Improve Water Resources Management Research Articles

Comparative metabolomics of leaves and stems of three Italian olive cultivars under drought stress

The Mediterranean will be one of the focal points of climate change. The predicted dry and hot summers will lead to water scarcity in agriculture, which may limit crop production and growth. The olive tree serves as a model woody plant for studying drought stress and improving water resource management; thus, it is critical to identify genotypes that are more drought tolerant and perform better under low irrigation or even rainfed conditions. In this study, the metabolomic approach was used to highlight variations in metabolites in stems and leaves of three Italian olive cultivars (previously characterized physiologically) under two and four weeks of drought stress. Phenolic and lipophilic profiles were obtained by gas chromatography-mass spectrometry and ultra-high performance liquid chromatography-mass spectrometry, respectively. The findings identified the leaf as the primary organ in which phenolic variations occurred. The Maurino cultivar exhibited a strong stress response in the form of phenolic compound accumulation, most likely to counteract oxidative stress. The phenolic compound content of ‘Giarraffa’ and ‘Leccino’ plants remained relatively stable whether they were exposed to drought or not. Variations in the lipid profile occurred in leaves and stems of all the cultivars. A high accumulation of compounds related to epicuticular wax components was observed in the leaf of ‘Giarraffa’, while a strong reduction of lipids and long-chain alkanes occurred in ‘Maurino’ when exposed to drought stress conditions.

Urban water security in multidisciplinary practices: A case of Lamphun Municipality, Thailand

Urban water security is a multi-faceted water, socioeconomic, and governance challenge. The complexity of translating the holistic approach into policies is an important need for improving water resource management to achieve prosperity, equity, sustainability, and good governance. The city of Lamphun is an example of the practice, which faced drought and major challenges in efficiently distributing water to industrial partners and downstream residents. The purpose of this study was to monitor urban water security and formulate a water management-based urban land use policy for Lamphun Municipality to mitigate water conflicts and ensure access to water for all residents. Therefore, the study examined urban water security using 4 indices: urban water resources, access, risks, and governance. A careful urban land use plan responsible for water security was proposed based on potential surface analysis techniques and public participation. The result of this research would not only guarantee water security, but also promote public participation and sustainable communities.

An improved canopy interception scheme into biogeochemical model for precise simulation of carbon and water fluxes in subtropical coniferous forest

The process-based Biome-BGC (Biome Biogeochemical Cycles) model is widely used to simulate the fluxes of carbon and water of terrestrial ecosystems. While exhibiting excellent performance in simulating carbon cycle processes, this model provides a relatively simple simulation of the water cycle processes, particularly in canopy interception, which may result in significant errors in evapotranspiration (ET) and soil moisture estimations. This study initially refined the temporal scale of the original Biome-BGC model from a daily scale to an hourly scale, and then incorporated a theoretical interception module into the hourly-scale model for correcting canopy interception algorithm, to enhance the accuracy of hydrological processes simulations. The modified model was tested using the half-hourly observed meteorological and eddy covariance flux data at the Qianyanzhou subtropical coniferous forest site. In comparison with the original daily-scale model, simulations from the hourly-scale model showed better agreement with measurements at Qianyanzhou forest site, with 30.61 % and 40.92 % lower annual average gross primary productivity (GPP) and ET simulations. Although the accuracy of GPP and ET simulations did not show a significant improvement after canopy interception correction, it notably enhanced the accuracy of canopy interception and soil moisture simulations. The canopy interception rates were 55.8 % and 44.1 % for the original daily-scale and hourly-scale model, obviously overstimated compared with canopy correction simulation (31.1 %), leading to a significant underestimation of runoff. The canopy interception and runoff simulations after interception correction were proven relatively more reasonable. Additionally, the coefficient of determination (R2) for measured vs simulated soil water content (SWC) were 0.38, 0.51 and 0.60 for the original daily-scale, hourly-scale, and canopy interception correction simulations respectively, with a notable improvement in accuracy. This study suggested that improvements in temporal scale and canopy interception for process-based biogeochemical model can provide a better understanding of the carbon and water exchanges in response to instantaneous meteorological conditions and support improved water resource management.

