The response of non-point source pollution to climate change in an orchard-dominant coastal watershed.

China is the largest global orchard distribution area, where high fertilization rates, complex terrain, and uncertainties associated with future climate change present challenges in managing non-point source pollution (NPSP) in orchard-dominant growing areas (ODGA). Given the complex processes of climate, hydrology, and soil nutrient loss, this study utilized an enhanced Soil and Water Assessment Tool model (SWAT-CO2) to investigate the impact of future climate on NPSP in ODGA in a coastal basin of North China. Our investigation focused on climate-induced variations in hydrology, nitrogen (N), and phosphorus (P) losses in soil, considering three Coupled Model Intercomparison Project phase 6 (CMIP6) climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Research results indicated that continuous changes in CO2 levels significantly influenced evapotranspiration (ET) and water yield in ODGA. Influenced by sandy soils, nitrate leaching through percolation was the principal pathway for N loss in the ODGA. Surface runoff was identified as the primary pathway for P loss. Compared to the reference period (1971-2000), under three future climate scenarios, the increase in precipitation of ODGA ranged from 15% to 28%, while the growth rates of P loss and surface runoff were the most significant, both exceeding 120%. Orchards in the northwest basin proved susceptible to nitrate leaching, while others were more sensitive to N and P losses via surface runoff. Implementing targeted strategies, such as augmenting organic fertilizer usage and constructing terraced fields, based on ODGA's response characteristics to future climate, could effectively improve the basin's environment.

Neonicotinoid insecticides in a large-scale agricultural basin system-Use, emission, transportation, and their contributions to the ecological risks in the Pearl River Basin, China.

Neonicotinoid pollution has increased rapidly and globally in recent years, posing significant risks to agricultural areas. Quantifying use and emission, transport and fate of these contaminants, and risks is critical for proper management of neonicotinoids in river basin. This study elucidates use and emissions of neonicotinoid pesticides in a typical large-scale agriculture basin of China, the Pearl River Basin, as well as the resulting agricultural non-point source pollution and related ecological risks using market surveys, data analysis, and the Soil and Water Assessment Tool. Neonicotinoid use in the basin was estimated at 1361 t in 2019, of which 83.1 % was used in agriculture. After application, approximately 99.1 t neonicotinoids were transported to the Pearl River, accounting for 7.2 % of the total applied. Estimated aquatic concentrations of neonicotinoids showed three seasonal peaks. Several distinct groups of neonicotinoid chemicals can be observed in the Pearl River, as estimated by the model. An estimated 3.9 % of the neonicotinoids used were transported to the South China Sea. Based on the present risk assessment result, several neonicotinoids posed risks to aquatic organism. Therefore, the use of alternative products and/or reduced use is deemed necessary. This study provides novel insights into the fate and ecological risks of neonicotinoid insecticides in large-scale watersheds, and underscores the need for greater efficiency of use and extensive environmental monitoring.

Water availability and extreme events under climate change scenarios in an experimental watershed of the Brazilian Atlantic Forest.

Climate change has diversified negative implications on environmental sustainability and water availability. Assessing the impacts of climate change is crucial to enhance resilience and future preparedness particularly at a watershed scale. Therefore, the goal of this study is to evaluate the impact of climate change on the water balance components and extreme events in Piabanha watershed in the Brazilian Atlantic Forest. In this study, extreme climate change scenarios were developed using a wide array of global climate models acquired from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Reports (AR6). Two extreme climate change scenarios, DryHot and WetCool, were integrated into the Soil and Water Assessment Tools (SWAT) hydrological model to evaluate their impacts on the hydrological dynamics in the watershed. The baseline SWAT model was first developed and evaluated using different model performance evaluation metrics such as coefficient of determination (R2), Nash-Sutcliffe (NSC), and Kling-Gupta efficiency coefficient (KGE). The model results illustrated an excellent model performance with metric values reaching 0.89 and 0.64 for monthly and daily time steps respectively in the calibration (2008 to 2017) and validation (2018 to 2023) periods. The findings of future climate change impacts assessment underscored an increase in temperature and shifts in precipitation patterns. In terms of streamflow, high-flow events may experience a 47.3 % increase, while low-flows could see an 76.6 % reduction. In the DryHot scenario, annual precipitation declines from 1657 to 1420 mm, with evapotranspiration reaching 54 % of precipitation, marking a 9 % rise compared to the baseline. Such changes could induce water stress in plants and lead to modifications on structural attributes of the ecosystem recognized as the Atlantic rainforest. This study established boundaries concerning the effects of climate change and highlighted the need for proactive adaptation strategies and mitigation measures to minimize the potential adverse impacts in the study watershed.

