Water Managers Research Articles

Uncertainty and sensitivity analysis of a travel‐time distribution‐based stream water electrical conductivity model

AbstractIn this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2‐year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute‐balance approach and (2) a water age‐based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age‐based model than the solute‐balance model; 4. using time‐variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS‐TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute‐balance model, the rainfall concentration Crain and in the age‐based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute‐balance model. Little change in parameter sensitivity over time was found for the age‐based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute‐balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.

Formation and toxicity alteration of halonitromethanes from Chlorella vulgaris during UV/chloramination process involving bromide ion

Frequent algal blooms cause algal cells and their algal organic matter (AOM) to become critical precursors of disinfection by-products (DBPs) during water treatment. The presence of bromide ion (Br−) in water has been demonstrated to affect the formation laws and species distribution of DBPs. However, few researchers have addressed the formation and toxicity alteration of halonitromethanes (HNMs) from algae during disinfection in the presence of Br−. Therefore, in this work, Chlorella vulgaris was selected as a representative algal precursor to investigate the formation and toxicity alteration of HNMs during UV/chloramination involving Br−. The results showed that the formation concentration of HNMs increased and then decreased during UV/chloramination. The intracellular organic matter of Chlorella vulgaris was more susceptible to form HNMs than the extracellular organic matter. When the Br−: Cl2 mass ratio was raised from 0.004 to 0.08, the peak of HNMs total concentration increased 33.99%, and the cytotoxicity index and genotoxicity index of HNMs increased 67.94% and 22.80%. Besides, the formation concentration and toxicity of HNMs increased with increasing Chlorella vulgaris concentration but decreased with increasing solution pH. Possible formation pathways of HNMs from Chlorella vulgaris during UV/chloramination involving Br− were proposed based on the alteration of nitrogen species and fluorescence spectrum analysis. Furthermore, the formation laws of HNMs from Chlorella vulgaris in real water samples were similar to those in deionized water samples. This study contributes to a better comprehension of HNMs formation from Chlorella vulgaris and provides valuable information for water managers to reduce hazards associated with the formation of HNMs.

Evaluation and Correction of Satellite-Based Rainfall Products (GPM) in the Upstream Part of Bengawan Solo Basin using MCCV Parameterization

In conservative manner, water managers utilize ground station dataset for hydrology analysis. Due to spatially sparse and unreliable precipitation gauges, many have shifted to the application of remote sensing products or satellite-based precipitation products (SBPPs). One of few notable SBPPs is the Global Precipitation Measurement (GPM), which has shown excellent performances. Despite its significant progress, systematic biases are inevitable in the process of recording precipitation. Consequently, a simple yet effective correction process is necessary to be carried out prior to SBPPs’ wide applications. This study proposes a non-complex transformation using Monte Carlo-Cross Validation (MCCV) for its function parameterization. The rainfall products in focus are the monthly GPM 3IMERGDF V06 in the Upper Bengawan Solo Basin, in Central Java. Evaluation using several statistical functions demonstrate that the GPM IMERG is capable in capturing the monthly rainfall, supporting by the mean correlation of 0,76. After the correction process, the GPM IMERG slightly improves its performance, indicated by the reduced error of approximately 16%. This correction procedure also allows for correction of the monthly rainfall overestimation, particularly those exceeding 600 mm/month.

Hydrologic Sensitivity of a Critical Turkish Watershed to Inform Water Resource Management in an Altered Climate

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or “reference” climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions.

