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Background: The CapaCiTY programme includes three, multi-centre, randomised controlled trials aiming to develop an evidence based adult chronic constipation treatment pathway. The trials were conducted in the United Kingdom, National Health Service, aiming to recruit 808 participants from 26 March 2015 to 31 January 2019. Sites were selected based on their responses to site feasibility questionnaires (2014–2015), a common tool employed by sponsors to assess a site's recruitment potential and ability to undertake the trial protocol. Failure to recruit the planned sample jeopardises reliability of results and wastes significant time and resources. The purpose of this study was to investigate barriers to recruitment in 2017. Methods: We conducted site feasibility assessments with thirty-nine sites prior to trial commencement. Twenty-seven were selected to participate in the CapaCiTY programme, twelve were deemed unsuitable. We compared site contracted recruitment rates with actual recruitment rates and conducted a telephone survey and analysis from 5 July to 7 December 2017 (n = 24) to understand barriers to recruitment. Three sites declined to participate in the survey. Results: At the time of survey, 15% of sites in the CapaCiTY programme were meeting recruitment targets, 85% were recruiting half or less of their target. Of these, 28% recruited no participants. The main barriers to recruitment were lack of resources, high workloads, lack of suitable participants and study design not being compatible with routine care. Despite multiple strategies employed to overcome these barriers, the trials were eventually stopped due to futility, recruiting only 34% of the programme sample size. Conclusions: Improving the reliability of site feasibility assessments could potentially save a substantial amount in failed research investments and speed up the time to delivery of new treatments. We recommend 1) investment in training researchers in conducting and completing site feasibility; 2) funders to require pilot and feasibility data in grant applications, with an emphasis on patient and public involvement in trial design; 3) conducting site feasibility assessment at the pre-award stage; 4) development of a national database of sites' previous trial recruitment performance; 5) data-driven site level assessment of recruitment potential. Trial registration: ISRCTN11791740; 16/07/2015, ISRCTN11093872; 11/11/2015, ISRCTN11747152; 30/09/2015. [ABSTRACT FROM AUTHOR]

Background: Participant non-response is a source of bias in all research, especially in randomised controlled trials. Participants followed up remotely can have high non-response rates. Four such trials have been conducted of a cover letter with content informed by behaviour change theory to overcome hypothesised barriers to responding to a mailed questionnaire. Pooled results to date have suggested further research to be worthwhile. We conducted an embedded randomised study within a trial of such cover letters in the hope that we would improve response rates to our postal quality of life questionnaires. Methods: One hundred forty-eight participants in the CODIFI2 diabetic foot ulcer sampling trial were randomised 1:1 to receive one of two different cover letters at follow-up timepoints: either a standard cover letter accompanying their postal follow-up questionnaires or to an 'enhanced' (theory-informed) cover letter. Questionnaires were mailed at 39, 52 and (for some participants) 104 weeks post randomisation. Outcome measures were response to mailing at each timepoint. Analysis was restricted to those for whom a questionnaire and letter was issued. Owing to limited recruitment, a reduced analysis plan, comprising solely observed response rates and 95% confidence intervals for difference in response rates was followed. Post hoc, we added our week 52 results to an already-published meta-analysis. Results: Sixty-seven out of 74 enhanced cover letter group (Enhanced) and 67/74 standard cover letter group (Standard) participants who had not already died or withdrawn were sent their first mailing at 39 weeks. The 39-week response rates were 47/67 (70.1%) and 39/67 (58.2%) for Enhanced and Standard participants, respectively. At week 52, the response rates were 45/64 (70.3%) and 35/63 (55.6%) for Enhanced and Standard participants, respectively. At week 104, the response rates were 24/33 (72.7%) and 19/33 (57.6%) for the Enhanced and Standard participants, respectively. Adding our week 52 results to a published meta-analysis increased the pooled estimate of differences in response rates to 0.04 (− 0.01 to 0.09) favouring enhanced letters. Conclusion: While this embedded randomised controlled trial observed greater response rates at all times among those randomised to the enhanced letter, the reduced sample size meant that these results are imprecise. Trial registration: ISRCTN registry ISRCTN74929588. Registered on 5 March 2019. [ABSTRACT FROM AUTHOR]

Although erosion has naturally been driven by rainfall patterns, human activities have increasingly influenced erosion rates in recent times. However, as climate change alters precipitation, future erosion control may once again depend primarily on climate. The primary goal of this investigation was to ascertain whether the issue of escalated erosion, typically linked to downstream dam effects, could diminish in significance due to projected climatic shifts later in this century. An erosion potential index (Ep), formulated as the ratio of the mass of sediment influx from upstream to the frequency of the sediment-carrying flow events, was computed on a tributary of the Godavari River, located downstream of a dam. Using the Soil & Water Assessment Tool (SWAT), virtual experiments were conducted to distinguish the impacts of the dam from projected climate changes on river geomorphology. Two control scenarios were created using the current climate data and simulated regulated and unregulated states of the basin. Employing climate projections and various mitigation scenarios (SSPs) for the 2060s and 2090s, this study estimated Ep values exclusively under future climatic conditions in an unregulated state of the basin. Results indicate that, in the unregulated state without the existence of the upstream dam, future climate impacts on erosion outweigh the current effects of the dam, underscoring the growing influence of climate on geomorphology. It suggests that existing structural interventions may lose their geomorphic significance in the face of future climate conditions. [ABSTRACT FROM AUTHOR]

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m3/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model. [ABSTRACT FROM AUTHOR]

Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the performance of a hydrological model by using satellite image classification (SIM) to extract land use (LU) information. Four methods, namely naive Bayes (NB), classification and regression trees (CART), support vector machine (SVM), and random forest (RF), are assessed for SIM using satellite images. CART demonstrated the highest overall accuracy (OA) of 0.996, followed by RF (OA = 0.994), SVM (OA = 0.797), and NB (OA = 0.543). The Soil and Water Assessment Tool (SWAT) model was employed in this study to construct and refine the hydrological model for simulating the streamflow in the basin. For this purpose, the ground-based layer was replaced by LU generated from the classification algorithms. Integrating the four classification approaches yielded a significant improvement in Nash-Sutcliffe efficiency of the SWAT model, increasing it from 0.37 to approximately 0.76. These findings highlight the effectiveness of using satellite image classification in enhancing the efficiency of hydrological models. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management. [ABSTRACT FROM AUTHOR]

The excessive extraction of groundwater is a globally significant issue, as it can lead to the permanent loss of groundwater system sustainability. Sustainable groundwater requires development that appropriately balances the needs of both humans and the environment. In this study, the exploitable groundwater (EGW) of the So-Yang-gang Dam (SYD) Basin was estimated based on simulated groundwater recharge rates using SWAT, and the sustainability of future groundwater development was evaluated under different climate change scenarios. The EGW in each sub-watershed of the SYD was estimated to range from 60 to 240 mm/year, with higher values in the upstream watersheds. A sustainability index (SI) was evaluated, ranging from 0.56 to 1.0 across various GCMs. The analysis revealed that uniform EGW across a watershed is overestimated value in sub-watersheds with low recharge rates, potentially accelerating groundwater depletion in those areas. Thus, a flexible EGW estimation approach is essential to balance groundwater conservation with human water demands. [ABSTRACT FROM AUTHOR]