An initial assessment of mobile dam efficiency in raising low-flow water levels in the Vietnamese Mekong Delta

In recent years, the Tien and Hau rivers have experienced a downtrend in water levels during the low flow season. This decrease has led to detrimental effects on agricultural production and ecological health in the VMD’s upper regions, such as the Long Xuyen Quadrangle (LXQ) and the Plain of Reeds (PoR). This research employs a numerical hydraulic model for a preliminary assessment of the potential effectiveness of mobile dams in increasing water levels in the VMD’s upper part. A calibrated 1D hydraulic model was utilized to evaluate the performance of the proposed mobile dams: one situated near the My Thuan bridge on the Tien river, and the other near the Can Tho bridge on the Hau river. Initial results indicate that setting the crest of mobile dams at -1.0 m could raise minimum and maximum water levels in the Tien river near Tan Chau by 1.0 m and 0.15 m, respectively, and in the Hau river near Chau Doc by 1.1 m and 0.07 m, respectively. Furthermore, these mobile dams could significantly enhance the minimum and maximum water levels in An Binh channel within the PoR and Ba The channel in the LXQ, by up to 0.55 m and 0.26 m, and 0.42 m and 0.29 m, respectively. These findings provide an essential foundation for initially understanding how mobile dams can improve water resource management in the VMD. However, additional investigation is required to assess the effects of mobile dams on salinity intrusion, ecological balance, hydrological dynamics, sediment deposition, and erosion.

Monthly streamflow forecasting based on meteorological data from a nearby station

ABSTRACT Monthly streamflow forecasting is critical for improving water resource management. In this study, several base-classifier data-mining algorithms – conjunctive rule (CR), isotonic regression (ISOR), sequential minimal optimization regression (SMOR) – as well as several hybrid data-mining techniques – disjoint aggregating or dagging (DA)-CR, DA-ISOR, and DA-SMOR – that combine dagging with these algorithms were developed and applied to forecasting streamflow 3 and 12 months into the future (i.e., Qt+3 and Qt+12) based on meteorological data from a nearby station. Thirty years of data (from 1988 to 2018) that included precipitation, minimum relative humidity, maximum relative humidity, evaporation, hours of sunshine, maximum temperature, minimum temperature, wind speed, and streamflow were collected at the Kermanshah synoptic station were input to develop and evaluate several models. Varying combinations of the input data were tested to find the optimal set to employ. The models were validated and compared using several quantitative statistical indices. Though all satisfactorily predicted monthly streamflow, the hybrid models (DA-CR, DA-ISOR, and DA-SMOR) outperformed the base-classifier models (CR, ISOR, and SMOR), proving that dagging improved data-mining models significantly. Of the hybrid models, D-SMOR was the best. The models developed in this study are cost-effective tools for quick and accurate monthly streamflow forecasting.

Pollution Characteristics and Risk Assessment of Heavy Metals in the Sediments of the Inflow Rivers of Dianchi Lake, China.

To explore the contamination status and identify the source of the heavy metals in the sediments in the major inflow rivers of Dianchi Lake in China, sediment samples were collected and analyzed. Specifically, the distribution, source, water quality, and health risk assessment of the heavy metals were analyzed using correlation analysis (CA), principal component analysis (PCA), the heavy metal contamination factor (Cf), the pollution load index (PLI), and the potential ecological risk index (PERI). Additionally, the chemical fractions were analyzed for mobility characteristics. The results indicate that the average concentration of the heavy metals in the sediment ranked in the descending order of Zn > Cr > Cu > Pb > As > Ni > Cd > Hg, and most of the elements existed in less-mobile forms. The Cfwas in the order of Hg > Zn > Cd > As > Pb > Cr > Ni; the accumulation of Hg, Zn, Cd, and As was obvious. Although the spatial variability of the heavy metal contents was pronounced, the synthetical evaluation index of the PLI and PERI both reached a high pollution level. The PCA and CA results indicate that industrial, transportation, and agricultural emissions were the dominant factors causing heavy metal pollution. These results provide important data for improving water resource management efficiency and heavy metal pollution prevention in Dianchi Lake.