Strategies to Build Customer Trust Based on Service Quality and Relationship Quality: Anchorage Study in Riau Islands

This study examines the potential of anchor anchor services in Riau Islands Province (Kepri), focusing on service quality and customer relationships that can affect the satisfaction of anchor anchor service users. Data from a survey of 100 vessels using anchor berthing services in Kabil Kepri waters shows that the quality of service and the quality of customer relationships significantly affect customer satisfaction. The results of regression analysis show that service quality and relationship quality simultaneously contribute greatly to customer satisfaction, with the value of the coefficient of determination reaching 68%. Service quality has a greater influence than relationship quality on customer satisfaction. The implications of this study show that quality service and good customer relations can increase customer satisfaction in using anchor anchor services. The conclusion of this study emphasizes the need for a comprehensive strategy in improving service quality and customer relations in anchorage management in Riau Islands waters. Suggestions for further research are further exploration of more representative indicators of service quality and customer relations as well as increased academic studies in the field of port marketing, especially related to anchorage objects.

Hybrid modeling of karstic springs: Error correction of conceptual reservoir models with machine learning

ABSTRACT Accurate spring discharge modeling and prediction is crucial for water management, helping authorities optimize use, manage variability, and prepare for droughts. Developing reliable simulation and forecasting tools is essential for effective management of groundwater resources from karstic springs. Although hybrid modeling approaches have been explored in hydrology, their application to spring discharge modeling is underexplored. Previous studies have focused on conceptual/distributed or data-driven models separately, missing the potential advantages of combining them. This creates a research gap in exploring the benefits of hybrid models for spring discharge. This study developed a hybrid model combining a conceptual GR5J model with Random Forests to simulate spring discharge from Bordeaux's largest karst aquifer. Model performance was assessed through comparison with the individual GR5J, RF, and benchmark models (weekly average of observed values). The hybrid model outperformed all models. Evaluation using actual meteorological data found the hybrid model achieved the highest accuracy by reducing GR5J simulation errors by 22%. When considering meteorological uncertainty, the hybrid model outperformed the individual GR5J, RF and benchmark models by 11, 30 and 47% respectively. The study findings suggest combining conceptual and machine learning approaches can improve spring discharge simulations, opening promising opportunities for enhanced forecasting in karst aquifers.

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

A Simple Artificial Neural Model to Predict Dambovita River Temperature Affected by Urban Heat Islands in Bucharest City

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures.

Analysing spatial variability in drought sensitivity of rivers using explainable artificial intelligence

Hydrological drought can have a severe negative impact on aquatic ecosystems and human living standards. Therefore, being able to predict and gain more insights in the spatial variability in drought sensitivity of rivers is of relevance for water managers. The drought sensitivity of a river is in this study represented by four drought metrics, of which three are relative towards the ecological minimal flow. Statistical and machine learning methods were evaluated to predict these metrics for rivers in the Flanders region of Belgium based on catchment characteristics and data on human interferences. XGBoost had the best performance, with an explained variance of 80 % to 90 %. After applying explaining AI on these models, insights were obtained in the spatial variability of the drought metrics. Irrigation is the most important variable, a high percentage of irrigation leads to a higher drought sensitivity. If there are a lot of human interferences, there is a higher drought sensitivity. Many of the observed dependencies can be explained by the differences in soil infiltration capacity and transferability of water for sandy versus clay soils. No clear dependence with the amount of forest or agriculture was observed, implying that the impact of forest and agriculture on the drought sensitivity of a river is complex.

