Watershed Model Research Articles

Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.

Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT

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.

IRainSnowHydro v1.0: A distributed integrated rainfall-runoff and snowmelt-runoff simulation model for alpine watersheds

Snowmelt runoff is an essential runoff component in alpine watersheds. On the Tibetan Plateau, the complex hydrometeorological and underlying surface conditions make a single runoff generation mode (either snowmelt-runoff or rainfall-runoff) cannot accurately simulate the runoff process. In this study, we developed a new method that combines the curve number, topographic index, and fractional snow cover to identify the sub-basin seasonal dominant runoff generation mode within the Jinsha River Basin. By constructing a surface ‘snow reservoir’ to depict snow melting impact on runoff generation, and quantitatively classifying the precipitation composition, an innovative integrated hydrological model named the distributed integrated Rainfall-runoff and Snowmelt-runoff simulation Hydrological model (iRainSnowHydro) is developed. With model, a method for identifying the seasonal varying dominant runoff generation mode is proposed. The results show that most sub-basins experience both snowmelt and rainfall driven runoff generation in spring, with snowmelt occurring earlier in regions of lower latitude and elevation. Besides, iRainSnowHydro performs well in daily runoff simulations at Zhimenda and Shigu stations with Nash coefficients of 0.81 and 0.85 in the calibration period, and 0.72 and 0.81 in the validation period. The correlation coefficient ranges from 0.92 to 0.96. Additionally, calculation through iRainSnowHydro indicates a noteworthy percentage of spring snowmelt water. Notably, the Zhimenda watershed, characterized by higher latitudes and elevations, displays an escalating trend from 56.6 % to 78.9 % of total precipitation for spring snowmelt water between 2014 and 2020, while the Shigu watershed maintains stable within 27 % ± 6 %. The methodologies outlined bear significance for simulating and predicting runoff in alpine watersheds and offers valuable insights into how snow cover responds to climate change on the Tibetan Plateau.

Modelling the Effects of Forest use Change on Brownification of Finnish Rivers under Atmospheric Pressure.

Browning of surface waters due to increased terrestrial loading of dissolved organic matter (DOM) is observed across the Northern Hemisphere. The effects influence several ecosystem services from freshwater productivity to water purification. Brownification is often explained by changes in large-scale anthropogenic pressures and ecosystem functioning (acidification, climate change, and land cover changes). This study examined the effect of forest use changes on water browning in Finland, considering the effects of global pressures. Our goal was to find the ecosystems and geographic areas that are most sensitive to environmental pressures that increase the loading of DOM. We were also looking for land use strategies that decrease browning. We combined mathematical watershed modelling to scenarios of climate change, atmospheric deposition, and forest use change. Changes included scenarios of forest harvest and protection on forest, that were derived from European Union's regulation. The study area covered 20 watersheds from south to north of Finland. In northern Finland brownification continue. In southern Finland global influence (atmospheric deposition, climate change) seem to weaken, giving more space for local forest use change having an influence on brownification. Forest use change was more influential in river basins dominated by organic soils than in mineral soils. Extending forest protection decreased brownification especially in areas where the influence of atmospheric pressure is decreasing. When forest protection is planned to provide a carbon storage and sequestration potential and to favor biodiversity, it has favorable effect on surface water quality as well.

Ecological Compensation Based on the Ecosystem Service Value: A Case Study of the Xin’an River Basin in China

To establish a sound ecological compensation (EC) mechanism in the Xin’an River Basin, this study suggested utilizing ecosystem service valuation to determine the compensation amount. In this study, the first step was to establish a reasonable watershed EC model using the ecological compensation supply coefficient (ECSC) based on the value spillover theory (VST) of the ecosystem services and the ecological compensation demand coefficient (ECDC). The second step was to classify the ecosystem services of the Xin’an River Basin into three categories, including supply service, regulating service, and cultural service, with 14 specific functions to determine the ecological compensation standard accounting scope in these services. Then, a case study on the Xin’an River Basin for EC standards was presented. The total ecosystem service value (ESV) in the Xin’an River Basin was estimated to be CNY 70.271 billion, with supply service accounting for 22.7%, regulating service accounting for 24.6%, and cultural service accounting for 52.7%. Based on the compensation scope, the ecosystem service values for the upper and lower limits of the EC were calculated as CNY 57.779 billion and CNY 17.292 billion. Combined with the results of the ECSC and ECDC, the upper and lower limits of the EC standard in the Xin’an River Basin were computed to be CNY 4.085 billion and CNY 1.438 billion, respectively. Therefore, the ESV-based EC model for the Xin’an River Basin can effectively address the challenge of inadequate EC in the watershed. It also facilitates balanced regional development and serves as a theoretical foundation and empirical evidence for the government to establish a unified national policy on cross-border river basin ecological compensation.

Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah and Wai Ruhu

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

Mutual impact of salinity and climate change on crop production water footprint in a semi-arid agricultural watershed: Application of SWAT-MODFLOW-Salt

Sustainable agriculture in intensively irrigated watersheds, particularly those in arid and semi-arid regions, requires enhanced management practices to maintain crop production, which depends on climate, available water resources, soil conditions, irrigation practices, and crop type. Among these factors, soil salinity and climate change are significant challenges to agricultural productivity. To investigate the long-term influence of salinity and climate change on crop production from 1999 to 2100 in irrigated semi-arid regions, we apply the water footprint (WF) concept using the hydro-chemical watershed model SWAT-MODFLOW-Salt, driven by five General Circulation Models (GCMs) and two climate scenarios (RCP4.5 and RCP8.5), to a 732 km2 irrigated stream-aquifer system within the Lower Arkansas River Valley (LARV), Colorado, USA. The study focused on calculating the green (WFgreen), blue (WFblue), and total (WFtotal) crop production WFs for 29 crops in the region, with and without including salinity effect on crop yield. Results reveal that during the baseline period (1999–2009), the total annual average WFgreen, WFblue, and WFtotal increased by 7.6 %, 4.4 %, and 6.5 %, respectively, under salinity stress, as crops experienced reductions of up to 4.6 %, 1.6 %, and 2.3 % in green, blue, and total crop yield. The mutual impact of salinity and the worst-case climate model (IPSL_CM5A_MR) under the higher emission scenario (RCP8.5) led to a 3.3 %, 1.9 %, and 3 % increase in green, blue, and total crop production WFs. Furthermore, the study highlighted that the proportion of green, blue, and total crop production WFs in the LARV exceeded the world average. This discrepancy was attributed to various factors, including different spatial and temporal crop distribution, irrigation practices, soil types, and climate conditions. Notably, salinity stress affected green crop yield and green WF more significantly than blue crop yield and blue WF across all GCM models. This finding underscores the importance of prioritizing management practices to control salinity-associated challenges within the region.

Watershed Modelling, Using the Geographical Information System (GIS) Approach (Federal University of Technology Owerri, Imo State Nigeria as Case study)

In a typical watershed area such as the Federal University of Technology, Owerri, Nigeria, watershed modelling is essential for the sustainable management of water resources, protection of ecosystems and more. In this study, we will be creating a watershed model for FUTO, this study will also cover other aspects such as estimating the depression-less flow direction, delineating the flow accumulation, extracting realistic drainage network using the Strahler’s stream order method and we will also delineate the watershed’s boundary and identify possible outlets / pour points, using the digital elevation model (DEM) gotten from Alaska satellite facility used as a main data source in combination with the PC version of ArcGIS software (ArcMap 10.7) and then we extract the hydrologic information from the DEM in ArcGIS using Hydrology tools. The estimation of depression-less flow direction is done by filling the digital elevation model (DEM). This includes performing fill on sinks to ensure proper delineation of basins and streams. If the sinks are not filled, a derived drainage network may be discontinuous. After the DEM has been filled, then we can now move on to delineating the flow accumulation in the study area. The results shows that by using watershed function in ArcGIS for watershed flow direction and accumulation in FUTO can be determined. The results also shows the drainage network that was extracted by Strahler’s method, showing different orders of streams and it also shows the watershed boundary and pour point / outlets along the Otamiri River which the watershed drains into. This work simply shows the applicability of GIS as a tool of watershed delineation and drainage extraction.

