Ecological restoration in the Yellow River Basin enhances hydropower potential

Domain: earth_science
Published: 2025-03-15
Editorial outcome: minor_revision

Abstract
Hydropower, an important renewable energy source worldwide, is threatened by reservoir sedimentation. Ecological restoration (ER) can mitigate this by reducing upstream sediment, thereby extending hydropower facilities’ lifespan. However, ER may also reduce runoff, potentially diminishing energy generation and complicating its overall impact on hydropower potential. Here, we examine China’s Yellow River, once the world’s most sediment-laden river, using eco-hydrological and reservoir regulation models to assess how large-scale ER influences the hydropower potential of the Xiaolangdi Reservoir, which controls 92.3% of the basin area. Our results indicate that, excluding upstream reservoirs’ operations and socioeconomic water use, Xiaolangdi could generate a total of ~2.7×1011 kWh of energy before facing diminished flexibility and efficiency caused by the exhaustion of sediment storage—57.3% more than without ER—equating to an additional ~100 billion kWh. This enhancement in hydropower potential primarily arises from the extended lifespan, despite a 6.9% reduction in average annual energy generation. These findings advance our understanding of the ecosystem-water-sediment-energy nexus, offering valuable insights for integrated watershed management globally.

Introduction
Ensuring access to affordable and clean energy is essential for achieving the Sustainable Development Goals1, necessitating a substantial increase in the share of renewable energy, such as hydro, solar, and wind. Hydropower has been the leading renewable energy source worldwide, with a total capacity of 1,268 GW by the end of 2023, accounting for 14% of global installed power capacity and 33% of global renewable power capacity2. In the transition to a net-zero emissions energy system, hydropower offers a cost-effective alternative to the high solar and wind installation costs at the current technological level3. Its flexibility and energy storage capacity can effectively balance the intermittent supply from solar and wind sources and meet peaks in energy demand on grids4–6. Consequently, hydropower has experienced renewed interest and rapid expansion in recent decades7, with an unprecedented 75% growth from 2000 to 20218, particularly in Asia-Pacific, South America, and Africa9. Nevertheless, vast untapped hydropower potential remains10, with considerable expansion planned or underway in the Global South11.

Despite being the most mature renewable technology, hydropower faces significant challenges, notably reservoir sedimentation. Sedimentation impairs reservoir’s storage capacity and abrades power turbines12, undermining the longevity of reservoirs and diminishing the operational flexibility of hydropower generation. Without adequate storage, hydropower facilities become reliant on seasonal flows, which might not occur when energy is needed, thus negating one of the primary advantages of hydropower over other renewable energy sources. Reservoirs worldwide lose approximately 0.5–1% of their initial storage capacity annually due to sedimentation13. A study of over 47,000 dams across 150 countries revealed that these dams had already lost 16% of their combined original storage capacity by 2022 due to trapped sediment, with this figure expected to rise to 26% by 205014. The higher erosion rates and increased sediment load caused by climate change and changes in watershed land use are likely to exacerbate reservoir sedimentation issues faster than previously anticipated15,16, amplifying the threat to the sustainability of hydropower.

Watershed land use management to control soil erosion can effectively mitigate reservoir sedimentation from upstream sources17. Vegetation measures, such as afforestation and grass planting, mitigate erosion by enhancing infiltration and reducing overland flows18,19, while engineering measures, such as terraces and check dams, decrease discharge velocity and increase surface storage, thereby reducing sediment transport19,20. These measures can extend the operational lifespan of hydropower facilities by reducing sediment flows into reservoirs18. However, large-scale afforestation often increases evapotranspiration (ET), leaving less water for runoff21—especially in semiarid and arid regions22—thereby diminishing hydropower potential, which depends directly on streamflow23. These opposing effects through changes in sediment and runoff raise an elusive question: what is the net impact of watershed land use management on hydropower potential? Existing studies typically focus on the separate effects through runoff or sediment change23,24 or seek future optimal management strategies by simultaneously considering these effects25,26. However, research integrating these opposing effects to analyze how real-world practices affect hydropower potential remains limited.

To address this knowledge gap, we select China’s Yellow River (YR) to explore how large-scale ecological restoration (ER) in the Yellow River Basin (YRB) affects the hydropower potential of the Xiaolangdi Reservoir, a strategic water conservancy project in the YR controlling 92.3% of the total basin area (Fig. 1a). The YR is an ideal case for this research, as it once carried the largest sediment load of any river in the world27,28. Severe sedimentation problems plagued its first dam, the Sanmenxia Reservoir, which lost more than 40% of its storage capacity within the first four years and underwent extensive reconstruction afterward29. To address the sediment issue, numerous soil and water conservation practices have been implemented since the 1970s, including terracing, check dam construction, and vegetation restoration20 (Supplementary Fig. 1). Among them, the Grain-for-Green Program (GFGP) implemented since 1999 is the largest and most successful30. Vegetation coverage of the YRB has significantly increased since 2000 (Fig. 1a), leading the greening process of China31. Along with these ER measures, the sediment load and streamflow of the YR have significantly decreased20 (Fig. 1b, c). The Xiaolangdi Reservoir, completed in 2001, has a total installed capacity of 1800 MW32. It has a total storage capacity of 12.65 billion m3, including a sediment storage capacity of 7.55 billion m3 for sediment deposition and a regulating storage capacity of 5.10 billion m3 for flow regulation and electricity generation32 (Supplementary Fig. 2). The operating period of the Xiaolangdi Reservoir coincides with the implementation of large-scale ER in the YRB, providing an excellent opportunity to analyze the overall impact of ER on its hydropower potential by affecting the sediment and runoff of the YR.

In this study, we use the designed sediment storage capacity of the Xiaolangdi Reservoir as a benchmark to evaluate total energy generation. Once this storage is filled, regulating storage is invaded, compromising the flexibility and efficiency of hydropower. Total energy generation is determined by monthly energy production and the sediment storage lifespan. As previously noted, energy production depends positively on reservoir inflow, while sediment storage lifespan depends inversely on sediment load. These factors are influenced by upstream land use, climate, socioeconomic water use, and reservoir operations. To isolate the impact of ER on hydropower potential, we design an idealized experiment simulating streamflow and sediment load of the YR under two scenarios: with and without ER implementation (2000–2019), while holding other influencing factors consistent. Both simulations use observed meteorological data to reflect climate variability, with upstream socioeconomic water use and reservoir operations excluded (Methods, Supplementary Table 1). The Geomorphology-Based Eco-Hydrological Model with Soil and Water Conservation (GBEHM-SWC), a distributed model that integrates the complex eco-hydrological interactions and human interferences in the YRB (Supplementary Fig. 3), is employed to simulate eco-hydrological and sediment dynamics. We then simulate the regulation of the Xiaolangdi Reservoir and its energy generation and sedimentation processes, comparing the total generated energy under both scenarios. By revealing the impact of ER in the YRB on the hydropower potential of the Xiaolangdi Reservoir, this study advances understanding of the ecosystem-water-sediment-energy nexus and provides valuable insights for integrated watershed management.

Results
Hydrological and sediment responses to ecological restoration
According to the Soil and Water Conservation Bulletin in Yellow River Basin33, by 2020, extensive ER measures had been implemented in the YRB, including the establishment of 12.64×104 km2 of forests, 6.08×104 km2 of terraces, 2.34×104 km2 of grasses, and 5.81 × 104 check dams (Supplementary Figs. 4 and 5). These measures led to significant increases in leaf area index (LAI) and gross primary productivity (GPP) across the basin (Fig. 2). Under the scenario with ER, the basin-averaged LAI from 2000 to 2019 increased by 6.33×10−2 m2 m−2 (a relative change of +10.78%) compared to the scenario without ER, while GPP increased by 47.50 g C m−2 (+13.04%). Notably, the differences between the two scenarios expanded over time. ER also effectively mitigated soil erosion, with basin-averaged erosion from 2000 to 2019 declining by 706.74 t km−2 (−57.11%). However, the large-scale implementation of ER increased basin-averaged ET by 11.39 mm (+3.02%), resulting in a corresponding reduction in basin-averaged runoff by 8.32 mm (−11.02%).

These eco-hydrological changes in the YRB caused by ER have led to significant reductions in both streamflow and sediment load of the YR (Fig. 3). Simulations indicate that, under the scenario with ER, the average annual streamflow into the Xiaolangdi Reservoir from 2000 to 2019 was 401.35×108 m3, a 7.9% decrease from the 435.82×108 m3 observed under the scenario without ER (Fig. 3a). The reduction in average annual sediment load was even more pronounced, falling from 7.54×108 t to 4.61×108 t, a reduction of 38.9% (Fig. 3c). With the implementation of ER measures, the disparity in streamflow and sediment load between the scenarios with and without ER widened significantly (Fig. 3b, d), showing an average reduction of 53.60×108 m3 (−11.7%) in streamflow and 3.28×108 t (−42.3%) in sediment load during the last five years (2015–2019).

Changes in energy generation and sedimentation of the Xiaolangdi Reservoir
Based on the simulated hydrological and sediment conditions of inflow into the Xiaolangdi Reservoir under different scenarios, we further simulated and compared the reservoir’s potential energy generation and sedimentation from 2002 onwards (Fig. 4), following the completion of construction at the end of 2001. The average annual energy generation from 2002 to 2019 under the scenario with ER was 121.00×108 kWh, a 6.9% decrease from the 130.01×108 kWh generated under the scenario without ER. The energy generation process and differences between the two scenarios exhibited intra-annual variation, with June to November contributing more than 60% of the annual energy generation under both scenarios. August and September showed the largest declines in monthly energy generation under the scenario with ER, with reductions of 17.8% and 11.5%, respectively. The average annual reservoir sedimentation also decreased with ER implementation, reducing from 5.44×108 m3 under the scenario without ER to 3.36×108 m3 under the scenario with ER, a decline of 38.3%. The flood season, spanning May to October, accounted for more than 90% of the annual sedimentation under both scenarios, with July and August contributing the most. The largest declines in monthly reservoir sedimentation under the scenario with ER also occurred in July and August, with reductions of 51.5% and 47.0%, respectively.

Accumulated energy generation enhanced by ecological restoration
Although the Xiaolangdi Reservoir exhibited higher average annual energy generation under the scenario without ER, the accompanying higher reservoir sedimentation significantly shortened the reservoir’s sediment storage lifespan compared to the scenario with ER (Fig. 5). The accumulated reservoir sedimentation under the scenario without ER was 97.91×108 m3 by December 2019, the endpoint of our simulation. Using the designed sediment storage capacity of the Xiaolangdi Reservoir, 75.50×108 m3, as the threshold for the reservoir’s lifespan, the sedimentation reached 75.90×108 m3 in July 2015, thereby exhausting the sediment storage capacity. The total energy generation under this scenario was 1713.63×108 kWh. However, under the scenario with ER, the accumulated reservoir sedimentation was only 60.43×108 m3 by December 2019, utilizing just 80% of the sediment storage capacity. Despite lower average annual energy generation, the accumulated energy generation under the scenario with ER exceeded that of the scenario without ER in May 2016, reaching 2177.96×108 kWh by December 2019. According to the trend derived from our simulation, the sediment storage capacity of the Xiaolangdi Reservoir under the scenario with ER would be exhausted in 2024, with a total energy generation of 2694.89×108 kWh, 57.3% higher than the scenario without ER, providing approximately 100 billion kWh of additional energy.