Partitioning evapotranspiration of Camellia oleifera during the growing season based on the Penman-Monteith model combined with the micro-lysimeter and stable isotope methods

Frequent seasonal droughts impact on crop growth and yield in the hilly areas of southern China. A good knowledge of its evapotranspiration partitioning of the main economic oil crop Camellia Oleifera is particularly important to increase water productivity and improve water resource management. The isotope-based method was applied to partition ET in the hilly Camellia Oleifera Forest of southern China. The isotopic composition of evapotranspiration vapor (δET), transpiration vapor (δT) and evaporation vapor (δE) were calculated by the Keeling plot method, the Craig-Gordon model (C-G model), and isotopic steady-state assumptions, respectively. The conventional Penman-Monteith model and the micro-lysimeter method (PM-L) were used to compare and assess the ET partitioning results (Ft). It showed that the Ft values calculated by the two methods were in good agreement. The isotopic composition of soil water at the evaporating front (δS) ahead and after DOY151 was sampled at 5 and 10 cm depth, respectively, where the correlation coefficients (R2) of the Ft values calculated by δ18O and δ2H were 0.84 and 0.66. Ft Calculated by δ18O was more accurate than δ2H because of the higher correlation coefficient of δ18O in the keeling plot regression and in the linear fit with PM-L. The isotopic composition of soil evaporation vapor（δE）calculated by dynamic εk (kinetic fractionation effects) values can be more accurate to quantify the ET partitioning results (with the R2 of 0.90). The mean values of Ft were 0.79 and 0.82 by the PM-L method and the isotope-based method respectively over the whole growing season, indicating the isotope method is robust in calculating ET partitioning for Camellia oleifera in the region. We also found that the seasonal variation of Ft is mainly controlled by temperature (T), soil water content (SWC), and leaf area index (LAI), all of which can be effectively expressed as power functions. The results provide potential assistance for water management in Camellia oleifera woodlands.

Living with technological drought: Experience of smallholding farmers of Bangladesh

Technological drought is caused by a lack of irrigation technology and a poor water management system. Here we present and analyse the results of a large survey of farmers in Bangladesh, designed to reveal their experience of and attitudes to, as well as factors and challenges of technological drought management. Also, we analysed the impact of water sources and the role of irrigation development to mitigate the technological drought taking North Bengal of Bangladesh as a case study. To explore the nature and characteristics of technological drought, we have used both qualitative and quantitative data from the study area. We used Principal Component Analysis (PCA) to find out the underlying dimensions and factor loadings of the technological drought using the field data. Moreover, we compared the relations and associations between socio-demographic, water source characteristics and technological drought perceptions. PCA results indicated seven underlying dimensions (challenges) out of thirty-one factors of technological drought related to river water management, irrigation technology, socio-economic conditions, land use, and electricity and fuel supply. The high population, lack of modern irrigation technology, upstream management problems, inefficiencies in the system, electricity connections, load shedding, and poor socio-economic conditions prevent people from getting the water they desperately need in the case study area. We also found a significant relationship between farmers’ socio-demographic and water source characteristics with technological drought perceptions. Although there has been increasing use of irrigation technology and improved water resource management in recent years, it is essential to highlight that there will likely be more challenges in the future if this trend continues.