The Nyabarongo river catchment in Rwanda has experienced significant changes in its land use and land cover (LULC) in recent decades, with profound implications for non-point source pollution. However, there are limited studies on non-point pollution caused by nutrient loss associated with land use and land cover changes in the catchment. This study investigates the spatiotemporal impacts of these changes on water quality considering nitrogen and phosphorus within the catchment from 2000 to 2020 and 2030 as a projection. The SWAT model was used in analysis of hydrological simulations, while the CA–Markov model was used for the future projection of LULC in 2030. The results revealed (1) the important changes in LULC in the study area, where a decrease in forestland was observed with a considerable increase in built-up land, grassland, and cropland; (2) that the R2 and NSE of the TN and TP in the runoff simulation in the catchment were all above 0.70, showing good applicability during calibration and validation periods; (3) that from 2000 to 2020 and looking to the projection in 2030, the simulated monthly average TN and TP levels have progressively increased from 15.36 to 145.71 kg/ha, 2.46 to 15.47 kg/ha, 67.2 to 158.8 kg/ha, and 9.3 to 17.43 kg/ha, respectively; and (4) that the most polluted land use types are agriculture and urban areas, due to increases in human activities as a consequence of population growth in the catchment. Understanding the patterns and drivers of these changes is critical for developing effective policies and practices for sustainable land management and protection of water resources. [ABSTRACT FROM AUTHOR]

Non-point source (NPS) pollution has a complex formation mechanism, and identifying its primary controlling factors is crucial for effective pollution treatment. In this study, the Baixi Reservoir Watershed, characterized by low-intensity development, was selected as the study area. A new methodology combining the Soil and Water Assessment Tool (SWAT) with the Random Forest (RF) algorithm was proposed to comprehensively identify the primary controlling factors of NPS pollution and analyze the interaction between factors. The results of the validated SWAT model showed that the annual intensity of total nitrogen (TN) load range was 0.677–11.014 kg ha−1 yr−1, and the total phosphorus (TP) load per unit area range was 0.020–0.110 kg ha−1 yr−1. Loads of sediment, TP, and TN exhibited significant seasonal variations, particularly in the Baixi basin, where sediment yield had the highest absolute change rate, with a value of up to 232.26. Random Forest models for TN and TP displayed high accuracy (R2 > 0.99) and robust generalization ability. Fertilization, sediment yield, and terrain slope were identified through RF models as the primary factors affecting TN and TP. By graphing partial dependency plots (PDPs) based on the results of the RF models to analyze the interaction between factors, the findings suggest a strong synergistic effect of two combined factors: fertilization and sediment yield. When fertilizer application exceeds 15 kg ha−1 yr−1 and sediment yield exceeds 3 kg ha−1 yr−1, there is a sharp increase in nitrogen and phosphorus load. Through the identification and analysis of the primary controlling factors of NPS pollution, this study provides a solid scientific foundation for developing effective watershed management strategies. [ABSTRACT FROM AUTHOR]

The quality of freshwater input from tributaries of the Western Mississippi Sound (WMSS) impacts the quality of coastal water. Hydrological and hydrodynamic models can be coupled to assess the impact of freshwater inflow from coastal watersheds. This study aims to compare the performance of a hydrodynamic model and a hydrological–hydrodynamic coupled model in detecting the effect of freshwater inflow from the coastal watersheds of the state of Mississippi into the WMSS. A hydrological model, the Soil and Water Assessment Tool (SWAT), and a hydrodynamic model, the visual Environmental Fluid Dynamics Code (vEFDC), were coupled to evaluate the difference between the hydrodynamical modelling approach, which employs an area-weighted approach to define flow and nutrient concentrations, and the more recent coupling model approach, which uses a hydrological model to determine the flow and nutrient load of the model. Furthermore, a nutrient load sensitivity analysis of the effect of freshwater inflow on water quality in the WMSS was conducted in addition to assessing the repercussions of tropical depressions. Hydrological assessments of the major tributaries watersheds of Saint Louis Bay (SLB) at the WMSS were performed using the SWAT model. After calibration/validation of the SWAT model, the streamflow output from the SWAT was incorporated into the vEFDC model. Finally, hydrodynamic simulation of the SWAT-vEFDC model was conducted, and water quality output was compared at different SLB locations. The salinity, dissolved oxygen, total nitrogen (TN), and total phosphorus (TP) were assessed by comparing the vEFDC and SWAT-vEFDC outputs. The results indicated that hydrological input from the SWAT alters the flow and nutrient concentration results as compared to an area-weighted approach. In addition, a major impact on the concentration of TN and TP occurred at the location where the freshwater flows into SLB. This impact diminishes further away from the point of freshwater inflow. Moreover, a 25% nutrient load variation did not demonstrate a difference in water quality at the WMSS besides TN and TP in a post-tropical depression scenario. Therefore, the SWAT-vEFDC coupled approach provided insights into evaluation of the area-weighted method, and of hydrological model output to the hydrodynamical model, the effect of freshwater inflow into coastal waters, and nutrient sensitivity analysis, which are important for integrated coastal ecosystems management. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the most serious environmental concerns as it threatens the sustainable agriculture and poses a grave threat to global food security. It is important to adopt the appropriate soil conservation measures to reduce the erosion hazard. In the present study, SWAT model has been applied to study the impact of various best management practices (BMPs) on sediment yield and nutrient losses from a Lower Sutlej River Basin, India. The impact of agricultural and structural BMPs was assessed both individually and in combinations to evaluate the best possible combination of BMPs. Three scenarios, viz., CT1-BMP-1 + CF-BMP-2 + CD-BMP-5 + Fertilizer level 1 (Scenario-1), CT1-BMP-1 + CF-BMP-2 + CB-BMP-3 + CD-BMP-5 + CT2-BMP-8 + Fertilizer level 2 (Scenario 2) and CT1-BMP-1 + CF-BMP-2 + CB-BMP-3 + BT-BMP-4 + CD-BMP-5 + GSS-BMP-6 + SBSS-BMP-7 + CT2-BMP-8 + Fertilizer level 3 (Scenario 3) were developed to study their impact on sediment yield and nutrient losses. The average annual sediment yield from watersheds ranges from 3.08 to 21.63 ton/ha/yr for the base scenario (without BMPs), 1.97 to 13.94 ton/ha/yr in scenario 1, 1.66 to 10.77 ton/ha/yr in scenario 2, and 1.04 to 7.78 ton/ha/yr in scenario 3. At the watershed level, the greatest decrease in sediment yield was obtained from check dam (28.72%), followed by bench terracing (25.62%), grade stabilization structures (22.74%), contour bunding (20.88%), stream bank stabilization structures (10.02%), contour trenching (8.99%), conservation tillage (6.66%) and contour farming (4.54%). The use of structural BMPs at the watershed level reduced sediment yields more effectively than agricultural BMPs. The implementation of all the potential BMPs in Scenario 3 minimized sediment yields to the extent of 66.25%. Model simulation demonstrated that a 30% reduction in fertilizer application under fertilizer scenario 3 resulted in the highest reduction in total nitrogen (24.04%), nitrate nitrogen (8.97%), and total phosphorus (11.75%). The study findings may be useful for promoting sustainable land and water resource management at the river basin level. [ABSTRACT FROM AUTHOR]