Methods
Data sources
This study utilized various datasets to analyze and simulate the vegetation dynamics in the YRB, the streamflow and sediment concentration of the YR, and the regulation of the Xiaolangdi Reservoir (Supplementary Table 2). Meteorological variables, such as daily minimum, maximum, and mean temperatures, relative humidity, wind speed, solar radiation, and sunshine duration, were interpolated from data at 113 national meteorological stations distributed across or near the YRB, provided by the China Meteorological Administration (http://data.cma.cn). Precipitation data were sourced from the China Gauge-based Daily Precipitation Analysis (CGDPA), a 0.25° gridded daily dataset integrated from observations of 113 national meteorological stations and other undisclosed meteorological stations using the climatology-based optimal method detailed by Shen and Xiong46. Geographic and soil texture data for the YRB were obtained from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) by Jarvis et al.47. and the soil information database by Shangguan et al.48. The border and DEM data for the Xiaolangdi Reservoir were sourced from Li et al.49. Soil hydraulic parameters were obtained from Dai et al.50. Vegetation type data were collected from the investigative vegetation maps of China (1:1,000,000)51,52 and the 30-m resolution Global Land Cover dataset53,54. ER construction records, including terracing, check dams, afforestation, and grass planting, were primarily collected from the Chinese National Water Resources Census (http://www.mwr.gov.cn/) and the Yellow River Conservancy Commission of the Ministry of Water Resources (http://www.yrcc.gov.cn/).

Comprehensive hydrological, ecological, and sediment datasets, such as Leaf Area Index (LAI), Gross Primary Productivity, evapotranspiration, etc., were employed for model calibration and validation. Specifically, this study aimed to simulate streamflow and sediment load without the influence of other reservoirs’ operations and socioeconomic water use upstream of the Xiaolangdi Reservoir. To this end, hydrological processes were first calibrated and validated using natural streamflow sequences from 1982 to 2000, obtained from the third national water resources survey and evaluation conducted by the Ministry of Water Resources (http://www.mwr.gov.cn/). This dataset restored surface water consumption, reservoir operations, and evaporation and leakage caused by the reservoirs, based on gauge observations. After establishing reliable hydrological modeling, sediment load was then calibrated and validated using observed sediment load from 1960 to 1980, during which most reservoirs on the YR had not yet been constructed, to approximate sediment processes without the influence of reservoir sedimentation. Observed sediment load data, along with data on the regulation and storage of the Xiaolangdi Reservoir, were provided by the Yellow River Conservancy Commission of the Ministry of Water Resources (http://www.yrcc.gov.cn/). More details on these datasets and their processing for simulating and validating vegetation, runoff, and sediment can be found in Yan et al.19,55 and Yang et al.56.

Simulation of streamflow and sediment load of the YR
The Geomorphology-Based Eco-hydrological Model with Soil and Water Conservation (GBEHM-SWC), a distributed physically-based eco-hydrological model, was used to simulate hydrological and sediment dynamics under varying vegetation cover and engineering conditions. The Geomorphology-Based Eco-hydrological Model (GBEHM)57, integrates a comprehensive set of processes, including hydrological dynamics governed by multi-layer soil water dynamics and energy balance, and erosion processes on hillslopes influenced by underlying surface characteristics and the hydraulic properties of overland flow. Furthermore, the model simulates kinematic wave flow routing and one-dimensional non-equilibrium sediment transport in river channels, offering a holistic representation of eco-hydrological interactions across both hillslope and channel systems (Supplementary Fig. 3). Developed from GBEHM, the GBEHM-SWC has been further refined to capture the impacts of soil and water conservation measures (ER measures in this study) on hydrological and sediment processes19. These measures are explicitly represented to depict their hydrological and sediment influences in the GBEHM-SWC. Terracing, afforestation, and grass planting on hillslopes first alter vegetation cover—a change observable through remote-sensed LAI data. They are also parameterized to enhance surface water storage capacity and reduce erosion capacity, integrated into hillslope hydro-sediment processes. Check dams in the river network system are conceptualized as unregulated reservoirs within each flow interval and incorporated into the flow routing process. These dams slow down discharge velocity or obstruct streamflow to facilitate sediment deposition, with stored water further infiltrating or evaporating. The outflow discharge and sediment concentration are calculated based on the simulated inflow and effective storage capacity of check dams, while dynamically tracking changes in water storage and sediment deposition within the dams. The parameterization scheme is detailed in Yan et al.19 and Yang et al.56. The GBEHM-SWC was extensively calibrated and validated in the YRB using monthly natural streamflow data from 38 hydrological stations and monthly observed sediment discharge data from 22 hydrological stations, demonstrating advanced performance with Nash-Sutcliffe efficiency coefficient (NSE) values for streamflow exceeding 0.8 (Supplementary Fig. 9) and NSE values for sediment discharge exceeding 0.5 at all mainstream stations (Supplementary Fig. 10).

Scenario analysis of ER impacts on energy generation of the Xiaolangdi Reservoir
We employed a single-factor fixed approach to assess the impacts of ER in the YRB on water and sediment changes of the YR and subsequently on the energy generation of the Xiaolangdi Reservoir. The scenario with ER represented current conditions with all restoration practices in place, while the scenario without ER reflected the pre-restoration landscape of the YRB (Supplementary Table 1). ER measures in the YRB, aimed at controlling severe soil erosion and reducing sediment transport, include terracing, afforestation, grass planting on hillslopes, and check dams in the river network. For the scenario with ER, actual ER measures from 2000 to 2019 and dynamic land use and LAI were used as inputs. The streamflow and sediment discharge into the Xiaolangdi Reservoir simulated by the GBEHM-SWC under this scenario showed high consistency with observed data from 2000 to 2019, with R2 values of 0.70 and 0.53 and NSE values of 0.67 and 0.45 for monthly natural streamflow and sediment discharge, respectively (Supplementary Figs. 11 and 12). In the scenario without ER, no ER measures were applied, and only the land use in 2000 and simulated natural vegetation dynamics based on the Biome-BGC model (Supplementary Fig. 13) were used as inputs.

The Biome-BGC model, a widely recognized tool for simulating vegetation ecophysiology, models the processes of energy, water, and carbon fluxes within terrestrial ecosystems and provides key variables like LAI. We used version 4.2 of the Biome-BGC model, which has been extensively validated and widely employed in previous studies58,59, to simulate the vegetation condition of the YRB from 2000 to 2019, accounting only for climatic influences and excluding human interference. The model was adapted to simulate vegetation dynamics on a 10-km grid cell scale, matching the spatial resolution of the eco-hydrological model. The NSE values for simulated monthly LAI for all vegetation types at five sample grids exceeded 0.85, averaging 0.90, demonstrating satisfactory performance. After calibrating the model parameters at the sample points, the Biome-BGC model was run for each grid. More details on the simulation process can be found in Yan et al.55.

Simulation of energy generation and sedimentation of the Xiaolangdi Reservoir
A random forest model was used to simulate the regulation rules of the Xiaolangdi Reservoir. Reservoir operation is typically based on its storage, purpose, and inflow60, and shows monthly variations attributed to seasonal inflow patterns, downstream irrigation water demands, and hydropower generation necessities61. Therefore, the monthly outflow released from the reservoir (Ot, m3/s) was predicted using the storage at the beginning of the month (St, 108 m3), the inflow of the month (It, m3/s) and the month before (It-1, m3/s), and the month itself (t) as explanatory variables. We used monthly regulation data of the Xiaolangdi Reservoir from June 2006 to March 2024 to build the model, with 80% of the data randomly selected for training and the remaining 20% for validation. The model showed high accuracy with NSE values of 0.88, 0.77, and 0.86 and R2 values of 0.94, 0.79, and 0.91 for training, validation, and all data, respectively (Supplementary Figs. 14 and 15).

Using the regulation rules model and hydrological and sediment output from the GBEHM-SWC, we simulated the regulation of the Xiaolangdi Reservoir under scenarios with and without ER (Supplementary Fig. 16). The simulation began in January 2002, following the completion of the reservoir construction at the end of 2001, with an initial recorded storage of 4.5 billion m3. The simulated storage was calculated using the water balance of the reservoir as Eq. (1):

St+1 = St + (It − Ot) * Δt * 10−8 (1)

where Δt is the total seconds of the corresponding month for each step size. To better reflect real-world reservoir regulation, we added two constraints: the maximum simulated storage should be less than the storage capacity at the simulated time, calculated as the initial storage capacity (12.65 billion m3) minus the accumulated sedimentation; the minimum simulated storage should be higher than the lowest values observed in the actual regulation of the reservoir (0.05 billion m3).

Based on simulated sediment concentration of the inflow into the Xiaolangdi Reservoir and outflow and storage of the reservoir, monthly energy generation and sedimentation of the Xiaolangdi Reservoir were calculated. According to a previous study62, the electricity generated by the Xiaolangdi Reservoir was calculated as Eq. (2):

Et = K * O’t * (Ht − H0) * Δt / 3600 (2)

where Et is the generated electricity (kWh); K is the power generation coefficient (8.5 for large hydropower stations)62; O’t is the water released for power generation, generally equal to Ot but capped at 1800 m3/s, the maximum flow rate that the turbine can handle62; Ht is the average fore-bay water level during month t, and H0 is the turbine layer elevation (129 m)62. Ht was calculated based on the average storage during month t and a quadratic polynomial function fitted to the relationship curve between storage and fore-bay water level of the Xiaolangdi Reservoir. The initial relationship was derived using DEM data (Supplementary Figs. 8 and 17). This relationship evolves with sedimentation in the Xiaolangdi Reservoir. Given the challenges in accurately predicting sedimentation distributions, we assumed that sediment initially accumulates at the reservoir’s lowest elevation and gradually increases over time. Consequently, as the reservoir silts up, changes in the storage–water level relationship curve are primarily reflected in shifts in the intercept. The relationship function was recalculated annually during the simulation. We validated our assumption using daily storage and water level data of the Xiaolangdi Reservoir spanning 2006–2023. Data points from different years demonstrated similar curvature to the initial relationship (Supplementary Fig. 8a). Additionally, the fitting functions for the storage–water level relationship, derived from the initial function with adjusted intercepts for all years, showed high R2 values exceeding 0.99 (Supplementary Table 3). The changes in the fitting function intercept and the changes in actual accumulated reservoir sedimentation over different years exhibit an inverse relationship (Supplementary Fig. 8b), indicating that the reduction in storage at any given water level is nearly equal in magnitude to the sedimentation during those years. This consistency supports the robustness of our assumption that sediment initially accumulates at the reservoir’s lowest elevation.