Investigation of climate change by analysing the rainfall pattern in kuzhithuraiyar sub-basin of India using GIS-based spatial analysis

Global climate change may have a significant effect on several environmental components in many different nations worldwide. Variations in the pattern of precipitation have an immediate impact on the management of water resources. To improve water resource management techniques, it is critical to investigate differences in the temporal and geographical rainfall patterns. The current study looks at rainfall patterns in the Kuzhithuraiyar sub-basin of the Kodayar basin, Kanyakumari District. Utilizing the mean monthly data from four distinct rain gauge locations, we have comprehensively compiled and analysed rainfall statistics over an extensive timespan of 30 years (1991–2020). For precision in temporal categorization, this data was meticulously divided into four seasonal classifications: pre-monsoon, post-monsoon, southwest monsoon (SW), and northeast monsoon (NE). Using ArcGIS 10.8, the rainfall pattern was analysed, and spatial distribution maps for the four seasons listed above were created using the inverse distance weighting (IDW) approach. The region receives an average of 1456.78 mm of precipitation each year, with the post-monsoon, pre-monsoon, SW monsoon, and NE monsoon providing 32.87 mm, 335.28 mm, 538.67 mm, and 549.97 mm, respectively. The results reveal that the model correlates mean rainfall and rainfall variability over areas of Tamil Nadu's Kuzhithuraiyar sub-basin with some accuracy in terms of the number of rainfall extremes and their spatial distribution maps.

A hydrogeomorphic dataset for characterizing catchment hydrological behavior across the Tibetan Plateau

Abstract. Hydrologic and geomorphic processes are intricately linked within the Earth system, jointly characterizing terrestrial hydrological behaviors and biogeochemical cycles across diverse temporal and spatial scales. The Tibetan Plateau provides an ideal setting for investigating the interactions between hydrological and geomorphic processes in a largely pristine natural environment. Nonetheless, the interactions remain largely unknown due to challenging physical conditions and data limitations. This study presents the inaugural version of a hydrogeomorphic dataset encompassing 18 440 catchments across the region. The dataset comprises 18 hydrogeomorphic metrics along with, in particular, the width-function-based instantaneous unit hydrograph (WFIUH) of each catchment. We find that the peak flow of WFIUH is positively related to slope and curvature but negatively related to catchment area, perimeter, length and circularity. The relationships of time to peak with the hydrogeomorphic metrics are similar to those of peak flow but in an oppositive direction. Catchment concentration time shows a positive relationship with catchment size but a strong negative correlation with catchment slope. The validity of the derived WFIUH has been confirmed by its successful integration into an hourly hydrological model for simulating flash-flood events. Uncertainties in the WFIUH can be attributed to the resolution of the digital elevation model (DEM) and the methods employed for calculating flow velocity. The dataset is publicly available via the Zenodo portal: https://doi.org/10.5281/zenodo.8280786 (Guo and Zheng, 2023). It can contribute to advancing our understanding of catchment hydrological behaviors and can provide simple and fast-routing unit hydrograph calculation for ungauged catchments in the Tibetan Plateau and hence improve water resources management and disaster mitigation in the region and its downstream areas.

Energy partitioning over an irrigated vineyard in arid northwest China: Variation characteristics, influence degree, and path of influencing factors