The future of algal blooms in lakes globally is in our hands

Lakes are fundamental to society and nature, yet they are currently exposed to excessive nutrients and climate change, resulting in algal blooms. In the future, this may change, but how and where still needs more scientific attention. Here, we explore future trends in algal blooms in lakes globally for >3500 ‘representative lakes’ for the year 2050, considering the attribution of both nutrient and climate factors. We soft-coupled a process-based lake ecosystem model (PCLake+) with a watershed nutrient model (MARINA-Multi) to assess trends in algal blooms in terms of the Trophic State Index for chlorophyll-a (TSI-Chla). Globally between 2010 and 2050, we show a rising trend in algal blooms under fossil-fuelled development (TSI-Chla increase in 91 % of lakes) and a declining trend under sustainable development (TSI-Chla decrease in 63 % of lakes). These changes are significantly attributed to nutrients. While not always significant, climate change attributions point to being unfavourable for lakes in 2050, exacerbating lake water quality. Our study stresses prioritising responsible nutrient and climate management on policy agendas. This implies that the future of algal blooms in lakes is in our hands.

Designing Sustainable Drainage Systems as a Tool to Deal with Heavy Rainfall—Case Study of Urmia City, Iran

Heavy rainfall, a natural phenomenon reinforced by climate change and global warming, can cause severe social, economic, and safety impacts. Due to the impact of climate change and global warming, heavy rainfall events have become more frequent and intense in recent years, underscoring the urgent need to develop robust stormwater management systems that can prevent related social, economic, and safety issues. This is of greater importance in developing countries. The present study identified areas in Urmia City, Iran, that require stormwater management to develop a comprehensive understanding of the hydrological processes within the study area and to prevent the subsequent effects of heavy rainfall. For this purpose, a combination of the watershed modeling system (WMS) and stormwater management model (SWMM) was employed. Also, three possible scenarios that could be implemented to address the issue of water flow in the medium were proposed. Results indicated that the scenario involving the application of a vegetative swale was the most promising solution. Overall, the results of the present study offer a valuable framework for decision-makers in regions facing heavy rainfalls to effectively manage and minimize the adverse impacts of such events.

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization

We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with 184 parameters of the SWAT + watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. These test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.

Calibration guide for watershed modeling with distributed groundwater modeling: application for the SWAT+ model

ABSTRACT We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989–2018) and projected future periods (2041–2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Digital Elevation Model (DEM) from Google Earth Pro and Freely Downloadable DEMs – A Case Study

Digital Elevation Models (DEMs) play a crucial role in watershed modelling and hydrological analysis. In recent years, the availability of high-resolution satellite imagery and remote sensing data has made it easier to generate DEMs using different software and platforms. Google Earth Pro (GEP) is one such platform that offers a user-friendly interface for generating DEMs, making it a popular choice among researchers and practitioners. However, the accuracy and reliability of the DEMs generated by GEP in different regions and under different environmental conditions have not been fully investigated. The increasing development activities change the earth’s topography, which in turn makes it necessary more frequent production of DEM. As it is a costlier task urban areas are given priority. This article focuses on a new methodology for generating DEM from GEP for a small area in the Ernakulam District of Kerala State, India. DEM prepared from GEP is then compared with different DEMs like SRTM 30m, ALOS PALSAR 12.5m and Cartosat 10m as well as ground surveyed values. It is observed that DEM prepared from GEP is as precise as other freely available DEMs as well as Cartosat 10m. Hence this DEM can be used for planning micro level developmental activities for smaller areas.

Mechanisms for carbon stock driving and scenario modeling in typical mountainous watersheds of northeastern China.

Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks, integrate the MCE-CA-Markov and InVEST models to evaluate the spatiotemporal variations of carbon stocks in mid- to high-latitude alpine watersheds in China under historical and future climate scenarios. Additionally, the study employs the Geographic Detector model to explore the driving mechanisms influencing the carbon storage capacity of watershed ecosystems. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Fractional Vegetation Cover (FVC), Digital Elevation Model (DEM), and Mean Annual Temperature (MAT) had the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the watersheds, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the watershed's southwest part. Therefore, given the vulnerability of mid- to high-latitude mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid- to high-latitude watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change.

