A collaborated framework to improve hydrologic ecosystem services management with sparse data in a semi-arid basin


 Applying various models to assess hydrologic ecosystem services (HESs) management has the potential to encourage efficient water resources allocation. However, can a single model designed on these principles be practical to carry out hydrologic ecosystem services management for all purposes? We address this question by fully discussing the advantages of the variable infiltration capacity (VIC) model, the soil and water assessment tool (SWAT), and the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The analysis is carried both qualitatively and quantitatively at the Yixunhe River basin, China, with a semi-arid climate. After integrating the advantages of each model, a collaborated framework and model selection method have been proposed and validated for optimizing the HESs management at the data sparse scenario. Our study also reveals that the VIC and SWAT model presents the better runoff reproducing ability of the hydrological cycle. Though the InVEST model has less accuracy in runoff simulation, the interannual change rate is similar to the other two models. Furthermore, the InVEST model (1.08 billion m3) has larger simulation result than the SWAT model (0.86 billion m3) for the water yield, while both models have close results for assessment of sediment losses.


INTRODUCTION
Ecosystem services have been defined as the benefits that humans derive from ecosystem communities that are formed by living and nonliving organisms that maintain the Earth's life support systems Millennium Ecosystem Assessment (MA) . As the irreplaceability of water for lives and organisms has been recognized, hydrologic ecosystem services (HESs) have received more attention among the various ecosystem services and have become a significant driver to address environmental crises, improve human well-being and achieve sustainability goals (Bai et al. ). HESs are a series of services that can be categorized into five classifications: improvement of extractive water supply, improvement of water supply, water damage mitigation, provision of water-related cultural services and water-associated supporting services (Brauman et al. ). InVEST, RIOS and SWAT models have been discussed qualitatively with regard to their practical applicability in managing HESs (Lüke & Hack ), while the InVEST, SWAT, VIC and ARIES models have been compared by their requirements about the data gaps (Vigerstol & Aukema ). To quantitatively test the utility among different models, Dennedy-Frank, who conducted a comparison between the SWAT and InVEST models at the annual scale, found that the amount of baseflow would be one of the reasons for the mismatch between these two models (Gassman et al. ; Dennedy-Frank et al. ). In general, most of the studies insist on the qualitative analysis of models' theoretical conceptualization, and prove that these models are capable of integrating local environmental protection and economic development in a manner which is efficient for making trade-offs in HESs management (Wainger et al. ). However, quantitative analysis between different models are still lacking.
When conducting the HESs management, the model specification, including data requirement, learning effort and simulation unit, would be crucial for practical applications and decision making. Addressing the advantages of different models would help users to utilize the limited available data to capture the maximum benefits of HESs.
Commonly, compared with the relatively small watershed suitability for the SWAT model (Qiao et al. ), the macro-calculation grids (1-50 km) of the VIC model could help rebuilding the hydrological cycle at territorial or national scale (Zhang et al. ), whereas the grids scale of the VIC model may not be flexible enough for interpreting the details in each administration unit, including watersheds, cities or provinces. Benefit from the calculation unit at pixel scales (30 m-10 km), the InVEST model is well-prepared to carry out practical measures for local policy makers (Gao et al. ), though the algorithm may not be sufficiently scientific for reproducing the hydrologic cycle as the VIC and SWAT model (Vigerstol & Aukema ).
Therefore, one single model cannot overcome all these inadequacies. Whether it is possible to develop a collaborated framework based on utilizing the features of each models to promote better HESs management has not been fully discussed through former studies.
Not only the characteristics of each model could affect the HESs management, but the demand for varied users would also be another critical factor. For example, scientists focus on the mechanism of hydrologic and ecological processes and how to optimize simulation accuracy to reveal more general natural principles (Gao et al. ; Hao et al. ). However, managers, including policymakers, stakeholders and engineers, are concerned that the model needs to be simply handled with clear results that would ultimately provide more intuitive choices for decision-making processes (Bagstad et al. ). Therefore, a practical model selection approach, which take the gaps in theoretical, data, scale and learning effort into consideration, would be meaningful for different users to choose HES models with their unique purpose.
The objective of this study is to propose and certify the applicability for a collaborated framework, which is composed by two hydrologic models (VIC and SWAT models) and one ecosystem model (InVEST model). A typical semi-arid catchment, the Yixunhe river basin in northern China, was selected as the study area. This study can be simply analyzed in two parts. The first part is to fully discuss the characteristics of each model both qualitatively and quantitatively. The second part is to use three scenarios to detect whether our proposed collaborated framework based on the characteristics of these models could improve the HESs management efficiency when data is lacking. It is noteworthy that water yield and sediment losses have been identified as two investigated HESs in our study.