In this study, a two-year experiment in an irrigated vineyard was conducted to investigate the variations in energy fluxes (net radiation, Rn; latent heat flux, LE; sensible heat flux, H; and soil heat flux, G) and quantify the influence of arid advection and environmental factors (vapor pressure deficit (VPD), Rn, air temperature, wind speed, precipitation, and volumetric soil water content) on energy partitioning in arid Northwest China. For the diurnal variation, the peak values of the energy fluxes appeared at approximately 14:00, except for G (which appeared at approximately 16:00). LE was the main consumer of daytime (Rn>0) energy during the growing season (average LE/(Rn–G) was 87% and 89% in the two years), even during the shooting and leaf-fall stages. Arid advection, which affects energy partitioning mainly by increasing atmospheric evaporative demand and providing heat energy, contributed 6–60% and 1–56% of the average daytime LE during the two growing seasons, especially at the fruiting stage. Moreover, advection accounted for more than half of average daytime energy imbalances. We found that the arid advection induced by irrigation could be attenuated by selecting a reasonable irrigation time. According to the structural equation modeling (SEM) analysis, Rn had the strongest direct and positive regulation effect on LE at the half-hourly and daily scales, indicating that LE was limited by energy rather than water in the study area. The influence of Rn on LE gradually weakened with an increase in the time scale, whereas the effect of VPD increased, which may have been due to the smaller time-lag effect between VPD and LE. The diurnal variations in LE at approximately 08:00–19:30 and 19:30–08:00 were mainly controlled by the direct positive effects of Rn and VPD, respectively. The results obtained in this study will provide a better understanding of surface processes and help improve water resource management in arid agricultural areas.

Characterization of subsurface geology and hydrogeology in Kribi -Cameroon using electrical resistivity soundings and 3D-Implicit modelling: Baseline for groundwater resource management

This scientific article aims to characterize the geology and hydrogeology of Kribi, Cameroon for the purposes of water resources management, infrastructure development, and environmental protection. The specific objectives include mapping the subsurface electrical resistivity structure, identifying groundwater potential zones, and constructing a hydrogeological model for groundwater modelling. The study employed the Schlumberger method to map the electrical resistivity structure of the 3D subsurface and utilized 3D implicit modelling techniques to delineate groundwater potential zones. The study revealed that Kribi exhibits a highly complex geology, characterized by three-layered electrical resistivity models, typical of sedimentary and metamorphic formations. The major lithologies identified include laterite, clay, sand, quartz, and granite. The region encompasses areas with low water potential and brackish zones alongside high groundwater production areas. The study established the presence of aquifers within Kribi, with the unconfined aquifer found at depths ranging from 2 m to 13 m, and the confined aquifer extending from 14 m to 40 m. The aquifers are notably thick and productive, indicating substantial groundwater resources. Static groundwater volumes were estimated at 50,179,381 m3 and 317,118,946 m3 for the unconfined and confined aquifers respectively. Despite this potential, water resources management in the area is non-existent. The study recommends the implementation of Integrated Water Resources Management (IWRM) to improve water resource management, with a primary focus on groundwater modelling. The northern and southern regions of Kribi exhibit high permeability, which is advantageous for groundwater recharge and storage projects but also poses challenges for environmental remediation efforts. Consequently, safeguarding the delineated area for groundwater provision is of utmost importance.

Environmental Controls on Evapotranspiration and Its Components in a Qinghai Spruce Forest in the Qilian Mountains.

Qinghai spruce forests, found in the Qilian mountains, are a typical type of water conservation forest and play an important role in regulating the regional water balance and quantifying the changes and controlling factors for evapotranspiration (ET) and its components, namely, transpiration (T), evaporation (Es) and canopy interceptions (Ei), of the Qinghai spruce, which may provide rich information for improving water resource management. In this study, we partitioned ET based on the assumption that total ET equals the sum of T, Es and Ei, and then we analyzed the environmental controls on ET, T and Es. The results show that, during the main growing seasons of the Qinghai spruce (from May to September) in the Qilian mountains, the total ET values were 353.7 and 325.1 mm in 2019 and 2020, respectively. The monthly dynamics in the daily variations in T/ET and Es/ET showed that T/ET increased until July and gradually decreased afterwards, while Es/ET showed opposite trends and was mainly controlled by the amount of precipitation. Among all the ET components, T always occupied the largest part, while the contribution of Es to ET was minimal. Meanwhile, Ei must be considered when partitioning ET, as it accounts for a certain percentage (greater than one-third) of the total ET values. Combining Pearson's correlation analysis and the boosted regression trees method, we concluded that net radiation (Rn), soil temperature (Ts) and soil water content (SWC) were the main controlling factors for ET. T was mainly determined by the radiation and soil hydrothermic factors (Rn, photosynthetic active radiation (PAR) and TS30), while Es was mostly controlled by the vapor pressure deficit (VPD), atmospheric precipitation (Pa), throughfall (Pt) and air temperature (Ta). Our study may provide further theoretical support to improve our understanding of the responses of ET and its components to surrounding environments.