Comparative analysis of HEC-HMS and SWAT hydrological models for simulating the streamflow in sub-humid tropical region in India.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash-Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.

A hybrid SWAT-ANN model approach for analysis of climate change impacts on sediment yield in an Eastern Himalayan sub-watershed of Brahmaputra.

The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The analysis was meticulously segmented into three distinct time spans: 2025-2049, 2050-2074, and 2075-2099. This innovative method integrates insights from multiple climate models under two Representative Concentration Pathways (RCP4.5 and RCP8.5), along with LULC projections generated through the Cellular Automata Markov model. By combining the strengths of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) techniques, the study aims to improve the accuracy of sediment yield simulations in response to changing environmental conditions. The non-linear autoregressive with external input (NARX) method was adopted for the ANN component of the hybrid model. The adoption of the hybrid SWAT-ANN approach appears to be particularly effective in improving the accuracy of sediment yield simulation compared to using the SWAT model alone, as evidenced by the higher coefficient of determination value of 0.74 for the hybrid model compared to 0.35 for the standalone SWAT model. In the context of the RCP4.5 scenario, during 2075-99, the study noted a 29.34% increase in sediment yield, accompanied by simultaneous rises of 42.74% in discharge and 27.43% in rainfall during the Indian monsoon season, spanning from June to September. In contrast, under the RCP8.5 scenario, for the same period, the increases in sediment yield, discharge, and rainfall for the monsoon season were determined to be 116.56%, 103.28%, and 64.72%, respectively. The present study's comprehensive analysis of the factors influencing sediment supply in the Puthimari River basin fills an important knowledge gap and provides valuable insights for designing proactive flood and erosion management strategies. The findings from this research are crucial for understanding the vulnerability of the Puthimari basin to climate and land use changes, and by incorporating these findings into policy and decision-making processes, stakeholders can work towards enhancing resilience and sustainability in the face of future hydrological and environmental challenges.

Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach.

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

Impacts of land use/land cover and climate change on landscape sensitivity in Tunca River sub-basin: Use in spatial planning and sectoral decision processes.

Managing landscape change is increasingly challenging due to rapid anthropogenic shifts. A delicate balance must be struck between the environment and change to ensure landscapes can withstand these impacts. This study conducted in the Tunca River sub-basin of Edirne province, aims to assess landscape sensitivity by examining the influence of land use/land cover (LULC) and climate change on landscape function processes. For this purpose, a methodology was developed based on ecosystem services to determine landscape sensitivity. The results revealed a LULC transformation that could lead to a 60% reduction in forest areas and a 5% and 20% increase in urban and irrigated agricultural areas, respectively. Water and erosion emerged as the most affected landscape function processes. Future scenarios from 2050 to 2070 indicate noteworthy changes in landscape sensitivity, showing an increase in sensitivity in the upper regions of the basin. The study identified high sensitivity in forested areas, moderate sensitivity in agricultural zones, and low sensitivity in micro-basins near residential areas. Protection and improvement strategies are recommended for areas with high and moderate sensitivity, while use-oriented strategies are suggested for those with low sensitivity. This study also establishes a scientific foundation for guiding the protection and management of ecologically sensitive basin areas, offering insights into the effects of landscape change processes at the micro-basin level in connection with climate change models.

Physically based vs. data-driven models for streamflow and reservoir volume prediction at a data-scarce semi-arid basin.

