Search Results (28)

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

Surface runoff and subsurface flow patterns were monitored in hillside runoff plots in almond and olive orchards with soils covered with spontaneous plants over two hydrological years. The experimental runoff plots were located on the south flank of the Sierra Nevada (Lanjarón, SE Spain) at 580 m a.s.l. with an area of 40 m² (10 m × 4 m). The surface and subsurface discharge were collected and measured at different soil depths (0, 5, 10, 25, and 50 cm), and the dissolved nutrient concentrations (NO₃–N, NH₄–N, PO₄–P, and K) were determined. According to the findings, the subsurface flow pathways drained most of the rainfall water compared with surface runoff, which was affected by plant cover. The influence of rainfall intensity (I₃₀) on surface runoff was more meaningful than that on subsurface flow. Throughout the monitoring period, the runoff coefficients at soil depths of 0, 5, 10, 25, and 50 cm averaged 0.04, 0.11, 0.14, 0.17, and 0.18, respectively. Subsurface flow was one of the dominant pathways for N and K loss, whereas P loss mainly occurred via surface runoff. Moreover, the concentrations in subsurface flow were higher than the recommended level for standard water quality for NO₃–N, NH₄–N, and PO₄–P. Subsurface flow was the main route of dissolved nutrient delivery, making these nutrients available to the root systems of trees, where nutrient uptake is more likely to occur. Thus, by lessening surface runoff and encouraging surface vegetation coverage to facilitate the recycling of nutrients and buffer the rainfall’s impact on the soil surface, nutrient loss control can be achieved. Full article

To study the characteristics of nitrogen (N) loss on slopes, different vegetation (bare soil, alfalfa), slopes (5°, 10°, 15°), and rainfall intensities (40, 60, 80 mm/h) were set as variable factors in simulated rainfall experiments. Surface runoff accounts for 60.38–96.16% of total runoff and most N loss (57.69–88.67% of NO₃⁻-N). Alfalfa can reduce average concentrations of N loss in runoff and reduce N loss in surface runoff by more than 48.29%, as well as subsurface runoff by 3.8%. Average N loss in subsurface runoff exceeds that of surface runoff. Rainfall intensity most affects N loss from surface runoff in bare soil conditions, and slope most affects N loss in subsurface runoff. Rainfall intensity in alfalfa treatments most influences runoff volume and N loss. The comprehensive effects of rainfall intensity, slope, and vegetation cover on the total loss of various forms of nitrogen in surface runoff can be described using a linear correlation equation, with a correlation coefficient between 0.84 and 0.91. Full article

Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics and nitrogen migration paths of surface and subsurface runoff under different rainfall intensities and slope gradients. The initial runoff generation time of subsurface runoff lagged that of surface runoff, and the lag time under different rainfall intensity and slope conditions ranges from 3.97 to 12.62 min. Surface runoff rate increased with increasing rainfall intensity and slope gradient; compared with a rainfall intensity of 40 mm/h, at a slope of 15°, average surface runoff rate at 60 and 80 mm/h increased by 2.38 and 3.60 times, respectively. Meanwhile, the subsurface runoff rate trended upwards with increasing rainfall intensity, in the order 5 > 15 > 10°. It initially increased and then decreased with increasing slope gradient, in the order 5 > 10 > 15°. Total nitrogen (TN) loss concentration of surface runoff shows a decrease followed by a stabilization trend; the concentration of TN loss decreases with decreasing rainfall intensity, and the stabilization time becomes earlier and is most obvious in 5° slope conditions. TN loss concentration in subsurface runoff decreased with increasing rainfall intensity, i.e., 40 > 60 > 80 mm/h. The surface runoff rainfall coefficient was mainly affected by rainfall intensity, a correlation between αs and slope gradients S was not obvious, and the fitting effect was poor. The subsurface runoff rainfall coefficient was mainly affected by slope gradient, the R² of all rainfall intensities was <0.60, and the fitting effect was poor. The main runoff loss pathway from the Miyun Reservoir slopes was surface runoff, which was more than 62.57%. At the same time, nitrogen loss was subsurface runoff, more than 51.14%. The proportion of surface runoff to total runoff increases with the increase of rainfall intensity and slope, with a minimum of 62.57%, and the proportion of nitrogen loss from subsurface runoff also decreases with increasing rainfall intensity but does not change with slope gradient. The order of different runoff modulus types was mixed runoff (surface and subsurface runoff occur simultaneously) > surface runoff > subsurface runoff. The surface and mixed runoff modulus increased significantly with increasing rain intensity under different rain intensities and slope gradients. Overall, rainfall intensity significantly affected slope surface runoff, and slope gradient significantly affected nitrogen loss. Full article

