Improve Water Resources Management Research Articles

Evaluating water-related health risks in East and Central Asian Islamic Nations using predictive models (2020–2030)

This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources, lack of sanitation, and absence of handwashing facilities is showing a potential rise in negative health impacts due to water pollution. The direct health influences of water-related problems are thoughtful. The increase in deaths and DALYs due to poor water quality and sanitation leads to a higher occurrence of waterborne diseases such as cholera, diarrhea, and dysentery. These conditions not only cause instant health disasters but also subsidize to long-term health issues which include chronic gastrointestinal disorders and malnutrition that is particularly among susceptible populations like children and the elderly. Employing various predictive models including autoregressive integrated moving average, exponential smoothing, support vector machines, and neural networks. The study evaluates their predictive capabilities by using mean absolute percentage error. Support vector machines is found to be the most accurate in forecasting deaths and disability-adjusted life years which is outperforming autoregressive integrated moving average, exponential smoothing, and neural networks. This research aims to inform stakeholders by providing insights into effective strategies for improving water resource management and public health interventions in the targeted regions.

Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.

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.

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.

Leak Detection in Urban Hydraulic Systems Using the K-BiLSTM-Monte Carlo Dropout Model

Utility companies lose approximately 35 liters of water for every 100 produced due to incorrect, illegal connections and the poor condition of pipes. This study develops an intelligent model to detect leaks using the Kalman filter, BiLSTM neural networks, and the Monte Carlo Dropout algorithm. Using data from the Empresa de Acueductos y Alcantarillados de Bogotá (EAAB), Colombia, autocorrelation analysis, PCA, cluster analysis, ADF and Durbin-Watson tests, Hurst exponent, spectral analysis, and wavelet transform were performed. Then, Kalman filtering techniques were applied, and a BiLSTM architecture controlled with Monte Carlo dropout was implemented. The results showed an accuracy of 87.48% in training and 80.48% in validation. Temporal analysis revealed a stationary behavior in the flow series, and the decrease in spectral intensity around 0.25 Hz was related to pressure perturbations caused by leaks. A detailed evaluation of pressure and flow signals identified leak patterns with high precision, demonstrating the effectiveness of the wavelet spectrogram in detecting energy disturbances. The novelty of the study lies in the integration of advanced artificial intelligence and combinatorial optimization techniques to improve water resource management, allowing early and accurate detection of leaks, significantly improving compared to traditional methods. Doi: 10.28991/CEJ-2024-010-07-01 Full Text: PDF

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.

Tracer movement and residence time distribution simulation: An initiative to improve the wetland water environment in the Helan Mountain impact plain

The hydraulic residence time (HRT) serves as a critical temporal scale for calculating the duration of pollutants within a water body. The spatial and temporal variations of HRT play a crucial role in developing location-specific water management strategies. In our study, we assessed the spatial and temporal distribution patterns of hydraulic residence time in Xinghai Lake, China – a representative case of large shallow lakes regulated by zoning – using a hydrodynamic-tracer model. The findings reveal that manipulating the outflow gates leads to a fluctuation range of 20 days in hydraulic residence time. Our analyses suggest that the operation of outflow gates significantly influences the reduction of hydraulic residence time. Compared with previous research, the spatial and temporal distribution patterns of hydraulic residence time examined in this study demonstrate the effectiveness of zonal regulation in improving water resource management.

Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture

Estimation of accurate soil physical and hydraulic properties are of prime importance for the management of water resources in agriculture-dominant regions. This study introduces a simplified framework for estimating soil physical and hydraulic properties crucial for managing agricultural water resources. The developed framework optimizes soil properties for the Regional Hydrological Extremes Assessment System (RHEAS) to enhance the performance of its core hydrological model, Variable Infiltration Capacity (VIC). These soil properties were optimized using six years (2015–2021) of satellite soil moisture observations from NASA’s Soil Moisture Active Passive (SMAP) mission with a modified Shuffled Complex Evolution (SCE-UA) optimization algorithm. A total of three most sensitive soil properties that control model soil moisture simulations, such as Ksat (Saturated hydraulic conductivity), expt (exponent parameter in Campbell’s equation for hydraulic conductivity), and bd (Bulk density) were optimized for the Lower Mekong River (LMR) basin. To better assess the impact of optimized soil properties, streamflow simulation as well as agricultural drought severity assessment, were estimated using the RHEAS framework’s VIC Routing module and Soil Moisture Deficit Index (SMDI) module, respectively. The streamflow simulation involved four approaches: an initial open-loop setup, one optimized with SMAP soil moisture data (SMAP), another optimized with actual streamflow data (Runoff), and a final one combining the previous two datasets (SMAP_Runoff). Switching from the initial setup to the SMAP-optimized model increased the Nash-Sutcliffe Efficiency (NSE) by 56.4 % and upgrading from the streamflow-optimized to the combined data model raised the NSE by 21.9 %. This showcases the benefits of optimizing soil properties for more accurate simulations. Furthermore, the optimized model accurately represented the severity and extent of historical agricultural droughts, aligning with regional drought reports of LMR basin. This framework offers a valuable tool for hydrological modeling and drought management, particularly in data-scarce and agriculture-intensive regions, informing agricultural water resource management, irrigation decision-making, and food security initiatives within the LMR basin and beyond.

Integrating Multi-source Remote Sensing Technology to Improve Water Resource Management Methods at Poyang Lake

Integrating Multi-source Remote Sensing Technology to Improve Water Resource Management Methods at Poyang Lake

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.