To calculate reservoir sedimentation, the sediment concentration of the outflow was derived using an empirical formula for the sediment delivery ratio of the Xiaolangdi Reservoir established in a previous study63. This formula characterizes the relationship between the sediment delivery ratio and its influencing factors—including average storage, outflow, inflow, and inflow sediment concentration of the Xiaolangdi Reservoir—as Eq. (3) and exhibits a high fitting accuracy (R2=0.86) with observed data from 2000 to 2015 (Supplementary Fig. 18):

ηt = 1.493 * (St / Ot)−1.008 * (It / Ot)−0.278 * ScIt−0.404 (3)

therefore, the sediment concentration of the outflow can be expressed as Eq. (4):

ScOt = ηt * ScIt * It / Ot (4)

where ηt is the sediment delivery ratio; St is the average storage during month t (108 m3); ScIt and ScOt are the sediment concentrations of the inflow and outflow, respectively (kg/m3). Using the mass conservation equation, the reservoir sedimentation volume can be calculated as Eq. (5):

ΔVt = (It * ScIt − Ot * ScOt) * Δt / ρ (5)

where ΔVt is the sedimentation volume of the reservoir in month t (m3); ρ is the dry density of the bed sediment, taken as 1200 kg/m3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "• Over-reliance on Simplified Models: The paper's heavy reliance on models, such as the Geomorphology-Based Eco-hydrological Model with Soil and Water Conservation (GBEHM-SWC) and random forest models for reservoir operations, might oversimplify the complex hydrological, sedimentation, and ecosystem interactions, which can result in inaccurate predictions or interpretations of real-world phenomena.\n• Inadequate Consideration of External Factors: The authors acknowledge that the study does not account for upstream reservoirs and socioeconomic water use. This oversight limits the generalizability and applicability of the findings, as upstream reservoirs and socio-economic water demands often play a crucial role in hydropower operations and reservoir sedimentation.\n• Limited Real-World Validation: The study primarily uses historical data from 1982 to 2000 for model calibration and validation. The lack of real-world data from recent years, particularly after 2019, undermines the relevance and reliability of the conclusions drawn, especially considering changing climatic and environmental conditions.\n• Lack of Climate Change Consideration: While the paper discusses the effects of land-use changes, it does not adequately address the impact of climate change on precipitation patterns and sediment flow. Ignoring these critical factors reduces the comprehensiveness of the analysis, especially given that climate change is a significant driver of hydrological variability.\n• Unrealistic Simplifications: The paper admits to some idealizations in the relationship between storage, forebay water level, and sedimentation rates in the Xiaolangdi Reservoir. These simplifications might not reflect the real-world complexities of reservoir dynamics and sediment deposition, leading to overestimation or underestimation of the reservoir's potential.\n• Exclusion of Socioeconomic Impacts: The focus is primarily on energy generation and sedimentation but neglects potential socioeconomic impacts such as water supply for agriculture, industrial use, or community livelihoods. Given the multipurpose nature of reservoirs, this omission creates a gap in understanding the broader implications of ecological restoration on society.\n• Unclear Policy Implications: While the study touches upon Payment for Ecosystem Services (PES) and integrated watershed management, the paper does not clearly articulate how these findings could be translated into actionable policy recommendations, especially for regions beyond China. The lack of practical guidance limits its usefulness for policymakers.\n• Ambiguous Causality Claims: The paper presents a serendipitous relationship between ecological restoration (ER) and hydropower potential, but fails to convincingly establish a direct causal link between ER measures and the observed increases in hydropower potential. This makes the claims about the benefits of ER speculative rather than firmly substantiated.", "reviewer_2": "I don’t think this work is of significant to the field. The topic of this work is highly important, and the authors present good simulated results of streamflow and sediment load (I didn’t see the comparisons between observed and simulated results concerning sediment load/SSC inflow and outflow of Xiaolangdi). However, It needs to be further explained why the discharge into the reservoir decreased marked after ecological restoration.\nThe MS only presents the variation of total sedimentation amount in the reservoir, however, distributions of sedimentation or reduction of utilizable capacity play the dominant role in influencing power generation. It won’t have much effects on power generation if the sedimentation occurred mainly in the dead storage.\nThe scenarios were too simplify and ideal, and results could hardly be proved or have some realistic directions for reality. In particular, the authors present that ‘By the end of 2019, the actual accumulated sedimentation of the Xiaolangdi Reservoir was 32.97×108 m3, accounting for only 54.6% of our simulation of the scenario with ER, indicating that the actual hydropower potential may be higher than our simulation. (L231-233)’, indicating that the role of current operational scheme named ‘water and sediment regulation scheme’ is extremely marked. Considering this effect, the accumulated energy generation without ecological restoration would also significant rise.", "reviewer_3": "The authors investigated how sediment can be effectively reduced through watershed management measures (e.g., ecological restoration), how sediment in reservoirs affects the longevity of reservoirs and hydroelectric power generation, and how increased power generation can be achieved by extending the lifespan of hydroelectric power facilities. This study sounds new because it touches change of hydropower potential due to ecological restoration which we did not pay much attention before. The article was well organized and written. However, there are some still some issues that are not fully developed. I believe the comments below can improve the completeness and quality of the manuscript.\nMajor comments\n1. The manuscript did not consider the potential feedbacks of climate change on hydropower generation. In particular, rainfall change can also have a significant impact on runoff and sedimentation, thus indirectly affecting hydropower generation.\n2. The manuscript considered idealized scenarios, excluding the operations of upstream reservoirs and socio-economic water use, which can significantly influence real-world hydrology and energy generation. The volume of water withdrawn from the Yellow River by human activities cannot be ignored. As far as we know, socio-economic water use accounts for a large proportion of the runoff.\n3. In modeling the relationship between storage volume and water level in the Xiaolangdi Reservoir, it was assumed that sediment initially accumulates at the reservoir's lowest elevation and gradually increases over time. However, this assumption may not be realistic and should be discussed.\n4. The discussion section lacks sufficient coverage of critical shortcomings in the experimental design, such as the failure to account for climate change, the omission of upstream reservoir operations and socio-economic water use, and the inadequate investigation the complex relationship between water and sediment.\nMinor comments\n1. In Figure 1c, the labeling in the diagram should not intersect the data lines.\n2. In this manuscript, the meteorological variables of daily minimum, maximum and mean temperature, relative humidity, wind speed, solar radiation and sunshine duration were processed using interpolation methods, while the precipitation data were processed using the CGDPA precipitation data product instead of interpolation. Some uncertainty may be reduced if the same meteorological data sources and methods are used.\n3. In Figure 2d, it seems better to put the labels in the nearby blank area instead of the bar graph\n4. In Figure 4, the blank area in the picture is too large, the layout should be adjusted appropriately and compactly.\n5. How were the areas for vegetation restoration determined in this study?\n6. The use of models to simulate changes in vegetation dynamics does not consider feedbacks from land use change and climate change."}, "author_response": "Response to Reviews (Ms. No. NCOMMS-24-57970)\nTo Reviewer #1:\nWe have followed all your valuable suggestions in our revision. Below are the reviewer’s previous\ncomments, followed by our responses.\nSincerely,\nShuai Wang (on behalf of the author team)\n***********\nReviewer #1 (Remarks to the Author):\n[Reviewer #1 Comment 1] Over-reliance on Simplified Models: The paper's heavy reliance on\nmodels, such as the Geomorphology-Based Eco-hydrological Model with Soil and Water\nConservation (GBEHM-SWC) and random forest models for reservoir operations, might\noversimplify the complex hydrological, sedimentation, and ecosystem interactions, which can result\nin inaccurate predictions or interpretations of real-world phenomena.\n[Response] Thank you for your comment. To isolate the impact of ecological restoration on\nhydropower potential, we have to rely on models to construct counterfactual scenarios. As stated in\nthe manuscript, the models demonstrate advanced performance and high accuracy: The Nash-\nSutcliffe efficiency coefficient (NSE) values for streamflow exceed 0.8 and NSE values for\nsediment discharge exceed 0.5 at all mainstream stations of the Yellow River, and NSE value for\nthe monthly outflow released from the Xiaolangdi reservoir exceeds 0.8. As for your concern about\noversimplify, the models actually integrate complex hydrological, sedimentation, and ecosystem\ninteractions. To address this, we revised our manuscript in the following aspects.\nFirst, we expanded the descriptions of the models in the Introduction and Methods sections to\nemphasize their capacity to integrate complex hydrological, sedimentation, and ecosystem\nprocesses: “The Geomorphology-Based Eco-Hydrological Model with Soil and Water Conservation\n(GBEHM-SWC), a distributed model that integrates the complex eco-hydrological interactions and\nhuman interferences in the YRB (Supplementary Fig. 2), was employed to simulate eco-\nhydrological and sediment dynamics.” (Line 109-113) and “The Geomorphology-Based Eco-\nhydrological Model (GBEHM)57, integrates a comprehensive set of processes, including\nhydrological dynamics governed by multi-layer soil water dynamics and energy balance, and\nerosion processes on hillslopes influenced by underlying surface characteristics and the hydraulic\nproperties of overland flow. Furthermore, the model simulates kinematic wave flow routing and\none-dimensional non-equilibrium sediment transport in river channels, offering a holistic\nrepresentation of eco-hydrological interactions across both hillslope and channel systems\n(Supplementary Fig. 2). Developed from GBEHM, the GBEHM-SWC has been further refined to\ncapture the impacts of soil and water conservation measures (ER measures in this study) on\nhydrological and sediment processes19. These measures are explicitly represented to depict their\nhydrological and sediment influences in the GBEHM-SWC.” (Line 345-354)\nSecond, we added the changes of different ecological and hydrological outputs under different\n1\nscenarios in the Results section: “According to the Soil and Water Conservation Bulletin in Yellow\nRiver Basin33, by 2020, extensive ER measures had been implemented in the YRB, including the\nestablishment of 12.64×104 km2 of forests, 6.08×104 km2 of terraces, 2.34×104 km2 of grasses, and\n5.81×104 check dams (Supplementary Fig. 3, Fig. 4). These measures led to significant increases in\nleaf area index (LAI) and gross primary productivity (GPP) across the basin (Fig. 2). Under the\nscenario with ER, the basin-averaged LAI from 2000 to 2019 increased by 6.33 ×10-2 m2 m-2 (a\nrelative change of +10.78%) compared to the scenario without ER, while GPP increased by 47.50 g\nC m-2 (+13.04%). Notably, the differences between the two scenarios expanded over time. ER also\neffectively mitigated soil erosion, with basin-averaged erosion from 2000 to 2019 declining by\n706.74 t km-2 (-57.11%). However, the large-scale implementation of ER increased basin-averaged\nET by 11.39 mm (+3.02%), resulting in a corresponding reduction in basin-averaged runoff by 8.32\nmm (-11.02%). These eco-hydrological changes in the YRB caused by ER have led to significant\nreductions in both streamflow and sediment load of the YR (Fig. 3).” (Line 120-133)\nAdded figures:\nSupplementary Fig. 2 Overview of the modelling framework of the Geomorphology-Based\nEco-hydrological Model (GBEHM).\nscenarios with and without ecological restoration. a-e, Maps of the average values of these\nvariables from 2000 to 2019 under the scenario with ecological restoration. f-j, Maps of the average\nvalues of these variables from 2000 to 2019 under the scenario without ecological restoration. k-o,\nchanges in the basin-average values of these variables from 2000 to 2019 under the two scenarios.