Physically based or data-driven models can be used for understanding basinwide hydrological processes and creating predictions for future conditions. Physically based models use physical laws and principles to represent hydrological processes. In contrast, data-driven models focus on input-output relationships. Although both approaches have found applications in hydrology, studies that compare these approaches are still limited for data-scarce, semi-arid basins with altered hydrological regimes. This study aims to compare the performances of a physically based model (Soil and Water Assessment Tool (SWAT)) and a data-driven model (Nonlinear AutoRegressive eXogenous model (NARX)) for reservoir volume and streamflow prediction in a data-scarce semi-arid region. The study was conducted in the Tersakan Basin, a semi-arid agricultural basin in Türkiye, where the basin hydrology was significantly altered due to reservoirs (Ladik and Yedikir Reservoir) constructed for irrigation purposes. The models were calibrated and validated for streamflow and reservoir volumes. The results show that (1) NARX performed better in the prediction of water volumes of Ladik and Yedikir Reservoirs and streamflow at the basin outlet than SWAT (2). The SWAT and NARX models both provided the best performance when predicting water volumes at the Ladik reservoir. Both models provided the second best performance during the prediction of water volumes at the Yedikir reservoir. The model performances were the lowest for prediction of streamflow at the basin outlet (3). Comparison of physically based and data-driven models is challenging due to their different characteristics and input data requirements. In this study, the data-driven model provided higher performance than the physically based model. However, input data used for establishing the physically based model had several uncertainties, which may be responsible for the lower performance. Data-driven models can provide alternatives to physically-based models under data-scarce conditions.

A spatio-temporal analysis of environmental fate and transport processes of pesticides and their transformation products in agricultural landscapes dominated by subsurface drainage with SWAT.

Pesticides are detected in surface water and groundwater, endangering the environment. In lowland regions with subsurface drainage systems, drained depressions become hotspots for transport of pesticides and their transformation products (TPs). This study focuses on detailed modelling of the degradation and transport of pesticides with different physico-chemical properties. The objective is to analyse complex hydrological transport processes, to understand the temporal and spatial dynamics of the degradation and transport of pesticides. The ecohydrological model SWAT+ simulates hydrological processes as well as agricultural management and pesticide degradation, and can therefore be used to develop pesticide loss reduction strategies. This study focuses on modelling of three pesticides (pendimethalin, diflufenican, and flufenacet), and two TPs, flufenacet-oxalic acid (FOA) and flufenacet sulfonic acid (FESA). The study area is a 100-hectare farmland in the northern German lowlands of Schleswig-Holstein that is characterized by an spacious drainage network of 6.3 km and managed according to common conventional agricultural practice. SWAT+ modelled streamflow with very good agreement between observed and simulated data during calibration and validation. Regarding pesticides, the model performance for highly mobile substances is better than for non-mobile pesticides. While the transport of the moderately to very mobile substances via tile drains played an important role in both wet and dry conditions, no transport via tile drains was modelled for the highly sorptive and non-mobile pendimethalin. In conclusion, the model can reliably represent the degradation of moderately to very mobile pesticides in small-scale tile drainage-dominated catchments, as well as surface runoff-induced peak loads. However, it has weaknesses in accounting for the subsurface transport of non-mobile substances, which can lead to an underestimation of the subsequent delivery after precipitation events and thus underestimates the total load.

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data.

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France).

Pesticides are a major source of pollution for ecosystems. In agricultural catchments, ponds serve as buffer areas for pesticide transfers and biogeochemical hotspots for pesticide dissipation. Some studies have highlighted the specific impact of ponds on the dynamics of pesticides, but knowledge of their cumulative effect at the watershed scale is scarce. Hence, using a modelling approach, we assessed the cumulative role of ponds in pesticide transfer in an agricultural basin (Southwest of France, 1110 km2). The Soil and Water Assessment Tool (SWAT) model was used to model the Save basin, including 197 ponds selected with a Multi-Criteria Decision Aiding Model based on their pesticide interception capacities. The daily discharge, the suspended sediment loads and two herbicide loads (i.e. S-metolachlor and aclonifen) in dissolved and particulate phases were accurately simulated from January 2002 to July 2014 at a daily time step. The presence of ponds resulted in a yearly mean reduction at the watershed outlet of respectively 61 % and 42 % of aclonifen and S-metolachlor fluxes compared to the simulations in the absence of ponds. Sediment-related processes were the most efficient for pesticide dissipation, leading to a mean dissipation efficiency by ponds of 51.0 % for aclonifen and 34.4 % for S-metolachlor. This study provides a first quantification of the cumulative role of ponds in pesticide transfer at the catchment scale in an intensive agricultural catchment.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China.

