Reservoir Operation Research Articles

Using hydrological modeling and satellite observations to elucidate subsurface and surface hydrological responses to the extreme drought

Climate change and anthropogenic activities have intensified extreme weather events globally. In the summer of 2022, the Yangtze River Basin (YRB) in China experienced an extreme drought, significantly impacting the ecosystems and society. However, the specific effects of this extreme drought on surface and subsurface hydrological dynamics remain unclear. Here we employed satellite-observed terrestrial water storage anomaly (TWSA) and a modified hydrological model with consideration of reservoir operation, human water consumption, and water diversion engineering to quantify how subsurface and surface water in YRB responded to such an extreme drought in 2022. Validation against a series of observations shows that the modified model has good performance in reproducing daily streamflow, reservoir water storage, lake water storage, and snow water equivalent. It achieved more precise GRACE TWSA estimates in the YRB with significant human intervention, and therefore it can fairly accurately quantify both surface and subsurface hydrological responses to the 2022 extreme drought. Compared to the same months (July-December) in 2015–2021, the drought in 2022 resulted in a decrease in precipitation and discharge of 373 km3 (36 %) and 324 km3 (50 %), respectively, while an increase in evapotranspiration of 156 km3 (29 %) in the YRB. In general, the surface water storage (SWS) is relatively low from July 2022, followed by subsurface water storage (SSWS) from August 2022, indicating an approximately one-month lag from the former to the latter. During the latter half year of 2022, the SWS and SSWS reduced by 48 km3 and 83 km3, respectively, suggesting the changes in the latter dominated the TWS variations. This study sheds light on the responses of surface and subsurface hydrology to extreme droughts, and the hydrological modeling framework with consideration of human activities proposed here holds applicability beyond the YRB.

The Changing Hydrology of an Irrigated and Dammed Yangtze River: Streamflow, Extremes, and Lake Hydrodynamics

AbstractUnderstanding the role of anthropogenic activities in the hydrological cycle is critical to support sustainable water management for the Yangtze River Basin (YRB), which experiences extensive dam operation, irrigation and water withdrawal. However, this remains challenging due to insufficient accuracies of existing process‐based models for fully depicting anthropogenic activities as part of the hydrological cycle. To this end, this study enhances a national‐scale coupled land surface‐hydrologic‐hydrodynamic model (CLHMS) with a dynamic irrigation scheme for distinct crops, an extended reservoir operation scheme incorporating both water storage anomalies and water demand anomalies, and a cost‐function‐based approach to link water demands with reservoir operation. The enhanced model is extensively validated against historical streamflow, water storage of 90 reservoirs, and irrigation water withdrawal in the YRB, and the water level and storage of the Poyang Lake (PYL). By setting up controlled experiments in the YRB, we show that the streamflow decreases by 2%–6% due to irrigation and water withdrawal, and manifests an attenuated seasonality due to reservoir operation. At the basin scale, the increasing trend of extreme flood peaks exhibits a reversal under human activities, with the flood mitigation effect of irrigation and water withdrawal accounting for up to 50% of that of reservoir operation. The hydrodynamics of the PYL also exhibits considerable human‐induced alterations, with a 1.79 m‐decrease in the water level at the end of flood season. Our study sheds light on quantifying anthropogenic hydrologic impacts at basin scales, with important implications for understanding the co‐evolution between anthropogenic activities and the hydrological cycle.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

A novel reservoir dispatching rules extraction framework based on hybrid embedding informer

Accurately capturing reservoir dispatching rules is crucial for mitigating carbon emissions and preventing flooding and inundation. However, the short operational duration of certain reservoirs poses challenges in acquiring sufficient data samples for reservoir rule simulation. Furthermore, the nonlinear and fluctuating characteristics of reservoir dispatching rules present obstacles to their precise prediction. To address these challenges, this study proposes a hybrid embedding-based Informer (Heformer) dispatching rules extraction framework. Initially, extensive data series are employed for modeling to acquire a comprehensive sample dataset. Subsequently, the Heformer model, based on the Informer architecture and integrating the Dimension-Segment-Wise (DSW) strategy and feature representation strategy, is introduced for capturing reservoir dispatching rules. To assess performance of the proposed model, it is compared against several recognized state-of-the-art models. Experimental results demonstrate that the proposed method effectively captures the operational characteristics of reservoirs, mitigates bias impact in reservoir prediction and operation, and provides a reference for extracting dispatching rules for reservoirs.