Study area
The Yixunhe River basin (YRB) is located in northeast

Data treatment
To minimize the system errors within these models, the same input data and interpolate methods were applied for the data pre-treatment. For all three models, the ET 0 (potential evapo-

Model description
In this section, the main object is to address whether the proposed collaborated framework could improve the HESs management efficiency in YRB. To achieve this target, the VIC, SWAT and InVEST models are estimated qualitatively and quantitatively to address the characteristics and applicability. The qualitative estimation focuses on the adaptability of the different models, such as the data requirement, learning efforts, the scale for the output results, etc. The quantitative estimation is the major focus on the model simulation process and results interpretation. It should be noted that in our research, whether the estimation provided by these models is accurate enough to make a good decision has not been specified as the major evaluation standard.
Our study pays more attention to whether these models can be well applied to handle and improve HESs assessment under data deficient scenarios. The specific introduction relating to these three models is presented in the introduction section of the supplementary information. Table 1 provides an overview comparison of three models, which has been retrieved from the model introductions. It is necessary to clarify the temporal and spatial scales for the requirements of the input data before starting the assessment of the adaptation for conducting HESs management (Cong et al. ). The InVEST model has relatively low requirements for input data and does not require a high level of expertise for preprocessing. In contrast, the VIC and SWAT models require more detailed input data, including daily precipitation, daily temperature and snow melting information. The VIC and SWAT models can provide daily runoff estimation in grid and sub-watershed scales, respectively, while the InVEST model can present the annual water yields at the pixel and sub-watershed scales.

Scenarios analysis
We selected 1960-1980 as our study period. It is acknowledged that human activities, including urbanization, irrigation methods and the construction of water reservoirs, were recognized as the major negative effects for HESs simulation (Wang et al. ). During the selected time period, the low urbanization and environmental protection regulation made it possible for us to minimized the disturbance of human activities (Xu et al. ), and to address the characteristics of these three models without unexpected errors. Furthermore, as an important eco-barrier for Beijing-Tianjin-Hebei region (Wu et al. ), the water yield would largely affect the water availability for residents. Moreover, the large agricultural area would accelerate soil erosion, which may reduce the reservoir's lifetime. Therefore, the water yield and sediment losses, which could increase the risk of the water crisis, have been chosen as two major HESs in our study (Chen et al. ).
In this study, we use the observation data sequences from 1960 to 1980, as shown in scenario A (Table 2) to fully discuss the characteristics of three models. Then, two scenarios (scenario B and C) were set to testify the proposed collaborated framework to see whether the water yield and sediment losses can be well-rebuilt, if there is a runoff data-sparse period (we hypothesized the runoff data of 1975-1980 was missing). Generally, scenario A works as the control group, where the HESs have been operated by the observation data from 1960 to 1980. Scenario B (without using a framework) and scenario C (using a framework) work as the experimental groups, where we create a data blank period from 1975 to 1980. The objective for establishing these three scenarios is to test whether the HESs in scenario B or scenario C is more similar to scenario A. The specific detailed information for each scenario is shown in Table 2.

Model performance
Generally, the calibrated models are well-prepared for runoff and HESs simulation ( Table 3)

Runoff of the three models
The annual mismatch between the simulation results of these three models were mainly due to the different requirements of the data, the divergent targets of the model builder, and the diverse water generation conceptual approaches (Lüke & Hack ). In general, the slopes for the runoff  (Figure 2(a)).
Owing to the daily scale input data and well-established hydrologic dynamic algorithms (Supplementary Equations (1)-(4)), the VIC and SWAT models are designed for better reproducibility of the hydrologic cycle, which results in the higher accuracy in runoff simulation. Specifically, the simulation by the VIC and SWAT models is more accurate than the InVEST models. Due to the simplified water balance equation (Supplementary Equation (8)), the InVEST model shows less accuracy in reflecting the annual runoff at the study catchment and demonstrates the highest deviation from the observed data (Figure 2(a), However, when considering interannual variations, all three models display a consistent change rate (Figure 2(b)).
In particular, the SWAT and VIC models have nearly the

)
, and could provide a reference for the correct investment to alleviate or utilize the HESs changes.

Water yield and sediment losses
As a classical hydrologic model, the VIC model has not been designed for extended HESs assessment, therefore, the water yield and sediment losses in this section have been quantified by the SWAT and InVEST models. When considering the water yield, though the SWAT and InVEST models have nearly the same interannual change rate of runoff  (Figure 2(b)), the annual water yield (per unit ha) diverges significantly in both the watershed and sub-watersheds scales.
Overall, the InVEST model shows a higher amount of five-years average water yield than the SWAT model, with 1.08 and 0.86 billion m 3 , respectively (Supplementary Table S3). In general, both models estimate that sub-watersheds Nos. 8 and 9 in the southernmost part of the study area contribute to the largest water yield per unit ha ( Figure 3(a)), which has revealed a high consistency between water yield and precipitation (Smith et al. ).
It should be noted that most of the top-ranking sub-watersheds (Nos. 2, 3 and 4) generated by the InVEST model are covered by agriculture in high altitude areas ( Figure 1(b)), which is below the change trend line (Figure 3(a)). However, the top-ranking sub-watersheds     (Zhou et al. ). In our study, the effect of precipitation is represented by the runoff or water yield. Therefore, we focus on the connection of sequencing between water yield and sediment losses in Figure 3. Significantly, two sub-watersheds (Nos. 1 and 3) presented a high heterogeneity with other sub-watersheds (Figure 3(a) and 3(b)). This feature may prove that the mechanism of water yield (runoff) would be another important factor for sediment losses.