Hydrology and meteorological data from relatively undisturbed watersheds aid in identifying effects on ecosystem services, tracking hydroclimatic trends, and reducing model uncertainties. Sustainable forest, water, and infrastructure management depends on assessing the impacts of extreme events and land use change on flooding, droughts, and biogeochemical processes. For example, global climate models predict more frequent high-intensity storms and longer dry periods for the southeastern USA. We summarized 17 years (2005–2021) of hydrometeorological data recorded in the 52 km², third-order Turkey Creek watershed at the Santee Experimental Forest (SEF), Southeastern Coastal Plain, USA. This is a non-tidal headwater system of the Charleston Harbor estuary. The study period included a wide range of weather conditions; annual precipitation (P) and potential evapotranspiration (PET) ranged from 994 mm and 1212 mm in 2007 to 2243 mm and 1063 in 2015, respectively. The annual runoff coefficient (ROC) varied from 0.09 in 2007 (with water table (WT) as deep as 2.4 m below surface) to 0.52 in 2015 (with frequently ponded WT conditions), with an average of 0.22. Although the average P (1470 mm) was 11% higher than the historic 1964–1976 average (1320 mm), no significant (α= 0.05) trend was found in the annual P (p = 0.11), ROC (p = 0.17) or runoff (p = 0.27). Runoff occurred on 76.4% of all days in the study period, exceeding 20 mm/day for 1.25% of all days, mostly due to intense storms in the summer and lower ET demand in the winter. No-flow conditions were common during most of the summer growing season. WT recharge occurred during water-surplus conditions, and storm-event base flow contributed 23–47% of the total runoff as estimated using a hydrograph separation method. Storm-event peak discharge in the Turkey Creek was dominated by shallow subsurface runoff and was correlated with 48 h precipitation totals. Estimated precipitation intensity–duration–frequency and flood frequency relationships were found to be larger than those found by NOAA for the 1893–2002 period (for durations ≥ 3 h), and by USGS regional frequencies (for ≥10-year return intervals), respectively, for the same location. We recommend an integrated analysis of these data together with available water quality data to (1) assess the impacts of rising tides on the hydroperiod and biogeochemical processes in riparian forests of the estuary headwaters, (2) validate rainfall–runoff models including watershed scale models to assess land use and climate change on hydrology and water quality, and (3) inform watershed restoration goals, strategies, and infrastructure design in coastal watersheds. Full article