Comparison of the performance of SWAT and hybrid M5P tree models in rainfall-runoff simulation.

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.

IoT Based Domestic Water Supply Monitoring and Theft Identification System

Abstract: In the urban and rural areas, the water supply to the residents and commercial establishments is provided with a fixed water flow rate. Now a day’s most people’s utilize the excess water by connecting the motor pump or suction pump to suck the water directly from the area street pipeline which is considered as water theft. It causes enormous complications during summertime and also reduces their environmental resources. In this project, the proposed system is used to develop an IoT-based water supply monitoring and theft identification system by recording the flow rates at the consumer or user end. To implement the proposed water supply system, each consumer can be provided with a PIC-based microcontroller kit to record the flow rate using a flow sensor. The electrically operated solenoid valve is provided to the consumers to monitor water usage. If any excess water deduction occurs in the water supply pipeline the excess flow rate and corresponding charges will sent to the consumer ID. The valve is turned off using IoT to stop the water supply when the consumer fails to pay or uses excessive water LCD is connected with a Node MCU to display data nearby. The proposed framework is completely automated; there is no need for manpower. The domestic water supply monitoring and theft identification system offers several benefits, including improved water resource management, reduction in water wastage, and financial savings. Identifying and addressing instances of theft, it promotes fair usage and conservation of water resources, ultimately contributing to sustainability.

Availability of the current and future water resources in Equatorial Central Africa: case of the Nyong forest catchment in Cameroon.

To anticipate disasters (drought, floods, etc.) caused by environmental forcing and reduce their impacts on its fragile economy, sub-Saharan Africa needs a good knowledge of the availability of current water resources and reliable hydroclimatic forecasts. This study has an objective to quantify the availability of water resources in the Nyong basin and predict its future evolution (2024-2050). For this, the SWAT (Soil and Water Assessment Tool) model was used. The performance of this model is satisfactory in calibration (2001-2005) and validation (2006-2010), with R2, NSE, and KGE greater than 0.64. Biases of - 11.8% and - 13.9% in calibration and validation also attest to this good performance. In the investigated basin, infiltration (GW_RCH), evapotranspiration (ETP), surface runoff (SURQ), and water yield (WYLD) are greater in the East, probably due to more abundant rainfall in this part. The flows and sediment load (SED) are greater in the middle zone and in the Southwest of the basin, certainly because of the flat topography of this part, which corresponds to the valley floor. Two climate models (CCCma and REMO) predict a decline in water resources in this basin, and two others (HIRHAM5 and RCA4) are the opposite. However, based on a statistical study carried out over the historical period (2001-2005), the CCCma model seems the most reliable. It forecasts a drop in precipitation and runoff, which do not exceed - 19% and - 18%, respectively, whatever the emission scenario (RCP4.5 or RCP8.5). Climate variability (CV) is the only forcing whose impact is visible in the dynamics of current and future flows, due to the modest current (increase of + 102 km2 in builds and roads) and future (increase of + 114 km2 in builds and roads) changes observed in the evolution of land use and land cover (LULC). The results of this study could contribute to improving water resource management in the basin studied and the region.