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

Modeling the sediment yield and estimating the best management practices in the Seybouse basin, Northeastern Algeria.

Identifying vulnerable areas to erosion within the watershed and implementing best management practices (BMPs) are crucial steps in mitigating watershed degradation by minimizing sediment yields. The present study evaluates and identifies the BMPs in the Seybouse basin, northeastern Algeria, using the Soil and Water Assessment Tool (SWAT) model. After successful calibration and validation, the model demonstrated a satisfactory ability to simulate monthly discharge and sediment. Then, the calibrated model was employed to evaluate the efficacy of diverse management practices in sediment control. In the SWAT, three soil and conservation practices, as well as vegetated filter strips (VFSs), grade stabilization structures (GSSs), and terracing were evaluated. The average annual sediment yield in the Seybouse watershed is determined to be 14.43 t/ha year, constituting 71% of the total soil loss. VFS demonstrated a sediment reduction of 37.30%, GSS 20.40%, and terracing 42.30%. Among these strategies, terracing results in the greatest reduction, followed by VFS. The results of this study area can be useful for informed decision-making regarding optimal watershed management strategies.

A CMIP6 multi-model based analysis of potential climate change effects on watershed runoff using SWAT model: A case study of kunhar river basin, Pakistan.

The hydrological regimes of watersheds might be drastically altered by climate change, a majority of Pakistan's watersheds are experiencing problems with water quality and quantity as a result precipitation changes and temperature, necessitating evaluation and alterations to management strategies. In this study, the regional water security in northern Pakistan is examined about anthropogenic climate change on runoff in the Kunhar River Basin (KRB), a typical river in northern Pakistan using Soil and Water Assessment tool (SWAT) and flow durarion curve (FDC). Nine general circulation models (GCMs) were successfully utilized following bias correction under two latest IPCC shared socioeconomic pathways (SSPs) emission scenarios. Correlation coefficients (R2), Nash-Sutcliffe efficiency coefficients (NSE), and the Percent Bias (PBIAS) are all above 0.75. The conclusions demonstrate that the SWAT model precisely simulates the runoff process in the KRB on monthly and daily timescales. For the two emission scenarios of SSP2-4.5 and SSP5-8.5, the mean annual precipitation is predicted to rise by 3.08 % and 5.86 %, respectively, compared to the 1980-2015 baseline. The forecasted rise in mean daily high temperatures is expected to range from 2.08 °C to 3.07 °C, while the anticipated increase in mean daily low temperatures is projected to fall within the range of 2.09 °C-3.39 °C, spanning the years 2020-2099. Under the two SSPs scenarios, annual runoff is estimated to increase by 5.47 % and 7.60 % due to climate change during the same period. Future socioeconomic growth will be supported by a sufficient water supply made possible by the rise in runoff. However, because of climate change, there is a greater possibility of flooding because of increases in both rainfall and runoff. As a result, flood control and development plans for KRB must consider the climate change's possible effects. There is a chance that the peak flow will move backwards relative to the baseline.

Stream macroinvertebrate communities in restored and impacted catchments respond differently to climate, land-use, and runoff over a decade.