Proposal and validation of the collaborated framework
The collaborated framework for different models would be another research interest for both the earth science and environmental management studies. When applying HESs management to elicit people's preferences, the data availability has always been considered as the main obstacle for HESs simulation. Nevertheless, in most cases, data gaps always exist during continuous monitoring periods. In this study, after fully discussing the advantage qualitatively and quantitatively for VIC, SWAT and InVEST models, we have proposed a collaborated framework (Figure 4) to try to take advantage of each model for carrying out better HESs management in data-deficient scenarios.
The collaborated framework was proposed based on the data availability and the characteristics of the three models in the previous section. When the input data can meet the requirement for all three models, the collaborated framework provides an approach for choosing the appropriate management tool for their unique needs. However, when the observation runoff is deficient, this framework could help users to achieve a better HESs assessment by using the VIC, SWAT and InVEST models.

Adaptability of the collaborated framework
When the data is sufficient for HESs reproduction, the target for our collaborated framework is to help different users fulfill their need for HESs management. In our framework, the data requirement, data time scale, learning efforts, spatial resolution and the operating platform have been considered for users to select the appropriate model to achieve their goals. Overall, the VIC and SWAT models are the better choice for researchers to build the hydrology cycle The designed purpose for these three models is different.
Both the VIC and SWAT models are designed for scientific use, therefore, the requirement for the parameter verification, observation data acquisition and model establishment are relatively high. Therefore, the VIC and SWAT models  Table 5. Generally, scenario C (using the framework) introduces a better performance than scenario B with nearly a 2% improvement for HESs assessment during the study period.
Specifically, with similar runoff simulation accuracy between the VIC and SWAT models as discussed in the section 'Runoff of the three models', a slight improvement in scenario C has been determined for the SWAT model. Our collaborated framework could improve the HESs management when the observation data is deficient.
Additionally, whether the collaborated framework could increase the accuracy for other extended HESs' modules (water purification, irrigation, etc.) should be further discussed. Also, our study encourages more researchers to apply our framework to other watersheds with different environmental backgrounds.

Implication
Though our study provided a meaningful approach to improve the HESs management, the limited HESs types in this research may encourage more works to enhance our collaborated framework. The SWAT and InVEST models may be more adaptable than the VIC model for multiple HESs analyses, such as hydropower production, municipal water supply and irrigation regulating. These various modules would help users to further explore how climate, land To address how our collaborated framework would help the HESs management with other modules, the implications are discussed below.

Municipal water supply
Compared with the water yield/surface runoff module of the SWAT and VIC models, the output of the InVEST model provides sufficient spatial resolution (Table 1) for further evaluating HESs, which is more closely related to an urban water supply administration unit. The resolution of pixel level provides effective support for drawing the water resources distribution and interannual variation in needed regions. The municipal water supply module of the InVEST model can also be applied for evaluating the situation of urban water scarcity, which would be practical for analyzing the balance between supply and demand of urban water in a given region. However, the basic need for the data would also be the climate factors and runoff, which could be introduced from our collaborated framework when the data is unavailable.

Hydropower production
The reservoir hydropower module, encapsulated in the Furthermore, when conducting a project that aims to study water resource management based on different irrigation scenarios, the SWAT and InVEST models can work together to make full use of the water scarcity module in the InVEST model and irrigation module in the SWAT model. Linking these two modules, we can identify whether the study catchment has the ability to expand the irrigation dimension and how nutrient export would change.

Limitation
Identification accuracy and resolution for the land use land cover maps would largely affect the models' simulation accuracy. In addition to the climatic factors, topographic conditions and theoretical calculation methods, the vegetation classification accuracy may also affect the results.
Specifically, in our study, the recognition ability of forests and shrubs, as well as shrubs and grasslands, are relatively low, which would lead to a mismatch on vegetation classification (Wu et al. ).

CONCLUSIONS
In this study we have discussed three models (VIC, SWAT and InVEST) for their applicability of HESs management in the YRB, China, and proposed a comprehensive management framework, including model selection and model collaborated approaches. Overall, the results indicate the following conclusions: (1) The VIC and SWAT models can meet the needs for precise water resource control and management at various time scales. The InVEST model is not suitable for overelaborate water quantity control and management, while low data requirement along with the diverse extended modules make the InVEST model an efficient decision-making tool.
(2) The VIC model could be the best choice for hydrologic cycle reproduction in identical areas. Moreover, although the InVEST model could not well reflect the actual hydrologic situation, it still can present valuable information for relative changes of the HESs.
(3) The collaborated framework aims at coupling these three models and fully uses the advantages of each model. After the validation processes, the proposed model collaborated framework is capable of improving HESs estimation and management in the study catchment.
(4) In order to test the adaptability of the collaborated framework, further similar studies under different climate conditions should be conducted. Furthermore, how to collaborate the extended module in the framework would also be a meaningful work in future.