\n[Reviewer #1 Comment 2] Inadequate Consideration of External Factors: The authors\nacknowledge that the study does not account for upstream reservoirs and socioeconomic water use.\nThis oversight limits the generalizability and applicability of the findings, as upstream reservoirs\nand socio-economic water demands often play a crucial role in hydropower operations and reservoir\n3\nsedimentation.\n[Response] Thank you for your comment. According to your comment, we revised our manuscript\nin the following aspects.\nFirst, we explained that excluding upstream reservoirs and socioeconomic water use in both\nwith and without ER scenarios ensures that other influencing factors remain consistent, allowing us\nto isolate the impact of ER: “As previously noted, energy production depends positively on reservoir\ninflow, while sediment storage lifespan depends inversely on sediment load. These factors are\ninfluenced by upstream land use, climate, socioeconomic water use, and reservoir operations. To\nisolate the impact of ER on hydropower potential, we simulated streamflow and sediment load of\nthe YR under two scenarios: with and without ER implementation (2000–2019), while holding other\ninfluencing factors consistent. Both simulations used observed meteorological data to reflect climate\nvariability, with upstream socioeconomic water use and reservoir operations excluded (Methods,\nSupplementary Table 1).” (Line 102-109)\nSecond, we clarified that the study’s primary objective is to determine the net impact of ER on\nhydropower potential rather than to simulate hydropower production accurately, and discussed the\nlimitations of our research design: “As this study primarily focuses on determining whether the net\nimpact of ER on hydropower potential is positive or negative, rather than accurately simulating real\nhydropower production, several simplifications and compromises were made in the experimental\ndesign. First, to isolate the impact of ER, other reservoirs and socio-economic water use upstream\nof the Xiaolangdi Reservoir were excluded in both scenarios. This approach ensures that the\ndifferences between scenarios reflect the hydrological and sediment responses to ER alone but limits\nthe direct comparability of the simulation results to real-world gauge observations. For example,\nthe accumulated sedimentation in the Xiaolangdi Reservoir under the scenario with ER was\noverestimated relative to observed values, suggesting that the actual hydropower potential may\nexceed our simulations. … These simplifications, while necessary for the scope of this study, may\naffect the precision of our results and should be addressed in future research to enhance accuracy\nand applicability.” (Line 272-299)\nThird, we discussed the effects of upstream reservoirs by comparing our simulated\nsedimentation in the Xiaolangdi Reservoir with observed value: “Besides the positive impact of ER\non hydropower potential, our results also highlight the significant contributions of other sediment\nmanagement measures. By the end of 2019, the accumulated sedimentation in the Xiaolangdi\nReservoir was 97.91×108 m3 under the scenario without ER and 60.43×108 m3 with ER, compared\nto the observed value of just 32.97×108 m3. This indicates that ER accounted for 57.7% of the actual\nsedimentation reduction of 64.94×108 m3, with the remaining 42.3% attributed to other measures\nnot included in our simulations. These measures likely involve reservoir operations such as sediment\nbypassing, sluicing, dredging, and drawdown flushing41, as well as the complex water and sediment\nregulation scheme (WSRS) of the YR28, 32. The WSRS, a coordinated regulation of three major\nreservoirs (Wanjiazhai, Sanmenxia, and Xiaolangdi) along the YR mainstream42, is generally\noperated every summer to enhance sediment delivery by unleashing artificial flood waves. Together\nwith ER in the YRB, the WSRS has greatly extended the life expectancy of the Xiaolangdi\nReservoir’s sediment storage capacity, which was initially projected to be exhausted by 2020.” (Line\n252-264)\n[Reviewer #1 Comment 3] Limited Real-World Validation: The study primarily uses historical data\n4\nfrom 1982 to 2000 for model calibration and validation. The lack of real-world data from recent\nyears, particularly after 2019, undermines the relevance and reliability of the conclusions drawn,\nespecially considering changing climatic and environmental conditions.\n[Response] Following your comment, we added the comparison of our results with the real-world\nmonthly natural streamflow and sediment data from 2000 to 2019 to show the reliability of our\nsimulation. This simulation of streamflow and sediment did not involve years after 2019. “The\nstreamflow and sediment discharge into the Xiaolangdi Reservoir simulated by the GBEHM-SWC\nunder this scenario showed high consistency with observed data from 2000 to 2019, with R2 values\nof 0.70 and 0.53 and NSE values of 0.67 and 0.45 for monthly natural streamflow and sediment\ndischarge, respectively (Supplementary Fig. 7, Fig. 8).” (Line 377-380)\nFurthermore, the calibration and validation of other variables also used the data from recent\nyears, such as LAI from 2000 to 2019, regulation of the Xiaolangdi Reservoir from June 2006 to\nMarch 2024, and storage and water level data of the Xiaolangdi Reservoir from 2006 to 2023.\nAdded figures:\nSupplementary Fig. 7 Observed and simulated monthly natural streamflow (a) and sediment\ndischarge (b) into the Xiaolangdi Reservoir from 2000 to 2019.\nsediment discharge (b) into the Xiaolangdi Reservoir and simulated values under the scenario\nwith ecological restoration from 2000 to 2019. The R2 and NSE values for sediment discharge are\nrelatively low due to the influence of reservoir operations on the observed data.\n[Reviewer #1 Comment 4] Lack of Climate Change Consideration: While the paper discusses the\neffects of land-use changes, it does not adequately address the impact of climate change on\nprecipitation patterns and sediment flow. Ignoring these critical factors reduces the\ncomprehensiveness of the analysis, especially given that climate change is a significant driver of\nhydrological variability.\n[Response] Thank you for your comment. Climate change was incorporated into our simulation.\nWe apologize for any misunderstanding and have clarified this in the Introduction section to\nemphasize its inclusion in our analysis: “To isolate the impact of ER on hydropower potential, we\nsimulated streamflow and sediment load of the YR under two scenarios: with and without ER\nimplementation (2000–2019), while holding other influencing factors consistent. Both simulations\nused observed meteorological data to reflect climate variability, with upstream socioeconomic water\nuse and reservoir operations excluded (Methods, Supplementary Table 1).” (Line 105-109)\nRevised table:\nSupplementary Table 1 Scenario settings.\nScenarios Meteorological Upstream Hillslope ER River-networks ER Output\nforcing input reservoirs’ LAI, land use (LU) Conceptual check Annual streamflow (Q, m3 yr-1),\noperations and and surface storage dams along river- annual runoff (R, mm yr-1),\nsocioeconomic capacity (SSC) networks annual actual evapotranspiration\nwater use (ET, mm yr-1), annual sediment\nload (S, t yr-1), hillslope erosion\nmodulus (Em, t km-2 yr-1)\nWith ecological Dynamic data None LAIGIMMS With check dams Q0, R0, ET0, S0, Em0\nrestoration from 2000 to 2019 Dynamic LU\nSSCER\nWithout ecological Dynamic data None LAIBGC No check dams Q1, R1, ET1, S1, Em1\nrestoration from 2000 to 2019 2000LU\nSSCnoER\n6\n[Reviewer #1 Comment 5] Unrealistic Simplifications: The paper admits to some idealizations in\nthe relationship between storage, forebay water level, and sedimentation rates in the Xiaolangdi\nReservoir. These simplifications might not reflect the real-world complexities of reservoir dynamics\nand sediment deposition, leading to overestimation or underestimation of the reservoir's potential.\n[Response] Thank you for your comment. According to your comment, we revised our manuscript\nin the following aspects.\nFirst, we compared the simplified relationships with observed data to validate their rationality:\n“H was calculated based on the average storage during month t and a quadratic polynomial function\nt\nfitted to the relationship curve between storage and fore-bay water level of the Xiaolangdi Reservoir.\nThe initial relationship was derived using DEM data (Supplementary Fig. 13, Fig. 14). This\nrelationship evolves with sedimentation in the Xiaolangdi Reservoir. Given the challenge of\naccurately predicting sedimentation patterns, we assumed that sediment initially accumulates at the\nreservoir’s lowest elevation and gradually increases over time. Consequently, as the reservoir silts\nup, changes in the storage–water level relationship curve are primarily reflected in shifts in the\nintercept. The relationship function was recalculated annually during the simulation. We validated\nour assumption using observed storage and water level data of the Xiaolangdi Reservoir spanning\n2006–2023. Data points from different years demonstrated similar curvature to the initial\nrelationship (Supplementary Fig. 14). The fitting functions for the storage–water level relationship,\nderived from the initial function with adjusted intercepts for all years, showed high R2 values\nexceeding 0.99 (Supplementary Table 3), confirming the robustness of our assumption.\nTo calculate reservoir sedimentation, the sediment concentration of the outflow was derived\nusing an empirical formula for the sediment delivery ratio of the Xiaolangdi Reservoir, established\nin a previous study63 based on observed data from 2000 to 2015, with a high fitting accuracy of 0.86\n(Supplementary Fig. 15)” (Line 425-442)\nSecond, we discussed the limitations of our simplification: “Third, sedimentation patterns,\nwhich affect reservoir storage, are shaped by complex hydrological and sediment processes, such\nas the movement of fine-grained sediment deposits45. Accurately modeling these processes over the\nlong term is challenging. Instead, we assumed changes in the relationship between storage and fore-\nbay water level with the accumulated sedimentation in the Xiaolangdi Reservoir. Although idealized,\nthis assumption aligns reasonably well with observed storage and water level data spanning 2006 to\n2023 (Methods, Supplementary Fig. 14, Table 3). … These simplifications, while necessary for the\nscope of this study, may affect the precision of our results and should be addressed in future research\nto enhance accuracy and applicability.” (Line 285-299)\nRelevant figures:\nReservoir.\nSupplementary Fig. 15 The relationship between the actual and calculated sediment delivery\nratio of the Xiaolangdi Reservoir from 2000 to 2015. Modified from Zhang et al., 2018.\nAdded table:\nSupplementary Table 3 Fitting functions for the storage–water level relationship from 2006–\n8\n2023, derived using the initial function with adjusted intercepts.\nYear Function R2 Year Function R2\n2006 y=0.0213x2-8.7906x+920.38 0.997 2015 y=0.0213x2-8.7906x+906.11 0.999\n2007 y=0.0213x2-8.7906x+917.24 0.998 2016 y=0.0213x2-8.7906x+906.25 0.998\n2008 y=0.0213x2-8.7906x+915.34 0.999 2017 y=0.0213x2-8.7906x+904.51 0.999\n2009 y=0.0213x2-8.7906x+914.76 0.999 2018 y=0.0213x2-8.7906x+903.53 0.995\n2010 y=0.0213x2-8.7906x+913.40 0.997 2019 y=0.0213x2-8.7906x+902.77 0.994\n2011 y=0.0213x2-8.7906x+911.14 0.999 2020 y=0.0213x2-8.7906x+904.35 0.997\n2012 y=0.0213x2-8.7906x+910.59 0.999 2021 y=0.0213x2-8.7906x+904.11 0.998\n2013 y=0.0213x2-8.7906x+909.64 0.999 2022 y=0.0213x2-8.7906x+903.64 0.998\n2014 y=0.0213x2-8.7906x+906.53 0.999 2023 y=0.0213x2-8.7906x+902.65 0.998\nInitial function of the storage–water level relationship: y=0.0213x2-8.7906x+934.75\n[Reviewer #1 Comment 6] Exclusion of Socioeconomic Impacts: The focus is primarily on energy\ngeneration and sedimentation but neglects potential socioeconomic impacts such as water supply\nfor agriculture, industrial use, or community livelihoods. Given the multipurpose nature of\nreservoirs, this omission creates a gap in understanding the broader implications of ecological\nrestoration on society.\n[Response] Following your comment, we expanded the discussion to include other potential\nsocioeconomic benefits, such as water supply for agriculture, industry, and community livelihoods,\nhighlighting the multipurpose role of reservoirs: “Beyond the focus on energy production in this\nstudy, the Xiaolangdi Reservoir provides other critical socioeconomic benefits, including flood and\nice jam control, agricultural irrigation, and the regulation of seasonal water supply to meet the\ndemands of cities and villages along the lower YR32. Since these functions depend on effective\nstorage capacity, their reliability is also affected by reservoir sedimentation16. The sediment load\nreduction achieved through ER mitigates sedimentation, enhancing the security of energy, food, and\nenvironment17. However, the broader impacts of ER on these benefits require further exploration,\nintegrating hydrological and sediment changes as demonstrated in this study.” (Line 213-220)\n[Reviewer #1 Comment 7] Unclear Policy Implications: While the study touches upon Payment\nfor Ecosystem Services (PES) and integrated watershed management, the paper does not clearly\narticulate how these findings could be translated into actionable policy recommendations, especially\nfor regions beyond China. The lack of practical guidance limits its usefulness for policymakers.