The pressure of nutrient pollution derived from wastewater treatment works and agricultural runoff is a reason for the decline in the ecological health of aquatic habitats. Projected residential development in catchments creates further nutrient loading that can be offset by nutrient management solutions that maintain ‘nutrient neutrality’ either onsite or elsewhere within the same catchment. This study developed an export coefficient model in conjunction with detailed farm business data to explore a nature-based solution to nutrient neutrality involving seven scenarios of crop conversion to mixed woodland or grazing grass in an area of intensive arable cultivation in the groundwater-fed Blackwater sub-catchment of the River Wensum, UK. When compared with the monitored riverine export of nutrients, the calculated nitrogen (N) and phosphorus (P) inputs under current land use showed that subsurface denitrification is removing 48–78% of the leached N and that P is accumulating in the field soils. The addition of 235 residential homes planned for 2018–2038 in the Blackwater will generate an additional nutrient load of 190 kg N a⁻¹ and 4.9 kg P a⁻¹. In six of the seven scenarios, the modelled fractions of crop conversion (0.02–0.21) resulted in the required reduction in P loading and more than sufficient reduction in N loading (196–1874 kg a⁻¹ for mixed woodland and 287–2103 kg a⁻¹ for grazing grass), with the additional reduction in N load above the requirement for nutrient neutrality potentially contributing to further improvement in water quality. The cost of land conversion is modelled in terms of crop gross margins and nutrient credits generated in the form of 0.1 kg units of N or P. For the range of scenarios considered, the annual cost per credit ranged from GBP 0.78–11.50 for N for mixed woodland (GBP 0.74–7.85 for N for grazing grass) and from GBP 160–782 for P for both scenarios. It is concluded that crop conversion is a viable option to achieve nutrient neutrality in arable catchments in eastern England when considered together with other nutrient management solutions. Full article

The determination of groundwater recharge (GWR) with an appropriate method is among the main subjects of groundwater resource management. This research is an event-scale quantitative assessment and a development of a water balance model of an aquifer response resulting from flash flooding and infiltration from the ephemeral streambed of some representative basins in Saudi Arabia. The methodology employed in the water balance analysis of this study starts with surface runoff rather than rainfall as in the literature, since the recharge process mainly occurs from runoff on the ground surface. A conceptual model of the local surface–subsurface interaction is developed to estimate the effective aquifer response (AR). The annual cumulative groundwater recharge (GWR) depths vary from 1.22 m to 6.97 m, with an average rise of 4.24 m at the studied wells; most of this happened following the May and April flash flood events. The results show also similar profile patterns and timings for most of the water level rises and falls. An average root mean square error (RMSE) of 0.46 m is obtained from the simulations and the coefficient of determination (R²) values range from 0.57 to 0.99. The results verified that the daily simulated groundwater elevations have shown very good agreement with the observed daily groundwater elevations over a time span of one year. In this work, the key issue to interpret the GWR depth from individual well samples is whether the model on the samples fits the measured groundwater level within the experimental runoff reaches (ERRs). The time steps (Δt) of the GWR simulation process are significantly affected by the high variability in the aquifer characteristics, as shown for the hydraulic conductivity values ranging from 5.3 m/day to 438 m/day within ERRs. This study highly recommends that groundwater recharge studies be site-specific. Otherwise, the extrapolation of the results even to adjacent basins is questionable. Full article

Subsurface perforated pipes drain infiltrated stormwater runoff while attenuating the peak flow. The Manning roughness coefficient (n) was identified as a fundamental parameter for estimating roughness in various subsurface channels. Hence, in this work, the performance of a six-row non-staggered sand-slot perforated pipe as a sample of the subsurface drainage is investigated experimentally in a laboratory flume at Universiti Sains Malaysia (USM) aimed at determining the Manning roughness coefficients (n) of the pipe and assessing the relationship between the Manning’s n and the hydraulic parameters of the simulated runoff flow under the conditions of the tailgate channel being opened fully (GFO) and partially (GPO), as well as the pipe having longitudinal slopes of 1:750 and 1:1000. Water is pumped into the flume at a maximum discharge rate of 35 L/s, and the velocity and depth of the flow are measured at nine points along the inner parts of the pipe. Based on the calculated Reynolds numbers ranging from 38,252 to 64,801 for both GFO and GPO conditions, it is determined that most of the flow in the perforated pipe is turbulent, and the calculated flow discharges and velocities from the outlets under GFO are higher than the flow and velocity rates under GPO with similar pipe slopes of 1:750 and 1:1000. The Manning coefficients are calculated at nine points along the pipe and range from 0.004 to 0.009. Based on the ranges of the calculated Manning’s n, an inverse linear relationships between the Manning coefficients and the flow velocity under GFO and GPO conditions are observed with the R² of 0.975 and 0.966, as well as 0.819 and 0.992 resulting from predicting the values of flow velocities with the equations v = ((0.01440 − n)/0.009175), v = ((0.01330 − n)/0.00890), v = ((0.02007 − n)/0.01814), and v = ((0.01702 − n)/0.01456) with pipe slopes of 1:750 and 1:1000, respectively. It is concluded that since the roughness coefficient (Manning’s n) of the pipe increases, it is able to reduce the flow velocity in the pipe, resulting in a lower peak of flow and the ability to control the quantity of storm water in the subsurface urban drainages. Full article