Evaluation of satellite rainfall estimates using PERSIANN-CDR and TRMM over three critical cells in Jordan

Abstract Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of Tropical Rainfall Measuring Mission (TRMM) and PERSIANN-CDR by analyzing their spatiotemporal patterns. Daily, monthly, seasonal, and annual precipitation estimates were evaluated using statistical measures and data from 71 rain gauges across three critical cells in Jordan from 2000 to 2013. The results showed that while both SREs had low accuracy on a daily scale, TRMM 3B43 performed better than PERSIANN-CDR at the monthly level. Additionally, TRMM 3B43 exhibited superior performance during the heavy rainy season, whereas PERSIANN-CDR showed better results during other seasons. In annual studies, TRMM 3B43 was found to be more accurate than PERSIANN-CDR for the north and south cells, while PERSIANN-CDR had a higher correlation coefficient for the middle cell. These findings can contribute to the development of more reliable and accurate SREs, thereby improving water resource management studies.

Assessing nitrate contamination in groundwater for public supply: A study in a small Brazilian town

Monitoring groundwater quality is essential for drinking water, avoiding risks to human health, and making it possible to identify and prevent environmental contamination. In Tupã, west of São Paulo-Brazil, the drinking water supply is carried out exclusively by underground water through the public service or alternative solutions using private tubular wells. In this context, this study evaluated the quality of drinking water from 25 private tubular wells in all areas of Tupã. Of these wells evaluated, 12% were contaminated with E. coli and 44% with total coliforms, indicating that the sanitary quality of the water is inadequate and may be directly associated with waterborne diarrheal diseases. The results of the physical-chemical analyses referring to water potability parameters, such as apparent color, turbidity, total dissolved solids, hardness, iron, ammonia, nitrite, chloride, and fluorine, met the requirements of Brazilian legislation. However, all samples showed NO3− contamination, whose concentrations ranged from 1.9 to 23.2 mg L−1NO3−-N, where 32% of the wells exceeded the maximum allowable limit of 10 mg L−1NO3−-N. The consumption of water contaminated with high concentrations of NO3− can cause health risks, especially to children due to their low body weight, causing methemoglobinemia or blue baby syndrome. In the study of the evolution of urban growth, the city's central area exhibited the most significant NO3− contamination because it is an older area with leakage problems and insufficient sewage system maintenance. The non-carcinogenic health risk associated with NO3− contamination was assessed using the Environmental Protection Agency (USEPA) methodology applied to oral ingestion and dermal contact in children and adults. The results revealed negligible risks from dermal contact. However, the assessment highlighted the susceptibility of children to nitrate contamination through oral ingestion, mainly from water from wells in the city's central area. This study emphasizes the importance of monitoring groundwater in Tupã, identifies potential sources of contamination, and provides information to improve water resources management. Implementing appropriate measures based on these findings can mitigate waterborne diseases, safeguard consumer health, and protect the environment.

Feasibility of monitoring floodplain on-farm water storages by integrating airborne and satellite LiDAR altimetry with optical remote sensing