Comparative study of reservoir operations using TLBO, PSO and DE optimization techniques: an experiment on the Hirakud reservoir, Odisha, India

ABSTRACT With the enduring progression of high population density, social economy and requirements of water supply, irrigation and hydropower, the water resource scarcity problem has been exacerbated in Odisha. Hence, the judicial operation of the Hirakud reservoir, which is considered the lifeline of Odisha, has become appreciably essential. Among various optimization techniques, metaheuristic algorithms are advanced techniques which can be applied for the optimal operation of a water reservoir. In this work, three metaheuristic algorithm-based optimization techniques, the Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching–Learning-Based Optimization (TLBO) algorithms, are applied for optimal water management of the Hirakud reservoir. From the result, it was found that the efficiency of TLBO for irrigation release during the non-monsoon period was 99.45% compared with PSO with an efficiency of 97.3% and DE with an efficiency of 95.6%. The efficiency of TLBO for hydropower generation was 99.6%, whereas for PSO it was 98.8% and for DE it was 98.5%. It is found from the above experiment that TLBO showed a better performance than PSO and DE optimization techniques for the water management of the Hirakud reservoir. These metaheuristic techniques will provide suitable guidance for reservoir operations at times when the presence of experts is a must but they may not be available.

Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation

Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation

REVIEW AND COMPARATIVE STUDY OF HYDROLOGICAL MODELS FOR RAINFALL-RUNOFF MODELLING

Water is considered as an important resources for human existence on the earth. In order to simulate or optimized hydrological data for various water resources management, several hydrological models are very useful to attain this aim for water resources management and as a decision support tools. A rainfall-runoff model is a quantitative prototype explaining the rainfall-runoff interactions at basin scale. The hydrological models have peculiarities in terms of capabilities for various water resources management. This paper tends to reviewed over fifty (50) papers that are peculiar to hydrological models as applicable to rainfall-runoff modeling. It involved evaluating and comparing different hydrological models used in simulating rainfall process converting into surface runoff for water use efficiency. Several runoff models such as Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), Precipitation-Runoff Modeling System (PRMS), Variable Infiltration Capacity model (VIC), LISt-based Erosion Model (LISEM), MIKE Surface Water - Groundwater Hydrology (MIKE SHE) and Runoff Prophet were critically assessed. Rainfall-runoff models are globally utilized for different applications to enhance water use efficiency across different sectors. However, types of hydrological models by examining various hydrological models, model accuracy by evaluating the accuracy and reliability of each model in predicting runoff from rainfall data, scope of applications by determining the adequacy of the models for numerous geographical regions and climatic circumstances, complexity and usability by assessing the complexity of the models, their data requirements, ease of use and computational efficiency, also the models advantages and limitations in capturing the dynamics of the rainfall-runoff process were critically assessed. This was to aid modeling objectives. It was inferred that HEC-HMS is widely applied for modelling precipitation-runoff processes in watersheds of various sizes, aiding in flood forecasting, reservoir operation, and water management for agricultural and urban water use efficiency. SWAT is used for assessing the impact of land management practices (e.g., crop rotation, irrigation, land use changes) on water resources, including runoff generation and water quality, thus optimizing water use efficiency in agriculture. PRMS is applied to model the transport of water via complex hydrological systems, aiding in watershed management and water use efficiency assessments. In conclusion, this comparative review seeks to guide water scientists, the users of hydrological models and hydrological engineers in selecting the most suitable models for their specific modelling needs for sustainable water resources management.