Satellite rainfall estimates are robust alternatives to gauge precipitation, especially in Africa, where several watersheds and regional water basins are poorly gauged or ungauged. In this study, six satellite precipitation products, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS); Tropical Applications of Meteorology Using Satellite and Ground-based Observations (TAMSAT); TRMM Multi-satellite Precipitation Analysis (TMPA); and the National Aeronautics and Space Administration's new Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (GPM) early run (IMERG-ER), late run (IMERG-LR), and final run (IMERG-FR), were used to force a gauge-calibrated Soil & Water Assessment Tool (SWAT) model for the Congo River Basin, Central Africa. In this study, the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) calibrated version of the SWAT was used as the benchmark/reference, while scenario versions were created as configurations using each satellite product identified above. CFSR was used as an independent sample to prevent bias toward any of the satellite products. The calibrated CFSR model captured and reproduced the hydrology (timing, peak flow, and seasonality) of this basin using the average monthly discharge from January 1984–December 1991. Furthermore, the results show that TMPA, IMERG-FR, and CHIRPS captured the peak flows and correctly reproduced the seasonality and timing of the monthly discharges (January 2007–December 2010). In contrast, TAMSAT, IMERG-ER, and IMERG-LR overestimated the peak flows. These results show that some of these precipitation products must be bias-corrected before being used for practical applications. The results of this study will be significant in integrated water resource management in the Congo River Basin and other regional river basins in Africa. Most importantly, the results obtained from this study have been hosted in a repository for free access to all interested in hydrology and water resource management in Africa. [ABSTRACT FROM AUTHOR]

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change. [ABSTRACT FROM AUTHOR]

This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), and support vector regression (SVR) models for rainfall-runoff modeling using gauged and satellite rainfall, and their fusions in the Gilgel Abay watershed, Ethiopia. Afterward, simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) techniques were applied to combine the outputs of individual models under three scenarios. The performance of the models was evaluated using Nash-Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results demonstrated that the ANFIS model outperformed all the other single models with validation stage NSE values of 0.864 and 0.875, and RMSE values of 23.58 and 21.84 m3/s for gauge and fusion rainfall data, respectively. Among the physical-based models, SWAT gave better modeling performance with the validation stage NSE values of 0.81 and 0.821 for gauge and fusion rainfall data, respectively. Moreover, an ensemble of artificial intelligence and physical-based models greatly improved the overall modeling performance. The NNE improved the performance of single models up to 15.7 and 21.2 5% for fusion and satellite-based rainfall modeling, respectively. [ABSTRACT FROM AUTHOR]

Hydrological models are vital for water management to determine in-stream flow, irrigational water, domestic water supply, and biodiversity conservation. This study formulates a hydrological model with a novel approach for streamflow and sediment load in the QGIS-supported Soil and Water Assessment Tool for the Halda River catchment, a unique ecological habitat for natural carp spawning and freshwater sources. The daily simulation uses an innovative stage-discharge relationship technique from available 15-day interval flow data. The model evaluation parameters R2 values 0.80 and 0.62, and NS values 0.81 and 0.61 for calibration and validation of streamflow suggested excellent agreement in the seasonal cycle and most of the monsoon peak flow. The streamflow/precipitation ratio indicates a significant influence of groundwater through infiltration. The baseflow shows a decreasing trend. The sediment load based on suspended sediment concentration at a downstream location is 1,625 tons/day. On the contrary, the model prediction is 30 times lower. The scattered sediment load data support the model estimate by considering relatively lower intervention or land use change in its upstream. This model provides a baseline for daily flow and sediment load for scenario modeling (e.g., climate change, land use change) for environmental flow estimation of the fish habitat, freshwater supply, irrigation, and salinity intrusion. [ABSTRACT FROM AUTHOR]

Climate change is one of the most critical factors impacting hydrological dynamic systems. This study investigated how climate change influences the hydrological dynamics within the Siliana watershed in northwestern Tunisia, employing the Soil and Water Assessment Tool (SWAT) model. The analysis compared streamflow patterns for the future period (2046–2072) with a baseline period (1979–2005). Simulations were carried out using four combinations of regional and global climate models from EURO-CORDEX, based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results indicate a projected annual precipitation decrease of 22% with RCP4.5 and 27% with RCP8.5, accompanied by a temperature rise of up to 7 °C under RCP8.5. Streamflow is anticipated to decrease by 44% under RCP4.5 and 69% under RCP8.5. Extreme events show intensified high flows of shorter durations and increased low flows. Analysis using the Standardized Precipitation Evapotranspiration Index (SPEI) revealed longer and more intense droughts. Under the RCP8.5 scenario, 24% of the watershed faces extreme drought, while 76% experiences severe drought conditions. These findings highlight notable changes in hydrological indicators, emphasizing the urgent need for adaptive strategies in water resource management within the Siliana Basin to mitigate the effects of climate change. [ABSTRACT FROM AUTHOR]

Modelling streamflow in snow-covered mountainous regions with complex hydrology and topography poses a significant challenge, particularly given the pronounced influence of temperature lapse rate (TLAPS) and precipitation lapse rate (PLAPS). The Present study area covers 54,990 km2 in the western Himalayas, including the Tibetan Plateau and the Indian portion of the USRB up to Bhakra Dam in Himachal Pradesh. In order to estimate the snowmelt and rainfall runoff contributions to the catchment, an integrated Soil and Water Assessment Tool (SWAT) model incorporates a Temperature Index with an Elevation Band approach. The uncertainty analysis of the SWAT model has been conducted using the Sequential Uncertainty Fitting algorithm (SUFI-2). Furthermore, machine-learning models such as Long Short-Term Memory (LSTM) neural networks and Random Forest (RF) are integrated with the SWAT model to enhance the accuracy of streamflow predictions resulting from snowmelt. The performance indices of a model for the monthly calibration period are R2 = 0.83, NSE = 0.82, P-BIAS = 2.3, P-factor = 0.82, and R-factor = 0.81. The corresponding values for the validation period are R^2 = 0.78, NSE = 0.77, P-BIAS = 5.7, P-factor = 0.72 and R-factor = 0.66. The results show that 63.08% of the Bhakra gauging station's annual streamflow has attributed to snow and glacier melt. The highest snow and glacier melt occur from May to August, while the minimum is observed from November to February. Regarding snowmelt forecasting, the LSTM model outperforms the RF model with an R2 value of 0.86 and 0.85 during training and testing, respectively. Additionally, sensitivity analysis highlights that soil and groundwater flow parameters, specifically SOL_K, SOL_AWC, and GWQMN, are the most sensitive parameters for streamflow modelling. The study confirms the effectiveness of SWAT for water resource planning and management in the mountainous USRB. [ABSTRACT FROM AUTHOR]