Groundwater-surface water interactions play a crucial role in hydrologic cycles, especially in arid and semi-arid basins. There is a growing interest in developing integrated hydrologic models to describe groundwater-surface water interactions and the associated processes. In this study, an integrated process-based hydrologic model, ParFlow, was tested and utilized to quantify the hydrologic responses, such as changes in surface runoff and surface/subsurface storage. We progressively conducted a complexity-increasing series of benchmarking cases to assess the performance of ParFlow in simulating overland flow and integrated groundwater-surface water exchange. Meanwhile, the overall performance and the computational efficiency were quantitatively assessed using modified Taylor diagrams. Based on the benchmarking cases, two case studies in the Heihe River Basin were performed for further validation and to diagnose the hydrologic responses under disturbance, named the Bajajihu (BJH) and Dayekou (DYK) cases, respectively. Both cases were 2D transects configured with in-situ measurements in the mid- and downstream of the Heihe River Basin. In the BJH case, simulated soil moisture by ParFlow was shown to be comparable with in-situ observations in general, with Pearson’s correlation coefficient (R) > 0.93 and root mean square difference (RMSD) < 0.007. In the DYK case, seven scenarios driven by remote sensing and reanalysis data were utilized to study hydrological responses influenced by natural physical processes (i.e., precipitation) and groundwater exploitations (i.e., pumping) that are critical to surface and subsurface storage. Results show that subsurface storage is sensitive to groundwater exploitation before an obvious stationary point. Moreover, a correlation analysis was additionally provided demonstrating the impacts of different factors on subsurface storage timeseries. It was found that pumping influences subsurface storage remarkably, especially under short-term but large-volume pumping rates. The study is expected to provide a powerful tool and insightful guidance in understanding hydrological processes’ effects in arid and semi-arid basins. Full article

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R²), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably. Full article

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions. Full article

Accurately converting nonpoint source pollution loads from the watershed scale to administrative scale is challenging. A promising solution is calculating the pass-through rate coefficient of nonpoint source pollutants (PTRE–NPS) at the watershed scale and discretizing the watershed units on grids with the same area but with different PTRE–NPS information. However, the pollution load of agricultural nonpoint sources has received far more attention than the PTRE–NPS. Moreover, as most of the existing PTRE–NPS results are obtained by distributed, semi-distributed models and the field monitoring of small watersheds, they are not easily extended to the national-scale management of nonpoint source pollution. The present study proposes a new conception of PTRE-NPS and tests it on different spatial scales by a coupled model, which captures the entry of agricultural nonpoint source pollutants into rivers and lakes. The framework includes five major modules: a pollutant driving and loss module, a surface runoff module, a soil erosion module, a subsurface runoff module, and a retention module. The model was applied in simulations of agricultural nonpoint source pollution in the Hongfenghu reservoir watershed with a karst hydro-geomorphology, which exists in the mountainous region of southwest China. On the watershed scale, the PTRE–NPS of total nitrogen (TN) and total phosphorous (TP) ranged from 0 to 2.62 (average = 0.18) and from 0 to 3.44 (average = 0.19), respectively. On the administrative scale, the PTRE–NPS of TN and TP were highest in Baiyun Town. The TN and TP loads of the agricultural nonpoint source pollution in the rivers and lakes of the Hongfenghu reservoir watershed were 1707.78 and 209.03 t, respectively, with relative errors of −45.36% and 13.07%, respectively. Most importantly, the developed framework can scientifically represent the generation–migration–transmission process of agricultural nonpoint source pollutions in each grid at both the watershed and administrative scales. Full article