Prediction of outflow temperature of reservoir based on theory-guided machine learning models and optimization of operation for improving outflow temperature

The proportion of hydropower within the energy-resource structure is gradually increasing. However, the construction of reservoirs inevitably leads to ecological and environmental issues, especially the release of low-temperature water in summer and its detrimental effects on fish spawning and reproduction. These issues can be alleviated by optimizing the reservoir operation scheme, but the large amount of time required for predicting the reservoir outflow temperature emerges as the primary constraint. In this study, we considered Pubugou (PBG) Reservoir and proposed an efficient and high-precision prediction method for reservoir outflow temperature based on theory-guided machine learning (TGML) algorithms. This method was applied to the multi-objective optimization of the reservoir operations, and an optimization operation plan was proposed to improve the outflow temperature. The research results indicate that the LightGBM machine learning model shows good predictive performance, with a maximum deviation of no more than 1 °C in predicting the outflow temperature. There is a significant competitive relationship between reservoir power generation and the outflow temperature. Compared with the current operational scheme, the maximum power operation plan results in a 4.4% increase in total power generation but causes the average outflow temperature during the fish spawning period to decrease by 0.1 °C. The total power generation of the improved outflow temperature operation scheme was 0.8% lower than that of the actual scheme, but the average outflow temperature during fish spawning increased by 0.2 °C. Properly increasing the discharge flow from the reservoir and lowering the water level can thus help alleviate the issue of outflow temperature.

Transcriptomic Analysis of the Effects of Changes in Temperature and Flow Velocity on Reproductive Ability of Coreius guichenoti

ABSTRACTThe establishment of reservoirs, which alter water temperature and flow dynamics, significantly affects the indigenous fish population, yet the underlying molecular mechanisms remain unclear. To investigate effects of the changing water temperature and flow rate on fish ovaries, we subjected female Coreius guichenoti to the combination of different water temperatures (17°C, 20°C and 23°C denoted as L, N and H, respectively) and flow velocities (0 and 0.5 m/s denoted as S and V, respectively). After 30‐day experiment period, we identified 393, 545, 300 and 657 differentially expressed transcripts in HV versus NV, LV versus NV, HS versus NV and LS versus NV, respectively. The transcriptome results showed that both environmental factors could significantly influence oogenesis, with water temperature exerting a more pronounced effect than flow velocity. In particular, the higher temperature (HV and HS) led to a tendency towards masculinization and even infertility in females, which were consistent with the histopathology results. By contrast, the lower temperature (LV and LS) promoted the progression of desirable female attributes, where static water conditions (LS) had a greater effect compared with flowing water (LV). These findings were of great significance for the adaptive operation of reservoirs to create reasonable and precise ecological flows for managing fish reproduction.

Using Optimization to Investigate the Adaptive Operation of Reservoirs under the Context of Climate Crisis

Using Optimization to Investigate the Adaptive Operation of Reservoirs under the Context of Climate Crisis

A novel formulation of an eco-friendly calcium nitrate-based heavy completion fluid

Calcium-nitrate-based transparent completion fluids are widely used in the oil and gas industry for well completion and stimulation operations in carbonate reservoirs. These fluids have many advantages, such as medium density, low corrosion, good temperature stability, low clay swelling, and high wettability. However, the density of calcium nitrate brine is limited by its solubility, which can be increased by the addition of alcohols. This study investigated the effects of adding different types of alcohols (G1, G2, and G3) to calcium nitrate brine on the properties and performance of the completion fluid for carbonate reservoirs. The fluid properties and performance were evaluated through a series of laboratory tests, including density measurement, corrosion test, viscosity measurement, rheology test, temperature stability test, clay swelling test, wettability test, and compatibility test. The results showed that the addition of alcohols to brine can improve or reduce the fluid density, viscosity, corrosion, temperature stability, clay swelling, wettability, and compatibility, depending on the type and amount of alcohols. The optimum fluids have been selected based on their highest density, lowest viscosity, lowest corrosion, highest temperature stability, lowest clay swelling, highest wettability change, and highest compatibility with formation fluids. The densities of the fluids CN4, CNG14, CNG24, and CNG34 were respectively 96, 101, 101.5, and 100 pounds per cubic foot (pcf). Their rates of corrosion on L80 steel were respectively 0.829, 0.589, 0.720, and 0.599 mils per year (mpy). Their apparent viscosities at 62.4 °F were respectively around 15, 120, 100, and 60 centipoise (cp). Their apparent viscosities at 176 °F were all around 10 to 25 mpy. The fluids remained clear with no evidence of suspended solid particles at 20 °F, indicating their resilience to low-temperature conditions and suitability for use in cold weather operations. The fluid CNG24 becomes cloudy at 285 °F, and the fluid CNG34 becomes cloudy from 212 °F onward, but the CN4 fluid remains clear and transparent at all temperatures. In the tested high temperatures, the effect on their pH was around the same. The fluids CN4, CNG24, and CNG34 had a lower swelling index than 5 ml per 2 g of bentonite clay. The contact angles of oil and carbonate-type thin sections after their wettabilities were affected by the fluids were also 99.5, 36.54, and 46.03°, respectively. Finally, they’re all relatively compatible with formation fluids. The best completion fluid for carbonate reservoirs was CNG24, which contained calcium nitrate and G2 alcohol. This fluid had the best overall performance and safety of the fluids tested.