Although the Carpathian Mts. area is considered as extremely prone to surface erosion which results in capacity loss of the dammed reservoirs, a lack of data to follow details of this process is perceivable. The research of the selected sediment fractions transport tracking was conducted using the capabilities of the digital platform—Macromodel DNS (Discharge‐Nutrient‐Sea) for the catchment with drinking water reservoir in the Polish part of Western Carpathian. The continuity of sediment transport simulation in two hydrologically different elements of the catchment—the river and the reservoir—was possible due to consolidation of two models in the platform—SWAT (Soil & Water Assessment Tool) and AdH/PTM (Adaptive Hydraulics Model/Particle Tracking Model). The result of those modules' integration was a database for tracking the individual sediment fractions delivered to the reservoir and deposited in specific reservoir zones. The implementation of climate and land use change scenarios allowed additionally to analyse the estimation of those processes in the future. The simulation outcomes consist of daily flows and monthly sediment loads at the reservoir inflow and the individual sediment particle fractions deposition location inside of the reservoir. [ABSTRACT FROM AUTHOR]

Uncertainty analysis is crucial before hydrological simulation to address model vulnerability in snow-covered mountainous river basins, ensuring effective soil conservation and water resource management planning. The present study is conducted on Sutlej River Basin, which is located in Western Himalayas and originated from Manasarovar Lake at Tibetans platue in China. The Soil and Water Assessment Tool (SWAT) model is calibrated; the parameter sensitivity and uncertainty of the SWAT were quantified using four optimization methods such as (i) sequential uncertainty fitting (SUFI-2), (ii) particle swarm optimization (PSO), (iii) generalized likelihood uncertainty estimation (GLUE), and (iv) parameter solution (ParaSol). The model is performed with calibration period (1982–2000) and validation period (2001–2013), using monthly observed streamflow at the Bhakra gauging station. The statistical performance criteria such as Nash–Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R2), values showing all four models, performed very well in the model calibration and validation. During the calibration period, SUFI-2 and ParaSol algorithms show higher performance with NSE and R2 values of 0.82 and 0.83. In the validation period, SUFI-2 shows high accuracy with NSE and R2 values of 0.77 and 0.78, while ParaSol also performed well with NSE and R2 values of 0.71 and 0.76. These results highlighted the superior performance of SUFI-2 and ParaSol compared to the other two (PSO, GLUE) techniques. Further, the values of p-factor and r-factor reveal that ParaSol method performed poor during both the calibration period (p-factor: 0.25, r-factor: 0.32) and the validation period (p-factor: 0.26, r-factor: 0.38). These results suggest that while ParaSol was effective in optimizing parameter sets, it fails in providing accurate estimates of uncertainty during both model calibration and validation, whereas other three methods perform very well in the model uncertainty analysis. This study fills a significant research gap by offering guidance on adjusting sensitive parameters, reducing uncertainty in streamflow simulation, and addressing the challenges specific to hydrological modeling in mountainous river basins, which is different from the previous studies conducted in plain river basins. [ABSTRACT FROM AUTHOR]

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Soil erosion has been causing an imbalance in nature and the environment. It is mainly caused naturally but is also due to human interventions leading to desertification and possible contamination. Therefore, engineering, geography, and cartography have been allies in applying erosion models to predict, address, and remediate the impacts. Therefore, the Revised Universal Soil Loss Equation (RUSLE) and Soil and Water Assessment Tool (SWAT) linked to Geographic Information Systems (GISs) could boost decision making as tools to mitigate issues. This study applies the RUSLE and SWAT models from a geotechnical point of view to analyze a sub-watershed at Idanha-a-Nova (Portugal) over 4 years, showing a predominant erosion risk class with losses lower than 5 t.ha−1.year−1 (60 to 86%), characterized as very low risk. The modeling permitted the development of soils erosion susceptibility charts, in addition to material availability and the suitability for construction areas, exposing a replicable methodology that could contribute to minimizing environmental impacts while encouraging a more intelligent use of the land towards a greener exploration. [ABSTRACT FROM AUTHOR]

Hydrological models serve as effective tools for managing water sources and assessing water components. Although basin hydrological models have been developed, selecting suitable software for modeling specific areas remains challenging. Hence, it's essential to opt for software capable of simulating the hydrological cycle with a simple structure and minimal input parameters. In northwest Iran, AWBM and ARCSWAT software were employed for simulation. AWBM is a simplistic program estimating runoff using precipitation and evaporation indicators, while ARCSWAT is a semi-distributed software for continuous flow simulation. ARCSWAT necessitates various inputs, including geological, land use, and slope maps, along with precipitation, temperature, relative humidity, and radiation statistics. The runoff simulation results during calibration and validation periods were assessed using two statistical indices: Nash Sutcliffe Efficiency (NSE) and the coefficient of determination (R2). A comparison of the statistical indicators revealed NSE values of 0.83 and 0.7 for calibration and validation periods, respectively, and R2 values of 0.83 and 0.8 for calibration and validation periods, respectively, for the semi-distributed SWAT model. In contrast, for the AWBM integrated model in the basin, NSE values were 0.63 and 0.48, and R2 values were 0.48 and 0.48 for calibration and validation periods, respectively, indicating better results in runoff simulation at the monthly time scale. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan'an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model's performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT's detailed physical process portrayal with Transformer's powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development. [ABSTRACT FROM AUTHOR]

The identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049). [ABSTRACT FROM AUTHOR]

Today, hydrological models are important tools for successfully managing water resources and understanding the hydrologic response of watersheds, especially in arid regions with limited water resources. The purpose of this study is to evaluate the potential of the Soil Water Assessment Tools model (SWAT) for predicting the hydrological regime of El Chique Dam watershed, in a semi-arid region of the state of Zacatecas, Mexico. The model was calibrated and validated on a daily and a monthly scale using the sequential uncertainty fitting algorithm (SUFI-2) within the Calibration and Uncertainty Program SWAT-CUP. The performance of the SWAT model was evaluated by means of four statistical measures: the Nash–Sutcliffe coefficient (NSE), the determination coefficient (R2), the Percentage of Bias (PBIAS) and the mean square error (RSR). The results showed a better performance of the model in the simulation on a monthly scale, obtaining values of NSE, R2, PBIAS and RSR of 0.78, 0.79, -14.00 and 0.47 during the calibration period, and 0.60, 0.61, -18.70 and 0.63 during the validation period, respectively. The most sensitive parameters that affect the behavior of runoff in the watershed are: ALPHA_BF, CH_K2, CN2, SOL_BD and OV_N. The overall hydrological water balance analysis showed that more than 90% of precipitation is lost by evapotranspiration, while 8.6% of precipitation contributed to streamflow. Thus, the SWAT model can predict runoff on a monthly scale very well and therefore, these results are expected to assist in parameter selection for calibration processes in other semi-arid regions, as well as in decision-making related to water resource management and planning in the watershed under study. [ABSTRACT FROM AUTHOR]