Identifying which environmental drivers underlie degradation and improvements of ecological communities is a fundamental goal of ecology. Achieving this goal is a challenge due to diverse trends in both environmental conditions and ecological communities across regions, and it is constrained by the lack of long-term parallel monitoring of environmental and community data needed to study causal relationships. Here, we identify key environmental drivers using a high-resolution environmental - ecological dataset, an ensemble of the Soil and Water Assessment Tool (SWAT+) model, and ecological models to investigate effects of climate, land-use, and runoff on the decadal trend (2012-2021) of stream macroinvertebrate communities in a restored urban catchment and an impacted catchment with mixed land-uses in Germany. The decadal trends showed decreased precipitation, increased temperature, and reduced anthropogenic land-uses, which led to opposing runoff trends - with decreased runoff in the restored catchment and increased runoff in the impacted catchment. The two catchments also varied in decadal trends of taxonomic and trait composition and metrics. The most significant improvements over time were recorded in communities of the restored catchment sites, which have become wastewater free since 2007 to 2009. Within the restored catchment sites, community metric trends were primarily explained by land-use and evaporation trends, while community composition trends were mostly associated with precipitation and runoff trends. Meanwhile, the communities in the impacted catchment did not undergo significant changes between 2012 and 2021, likely influenced by the effects of prolonged droughts following floods after 2018. The results of our study confirm the significance of restoration and land-use management in fostering long-term improvements in stream communities, while climate change remains a prodigious threat. The coupling of long-term biodiversity monitoring with concurrent sampling of relevant environmental drivers is critical for preventative and restorative management in ecology.

The overlap between randomised evaluations of recruitment and retention interventions: An updated review of recruitment (Online Resource for Recruitment in Clinical triAls) and retention (Online Resource for Retention in Clinical triAls) literature.

BACKGROUND: The Online Resource for Recruitment in Clinical triAls (ORRCA) and the Online Resource for Retention in Clinical triAls (ORRCA2) were established to organise and map the literature addressing participant recruitment and retention within clinical research. The two databases are updated on an ongoing basis using separate but parallel systematic reviews. However, recruitment and retention of research participants is widely acknowledged to be interconnected. While interventions aimed at addressing recruitment challenges can impact retention and vice versa, it is not clear how well they are simultaneously considered within methodological research. This study aims to report the recent update of ORRCA and ORRCA2 with a special emphasis on assessing crossover of the databases and how frequently randomised studies of methodological interventions measure the impact on both recruitment and retention outcomes. METHODS: Two parallel systematic reviews were conducted in line with previously reported methods updating ORRCA (recruitment) and ORRCA2 (retention) with publications from 2018 and 2019. Articles were categorised according to their evidence type (randomised evaluation, non-randomised evaluation, application and observation) and against the recruitment and retention domain frameworks. Articles categorised as randomised evaluations were compared to identify studies appearing in both databases. For randomised studies that were only in one database, domain categories were used to assess whether the methodological intervention was likely to impact on the alternate construct. For example, whether a recruitment intervention might also impact retention. RESULTS: In total, 806 of 17,767 articles screened for the recruitment database and 175 of 18,656 articles screened for the retention database were added as result of the update. Of these, 89 articles were classified as 'randomised evaluation', of which 6 were systematic reviews and 83 were randomised evaluations of methodological interventions. Ten of the randomised studies assessed recruitment and retention and were included in both databases. Of the randomised studies only in the recruitment database, 48/55 (87%) assessed the content or format of participant information which could have an impact on retention. Of the randomised studies only in the retention database, 6/18 (33%) assessed monetary incentives, 4/18 (22%) assessed data collection location and methods and 3/18 (17%) assessed non-monetary incentives, all of which could have an impact on recruitment. CONCLUSION: Only a small proportion of randomised studies of methodological interventions assessed the impact on both recruitment and retention despite having a potential impact on both outcomes. Where possible, an integrated approach analysing both constructs should be the new standard for these types of evaluations to ensure that improvements to recruitment are not at the expense of retention and vice versa.

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.

Water conservation pattern of Fangcheng River Basin in Beibu Gulf and its response to precipitation.