\n[Response] Thank you for your comment. According to your comment, we revised our manuscript\nin the following aspects.\nFirst, we highlighted the global relevance of our findings by discussing how similar challenges\nare faced by other rivers worldwide: “Sediment is a fundamental challenge for hydropower\ndevelopment17, with reservoirs worldwide losing storage capacity much faster than expected due to\nclimate change and human activities16. For example, reservoirs in the upper Yangtze River, China’s\n9\nlargest hydropower production region, have been affected by increasing sediment flux from the\nheadwaters in recent decades16; the Tarbela Reservoir in the Indus River, Pakistan’s largest\nhydropower and irrigation project, lost ~30% of its original storage capacity between 1974 and 2006,\nfar exceeding initial expectations38; the Manwan and Dachaoshan Dams in the Mekong River have\nlost over 50% of their storage capacity because of sediment accumulation9; the Jirau and Santo\nAntonio Dams in the Madeira River have had to use dredgers to remove sediment accumulating at\n“unexpected” rates within five years of their completion15. As reservoirs are usually constructed at\nthe most viable sites, their storage capacity loss to sedimentation is challenging to offset by building\nnew ones14. The experiences from the YR can be extended to other rivers worldwide to mitigate\nreservoir sedimentation and enhance hydropower potential through rational watershed land use\nmanagement.” (Line 221-233)\nSecond, we proposed the actionable PES mechanism by pointing out who should bear the cost\nand how to implement PES practices: “While controlling erosion at its source is widely\nrecommended, it remains poorly implemented in reservoir sediment management due to the lack of\ndirect benefits for land users39. In the YRB, ER was primarily funded by China’s central and local\ngovernments31 and separated from the construction of the Xiaolangdi Reservoir, yet it yielded the\nunintended but beneficial outcome of enhanced hydropower potential. This bonus benefit highlights\nthe feasibility of payment for ecosystem services (PES) in other rivers with hydropower and clarifies\nwho should bear the cost. If simulations of the ecosystem-water-sediment-energy nexus for other\nrivers indicate that effective upstream land use management can increase energy generation from\ncertain existing or planned reservoirs, hydropower companies could allocate a portion of their\nadditional profits to compensate upstream communities for adopting sediment-reducing land use\nmeasures. Drawing on real-world practices in the YRB, these measures include converting cropland\nto terraces, forests, or grasslands, planting vegetation in landslide-prone areas, constructing check\ndams, and removing excess sediment from these dams20. By achieving targeted sediment load\nreductions through such measures, upstream farmers could receive compensation in the form of cash\npayments or rewards such as reduced energy costs from hydropower companies. Such PES\nmechanisms have proven successful in some small watersheds like Indonesia’s Way Besai\nwatershed40. As our study demonstrates their significant potential for large-scale integrated\nwatershed management, they merit further expansion worldwide.” (Line 234-251)\nThird, besides the more generally applicable PES suggestion, we also gave the actionable\nsuggestion to rivers with cascade reservoirs based on our findings: “Together with ER in the YRB,\nthe WSRS has greatly extended the life expectancy of the Xiaolangdi Reservoir’s sediment storage\ncapacity, which was initially projected to be exhausted by 2020. Such coordinated reservoir\nregulation provides a promising complement to watershed PES and could be applied to other rivers\nwith cascade reservoirs, such as the Yangtze, Nile, and Mekong. Effective operation of cascade\nreservoirs requires two regulation phases: in the first phase, clear water is released from the\ndownstream reservoir to lower its water level; in the second phase, floodwaters from the coordinated\noperation of upstream reservoirs scour previously deposited sediment in the downstream reservoir,\ngenerating an artificial hyperpycnal flow with high sediment concentration and fluid density42. In\nthis way, sedimentation of downstream reservoirs can be effectively mitigated.” (Line 262-271)\n[Reviewer #1 Comment 8] Ambiguous Causality Claims: The paper presents a serendipitous\nrelationship between ecological restoration (ER) and hydropower potential, but fails to convincingly\n10\nestablish a direct causal link between ER measures and the observed increases in hydropower\npotential. This makes the claims about the benefits of ER speculative rather than firmly substantiated.\n[Response] We employed a single-factor fixed approach to isolate the impacts of ER in the YRB on\nwater and sediment changes of the YR, and subsequently on the energy generation of the Xiaolangdi\nReservoir. By holding other influencing factors consistent while varying ER measures in the\nsimulation, we established a direct causal link.\nIn the previous version, we used the terms “serendipitously” and “serendipity” to introduce\nthat ER was not designed for enhancing hydropower yet yielded the unintended benefits. We are\nsorry for the misunderstanding and revised our statement as follows: “In the YRB, ER was primarily\nfunded by China’s central and local governments31 and separated from the construction of the\nXiaolangdi Reservoir, yet it yielded the unintended but beneficial outcome of enhanced hydropower\npotential. This bonus benefit highlights the feasibility of payment for ecosystem services (PES) in\nother rivers with hydropower and clarifies who should bear the cost.” (Line 235-239)\nWe have followed all your valuable comments and suggestions in our revision. The detailed\nresponses are as follows. As before, the reviewer’s previous comments are followed by our\nresponses.\nSincerely,\nShuai Wang (on behalf of the author team)\n***********\nReviewer #2 (Remarks to the Author):\n[Reviewer #2 Comment 1] I don’t think this work is of significant to the field. The topic of this\nwork is highly important, and the authors present good simulated results of streamflow and sediment\nload (I didn’t see the comparisons between observed and simulated results concerning sediment\nload/SSC inflow and outflow of Xiaolangdi). However, it needs to be further explained why the\ndischarge into the reservoir decreased marked after ecological restoration.\n[Response] Thank you for your positive comment regarding the accuracy of our simulations. The\nstreamflow and sediment load into the Xiaolangdi Reservoir are measured at the Sanmenxia station,\nwhich serves as the upper boundary of the reservoir. To address your comment, we have revised the\ncaptions of Supplementary Fig. 5 and Fig. 6 to clearly indicate this information. Additionally, the\nsediment concentration outflow of the Xiaolangdi Reservoir was derived using an empirical formula\nfor the sediment delivery ratio (SDR). In the revised manuscript, we compared the calculated SDR\nwith the observed SDR to validate this approach: “To calculate reservoir sedimentation, the\nsediment concentration of the outflow was derived using an empirical formula for the sediment\ndelivery ratio of the Xiaolangdi Reservoir, established in a previous study63 based on observed data\nfrom 2000 to 2015, with a high fitting accuracy of 0.86 (Supplementary Fig. 15)” (Line 439-442)\nWe have also elaborated on the reasons for the marked decrease in discharge into the reservoir\nfollowing ecological restoration. These explanations are included in the Introduction and Results\nsections for better clarity: “However, large-scale afforestation often increases evapotranspiration\n(ET), leaving less water for runoff21—especially in semiarid and arid regions22—thereby\ndiminishing hydropower potential, which depends directly on streamflow23.” (Line 69-72) and\n“However, the large-scale implementation of ER increased basin-averaged ET by 11.39 mm\n(+3.02%), resulting in a corresponding reduction in basin-averaged runoff by 8.32 mm (-11.02%).”\n(Line 129-131)\nRevised captions and added figure:\nSupplementary Fig. 5 Comparison of the observed and simulated monthly streamflow from\n1982 to 2000 at mainstream hydrological stations of the Yellow River. The streamflow into the\nXiaolangdi Reservoir is measured at the Sanmenxia station.\nSupplementary Fig. 6 Comparison of the observed and simulated monthly sediment load from\n12\n1960 to 1980 at mainstream hydrological stations of the Yellow River. The sediment load into\nthe Xiaolangdi Reservoir is measured at the Sanmenxia station.\nSupplementary Fig. 15 The relationship between the actual and calculated sediment delivery\nratio of the Xiaolangdi Reservoir from 2000 to 2015. Modified from Zhang et al., 2018.\n[Reviewer #2 Comment 2] The MS only presents the variation of total sedimentation amount in\nthe reservoir, however, distributions of sedimentation or reduction of utilizable capacity play the\ndominant role in influencing power generation. It won’t have much effects on power generation if\nthe sedimentation occurred mainly in the dead storage.\n[Response] Thank you for your comment. According to the comment, we explained why sediment\nstorage capacity was selected as a benchmark in our study: “In this study, we used the designed\nsediment storage capacity of the Xiaolangdi Reservoir as a benchmark to evaluate total energy\ngeneration. Once this storage is filled, live storage is invaded, compromising the flexibility and\nefficiency of hydropower.” (Line 99-101).\nWe also clarified the importance of maintaining flexibility in hydropower generation: “In the\ntransition to a net-zero emissions energy system, hydropower offers a cost-effective alternative to\nthe high solar and wind installation costs at the current technological level3. Its flexibility and energy\nstorage capacity can effectively balance the intermittent supply from solar and wind sources and\nmeet peaks in energy demand on grids4, 5, 6.” (Line 43-47) and “Sedimentation impairs reservoir’s\nstorage capacity and abrades power turbines12, undermining the longevity of reservoirs and\ndiminishing the operational flexibility of hydropower generation. Without adequate storage,\nhydropower facilities become reliant on seasonal flows, which might not occur when energy is\nneeded, thus negating one of the primary advantages of hydropower over other renewable energy\nsources.” (Line 53-57)\ndoes not alter the conclusions of our study: “Fourth, sediment storage exhaustion was used as a\nbenchmark to evaluate and compare total energy generation across scenarios; while it reduces\nreservoir flexibility and efficiency, power generation remains possible. Analysis of the relationship\nbetween accumulated sedimentation and energy generation (Supplementary Fig. 16) showed that,\nregardless of the storage threshold chosen as the benchmark, the scenario with ER consistently\nyielded higher energy output—generally exceeding 50% more—underscoring the robustness of our\nfindings.” (Line 291-297)\nAdded figure:\nSupplementary Fig. 16 The relationship between accumulated sedimentation and energy\ngeneration of the Xiaolangdi Reservoir under scenarios with and without ecological restoration.\n[Reviewer #2 Comment 3] The scenarios were too simplify and ideal, and results could hardly be\nproved or have some realistic directions for reality. In particular, the authors present that ‘By the\nend of 2019, the actual accumulated sedimentation of the Xiaolangdi Reservoir was 32.97×108 m3,\naccounting for only 54.6% of our simulation of the scenario with ER, indicating that the actual\nhydropower potential may be higher than our simulation. (L231-233)’, indicating that the role of\ncurrent operational scheme named ‘water and sediment regulation scheme’ is extremely marked.\nConsidering this effect, the accumulated energy generation without ecological restoration would\nalso significant rise.\n[Response] Thank you for your comment. According to your comment, we revised our manuscript\nto better clarify the research design and objective, analyze the impact of other measures, and provide\npractical directions for real-world application.\nFirst, we explained that the simplified scenarios were designed to isolate the impact of ER by\n14\nholding other influencing factors consistent: “As previously noted, energy production depends\npositively on reservoir inflow, while sediment storage lifespan depends inversely on sediment load.