Groundwater Sustainability and Land Subsidence in California’s Central Valley

The Central Valley of California is one of the most prolific agricultural regions in the world. Agriculture is reliant on the conjunctive use of surface-water and groundwater. The lack of available surface-water and land-use changes have led to pumping-induced groundwater-level and storage declines, land subsidence, changes to streamflow and the environment, and the degradation of water quality. As a result, in part, the Sustainable Groundwater Management Act (SGMA) was developed. An examination of the components of SGMA and contextualizing regional model applications within the SGMA framework was undertaken to better understand and quantify many of the components of SGMA. Specifically, the U.S. Geological Survey (USGS) updated the Central Valley Hydrologic Model (CVHM) to assess hydrologic system responses to climatic variation, surface-water availability, land-use changes, and groundwater pumping. MODFLOW-OWHM has been enhanced to simulate the timing of land subsidence and attribute its inelastic and elastic portions. In addition to extending CVHM through 2019, the new version, CVHM2, includes several enhancements as follows: managed aquifer recharge (MAR), pumping with multi-aquifer wells, inflows from ungauged watersheds, and more detailed water-balance subregions, streamflow network, diversions, tile drains, land use, aquifer properties, and groundwater level and land subsidence observations. Combined with historical approximations, CVHM2 estimates approximately 158 km3 of storage loss in the Central Valley from pre-development to 2019. About 15% of the total storage loss is permanent loss of storage from subsidence that has caused damage to infrastructure. Climate extremes will likely complicate the efforts of water managers to store more water in the ground. CVHM2 can provide data in the form of aggregated input datasets, simulate climatic variations and changes, land-use changes or water management scenarios, and resulting changes in groundwater levels, storage, and land subsidence to assist decision-makers in the conjunctive management of water supplies.

Irrigation Distribution Network Design Parameters and Their Influence on Sustainability Management

In 2030, the world population will exceed 8.5 billion, increasing the challenges to satisfy basic needs for food, shelter, water, and/or energy. Irrigation plays a vital role in productive and sustainable agriculture. In the current context, it is determined not only by water availability but also by optimal management. Several authors have attempted to measure the performance of irrigation networks through various approaches in terms of technical indicators. To improve the sustainability in the pipe sizing of the pressurised irrigation networks, 25 different models were evaluated to discuss the advantages and disadvantages to consider in future methodologies to size water systems, which guarantee the network operation but contribute to improving the sustainability. They enable water managers to use them as tools to reduce a complex evaluation of the performance of a system, and focusing on better management of resources and sustainability indicators for agricultural ecosystems are clear and objective values.

Can we rely on drought‐ending “miracles” in the Colorado River Basin?

AbstractUnexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought‐busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard‐to‐predict, ultra‐high precipitation events occurring during the late‐snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry‐to‐wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. In view of the potentially wetter but less‐snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

Tracking aquatic animals for fisheries management in European waters

AbstractAcoustic telemetry (AT) has emerged as a valuable tool for monitoring aquatic animals in both European inland and marine waters over the past two decades. The European Tracking Network (ETN) initiative has played a pivotal role in promoting collaboration among AT researchers in Europe and has led to a significant increase in the number of tagged and observed aquatic animals in transboundary European waters. While AT benefits decision‐making and delivers essential data to management bodies, its potential for management decision‐making mechanisms has yet to be fully harnessed. We reviewed existing research, studies, and organisational initiatives related to aquatic animal tracking and their utility in fisheries management in European waters. We found that AT has already contributed to many aspects of fisheries management, such as improved understanding of stock dynamics, identification of critical habitats, assessment of migration routes, and evaluation of the effectiveness of conservation measures. However, broader utilisation of tracking technologies is needed. By leveraging the full potential of AT, managers can make more informed decisions to protect, restore, and sustainably manage European waters and creatures that live therein.

Lettuce evapotranspiration and crop coefficients using eddy covariance and remote sensing observations