Extreme flood events present a significant challenge for operators and managers of large drinking water reservoirs. Detailed flood response analysis can predict the hydrology response of a reservoir to changing climate conditions and can aid in managing the reservoir in anticipation of extreme events. Herein, the Soil and Water Assessment Tool (SWAT), a watershed model, was used in conjunction with a reservoir management model, the Operational Analysis and Simulation of Integrated Systems (OASIS) model, to evaluate extreme flood events across a set of initial reservoir storage capacities across various CMIP6 climate scenarios. The SWAT model was calibrated and validated with PRISM climate data in conjunction with land and soil cover data and multi-site gauged stream discharges. The validated model demonstrated satisfactory performance (NSE = 0.55 and R2 = 0.56) for total reservoir inflow. The resulting inflow values from SWAT were utilized to set up a calibrated/validated OASIS model (NSE = 0.55 and R2 = 0.68). OASIS was then used to assess alternative operating rules for the reservoir under varying climate scenarios (RCP4.5 and RCP8.5) and extreme events (synthetic hurricanes). Focusing on a major reservoir in the Northeastern United States, the analysis of the reservoir response was based on (1) reservoir volume–elevation curve, (2) daily reservoir inflow, (3) daily precipitation, (4) spillway flow, and (5) reservoir evaporation. Projected future scenarios indicate a >20% increase in precipitation in April compared to historical records, coupled with likely reduced runoff from November to March. With extreme conditions most likely in the month of April, RCP4.5 and RCP8.5 projections suggest that most scenarios result in a 10–15% increase in the mean of 3D30Y runoff volumes, and a 150% increase under the most extreme conditions. For 7D30Y runoff volumes in April, the RCP4.5 and RCP8.5 analyses reveal an increased likelihood of the reservoir elevation reaching overspill flow levels during the latter half of the simulation period (2020 to 2080). Our findings indicate that simulations with SWAT coupled with OASIS can assist reservoir managers in regulating water levels in anticipation of extreme precipitation events. [ABSTRACT FROM AUTHOR]

Quantifying the attribution of climate change and human activities on runoff and suspended sediment load is crucial for formulating future watershed management measures, especially in the ecologically fragile alpine region, which is more susceptible to climate change and human activities. In this study, the temporal changes in runoff‐suspended sediment load and the potential impact factors (i.e., precipitation, potential evapotranspiration (PET), land use/land cover change (LULC) and reservoir operation) were investigated in the Lhasa River Basin (LRB) from 1956 to 2020. In addition, the contributions of those factors to the changes in runoff and suspended sediment load were quantitatively evaluated based on physically‐based Soil and Water Assessment Tools (SWAT). The results indicated that annual runoff and suspended sediment load showed an increasing but insignificant trend during 1956–2020, while annual precipitation and PET showed a significant increasing trend at a rate of 0.94 and 1.07 mm/yr, respectively. LULC mainly presented an increase in forestland area followed by a decrease in grassland and bare land area due to the implementation of ecological projects. Runoff and suspended sediment load changed abruptly at approximately 1995 and 2005 based on three abrupt change identification methods, thus, the study period was further divided into three substages: baseline period (P0, 1956–1994), dramatically increased period (P1, 1995–2004) and slightly decreased period (P2, 2005–2020). The attribution analysis showed that climate change was the dominant contributor to runoff and suspended sediment load increments during P1. LULC caused the decline in runoff and suspended sediment load in both P1 and P2, with their contribution increasing significantly from 0.40% and 4.73% in P1 to 50.24% and 51.79% in P2, respectively. Reservoir operation was the second key factor in runoff and suspended sediment load reduction in P2, contributing 42.07% and 43.72%, respectively. These findings provide the scientific foundation for reasonably allocating water resources and statistical support for the benefit evaluation of implementing ecological projects in the basin. [ABSTRACT FROM AUTHOR]

The study focuses on the uncertainty quantification and bias correction of hydrological projections using Bayesian applications. The climate change impact assessment on streamflow has been done using Soil and Water Assessment Tool (SWAT) model in Bharathapuzha river basin, India. The uncertainty quantification has been done by using Generalised Likelihood Uncertainty Estimation (GLUE) algorithm and the ensemble spread in the streamflow projections is quantified as the total uncertainty. A Hierarchical Bayesian Algorithm is adopted in the current study to remove the systematic bias in the projections of extreme streamflow. The approach established a probabilistic correction to the projected streamflow based on the biases in daily scale hindcast streamflow simulations with the corresponding observed historical streamflow data. The procedure is applied to the ensemble streamflow predictions for the Bharathapuzha catchment and over 10 times reduction in RMSE is observed in the bias corrected streamflow. The skill of the procedure in correcting the streamflow across different terciles is studied using the concept of reliability and significant improvement is observed in the reliability of high and medium flow ranges. The average width of the ensemble streamflow simulation band for the period 2021–2030 is seen to reduce from 5560 cumec to 2188 cumec after the correction procedure is applied. Highlight: The parametric uncertainty in the streamflow projection is accounted by calibrating the hydrological model using the GLUE procedure. A Hierarchical Bayesian approach has been adopted for bias correction of the streamflow projections. A higher reliability is observed in the bias-corrected streamflow, especially in high and medium flow ranges. The average width of the ensemble streamflow simulation band has reduced from 5560 cumec to 2188 cumec after the bias correction. [ABSTRACT FROM AUTHOR]

Hydrological models are critical for water resources planning and management. The precision and reliability of the simulations hinge greatly on the accessibility and quality of available input data. Particularly in developing nations, the major challenge in modeling is the scarcity of fine-scale spatiotemporal input data, specifically precipitation. Remotely sensed weather data has been increasingly used in recent years. However, they possess bias compared to ground observations due to the nature of indirect measurement and may affect the simulated water balance. To address these limitations, we explored data assimilation techniques to improve the Global Precipitation Measurement product (IMERG) precipitation using limited ground observations. Multiple assimilation methods are applied by incorporating Linear scaling Correction Factor (CF) and Power Transformation Function methods (PF). The assimilated IMERG precipitation from the most effective method identified, is utilized in an eco-hydrological model, and the resulting stream flow simulations are validated against observed flow data. The findings indicate that assimilated precipitation enhances the monthly flow statistics in both CF and PF methods and also in conditional merged precipitation. An ensemble of hydrological simulations, outperformed those based on raw IMERG precipitation. Additionally, the hydrological simulations are compared with observed gauge precipitation data and the widely used Climate Forecast System Reanalysis (CFSR) dataset in data-limited watersheds. The simulations utilizing the assimilated IMERG dataset (NSE = 0.52) are comparable to gauge precipitation-based simulations (NSE = 0.61) and significantly superior to CFSR-based simulations (NSE=-0.2). These results highlight the potential of utilizing assimilated remote sensing data for hydrological modeling in data-limited watersheds, leading to improved simulation accuracy and reliability. [ABSTRACT FROM AUTHOR]