\nThese factors are influenced by upstream land use, climate, socioeconomic water use, and reservoir\noperations. To isolate the impact of ER on hydropower potential, we simulated streamflow and\nsediment load of the YR under two scenarios: with and without ER implementation (2000–2019),\nwhile holding other influencing factors consistent. Both simulations used observed meteorological\ndata to reflect climate variability, with upstream socioeconomic water use and reservoir operations\nexcluded (Methods, Supplementary Table 1).” (Line 102-109)\nSecond, we clarified that the study’s primary objective is to determine the net impact of ER on\nhydropower potential rather than to simulate hydropower production accurately, and discussed the\nlimitations of our research design: “As this study primarily focuses on determining whether the net\nimpact of ER on hydropower potential is positive or negative, rather than accurately simulating real\nhydropower production, several simplifications and compromises were made in the experimental\ndesign. First, to isolate the impact of ER, other reservoirs and socio-economic water use upstream\nof the Xiaolangdi Reservoir were excluded in both scenarios. This approach ensures that the\ndifferences between scenarios reflect the hydrological and sediment responses to ER alone but limits\nthe direct comparability of the simulation results to real-world gauge observations. For example,\nthe accumulated sedimentation in the Xiaolangdi Reservoir under the scenario with ER was\noverestimated relative to observed values, suggesting that the actual hydropower potential may\nexceed our simulations. … These simplifications, while necessary for the scope of this study, may\naffect the precision of our results and should be addressed in future research to enhance accuracy\nand applicability.” (Line 272-299)\nThird, to address the role of other sediment management measures, we compared our simulated\nsedimentation data with observed values. This comparison highlights the significant contributions\nof both ER and the WSRS in mitigating sedimentation: “Besides the positive impact of ER on\nhydropower potential, our results also highlight the significant contributions of other sediment\nmanagement measures. By the end of 2019, the accumulated sedimentation in the Xiaolangdi\nReservoir was 97.91×108 m3 under the scenario without ER and 60.43×108 m3 with ER, compared\nto the observed value of just 32.97×108 m3. This indicates that ER accounted for 57.7% of the actual\nsedimentation reduction of 64.94×108 m3, with the remaining 42.3% attributed to other measures\nnot included in our simulations. These measures likely involve reservoir operations such as sediment\nbypassing, sluicing, dredging, and drawdown flushing41, as well as the complex water and sediment\nregulation scheme (WSRS) of the YR28, 32. The WSRS, a coordinated regulation of three major\nreservoirs (Wanjiazhai, Sanmenxia, and Xiaolangdi) along the YR mainstream42, is generally\noperated every summer to enhance sediment delivery by unleashing artificial flood waves. Together\nwith ER in the YRB, the WSRS has greatly extended the life expectancy of the Xiaolangdi\nReservoir’s sediment storage capacity, which was initially projected to be exhausted by 2020.” (Line\n252-264)\nFinally, based on our findings, we proposed actionable recommendations for watershed\npayment for ecosystem services (PES) and coordinated reservoir regulation to enhance the practical\napplicability of our work: “While controlling erosion at its source is widely recommended, it\nremains poorly implemented in reservoir sediment management due to the lack of direct benefits\nfor land users39. In the YRB, ER was primarily funded by China’s central and local governments31\nand separated from the construction of the Xiaolangdi Reservoir, yet it yielded the unintended but\n15\nbeneficial outcome of enhanced hydropower potential. This bonus benefit highlights the feasibility\nof payment for ecosystem services (PES) in other rivers with hydropower and clarifies who should\nbear the cost. If simulations of the ecosystem-water-sediment-energy nexus for other rivers indicate\nthat effective upstream land use management can increase energy generation from certain existing\nor planned reservoirs, hydropower companies could allocate a portion of their additional profits to\ncompensate upstream communities for adopting sediment-reducing land use measures. Drawing on\nreal-world practices in the YRB, these measures include converting cropland to terraces, forests, or\ngrasslands, planting vegetation in landslide-prone areas, constructing check dams, and removing\nexcess sediment from these dams20. By achieving targeted sediment load reductions through such\nmeasures, upstream farmers could receive compensation in the form of cash payments or rewards\nsuch as reduced energy costs from hydropower companies. Such PES mechanisms have proven\nsuccessful in some small watersheds like Indonesia’s Way Besai watershed40. As our study\ndemonstrates their significant potential for large-scale integrated watershed management, they merit\nfurther expansion worldwide.” (Line 234-251) and “Together with ER in the YRB, the WSRS has\ngreatly extended the life expectancy of the Xiaolangdi Reservoir’s sediment storage capacity, which\nwas initially projected to be exhausted by 2020. Such coordinated reservoir regulation provides a\npromising complement to watershed PES and could be applied to other rivers with cascade\nreservoirs, such as the Yangtze, Nile, and Mekong. Effective operation of cascade reservoirs requires\ntwo regulation phases: in the first phase, clear water is released from the downstream reservoir to\nlower its water level; in the second phase, floodwaters from the coordinated operation of upstream\nreservoirs scour previously deposited sediment in the downstream reservoir, generating an artificial\nhyperpycnal flow with high sediment concentration and fluid density42. In this way, sedimentation\nof downstream reservoirs can be effectively mitigated.” (Line 262-271)\nWe have followed all your very useful suggestions in our revision. The detailed responses are as\nfollows.\nSincerely,\nShuai Wang (on behalf of the author team)\n***********\nReviewer #3 (Remarks to the Author):\nThe authors investigated how sediment can be effectively reduced through watershed management\nmeasures (e.g., ecological restoration), how sediment in reservoirs affects the longevity of reservoirs\nand hydroelectric power generation, and how increased power generation can be achieved by\nextending the lifespan of hydroelectric power facilities. This study sounds new because it touches\nchange of hydropower potential due to ecological restoration which we did not pay much attention\nbefore. The article was well organized and written. However, there are some still some issues that\nare not fully developed. I believe the comments below can improve the completeness and quality of\nthe manuscript.\n[Response] We thank you for your positive comment.\nMajor comments\n[Reviewer #3 Comment 1] 1. The manuscript did not consider the potential feedbacks of climate\nchange on hydropower generation. In particular, rainfall change can also have a significant impact\non runoff and sedimentation, thus indirectly affecting hydropower generation.\n[Response] Thank you for your comment. Climate change was incorporated into our simulation.\nWe apologize for any misunderstanding and have clarified this in the Introduction section to\nemphasize its inclusion in our analysis: “To isolate the impact of ER on hydropower potential, we\nsimulated streamflow and sediment load of the YR under two scenarios: with and without ER\nimplementation (2000–2019), while holding other influencing factors consistent. Both simulations\nused observed meteorological data to reflect climate variability, with upstream socioeconomic water\nuse and reservoir operations excluded (Methods, Supplementary Table 1).” (Line 105-109)\n[Reviewer #3 Comment 2] 2. The manuscript considered idealized scenarios, excluding the\noperations of upstream reservoirs and socio-economic water use, which can significantly influence\nreal-world hydrology and energy generation. The volume of water withdrawn from the Yellow River\nby human activities cannot be ignored. As far as we know, socio-economic water use accounts for\na large proportion of the runoff.\n[Response] Thank you for your comment. According to your comment, we revised our manuscript\nin the following aspects.\nFirst, we explained that excluding upstream reservoirs and socioeconomic water use in both\nwith and without ER scenarios ensures that other influencing factors remain consistent, allowing us\nto isolate the impact of ER: “As previously noted, energy production depends positively on reservoir\n17\ninflow, while sediment storage lifespan depends inversely on sediment load. These factors are\ninfluenced by upstream land use, climate, socioeconomic water use, and reservoir operations. To\nisolate the impact of ER on hydropower potential, we simulated streamflow and sediment load of\nthe YR under two scenarios: with and without ER implementation (2000–2019), while holding other\ninfluencing factors consistent. Both simulations used observed meteorological data to reflect climate\nvariability, with upstream socioeconomic water use and reservoir operations excluded (Methods,\nSupplementary Table 1).” (Line 102-109)\nSecond, we clarified that the study’s primary objective is to determine the net impact of ER on\nhydropower potential rather than to simulate hydropower production accurately, and discussed the\nlimitations of our research design: “As this study primarily focuses on determining whether the net\nimpact of ER on hydropower potential is positive or negative, rather than accurately simulating real\nhydropower production, several simplifications and compromises were made in the experimental\ndesign. First, to isolate the impact of ER, other reservoirs and socio-economic water use upstream\nof the Xiaolangdi Reservoir were excluded in both scenarios. This approach ensures that the\ndifferences between scenarios reflect the hydrological and sediment responses to ER alone but limits\nthe direct comparability of the simulation results to real-world gauge observations. For example,\nthe accumulated sedimentation in the Xiaolangdi Reservoir under the scenario with ER was\noverestimated relative to observed values, suggesting that the actual hydropower potential may\nexceed our simulations. … These simplifications, while necessary for the scope of this study, may\naffect the precision of our results and should be addressed in future research to enhance accuracy\nand applicability.” (Line 272-299)\nThird, we discussed the effects of upstream reservoirs by comparing our simulated\nsedimentation in the Xiaolangdi Reservoir with observed value: “Besides the positive impact of ER\non hydropower potential, our results also highlight the significant contributions of other sediment\nmanagement measures. By the end of 2019, the accumulated sedimentation in the Xiaolangdi\nReservoir was 97.91×108 m3 under the scenario without ER and 60.43×108 m3 with ER, compared\nto the observed value of just 32.97×108 m3. This indicates that ER accounted for 57.7% of the actual\nsedimentation reduction of 64.94×108 m3, with the remaining 42.3% attributed to other measures\nnot included in our simulations. These measures likely involve reservoir operations such as sediment\nbypassing, sluicing, dredging, and drawdown flushing41, as well as the complex water and sediment\nregulation scheme (WSRS) of the YR28, 32. The WSRS, a coordinated regulation of three major\nreservoirs (Wanjiazhai, Sanmenxia, and Xiaolangdi) along the YR mainstream42, is generally\noperated every summer to enhance sediment delivery by unleashing artificial flood waves. Together\nwith ER in the YRB, the WSRS has greatly extended the life expectancy of the Xiaolangdi\nReservoir’s sediment storage capacity, which was initially projected to be exhausted by 2020.” (Line\n252-264)\n[Reviewer #3 Comment 3] 3. In modeling the relationship between storage volume and water level\nin the Xiaolangdi Reservoir, it was assumed that sediment initially accumulates at the reservoir's\nlowest elevation and gradually increases over time. However, this assumption may not be realistic\nand should be discussed.\n[Response] Thank you for your suggestion. Following to your suggestion, we revised our\nmanuscript in the following aspects.\nFirst, we compared the simplified relationships with observed data to validate their rationality:\n18\n“H was calculated based on the average storage during month t and a quadratic polynomial function\nt\nfitted to the relationship curve between storage and fore-bay water level of the Xiaolangdi Reservoir.