Estimating changes in streamflow attributable to wildfire in multiple watersheds using a semi‐distributed watershed model

AbstractMore than half of water supply in the western United States is sourced from forested lands that are increasingly under wildfire risk. Studies have begun to isolate the effects of wildfire on streamflow, but they have typically used coarse temporal resolutions that cannot account for the numerous, interconnected watershed processes that control the responses to rainfall events. In this study, we employed a method to isolate fine‐scale (daily) effects of fire. Wildfire effects were estimated as the difference between measured post‐fire streamflow and unburned scenarios of post‐fire streamflow simulated by a hydrologic model calibrated to pre‐fire conditions. The method was applied to track hydrologic recovery after wildfires in six burned watersheds across the western United States: North Eagle Creek, NM (2012 Little Bear Fire), Lopez Creek, CA (1985 Las Pilitas Fire), City Creek, Devil Canyon Creek, East Twin Creek, and Plunge Creek, CA (2003 Old Fire). All six watersheds experienced prolonged increases of post‐fire streamflow, with the most consistent changes occurring during periods of low streamflow. Following 6 years of increased streamflow, Lopez Creek experienced 6 years of reduced streamflow before returning to the pre‐fire hydrologic regime. North Eagle Creek and the four watersheds affected by the Old Fire continued to have elevated streamflow 9 and 18 years post‐fire, respectively, without returning to the pre‐fire hydrologic regime.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

Enhancing regional flood frequency analysis by integrating site-similarity measures with watershed modeling

This paper introduces and applies a novel methodology that integrates watershed modeling with the traditional regional flood frequency analysis. The methodology introduces a novel site-similarity measure that relies on hydrologic simulations and accounts for the effect of land depressions on streamflow generation. The new measure is tested along with other traditional measures for regional flood frequency analysis in the Canadian prairies. The case study is chosen carefully to critically test the new methodology. An application of 30 combinations of the new and traditional site similarity measures is assessed for pooling 109 sites. The homogeneity of the clustered groups is evaluated, and different probability distributions are applied to describe at-site and regional annual maximum flows. The results present enhanced groups’ homogeneity when the new measure is employed due to a better representation of the hydrologic similarity between the pooled sites. Furthermore, the regionally estimated quantiles are found susceptible to the chosen site similarity measures in the pooling process, which highlights the importance of considering the proposed measure that describes a key hydrologic aspect when land depressions exist.

Provincial emission inventory of atmospheric polycyclic aromatic hydrocarbons (PAHs) in China

China, as the largest global emitter of atmospheric polycyclic aromatic hydrocarbons (PAHs), exhibits significant variation in energy structure and technological capabilities among provinces. It is vital to construct a provincial-level emission inventory of PAHs, fully considering these provincial disparities, for assessing the environmental budgets and instituting effective mitigation measures. This study constructed a dataset of provincial PAHs emission factors based on downscaling estimates by incorporating the Human Development Index (HDI). These emission factors were combined with the latest activity rate data to estimate PAHs emissions from individual provinces in China from 2007 to 2017. The national total PAHs emissions in our study were 11.8–12.3% higher than the previous emission inventory results. The validation results indicated that the inventory-based PAHs emissions were significantly correlated with the simulated emissions by an atmospheric box model and watershed model. From 2007 to 2017, nearly three-quarters of provinces declined in PAHs emissions resulting from technological advancements and environmental standard improvements, leading to a 34.4% reduction (from 118.5 to 77.8 Gg) of the national total. The industrial sector was consistently the dominant contributor (51.9–66.4%) with a growing trend in its share. This growth was dominantly contributed by less developed provinces, such as Heilongjiang, Ningxia, and Qinghai, indicating that there was a shift of industrial sectors and associated PAHs emissions from highly concentrated industrial regions to nascent industrial regions. This study provides essential data support for a comprehensive understanding of each province's emission responsibilities regarding PAHs and for assessing the potential risks that PAHs pose to human health and ecosystems in these provinces.

A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico

Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.