Maravanthe Beach has been witnessing erosion due to waves caused by tidal forces, destroying vegetation, and causing enormous damage to people's lives, livestock, and property. Hence, an attempt has been made to understand the shoreline change analysis using the automatic shoreline extraction method and determine the rate of change in terms of erosion and accretion rate before and after the construction of coastal structures during the period from 2014 to 2023. The SWAT model was applied to analyze the influence of surface runoff on shoreline changes near Maravanthe Beach for the periods 2004–2014 and 2012–2023. Also, runoff was chosen as one of the parameters for validation of the analysis. It was noticed that the shoreline stretch had experienced significant erosion prior to the construction of shore protection infrastructure. Soon after the deployment of certain shore structures, the shoreline progressed from a high erosion stretch to a low erosion stretch. This study reveals that Maravanthe Beach witnessed greater erosion during the period 2014–2018 than it did from 2019–2023 and also observed a decrease in erosion from 11% to 3% and an increase in the accretion zones from 2 to 9%. [ABSTRACT FROM AUTHOR]

Analysing the impacts of climate change on water resources is crucial to identify vulnerabilities and prioritize actions. We investigated climate change impacts on drinking water supply, emphasizing sustainable agriculture as an adaptation strategy, using the SWAT model in the Cávado River basin, Portugal. Our study highlights an increase in months with river discharge below ecological flow post water abstraction due to climate change. Notably, nitrate concentration was more influenced by sustainable agriculture practices than climate change. Our study highlights the vital role of adaptive strategies, especially sustainable agriculture, in securing water resources amidst challenges posed by climate change. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive comparison between traditional hydrological models and advanced machine learning (ML) techniques in predicting streamflow dynamics. Traditional models, namely the Soil and Water Assessment Tool (SWAT) and Génie Rural à 4 Paramètres Journalier (GR4J), are juxtaposed against ML models, including Random Forest (RF), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Both SWAT and GR4J demonstrated commendable performance, with GR4J displaying marginally superior predictive accuracy, evidenced by its tighter RMSE values. In the realm of ML, RF exhibited exceptional prowess in integrating diverse climatic features, especially in a scenario integrating comprehensive meteorological data. ANN showcased consistent performance across different input scenarios, emphasising its robustness. LSTM and BiLSTM, tailored for time series data, underscored the importance of precipitation's temporal dynamics in streamflow predictions. A notable revelation is the significance of choosing appropriate input data, with certain scenarios outperforming others based on the amalgamation of meteorological parameters. The flow duration curve (FDC) analysis further highlighted the model capabilities, with RF and BiLSTM excelling in capturing extreme flows, while traditional models resonated more with medium flow regimes. This research offers vital insights for hydrologists and decision-makers, aiding in informed model selection for streamflow predictions. [ABSTRACT FROM AUTHOR]

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evapotranspiration (ET) estimation is essential for managing agricultural water demand at the basin scale and allocating irrigation water. Many uncertainties, such as those related to the model's structure, initial conditions, and parameter set, cascade into the ET calculation, producing unreliable results, even though water modelers and managers depend on stand-alone ET estimation models for planning and management. Utilizing an ensemble-based data assimilation (EDA) methodology, this study investigated how remotely-sensed ET can enhance simulations of the popular Surface Energy Balance Algorithm for Land (SEBAL) ET model while taking uncertainties into account. This watershed-scale study was carried out in the Maroon Basin situated in southwestern Iran. The SEBAL model was employed to simulate ET. The particle filter-based DA method was then applied to enhance the model's performance. Afterward, the SWAT model was utilized to simulate the performance of products and runoff using ET taken from the SEBAL model. The study findings demonstrated that employing DA in SEBAL ET produced a more reliable and accurate model simulation. These findings paved the way for future research by highlighting the significance of digital farming tools in the management of water resources and sound agricultural planning and management. [ABSTRACT FROM AUTHOR]

Significant concerns have been raised about the use of tactical officers, who are suggested to be predisposed to use force—particularly deadly force—when interacting with the public. Given this, we conducted a systematic review of research that compares the decision-making of officers with tactical experience and those without tactical experience (i.e. patrol and recruits). Specifically, we examined the following research questions: (1) across officers who received different training (i.e. officers with tactical experience and officers without tactical experience), are there differences in use-of-force decision-making (e.g. reaction time and decisions to use force)?; and (2) what cognitive processes explain any differences in decision-making (e.g. gaze control and mental models)? Using the PRISMA guidelines we conducted a systematic literature review in which six databases were used to identify articles. Of the 388 articles that were screened, we conducted a narrative synthesis of 10 studies and found that tactical officers are not only able to make more accurate decisions to use deadly force but are also able to make those decisions faster than officers without tactical training. The enhanced decision-making observed in tactical officers was largely explained by where tactical officers fixate their gaze during unfolding scenarios, as well as an enhanced ability to predict how a situation will unfold. Based on our findings, tactical officers are not primed to use (deadly) force as has been suggested. We call for more research that explores the decision-making processes of tactical officers and the extent to which they have expertise in managing potential use-of-force encounters. [ABSTRACT FROM AUTHOR]

Abstract Background The CapaCiTY programme includes three, multi-centre, randomised controlled trials aiming to develop an evidence based adult chronic constipation treatment pathway. The trials were conducted in the United Kingdom, National Health Service, aiming to recruit 808 participants from 26 March 2015 to 31 January 2019. Sites were selected based on their responses to site feasibility questionnaires (2014–2015), a common tool employed by sponsors to assess a site’s recruitment potential and ability to undertake the trial protocol. Failure to recruit the planned sample jeopardises reliability of results and wastes significant time and resources. The purpose of this study was to investigate barriers to recruitment in 2017. Methods We conducted site feasibility assessments with thirty-nine sites prior to trial commencement. Twenty-seven were selected to participate in the CapaCiTY programme, twelve were deemed unsuitable. We compared site contracted recruitment rates with actual recruitment rates and conducted a telephone survey and analysis from 5 July to 7 December 2017 (n = 24) to understand barriers to recruitment. Three sites declined to participate in the survey. Results At the time of survey, 15% of sites in the CapaCiTY programme were meeting recruitment targets, 85% were recruiting half or less of their target. Of these, 28% recruited no participants. The main barriers to recruitment were lack of resources, high workloads, lack of suitable participants and study design not being compatible with routine care. Despite multiple strategies employed to overcome these barriers, the trials were eventually stopped due to futility, recruiting only 34% of the programme sample size. Conclusions Improving the reliability of site feasibility assessments could potentially save a substantial amount in failed research investments and speed up the time to delivery of new treatments. We recommend 1) investment in training researchers in conducting and completing site feasibility; 2) funders to require pilot and feasibility data in grant applications, with an emphasis on patient and public involvement in trial design; 3) conducting site feasibility assessment at the pre-award stage; 4) development of a national database of sites’ previous trial recruitment performance; 5) data-driven site level assessment of recruitment potential. Trial registration ISRCTN11791740; 16/07/2015, ISRCTN11093872; 11/11/2015, ISRCTN11747152; 30/09/2015.