\nThe initial relationship was derived using DEM data (Supplementary Fig. 13, Fig. 14). This\nrelationship evolves with sedimentation in the Xiaolangdi Reservoir. Given the challenge of\naccurately predicting sedimentation patterns, we assumed that sediment initially accumulates at the\nreservoir’s lowest elevation and gradually increases over time. Consequently, as the reservoir silts\nup, changes in the storage–water level relationship curve are primarily reflected in shifts in the\nintercept. The relationship function was recalculated annually during the simulation. We validated\nour assumption using observed storage and water level data of the Xiaolangdi Reservoir spanning\n2006–2023. Data points from different years demonstrated similar curvature to the initial\nrelationship (Supplementary Fig. 14). The fitting functions for the storage–water level relationship,\nderived from the initial function with adjusted intercepts for all years, showed high R2 values\nexceeding 0.99 (Supplementary Table 3), confirming the robustness of our assumption.” (Line 425-\n438)\nSecond, we discussed the limitations of our simplification: “Third, sedimentation patterns,\nwhich affect reservoir storage, are shaped by complex hydrological and sediment processes, such\nas the movement of fine-grained sediment deposits45. Accurately modeling these processes over the\nlong term is challenging. Instead, we assumed changes in the relationship between storage and fore-\nbay water level with the accumulated sedimentation in the Xiaolangdi Reservoir. Although idealized,\nthis assumption aligns reasonably well with observed storage and water level data spanning 2006 to\n2023 (Methods, Supplementary Fig. 14, Table 3). … These simplifications, while necessary for the\nscope of this study, may affect the precision of our results and should be addressed in future research\nto enhance accuracy and applicability.” (Line 285-299)\nRelevant figures:\nSupplementary Fig. 14 The relationship between storage and water level of the Xiaolangdi\nReservoir.\nSupplementary Table 3 Fitting functions for the storage–water level relationship from 2006–\n2023, derived using the initial function with adjusted intercepts.\nYear Function R2 Year Function R2\n2006 y=0.0213x2-8.7906x+920.38 0.997 2015 y=0.0213x2-8.7906x+906.11 0.999\n2007 y=0.0213x2-8.7906x+917.24 0.998 2016 y=0.0213x2-8.7906x+906.25 0.998\n2008 y=0.0213x2-8.7906x+915.34 0.999 2017 y=0.0213x2-8.7906x+904.51 0.999\n2009 y=0.0213x2-8.7906x+914.76 0.999 2018 y=0.0213x2-8.7906x+903.53 0.995\n2010 y=0.0213x2-8.7906x+913.40 0.997 2019 y=0.0213x2-8.7906x+902.77 0.994\n2011 y=0.0213x2-8.7906x+911.14 0.999 2020 y=0.0213x2-8.7906x+904.35 0.997\n2012 y=0.0213x2-8.7906x+910.59 0.999 2021 y=0.0213x2-8.7906x+904.11 0.998\n2013 y=0.0213x2-8.7906x+909.64 0.999 2022 y=0.0213x2-8.7906x+903.64 0.998\n2014 y=0.0213x2-8.7906x+906.53 0.999 2023 y=0.0213x2-8.7906x+902.65 0.998\nInitial function of the storage–water level relationship: y=0.0213x2-8.7906x+934.75\n[Reviewer #3 Comment 4] 4. The discussion section lacks sufficient coverage of critical\nshortcomings in the experimental design, such as the failure to account for climate change, the\nomission of upstream reservoir operations and socio-economic water use, and the inadequate\ninvestigation the complex relationship between water and sediment.\n[Response] Thank you for your suggestion. According to your suggestion, we discussed the\nlimitations of our study as: “As this study primarily focuses on determining whether the net impact\nof ER on hydropower potential is positive or negative, rather than accurately simulating real\nhydropower production, several simplifications and compromises were made in the experimental\ndesign. First, to isolate the impact of ER, other reservoirs and socio-economic water use upstream\nof the Xiaolangdi Reservoir were excluded in both scenarios. This approach ensures that the\ndifferences between scenarios reflect the hydrological and sediment responses to ER alone but limits\nthe direct comparability of the simulation results to real-world gauge observations. For example,\nthe accumulated sedimentation in the Xiaolangdi Reservoir under the scenario with ER was\noverestimated relative to observed values, suggesting that the actual hydropower potential may\nexceed our simulations. Second, large-scale land use change may indirectly influence hydropower\npotential by affecting precipitation, partially offsetting its impact on local water availability23, 43.\nWhile this complex feedback between land use change and climate was not incorporated into our\nsimulations—given prior findings that precipitation increase from vegetation greening was\nstatistically insignificant in North China44—it remains an important consideration for future\nsimulations in other regions. Third, sedimentation patterns, which affect reservoir storage, are\nshaped by complex hydrological and sediment processes, such as the movement of fine-grained\nsediment deposits45. Accurately modeling these processes over the long term is challenging. Instead,\nwe assumed changes in the relationship between storage and fore-bay water level with the\naccumulated sedimentation in the Xiaolangdi Reservoir. Although idealized, this assumption aligns\n20\nreasonably well with observed storage and water level data spanning 2006 to 2023 (Methods,\nSupplementary Fig. 14, Table 3). Fourth, sediment storage exhaustion was used as a benchmark to\nevaluate and compare total energy generation across scenarios; while it reduces reservoir flexibility\nand efficiency, power generation remains possible. Analysis of the relationship between\naccumulated sedimentation and energy generation (Supplementary Fig. 16) showed that,\nregardless of the storage threshold chosen as the benchmark, the scenario with ER consistently\nyielded higher energy output—generally exceeding 50% more—underscoring the robustness of our\nfindings.” (Line 272-299)\nMinor comments\n[Reviewer #3 Comment 5] 1. In Figure 1c, the labeling in the diagram should not intersect the data\nlines.\n[Response] We revised the Figure accordingly.\nFig. 1 Location of the Yellow River Basin and changes in vegetation, sediment load, and\nstreamflow. a, Annual MODIS LAI trend for the entire basin from 2000 to 2023. b and c, Annual\nsediment load and streamflow at the Tongguan station from 1956 to 2023.\n21\n[Reviewer #3 Comment 6] 2. In this manuscript, the meteorological variables of daily minimum,\nmaximum and mean temperature, relative humidity, wind speed, solar radiation and sunshine\nduration were processed using interpolation methods, while the precipitation data were processed\nusing the CGDPA precipitation data product instead of interpolation. Some uncertainty may be\nreduced if the same meteorological data sources and methods are used.\n[Response] Thank you for your comment. We explained in the revision that the CGDPA\nprecipitation data were also using the same meteorological data sources: “Meteorological variables,\nsuch as daily minimum, maximum, and mean temperatures, relative humidity, wind speed, solar\nradiation, and sunshine duration, were interpolated from data at 113 national meteorological stations\ndistributed across or near the YRB, provided by the China Meteorological Administration\n(http://data.cma.cn). Precipitation data were sourced from the China Gauge-based Daily\nPrecipitation Analysis (CGDPA), a 0.25° gridded daily dataset integrated from observations of 113\nnational meteorological stations and other undisclosed meteorological stations using the\nclimatology-based optimal method detailed by Shen and Xiong46.” (Line 312-315)\n[Reviewer #3 Comment 7] 3. In Figure 2d, it seems better to put the labels in the nearby blank area\ninstead of the bar graph\n[Response] We revised the Figure accordingly.\nFig. 3 Streamflow (a) and sediment load (c) into the Xiaolangdi Reservoir from 2000 to 2019\nunder scenarios with and without ecological restoration, and their differences (b and d).\n22\n[Reviewer #3 Comment 8] 4. In Figure 4, the blank area in the picture is too large, the layout should\nbe adjusted appropriately and compactly.\n[Response] We revised the Figure accordingly.\nFig. 4 Accumulated sedimentation (a) and energy generation (b) of the Xiaolangdi Reservoir\nunder scenarios with and without ecological restoration.\n[Reviewer #3 Comment 9] 5. How were the areas for vegetation restoration determined in this\nstudy?\n[Response] Following your comment, we pointed out how we determined the areas in the revision:\n“According to the Soil and Water Conservation Bulletin in Yellow River Basin33, by 2020, extensive\nER measures had been implemented in the YRB, including the establishment of 12.64×104 km2 of\nforests, 6.08×104 km2 of terraces, 2.34×104 km2 of grasses, and 5.81×104 check dams\n(Supplementary Fig. 3, Fig. 4).” (Line 120-123)\n[Reviewer #3 Comment 10] 6. The use of models to simulate changes in vegetation dynamics does\nnot consider feedbacks from land use change and climate change.\n23\n[Response] Thank you for your comment. According to the comment, we explained why we did not\nconsider the feedbacks discussed the limitation of our study: “Second, large-scale land use change\nmay indirectly influence hydropower potential by affecting precipitation, partially offsetting its\nimpact on local water availability23, 43. While this complex feedback between land use change and\nclimate was not incorporated into our simulations—given prior findings that precipitation increase\nfrom vegetation greening was statistically insignificant in North China44—it remains an important\nconsideration for future simulations in other regions.” (Line 281-285)\nReference:\nLi Y, Piao S, Li LZX, Chen A, Wang X, Ciais P, et al. Divergent hydrological response to large-\nscale afforestation and vegetation greening in China. Science Advances 2018, 4(5): aar4182.\n24"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have added a few information concerning my previous comments, however, some problems are still far from clear.\n1. The authors indicated that the sediment concentration of the outflow was derived using an empirical formula for the sediment delivery ratio of the Xiaolangdi Reservoir, established in a previous study63 based on observed data from 2000 to 2015. The empirical formula for sediment delivery ration in the previous study must have considered the regulation of cascade reservoirs in the YR, while in the present study, the authors mentioned that they exclude the reservoir regulation in their simulation. Therefore, it is quite confusing how they can fit well.\n2. What do you mean by ‘designed sediment storage capacity’? The authors haven’t response to my doubts in Comment 2, most of the replies are irrelevant. Sedimentation processes and distribution play crucial roles in the efficiency of reservoir power generation. For instance, large amount of sedimentation in the regulating storage capacity will have great effects in reservoir regulation and power generation, in contrast, the same amount of sedimentation in the dead storage capacity will have limited effects. It should be noted that, sedimentation in the reservoir could happen in the dead storage capacity and regulating storage capacity simultaneously, which couldn’t be simplified as sedimentation in the dead capacity first, and then in the regulating storage capacity.\n3. It is quite understandable that in order to isolate the role of ER, this study needs to make some simplifications. However, these simplifications must be reasonable and fit well with the current situations in field. For example, the authors excluded the role of cascade reservoir regulation, which is a very important factor. Although a comparison result can be obtained between with ER and without ER, it doesn’t make any sense based on simplification out of line with the reality. Not to mention the accurate power generation result, the influencing degree of ER is not reliable. It’s better to consider there crucial factors in both scenarios.", "reviewer_3": "The authors have addressed my comments, and it can be acceptable."}, "author_response": "Response to Reviews (Ms. No. NCOMMS-24-57970A-Z)\nTo Reviewer #1:\nThank you for your positive comment and the time that you have spent reviewing our manuscript.\nSincerely,\nShuai Wang and Bojie Fu (on behalf of the author team)\n***********\nReviewer #1 (Remarks to the Author):\nWell done!\nWe have followed all your valuable comments and suggestions in our revision. Below are the\nreviewer’s previous comments, followed by our responses.\nSincerely,\nShuai Wang and Bojie Fu (on behalf of the author team)\n***********\nReviewer #2 (Remarks to the Author):\nThe authors have added a few information concerning my previous comments, however, some\nproblems are still far from clear.\n[Response] Thank you for taking the time to review our manuscript. In response to your concerns,\nwe consulted with experts from the Yellow River Conservancy Commission, the authority\nresponsible for managing and regulating the Yellow River Basin. We have added further details\nabout the Xiaolangdi Reservoir and strengthened the support for our simplifications and\nassumptions. We trust that these revisions address your concerns.\n[Reviewer #2 Comment 1] 1. The authors indicated that the sediment concentration of the outflow\nwas derived using an empirical formula for the sediment delivery ratio of the Xiaolangdi Reservoir,\nestablished in a previous study63 based on observed data from 2000 to 2015. The empirical formula\nfor sediment delivery ration in the previous study must have considered the regulation of cascade\nreservoirs in the YR, while in the present study, the authors mentioned that they exclude the reservoir\nregulation in their simulation. Therefore, it is quite confusing how they can fit well.\n[Response] Thank you for your comment. The empirical formula for the sediment delivery ratio of\nthe Xiaolangdi Reservoir captures its relationship with key influencing factors, including average\nstorage, outflow, inflow, and inflow sediment concentration. These factors reflect the reservoir’s\nhydrological and sediment conditions as well as its operation. While the regulation of cascade\nreservoirs in the YR and other upstream influencing factors affect inflow and inflow sediment\nconcentration into the Xiaolangdi Reservoir, they do not alter the fundamental empirical\nrelationship. Since the formula was derived from a broad range of observed inflow and inflow\nsediment concentration values, it remains applicable in our simulation, where upstream reservoir\nregulation was excluded.