Assessing the spatiotemporal patterns of watershed water conservation under the influence of the South Asian monsoon climate and its response to precipitation is essential for revealing the evolving patterns of water conservation under different temporal scales. Following the principles of water balance and using the Soil and Water Assessment Tool (SWAT) model, we investigated the spatiotemporal patterns of water conservation and its response to precipitation in the Fangcheng River Basin of Beibu Gulf. The results showed that water conservation in Fangcheng River Basin calculated by SWAT model were 1637.4 mm·a-1, accounting for 50.7% of the mean annual precipitation. The variation of water conservation in different sub-basins was obviously different. Sub-basins with high forest coverage and steep slopes exhibited higher water conservation, while sub-basins with other land use types (such as cropland and grassland), gentle slopes, and intense human activities showed lower water conservation. At the monthly scale, both water conservation and its variation showed similar response characteristics to precipitation in the basin. The response of water conservation variation to sub-precipitation events could be classified into two types. For the short-term rainfall events (duration≤2 days), water conservation variation showed a linear relationship. For the medium to long-term rainfall events (2 days

Trial Forge Guidance 4: a guideline for reporting the results of randomised Studies Within A Trial (SWATs).

BACKGROUND: Evidence to support decisions on trial processes is minimal. One way to generate this evidence is to use a Study Within A Trial (SWAT) to test trial processes or explore methodological uncertainties. SWAT evidence relies on replication to ensure sufficient power and broad applicability of findings. Prompt reporting is therefore essential; however, SWAT publications are often the first to be abandoned in the face of other time pressures. Reporting guidance for embedded methodology trials does exist but is not widely used. We sought therefore to build on these guidelines to develop a straightforward, concise reporting standard, which remains adherent to the CONSORT guideline. METHODS: An iterative process was used to develop the guideline. This included initial meetings with key stakeholders, development of an initial guideline, pilot testing of draft guidelines, further iteration and pilot testing, and finalisation of the guideline. RESULTS: We developed a reporting guideline applicable to randomised SWATs, including replications of previous evaluations. The guideline follows the Consolidated Standards for Reporting Trials (CONSORT) statement and provides example text to ensure ease and clarity of reporting across all domains. CONCLUSIONS: The SWAT reporting guideline will aid authors, reviewers, and journal editors to produce and review clear, structured reports of randomised SWATs, whilst also adhering to the CONSORT guideline. TRIAL REGISTRATION: EQUATOR Network - Guidelines Under Development ( https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-clinical-trials/#SWAT ). Registered on 25 March 2021.

Effects of an increased financial incentive on follow-up in an online, automated smoking cessation trial: A randomised controlled Study Within a Trial (SWAT).

INTRODUCTION: Poor retention in clinical trials can impact on statistical power, reliability, validity and generalisability of findings and is a particular challenge in smoking cessation studies. In online trials with automated follow up mechanisms, poor response also increases resource-need for manual follow up. This study compared two financial incentives on response rates at 6 months follow up, in an online, automated smoking cessation feasibility trial of a cessation smartphone app (Quit Sense). METHODS: A study within a trial (SWAT), embedded within a host randomised controlled trial. Host trial participants were randomised 1:1 to receive either a £10 or £20 voucher incentive, for completing the 6-month questionnaire. Stratification for randomisation to the SWAT was by minimisation to ensure an even split of host trial arm participants, and by 6-week response rate. Outcome measures were: questionnaire completion rate, time to completion, number of completers requiring manual follow up and completeness of responses. RESULTS: 204 participants were randomised to the SWAT. The £20 and £10 incentives did not differ in completion rate at 6 months (79% versus 74%; p=0.362) but did reduce the proportion of participants requiring manual follow up (46% versus 62%; p=0.018) and the median completion time (7 days versus 15 days; p=0.008). Measure response completeness rates were higher among £20 incentive participants, though differences were small for the host trial's primary smoking outcome. CONCLUSIONS: Benefits to using relatively modest increases in incentive for online smoking cessation trials include more rapid completion of follow up questionnaires and reduced manual follow up. IMPLICATIONS: A modest increase in incentive (from £10 to £20) to promote the completion of follow up questionnaires in online smoking cessation trials may not increase overall response rates but could lead to more rapid data collection, a reduced need for manual follow-up and reduced missing data among those who initiate completing a questionnaire. Such an improvement may help to reduce bias, increase validity and generalisability, and improve statistical power in smoking cessation trials.