Abstract Background Participant non-response is a source of bias in all research, especially in randomised controlled trials. Participants followed up remotely can have high non-response rates. Four such trials have been conducted of a cover letter with content informed by behaviour change theory to overcome hypothesised barriers to responding to a mailed questionnaire. Pooled results to date have suggested further research to be worthwhile. We conducted an embedded randomised study within a trial of such cover letters in the hope that we would improve response rates to our postal quality of life questionnaires. Methods One hundred forty-eight participants in the CODIFI2 diabetic foot ulcer sampling trial were randomised 1:1 to receive one of two different cover letters at follow-up timepoints: either a standard cover letter accompanying their postal follow-up questionnaires or to an ‘enhanced’ (theory-informed) cover letter. Questionnaires were mailed at 39, 52 and (for some participants) 104 weeks post randomisation. Outcome measures were response to mailing at each timepoint. Analysis was restricted to those for whom a questionnaire and letter was issued. Owing to limited recruitment, a reduced analysis plan, comprising solely observed response rates and 95% confidence intervals for difference in response rates was followed. Post hoc, we added our week 52 results to an already-published meta-analysis. Results Sixty-seven out of 74 enhanced cover letter group (Enhanced) and 67/74 standard cover letter group (Standard) participants who had not already died or withdrawn were sent their first mailing at 39 weeks. The 39-week response rates were 47/67 (70.1%) and 39/67 (58.2%) for Enhanced and Standard participants, respectively. At week 52, the response rates were 45/64 (70.3%) and 35/63 (55.6%) for Enhanced and Standard participants, respectively. At week 104, the response rates were 24/33 (72.7%) and 19/33 (57.6%) for the Enhanced and Standard participants, respectively. Adding our week 52 results to a published meta-analysis increased the pooled estimate of differences in response rates to 0.04 (− 0.01 to 0.09) favouring enhanced letters. Conclusion While this embedded randomised controlled trial observed greater response rates at all times among those randomised to the enhanced letter, the reduced sample size meant that these results are imprecise. Trial registration ISRCTN registry ISRCTN74929588. Registered on 5 March 2019.

Abstract Water conservation (WC) is a critical ecological service function in the Yellow River Basin (YRB). There is currently a lack of detailed exploration of WC development processes and the impact mechanisms of driving factors at spatiotemporal scales in the YRB. By collecting data on DEM, land use, soil, meteorology, reservoirs, and observed discharge, this study established a large-scale WC model using the soil and water assessment tool (SWAT). The abrupt change test, empirical orthogonal function (EOF), wavelet analysis, hierarchical partitioning analysis (HPA), geodetectors, and aridity index were employed to analyze the multi-spatiotemporal characteristics and driving forces of WC calculated using the water balance method. The results are as follows: (1) The average WC among the YRB was 9.11 mm (74.68 × 108 m3) from 1960 to 2020. Pasture and forests contributed to 48.65% and 22.05% of the average annual WC, respectively. (2) WC exhibited four forms: less/more in the YRB, more in the southeast (northwest), and less in the northwest (southeast). (3) Forests and pastures in land use had higher average WC capacity, while Gansu, Shaanxi, and Qinghai ranked in the top three for average WC among the nine provinces. (4) Precipitation was the major driving force affecting WC variations, with the interaction between precipitation and actual evapotranspiration being the most significant. (5) Drought was a significant cause of negative WC. Protecting and managing crucial WC areas was essential for improving the ecological environment. This research elucidates the driving forces of WC in the YRB, providing scientific support for improving regional WC and promoting sustainable development.

【Objective】 The Fuhe River Basin is an important food production region in Northwestern China. It is increasingly affected by droughts. This paper analyses the occurrence of droughts and their key determinants in this region. 【Method】 The analysis was based on modelling. We simulated the changes in monthly soil water content from 1962 to 2019 using the SWAT model. The resulting data were then used to calculate the standardized soil moisture index. The characteristics of agricultural droughts were analyzed using the theory of runs, and the key determinants of the droughts were analyzed using the geographical detector. 【Result】 ① The coefficient of determination (R2) and the Nash efficiency coefficient (ENS) between the modelled and measured results in the calibration are both higher than 0.80; the coefficient of determination between the predicted soil moisture content and the measured soil moisture content is 0.52. ② The duration of droughts in different regions in the basin varied from 3.60 to 4.31 months, and the drought intensity ranged from 4.91 to 5.61, both varying spatially, higher in the North and South and lower in the center. ③ The influence of temperature, solar radiation, and wind speed on spatial variation in the droughts, characterized by the q-value, exceeds 0.30. Other environmental factors are nonlinearly interacted in their influence on the droughts. 【Conclusion】 The standardized soil moisture index can quantitatively characterize droughts in the Fuhe River Basin. Droughts in the basin vary spatially, with temperature, solar radiation and wind speed being the main contributing factors.

Abstract Although the Carpathian Mts. area is considered as extremely prone to surface erosion which results in capacity loss of the dammed reservoirs, a lack of data to follow details of this process is perceivable. The research of the selected sediment fractions transport tracking was conducted using the capabilities of the digital platform—Macromodel DNS (Discharge‐Nutrient‐Sea) for the catchment with drinking water reservoir in the Polish part of Western Carpathian. The continuity of sediment transport simulation in two hydrologically different elements of the catchment—the river and the reservoir—was possible due to consolidation of two models in the platform—SWAT (Soil & Water Assessment Tool) and AdH/PTM (Adaptive Hydraulics Model/Particle Tracking Model). The result of those modules' integration was a database for tracking the individual sediment fractions delivered to the reservoir and deposited in specific reservoir zones. The implementation of climate and land use change scenarios allowed additionally to analyse the estimation of those processes in the future. The simulation outcomes consist of daily flows and monthly sediment loads at the reservoir inflow and the individual sediment particle fractions deposition location inside of the reservoir.