Lettuce (Lactuca sativa L.) is a high-value crop for irrigation districts in the low deserts of the USA Southwest. To ensure maximal crop quality, negligible soil salinity stress, minimal nutrient loss and reduced pathogen susceptibility, lettuce irrigation must meet, but not exceed, crop water use requirements. However, lettuce crop water use information is outdated in this region: prior studies were conducted at least four decades ago (1960–1980) and do not represent current varieties, management practices, and climate. To address this shortcoming, 12 commercial sites in Yuma, Arizona, USA were evaluated between 2016 and 2020 to update lettuce water use requirements and crop coefficients. The study measured crop evapotranspiration (ETc) using eddy covariance observations at eight iceberg and four romaine sites, where planting dates varied throughout the fall. Observed ETc and remote sensing data were used to model the daily soil water balance and derive crop coefficients: single (Kc), basal (Kcb), and soil evaporation (Ke). The analysis was supported by lettuce crop height estimates and fractional vegetative cover (fc) via remote sensing. Days to maturity averaged 75 ± 15 and 89 ± 12 days for romaine and iceberg, respectively, where season lengths increased as planting dates progressed from early fall to late winter. Average planting date for romaine sites was about 20 days earlier than average iceberg sites. When growing intervals are cast in heat units, dependence on crop type and time of planting was reduced. Average cumulative growing-degree-day and enhanced-degree-day metrics were 1133 ± 87 and 754 ± 48 °C-days, respectively. Seasonal lettuce ETc averaged 278 ± 24 mm. Cumulative irrigation applied, plus precipitation, averaged 355 ± 88 mm. Lettuce Kc for sites varied from 0.90 ± 0.13 to 1.19 ± 0.11 and Kcb from 0.20 ± 0.05 to 1.01 ± 0.11 for the initial and mid-season growth stages, respectively. These updates will help growers improve their irrigation efficiency for lettuce and provide important documentation needed by water managers.

A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part II: Thermodynamic and dynamic climate change scenarios

This study is the second of a two-part series presenting a novel weather regime-based stochastic weather generator to support bottom-up climate vulnerability assessments of water systems in California. In Part 2 of this series, we present how the model is used to develop an ensemble of climate change scenarios based on both thermodynamic and dynamic signals of climate change. The ensemble includes a suite of 30 climate change scenarios, each consisting of 1000 years of simulated daily climate data (precipitation, maximum temperature, minimum temperature) at a 6 km resolution across the entire state of California. The 30 scenarios represent a range of plausible climate changes to temperature, average precipitation, and precipitation extremes that are reflective of thermodynamic responses of the atmosphere to warming. An additional two scenarios are also created that represent changes in the frequency of weather regimes (e.g., dynamic climate change). Results from these scenarios reveal that when the effects of anthropogenic climate change are combined with plausible realizations of natural climate variability, the severity of extremes in California is amplified significantly. In addition, recent changes in the frequency of large-scale patterns of atmospheric circulation can have impacts of similar magnitude to large (>10%) declines in average precipitation, particularly with respect to drought. The scenarios developed in this work are designed to allow water managers to systematically test the sensitivity of their water system to different combinations of climate change, so that key vulnerabilities can be discovered and then addressed through adaptation planning.

Automated Water Monitoring & Managing System for Aquaculture Using IOT

In recent years, the Internet of Things (IoT) has emerged as a transformative technology with profound implications for various sectors, including environmental monitoring and management. This paper presents an innovative IoT-based automated water quality monitoring and management system (AWQMS) designed to revolutionize the way we monitor and safeguard water resources.The AWQMS leverages a network of IoT-enabled sensors deployed at strategic locations throughout water bodies, continuously measuring crucial parameters such as pH, dissolved oxygen, turbidity, temperature, and conductivity. These sensors are equipped with wireless communication capabilities, enabling real-time data transmission to a centralized data acquisition and processing platform. At the heart of the AWQMS lies advanced data analytics and machine learning algorithms, which analyze incoming data streams to detect anomalies and deviations from predefined water quality thresholds. Key Word: Automated Water Monitoring and Managing system, IoT based solution, sensor-based monitoring, Real time monitoring system, cost-effectiveness.