Small water storages (≤ 500 ha surface area) enhance water supply for agricultural, human and livestock consumption. Their oftentimes large numbers and wide geographic spread, plus inaccurate or absent in situ metering, make their inclusion in water resource management difficult. This research assessed the capabilities of satellite optical remote sensing and LiDAR altimetry to characterise small water storages used for irrigation, so they can be included in water resources assessments. Landsat and Sentinel-2 water maps were integrated with airborne and satellite LiDAR altimetry to assess: (i) locations; (ii) elevation-area-volume relations; (iii) dates of construction/decommissioning; (iv) actual evaporation losses; and (v) storage volume changes of floodplain on-farm water storages (FoFWS) used primarily for cotton irrigation in the semi-arid Barwon-Darling region (142,173 km2), Australia. These remotely sensed characteristics would enable the representation of FoFWS in river system models and in regional water management plans, thus enhancing socioeconomic and environmental outcomes. Results from Landsat water maps showcased the growth in FoFWS surface area. In January 1988 there were 17 FoFWS with a total maximum surface area of 101.89 ha. By December 2021, the then 105 FoFWS maximum surface area was 5587.68 ha; a 55-times increase. For the first time in a regional context, mean annual (2000/01–2020/21) standing water evaporation losses were estimated for all FoFWS. These accounted to 52.47 MCM/year, which is a substantial ∼27% of the estimated long-term mean regional irrigation diversion limit (189 MCM/year). Elevation measurements from satellite LiDAR altimetry data, specifically GEDI and ICESat-2, were evaluated to assess if water volumes in FoFWS can be estimated through elevation-area-volume relations from a 1 m digital elevation model derived from airborne LiDAR data. Although GEDI's spatially variable footprint and ICESat-2's 91-day repeat cycle render them insufficient to monitor changes in water volumes in FoFWS, both satellite LiDAR altimeters provided accurate water level observations, with root mean squared differences (RMSD) of 0.38 m for GEDI and 0.12 m for ICESat-2, when compared to in situ water level measurements. ICESat-2's 0.7 m along-track sampling interval also detected changes of FoFWS structural features, such as increases in wall heights in recent years. This study demonstrated the value of integrating high-resolution airborne LiDAR data with satellite altimetry (LiDAR and/or radar interferometry), along with satellite optical water maps and rates of actual evaporation, to monitor FoFWS and other similar individually small yet collectively large reservoirs. Where similar data are available globally, then accurate information about small and dispersed water storages across poorly metered irrigated agricultural landscapes can be retrieved. Doing so will improve water resource management in the Barwon-Darling region, and in similar semi-arid and arid areas globally where irrigated agriculture is present.

Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework

Developing reliable streamflow forecasting models is critical for hydrological tasks such as improving water resource management, analyzing river patterns, and flood forecasting. In this research, for the first time, an emerging multi-level TOPSIS (technique for order preference by similarity to the ideal solution) -based hybridization comprised of the Boruta classification and regression tree (Boruta-CART) feature selection, multivariate variational mode decomposition (MVMD), and a hybrid Convolutional Neural Network (CNN) Bidirectional Gated Recurrent Unit (CNN-BiGRU) deep learning was adopted to multi-temporal (one and three days ahead) forecast the daily streamflow in the Rivers of Prince Edward Island, Canada. For this aim, in the first step, the Boruta-CART feature selection technique determines the most effective lagged components among all the antecedent two-day information (i.e., t-1 and t-2) of hydro-meteorological features (from 2015 to 2020), including the water level, mean air temperature, heat degree days, total precipitation, dew point temperature, and relative humidity in the Bear and Winter Rivers of Prince Edward Island, Canada. Afterwards, a multivariate variational mode decomposition (MVMD) decomposes the input time series to decrease the complexity and non-linearity of the non-stationary ones before feeding the deep learning (DL) models. Here, the CNN-GRU was employed as the primary DL model, along with the kernel extreme machine method (KELM), random variational function link (RVFL), and hybrid CNN bidirectional recurrent neural network (CNN-BiRNN) as the comparative models. A TOPSIS scheme applying several performance measures like the correlation coefficient (R), root mean square error (RMSE), and reliability was designed for the robustness assessment of the hybrid (MVM-CNN-BiGRU, MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM) and standalone models. The computational outcomes revealed that in the Bear River, the MVM-CNN-BiGRU, owing to its best forecasting performance (one day ahead: TOPSIS score 1, R = 0.960, RMSE = 0.098, and reliability = 65.082; three days ahead: TOPSIS score = 0.999, R = 0.924, and RMSE = 0.33) outperformed the other hybrid models, followed by the MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM, respectively. Moreover, in the Winter River, the MVM-CNN-BiGRU in terms of (one-day ahead: TOPSIS score = 0.890, R = 0.955, RMSE = 0.274, and reliability = 34.004; three-days ahead: TOPSIS score = 0.686, R = 0.924, and RMSE = 0.330) was superior to the other models. The provided expert system could be vital in the local flood decision-making process, in the absence of streamflow information as input modeling, during the flood seasons to reduce flood damage in residential areas.