Cap‐Haïtien, the second largest city in Haiti, is highly vulnerable to earthquakes, landslides, and flooding. The rapid pace of urbanization and deforestation has exacerbated the risk of flooding, resulting in disasters in November 2012, 2016, and 2022. This study aims to assess the impact of urbanization and deforestation on river flooding in Cap‐Haïtien by applying the hydrological model Soil Water Assessment Tool (SWAT) and the hydrodynamic model Sobek‐Rural. We examined the current situation and a scenario of future urbanization and deforestation. Urbanization and deforestation are found to play a pivotal role in the production and deposition of sediment along the lower Haut‐du‐Cap River reaches. The existing hydraulic capacity of the river and its drainage system cannot handle the estimated peak flows. The mountain ravines west of the city are found to be the primary source of sediment‐laden flash floods. We recommend retention basins, drainage extensions, and pragmatic public policies to mitigate flood risk. Comprehensive strategies are needed to address the detrimental effects of urbanization and deforestation on flooding in Cap‐Haïtien and similar regions where a lack of water governance has worsened the flooding alongside urbanization and deforestation. We generalize our experiences from Cap‐Haïtien into a broader framework for data‐scarce areas. [ABSTRACT FROM AUTHOR]

Jordan is severely affected by climate change, it suffers from significance fluctuation and decrease in the amounts of the annual precipitation basically during the last decade which had dire consequences for farmers and the provision of fresh water. In this study, the impact of climate change on the Al-Wala basin was analyzed during the period 2013 to 2024 using Geomatics techniques, Google Earth Engine (GEE) and machine learning codes. Soil and Water Assessment Tool (SWAT) model was used to simulate the hydrological process up to year 2064. Moreover, the Meteorological Research Institute Earth System Model (MRI-ESM2-0) was used to predict the change of water surface area of the Al-Wala dam lake in the future. Annual satellite images: Lanadsat and sentinel, covering the period of the study area were downloaded and enhanced. They permit to provide the necessary information to carry out this study. As result, an important fluctuation of the amount of annual rainfall quantity was observed as well as, the amounts of annual rainfall expected to increase and decrease wobbly for several years in the future. Overall the average annual runoff will increase by 10% compared to the baseline scenario. The minimum temperature is expected to be higher than their rates throughout the year by 0.09°- 0.11o C, this will increase the evaporation rates with about 0.03%. The analysis of the sensitivity using the SWAT model was identified by 6 parameters out of 17. The regression coefficient (R2), Nash and Sutcliffe efficiency (NSE), on monthly basis, were above 0.60 for both of them which indicates satisfactory model results. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study was to evaluate fish habitat suitability by simulating hydrodynamic and water quality factors using SWAT and HEC-RAS linked simulation considering time-series analysis. A 2.9 km reach of the Bokha stream was selected for the habitat evaluation of Zacco platypus, with hydrodynamic and water quality simulations performed using the SWAT and HEC-RAS linked approach. Based on simulated 10-year data, the aquatic habitat was assessed using the weighted usable area (WUA), and minimum ecological streamflow was proposed from continuous above threshold (CAT) analysis. High water temperature was identified as the most influential habitat indicator, with its impact being particularly pronounced in shallow streamflow areas during hot summer seasons. The time-series analysis identified a 28% threshold of WUA/WUAmax, equivalent to a streamflow of 0.48 m3/s, as the minimum ecological streamflow necessary to mitigate the impact of rising water temperatures. The proposed habitat modeling method, linking watershed-stream models, could serve as a useful tool for ecological stream management.

Abstract Water-scarce regions, like the Mediterranean, face worsening conditions due to climate change, intensifying pressure on key economic sectors such as hydropower. Additionally, environmental conservation policies, particularly the implementation of environmental flows, present challenges for hydropower systems. Certainty regarding the impact of these factors on future hydropower production is crucial for informed decision-making in the transition to sustainable energy. This study introduces S + HydPower, a tool coupled with SWAT+ to assess climate change and watershed management effects on small hydropower plant (SHP) systems. In this study, we used this tool to investigate the consequences of implementing environmental flows and climate change on run-of-river SHPs in the Catalan River Basin District (CRBD), in Catalonia. The results show that applying environmental flows would lead to a significant 27% reduction in SHP production. However, this reduction would represent only 0.25% of the region’s current energy demand. Furthermore, the study reveals a potential 38% to 73% reduction in SHP production by the end of the twenty-first century due to the combined effects of environmental flows and climate change. This suggests a substantial decline in run-of-river SHP’s contribution to the CRBD’s electricity supply. These findings emphasize the need to explore alternative and sustainable energy sources to ensure the long-term reliability and resilience of the region’s energy supply.

The consequences of human interventions on land use have been empirically demonstrated to affect substantially hydrological processes and ecosystem services within watershed environments. Since 1970, Malaysia has faced deforestation, driven mainly by logging and agricultural expansion, aligning with its developmental goals by 2020. From 1970 to 2000, deforestation led to a 25.5% decline in forested land, causing a significant 10.2% rise in excess runoff. Moreover from 2001 to 2021, the nation lost 17% of its total land to deforestation. These trends emphasize the need for a thorough investigation of sustainable conservation efforts in Malaysia. This study focuses on the Langat basin in Malaysia, evaluating past and future land use changes and their effects on the basin’s hydrological response. The study employed key informant reports, population growth data, observed land use change, field survey and agricultural land availability considered for developing change scenarios. We emphasized the significance of integrating diverse modeling methods to analyze LULC changes effectively. The use of a semi-distributed hydrological model, SWAT, in combination with Markov chain and Multi-Layer Perceptron (MLP) model and Geographic Information Systems (GIS) techniques proved to be an integrated and suitable tool for comprehensive change analysis and modeling of land use. Markov chain modeling is valuable for predicting land use changes over time, providing input scenarios for SWAT simulations. MLP is a powerful algorithm to capture non-linear relationships and complex patterns in the data, enhancing the modeling accuracy. The simulation results, based on historic land use data (1984-2006-2010) and projected future land use maps (2030-2050-2080), revealed a consistent pattern of urban expansion and deforestation leading to increased streamflow. Projections indicated a substantial rise in streamflow by 20%, 61%, and 71% for the 2030s, 2050s, and 2080s, respectively. To mitigate potential flood and sediment loss, it is crucial to involve local stakeholders such as local communities, government bodies, environmental organizations, and businesses. Such analysis facilitates understanding their perspectives and concerns regarding afforestation and urban expansion control, informing future development programs and land use planning effectively.

According to many authors, hydrological modelling is one of the fundamental techniques for determining the impacts of various environmental changes on the quantitative characteristics of the aquatic environment. This study examines the effects of landscape changes induced by the need to respond to climate change and the natural urban development of the rural setting on the discharge from the small catchment. The SWAT model was applied to a small catchment of Kopaninsky Stream in the traditional agricultural part of the Czech Republic. According to the results, the most effective approach for reducing the consequences of hydrological extremes in the catchment and retaining more water in the landscape is the properly spaced placement of grassland and forest enclaves in the developing urban structure. In practice, each land use category’s overall percentage of representation is less significant than its relative arrangement.