrogels. The hydrogels exhibited high Q in distilled water, low-salt solutions, and low-urea (1 wt%) solutions. In the latter, the Q∞ was as high as 768.99 g/g. The hydrogels also showed excellent cyclic swelling performance and maintained high Q∞ (759.99 g/g) after six cycles. Crucially, the hydrophobic association hydrogels demonstrated superior swelling and water retention properties compared to conventional hydrogels. Thus, the hydrogels have applications in water management in various fields including agriculture and horticulture.

The effect of climate uncertainty on water management for long-run aquifer abundance

The adaptation of agriculture to climate change depends on the ability to integrate irrigation practices into water management applications. This requires robust water use estimates and the evaluation of management practices as a tool to conserve water. We combine a landscape level agricultural management model that accounts for risk and uncertainty through climate change with an aquifer model. The modelling system produces estimates of the long run aquifer abundance for management scenarios for a study area in the Lower Mississippi River Basin, United States. Except in the driest climate outcome, the aquifer abundance rises over time. We find that the increase in the aquifer for most climate outcomes occurs through judicious crop choice and aquifer recharge followed by a use of on-farm reservoirs later in the study period. Our modelling points to the greatest sensitivity of the long run aquifer abundance to fluctuations in crop prices and production costs followed by the cost of surface water for aquifer recharge.

Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions

Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.

Temperature change-informed future multisite streamflow generation to support water supply vulnerability assessments under climate change

Stochastically generated streamflow time series are increasingly used for various water management and hazard assessment applications. The sequences provide realizations, preserving the temporal and spatial characteristics observed in the historic data. However, the streamflow simulations are more desirable if climate variability is utilized to account for nonstationarity in past and future climate. This study proposes a nonstationary-based approach for stochastically generating future multisite daily streamflow to evaluate the vulnerability of water supply systems. The approach is developed based on a national-wide relationship between annual daily maximum temperature and annual streamflow, which is newly identified in this study. This approach is attractive since it can avoid limitations and uncertainties introduced during realization and bias correction processes for climate model-based rainfall information. While the approach is developed by coupling annual and daily simulations, it includes (1) a wavelet decomposition-based autoregressive simulation to impose the signal of regional climate covariate; (2) clustering-based spatial pattern recognition and simulation; and (3) block bootstrapping and vine copula-based simulation for multisite streamflow simulation. The approach is used to evaluate the water security of the regional water supply systems for multiple basins over South Korea. Results show that the generated sequences properly preserve many of the historical characteristics across multiple basins. When two future scenarios were generated based on 1 and 2 °C warming, significant decreases in streamflow are projected, particularly for the 2 °C scenario. This analysis suggests that regional water supply systems could substantially be threatened under the warming condition.

Determination of Crop Water Requirement and Crop Coefficient at Different Growth Stages of Tomato by Using Weighing Type Lysimeter in Raichur Region

The water requirement for any cropping system is important consideration for designing and managing irrigation systems. Water requirement of crops varies substantially over the growing season mainly due to variation in crop cover and climatic conditions. For estimating crop water requirement of tomato crop, we have taken field experiment in weighing lysimeter for three seasons rabi - (7th September 2021 to 5th January 2022); Summer - (15th January 2022 to 15th May 2022) and Kharif - (6th June 2022 to 4th October 2022). The crop water requirement helps us to develop the crop coefficients for various growth stages (initial, development, mid and late season) for particular climatic conditions. Further, with exact crop coefficients derived from weighing type lysimeter will be helpful for determining water requirement. The results showed that the crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 22.76, 110.71, 173.47 and 68.71 mm, respectively. With respect to crop coefficient (Kc), it is estimated to be 0.38, 0.64, 1.12 and 0.66 for initial, development, mid-season and late season stages, respectively during rabi season. The crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 26.53, 164.76, 235.56 and 121.51 mm, respectively. With respect to crop coefficient (Kc), it is estimated to be 0.56, 0.87, 1.22 and 0.78 for initial, development, mid-season and late season stages, respectively during summer season. The crop evapotranspiration (ETc) for initial, development, mid-season and late-season being 32.31, 131.38, 173.51 and 60.13 mm, respectively. With respect to Kc, it is estimated to be 0.36, 0.77, 1.13 and 0.74 for initial, development, mid-season and late season stages respectively during kharif season. The measured Kc values were significantly different from the FAO-56 reported values. Therefore, local calibration of crop coefficients is an essential for efficient irrigation water management and precise water applications.

Testing and applying baseflow approaches to environmental flow needs

Environmental flow need (EFN) is defined as the quantity, quality, and timing of flow that are required to maintain the health of riverine ecosystems and human and societal sustainable development. Numerous research frameworks have been developed and used with a range of sophistication, data requirements, and management strategies. Recently, 90% of the monthly baseflow derived from groundwater has been proposed as the presumptive standard for the groundwater contribution of EFN. However, this proposed groundwater presumptive standard has not been compared to the field-based EFN, which limited its practical application in water management. Therefore, we ask whether baseflow can be used as an index of EFN, and if so, is 90% of monthly baseflow robust for aquatic ecosystems? We selected nine watersheds in the Okanagan Basin (Canada) with well-established EFN based on extensive fieldwork and local experts' knowledge to address this research question. Four baseflow separation methods were adopted to derive the long-term monthly baseflow. We then compared the baseflow against field-based EFN and found that different ranges of baseflow can represent field-based EFN. Specifically, 86%-159%, 93%-151%, and 128%-139% of monthly baseflow can be used as EFN standards for snow-melting, summer, and fall-winter, respectively. Further, we found that 102%-150% of the monthly baseflow should be maintained, implying that the presumptive standard underestimated EFN in our study region. Finally, we recommended that baseflow can be an effective index for EFN; however, watershed management should recognize local aquatic conditions in applying the baseflow EFN index for conjunctive watershed management.