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km², which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems. Full article

Green roofs (GRs) are considered sustainable solutions for the adaptation of urban water management to climate change. The use of GRs is particularly promising in urban environments like the Metropolitan Area of Milan, the most urbanized area in Italy. In this work, we evaluated the subsurface runoff coefficient at the event-time scale, for more than one year of observations, of 68 small-scale test beds comprising different configurations of green roofs (e.g., different vegetations, types and depths of growing media, and different slopes) installed in the Metropolitan Area of Milan. The objectives of this study are three-fold. Firstly, the controlling factors of the hydraulic have been assessed for efficiency. We calculated a mean drainage flow rate of 51%, finding that growing media play a significant role in determining the drainage flow during the spring, at the beginning of the vegetative period. During this season, water retention in fertilized beds increases significantly. At the beginning of the summer, the vegetation cover is able to significantly reduce the drainage flow, playing an even more crucial role with respect to the growing medium material. However, we found that the vegetation type (grass field and Sedum) does not play a significant role in the retention processes. Secondly, the delay of the peak flow rate was determined. We found a precipitation peak delay from 1 to 2 h, which would be sufficient to guarantee environmental benefits for urban drainage. Finally, the factors controlling the hydraulic efficiency of GRs for individual precipitation events were assessed. We found that soil moisture and cumulated precipitation are both significant factors determining the drainage flow rate. In conclusion, we point out that soil moisture is one of the main parameters characterizing GR drainage and should be further considered in future research efforts devoted to the analysis of GR performance. Full article

The transport of agricultural nonpoint source (NPS) pollutants in water pathways is affected by various factors such as precipitation, terrain, soil erosion, surface and subsurface flows, soil texture, land management, and vegetation coverage. In this study, based on the transmission mechanism of NPS pollutants, we constructed a five-factor model for predicting the path-through rate of NPS pollutants. The five indices of the hydrological processes, namely the precipitation index (α), terrain index (β), runoff index (TI), subsurface runoff index (LI), and buffer strip retention index (RI), are integrated with the pollution source data, including the rural living, livestock and farmland data, obtained from the national pollution source census. The proposed model was applied to the headwater of the Miyun Reservoir watershed for identifying the areas with high path-through rates of agricultural NPS pollutants. The results demonstrated the following. (1) The simulation accuracy of the model is acceptable in mesoscale watersheds. The total nitrogen (TN) and total phosphorus (TP) agriculture loads were determined as 705.11 t and 3.16 t in 2014, with the relative errors of the simulations being 19.62% and 24.45%, respectively. (2) From the spatial distribution of the agricultural NPS, the TN and TP resource loads were mainly distributed among the upstream of Dage and downstream of Taishitun, as well as the towns of Bakshiying and Gaoling. The major source of TN was found to be farmland, accounting for 47.6%, followed by livestock, accounting for 37.4%. However, the path-through rates of TP were different from those of TN; rural living was the main TP source (65%). (3) The path-through rates of agricultural NPS were the highest for the towns of Wudaoying, Dage, Tuchengzi, Anchungoumen, and Huodoushan, where the path-through rate of TN ranged from 0.17 to 0.26. As for TP, it was highest in Wudaoying, Kulongshan, Dage, and Tuchengzi, with values ranging from 0.012 to 0.019. (4) A comprehensive analysis of the distribution of the NPS pollution load and the path-through rate revealed the towns of Dage, Wudaoying, and Tuchengzi as the critical source areas of agricultural NPS pollutants. Therefore, these towns should be seriously considered for effective watershed management. In addition, compared with field monitoring, the export coefficient model, and the physical-based model, the proposed five-factor model, which is based on the path-through rate and the mechanism of agricultural NPS pollutant transfer, cannot only obtain the spatial distribution characteristics of the path-through rate on a field scale but also be applicable to large-scale watersheds for estimating the path-through rates of NPS pollutants. Full article