New approach to domestic grey water footprinting: country-scale accounting using statistical methods in Türkiye

ABSTRACT This study aimed to develop an approach for country-scale domestic grey water footprint (GWFdomestic) accounting and examine spatio-temporal differences using statistical methods. In this scope, the GWFdomestic was calculated as the amount of water required to reduce the total nitrogen concentrations of domestic wastewater released into receiving media from 81 cities in Türkiye. GWFdomestic values were estimated based on the data on wastewater amount and applied wastewater treatment process. GWFdomestic was calculated by dividing the pollutant load of discharged water by the critical concentration in the surface water. The empirical results showed that (a) the produced wastewater amount increased up to 125 m3/year in some cities. (b) GWFdomestic values showed a difference between 330 and 1,900 depending on the level of treatment, and the average value was about 750 m3/ca.year. (c) A total of 81 cities were grouped under four categories, and applied water treatment technology was the main characteristic of this classification. (f) GWFdomestic has not statistically significantly changed over time in a large part of the country. It can be concluded that country-scale GWFdomestic accounting can assist water managers in developing prevention measures by analyzing spatio-temporal differences in the water footprint of domestic discharges.

AI-Forecast: an innovative and practical tool for short-term water demand forecasting

ABSTRACT Water management is a major contemporary and future challenge. In an increasing water demand scenario related to climate change, a water distribution system must ensure equal access to water for all users. In this context, a reliable short-term water demand forecasting system is crucial for reliable water management. However, despite the abundance of studies in the scientific literature, few examples highlight complete tools for providing such models to real water utilities and water managers. This study presents AI-Forecast, an innovative tool developed to predict water demand with state-of-the-art models. Such tool is based on the data-driven logic, and it is designed to provide a complete data-driven chain that starts from the data and arrives to the short-term water demand prediction. AI-Forecast can import data, properly manage them, and assess tasks like outlier detection and missing data imputation. Eventually, it can implement state-of-the-art forecasting models and provide the forecasts. The prediction is shown through an intuitive web interface, which is designed to highlight the major information related to the prediction accuracy. Although this tool does not provide a new prediction algorithm, it proposes a complete data-driven chain that is practically designed to take such models in practice to real water utilities.

Large-Scale Hydrological Models and Transboundary River Basins

Large-scale hydrological modeling is an emerging approach in river hydrology, especially in regions with limited available data. This research focuses on evaluating the performance of two well-known large-scale hydrological models, namely E-HYPE and LISFLOOD, for the five transboundary rivers of Greece. For this purpose, discharge time series at the rivers’ outlets from both models are compared with observed datasets wherever possible. The comparison is conducted using well-established statistical measures, namely, coefficient of determination, Percent Bias, Nash–Sutcliffe Efficiency, Root-Mean-Square Error, and Kling–Gupta Efficiency. Subsequently, the hydrological models’ time series are bias corrected through scaling factor, linear regression, delta change, and quantile mapping methods, respectively. The outputs are then re-evaluated against observations using the same statistical measures. The results demonstrate that neither of the large-scale hydrological models consistently outperformed the other, as one model performed better in some of the basins while the other excelled in the remaining cases. The bias-correction process identifies linear regression and quantile mapping as the most suitable methods for the case study basins. Additionally, the research assesses the influence of upstream waters on the rivers’ water budget. The research highlights the significance of large-scale models in transboundary hydrology, presents a methodological approach for their applicability in any river basin on a global scale, and underscores the usefulness of the outputs in cooperative management of international waters.

Research on out-of-sample prediction method of water quality parameters based on dual-attention mechanism

Predicting water quality data is an important measure for ecological environment protection in watersheds. Aiming at the problem that existing prediction algorithms rarely analyze the characteristics of future changes in water quality indicators, this paper proposes an out-of-sample prediction model for water quality parameters based on the dual-attention mechanism. The model adopts the Encoder-Decoder architecture to realize the prediction of data series, and combines the dual attention of dimension and time step to improve the prediction performance of out-of-sample data. The model is used to predict the water quality parameters of a multi-parameter river, analyze the trend of the out-of-sample data, and compare the prediction results with the traditional LSTM network and Encoder-Decoder LSTM network, the prediction accuracies of the water quality indicators are improved, and the prediction accuracy of the out-of-sample data of the water quality indicators reaches 80%. This will be of great significance to the comprehensive management of river waters and the high-quality development of ecological environment.