\nWe apologize for any confusion and have revised the statements as follows: “To calculate\nreservoir sedimentation, the sediment concentration of the outflow was derived using an empirical\nformula for the sediment delivery ratio of the Xiaolangdi Reservoir established in a previous study63.\nThis formula characterizes the relationship between the sediment delivery ratio and its influencing\nfactors—including average storage, outflow, inflow, and inflow sediment concentration of the\nXiaolangdi Reservoir—and exhibits a high fitting accuracy (R2 = 0.86) with observed data from\n2000 to 2015 (Supplementary Fig. 18):\n𝜂 =1.493·(𝑆̅ ⁄𝑂 )−1.008·(𝐼 ⁄𝑂 )−0.278·𝑆𝑐𝐼 −0.404\nt 𝑡 𝑡 𝑡 𝑡 𝑡\ntherefore, the sediment concentration of the outflow can be expressed as:\n2\n𝑆𝑐𝑂 =𝜂 ·𝑆𝑐𝐼 ·𝐼 ⁄𝑂\nt 𝑡 𝑡 𝑡 𝑡\nwhere η is the sediment delivery ratio; 𝑆̅ is the average storage during month t (108 m3); ScI and\nt 𝑡 t\nScO are the sediment concentrations of the inflow and outflow, respectively (kg/m3).” (Lines 456-\nt\n466)\n[Reviewer #2 Comment 2] 2. What do you mean by ‘designed sediment storage capacity’? The\nauthors haven’t response to my doubts in Comment 2, most of the replies are irrelevant.\nSedimentation processes and distribution play crucial roles in the efficiency of reservoir power\ngeneration. For instance, large amount of sedimentation in the regulating storage capacity will have\ngreat effects in reservoir regulation and power generation, in contrast, the same amount of\nsedimentation in the dead storage capacity will have limited effects. It should be noted that,\nsedimentation in the reservoir could happen in the dead storage capacity and regulating storage\ncapacity simultaneously, which couldn’t be simplified as sedimentation in the dead capacity first,\nand then in the regulating storage capacity.\n[Response] Thank you for your comment. Sediment storage capacity refers to the volume of a\nreservoir planned for the deposition of sediment. We apologize for not providing this information\nearlier and have included it in the revised manuscript: “The Xiaolangdi Reservoir, completed in\n2001, has a total installed capacity of 1,800 MW32. It has a total storage capacity of 12.65 billion\nm3, including a sediment storage capacity of 7.55 billion m3 for sediment deposition and a regulating\nstorage capacity of 5.10 billion m3 for flow regulation and electricity generation32 (Supplementary\nFig. 2).” (Lines 91-94) Additionally, we have added Supplementary Fig. 2 to illustrate the storage\nconfiguration of the Xiaolangdi Reservoir. The distinction between sediment storage and regulating\nstorage is based on water levels: most of the sediment storage is below 245 m, while the regulating\nstorage is situated between 245 m and 275 m. The dead storage, located below 230 m, is part of the\nsediment storage. According to the reservoir’s design, the sediment storage and regulating storage\ntogether constitute the total storage capacity.\nRegarding your concerns about sedimentation processes and distribution, we agree that these\nfactors are crucial for reservoir regulation, and indeed, sedimentation can occur in both the dead\nstorage and regulating storage simultaneously. However, for the Xiaolangdi Reservoir, a comparison\nof the observed longitudinal profile of the thalweg between 2023 and 1999 (please see\nSupplementary Fig. 2b below) indicates that most sedimentation has occurred below the 245 m\nwater level, within the sediment storage zone. Modeling sedimentation distribution over the long\nterm remains challenging. As a simplification, we assumed that sediment initially accumulates at\nthe reservoir’s lowest elevation and gradually increases over time; therefore, in our simulation,\nsedimentation occurs first within the sediment storage, and once this storage is filled, the regulating\nstorage is invaded, compromising the flexibility and efficiency of hydropower. Although this\nassumption is idealized, it is supported by daily storage and water level data of the Xiaolangdi\nReservoir spanning 2006–2023: “Given the challenges in accurately predicting sedimentation\ndistributions, we assumed that sediment initially accumulates at the reservoir’s lowest elevation and\ngradually increases over time. Consequently, as the reservoir silts up, changes in the storage–water\nlevel relationship curve are primarily reflected in shifts in the intercept. The relationship function\nwas recalculated annually during the simulation. We validated our assumption using daily storage\nand water level data of the Xiaolangdi Reservoir spanning 2006–2023. Data points from different\nyears demonstrated similar curvature to the initial relationship (Supplementary Fig. 8a).\nfunction with adjusted intercepts for all years, showed high R2 values exceeding 0.99\n(Supplementary Table 3). The changes in the fitting function intercepts and the changes in actual\naccumulated reservoir sedimentation over different years exhibit an inverse relationship\n(Supplementary Fig. 8b), indicating that the reduction in storage at any given water level is nearly\nequal in magnitude to the sedimentation during those years. This consistency supports the\nrobustness of our assumption that sediment initially accumulates at the reservoir’s lowest elevation.”\n(Lines 442-455).\nWe have also discussed the limitations of this simplification in the revised manuscript: “Third,\nsedimentation distributions, which affect reservoir storage, are shaped by complex hydrological and\nsediment processes, such as the movement of fine-grained sediment deposits45. Accurately modeling\nthese processes over the long term remains challenging. Instead, we assumed that sediment initially\naccumulates at the reservoir’s lowest elevation and gradually increases over time. This assumption\nimplies that sedimentation occurs first in the sediment storage of the Xiaolangdi Reservoir (mainly\nbelow the water level of 245 m, Supplementary Fig. 2) and enables us to simulate changes in the\nrelationship between storage and forebay water level as sedimentation accumulates. Although\nidealized, this assumption aligns reasonably well with daily storage and water level data spanning\n2006 to 2023 (Methods, Supplementary Fig. 8, Table 3). … These simplifications, while necessary\nfor the scope of this study, may affect the precision of our results and should be addressed in future\nresearch to enhance accuracy and applicability.” (Lines 296-312)\nRelevant figures and table:\nsource from Yellow River sediment bulletin 2023.\nSupplementary Fig. 8 The relationship between storage and water level of the Xiaolangdi\nReservoir. The fitting functions for the storage–water level relationship from 2006–2023, derived\nusing the initial function with adjusted intercepts, can be found in Supplementary Table 3.\nSupplementary Table 3 Fitting functions for the storage–water level relationship from 2006–\n2023, derived using the initial function with adjusted intercepts.\nYear Function R2 Year Function R2\n2006 y=0.0213x2-8.7906x+920.38 0.997 2015 y=0.0213x2-8.7906x+906.11 0.999\n2007 y=0.0213x2-8.7906x+917.24 0.998 2016 y=0.0213x2-8.7906x+906.25 0.998\n2008 y=0.0213x2-8.7906x+915.34 0.999 2017 y=0.0213x2-8.7906x+904.51 0.999\n2009 y=0.0213x2-8.7906x+914.76 0.999 2018 y=0.0213x2-8.7906x+903.53 0.995\n2010 y=0.0213x2-8.7906x+913.40 0.997 2019 y=0.0213x2-8.7906x+902.77 0.994\n2011 y=0.0213x2-8.7906x+911.14 0.999 2020 y=0.0213x2-8.7906x+904.35 0.997\n2012 y=0.0213x2-8.7906x+910.59 0.999 2021 y=0.0213x2-8.7906x+904.11 0.998\n2013 y=0.0213x2-8.7906x+909.64 0.999 2022 y=0.0213x2-8.7906x+903.64 0.998\n2014 y=0.0213x2-8.7906x+906.53 0.999 2023 y=0.0213x2-8.7906x+902.65 0.998\nInitial function of the storage–water level relationship: y=0.0213x2-8.7906x+934.75\n5\n[Reviewer #2 Comment 3] 3. It is quite understandable that in order to isolate the role of ER, this\nstudy needs to make some simplifications. However, these simplifications must be reasonable and\nfit well with the current situations in field. For example, the authors excluded the role of cascade\nreservoir regulation, which is a very important factor. Although a comparison result can be obtained\nbetween with ER and without ER, it doesn’t make any sense based on simplification out of line with\nthe reality. Not to mention the accurate power generation result, the influencing degree of ER is not\nreliable. It’s better to consider there crucial factors in both scenarios.\n[Response] Thank you for your comment. We fully agree that factors such as cascade reservoir\nregulation and socioeconomic water use influence the hydrology and sediment dynamics of the YR,\nthereby affecting the hydropower generation of the Xiaolangdi Reservoir. To better align our results\nwith real-world conditions, we have compared our simulated streamflow, sediment load, energy\ngeneration, and sedimentation with observed values from the Xiaolangdi Reservoir. Unlike our\nmodeled ER scenario, the real-world scenario includes ER measures as well as additional factors\nsuch as upstream reservoirs’ operations and socioeconomic water use. Therefore, the differences\nbetween our ER scenario and real-world observations can be attributed to these unaccounted factors.\nThe revisions are as follows: “By the end of 2019, the accumulated sedimentation in the Xiaolangdi\nReservoir was 97.91×108 m3 under the scenario without ER and 60.43×108 m3 with ER, compared\nto the observed value—representing the actual scenario with all influencing factors, including ER—\nof just 32.97×108 m3. This indicates that ER accounted for 57.7% of the actual sedimentation\nreduction of 64.94×108 m3, with the remaining 42.3% attributed to other measures not included in\nour simulations.” (Lines 255-260); “Our simplification ensures that differences between scenarios\nreflect the hydrological and sediment responses attributable solely to ER, although it limits direct\ncomparability with real-world gauge observations. In practice, upstream reservoirs’ operations and\nsocioeconomic water use reduce the actual streamflow and sediment load entering the Xiaolangdi\nReservoir compared to our ER simulation (Supplementary Fig. 6). Although this results in lower\naverage annual energy generation and sedimentation in reality, the relationship between\naccumulated sedimentation and energy generation suggests that the actual accumulated hydropower\nproduction may exceed our simulations (Supplementary Fig. 7).” (Lines 284-291)\nFurthermore, we have added the following explanation in the revised manuscript to clarify our\nrationale for not including upstream reservoir operations and socioeconomic water use: “Simulating\nthe long-term operations of upstream reservoirs and socioeconomic water use is challenging. These\nprocesses depend on upstream streamflow and sediment load. If included in the scenarios, these\nprocesses would vary in tandem with changes in upstream streamflow and sediment load driven by\nER, rather than remaining consistent across different scenarios, making it complex and difficult to\nisolate the impact of ER. Our simplification ensures that differences between scenarios reflect the\nhydrological and sediment responses attributable solely to ER, although it limits direct\ncomparability with real-world gauge observations.” (Lines 279-286)\nAdditionally, we emphasized that our study is an idealized experiment and that the simulated\nhydropower represents hydropower potential rather than actual generation. Therefore, the core\nfocus of our study—integrating the opposing effects through sediment and runoff changes to\nunderstand the overall impact of ER on hydropower potential—is not compromised by our exclusion\nof these factors. These points are underscored in the revised manuscript, including in the title\n“Ecological restoration in the Yellow River Basin enhances hydropower potential,” as well as in the\nfollowing statements: “To isolate the impact of ER on hydropower potential, we designed an\n6\nidealized experiment simulating streamflow and sediment load of the YR under two scenarios: with\nand without ER implementation (2000–2019), while holding other influencing factors consistent.”\n(Lines 107-110); “the estimated hydropower reflects the potential of the Xiaolangdi Reservoir in\nthe absence of upstream reservoirs’ operations and socioeconomic water use.” (Lines 277-279)\nRelevant figures:\nSupplementary Fig. 6 Comparison of the streamflow, sediment load, energy generation, and\nsedimentation of the Xiaolangdi Reservoir under actual scenario and scenarios with and\nwithout ecological restoration.\ngeneration of the Xiaolangdi Reservoir under different scenarios.\nThank you for your positive comment and the time that you have spent reviewing our manuscript.\nSincerely,\nShuai Wang and Bojie Fu (on behalf of the author team)\n***********\nReviewer #3 (Remarks to the Author):\nThe authors have addressed my comments, and it can be acceptable.\n9"}]

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