Optimization of reservoir operation by sine cosine algorithm: A case of study in Algeria

The optimal operation of the reservoir has vital importance in water engineering. In the presented article, a new optimization method, named sine cosine algorithm (SCA) was employed to obtain operating policy for an irrigation system. The SCA was utilized for the monthly operation of the Boukerdane Dam placed in the north of Algeria. The fitness function was the minimization of the total shortage for the studied period. Three scenarios considering three different seasons of inflow (dry, normal and wet) are used to optimize the reservoir system’s operation. The SCA outputs were compared with particle swarm optimization (PSO) and kidney algorithm (KA). The outcomes indicated that the SCA surpassed the PSO and KA in convergence rate. The general results indicated the low speed of KA and PSO in achieving convergence. The results indicated that the highest RES (resiliency index), SUS (sustainability index) and REL (reliability index) achieved by the SCA were 65, 86 and 92 %, respectively. Comparing the third scenario with the first and second scenarios, it was observed that the third scenario (wet seasons) improved the results.

MILP and PSO approaches for solving a hydropower reservoirs intraday economic optimization problem

AbstractShort-term hydropower generation with several water reservoirs requires deciding, for each moment in time, the volume of water (outflow) that is released from every reservoir to be turbined and generate energy. Knowing the price of energy at every time period, the objective is to maximize the income earned from the generated energy. In this paper, we present (1) a Hydropower Reservoirs Operation Optimization problem with a higher level of detail than those found in the literature, encompassing temporal delays, water hammer effects, and increased temporal discretization, among others features, and (2) two distinct approaches for addressing this problem: MILP and PSO. These methods are compared across instances of varying nature to evaluate their performance. We make our code available on GitHub: https://github.com/baobabsoluciones/flowing-basin.

Climate change impact on hydropower generation and adaptation through reservoir operation in a Himalayan river, Tamor

ABSTRACT Integrated assessment of climate change impact and water resource development scenario is crucial for planning and management. In the Himalayan river basin, it is of utmost importance considering the vulnerability to climate change and the pace of water resource development. This study focused on Tamor river basin (TRB), investigating the impacts of climate change on the energy generation from hydropower projects and analyzes the adaptation through reservoir operation. Analyzing the three run-of-river (RoR) hydropower projects and a storage project, this study projects future energy generation. Results showed that RoR is highly susceptible to the impacts, demonstrated by significant reduction during pre-monsoon up to -53% and increment at annual scale up to 28%. In Tamor storage project, the particle-swarm optimization approach generated operational strategies according to altered streamflow conditions. This resulted in adaptation to the projected decrease in March-June flow through flexible operation rules, yielding positive impact on energy generation (up to +7.3% on an annual scale). The new set of rules will adapt to the flow deficit and increase the dry season flow downstream, almost by double than the historical baseline. This research highlights the significance of reservoir project and its optimized operation in effectively managing water under changing climate.

Risk-benefit comparative analysis of different control methods for reservoirs under the water to electricity mode

Abstract For some small hydropower stations that are not under the control of power dispatching department, there are many ways to implement the reservoir operation scheme formulated under the premise of determining power by water. This paper respectively selects the output, flow and water level process as the control conditions, explores the risks and benefits of reservoir operation with different control variables, and selects the optimal operation scheme through comprehensive comparative analysis. Simulation results of a hydropower station in the Han River Basin show that when the output is taken as the control condition, the waste water is small, the power generation benefit is high, and the risk of the water level outside boundary is within the acceptable range. Considering the risk and benefit of reservoir operation and taking output as the control condition, it has significant advantages over water level and flow.

Nutritional status of the reservoir tributary backwater area and implications for nutrient control

Understanding the spatial and temporal distribution and driving factors of the nutritional status in the backwater area of typical tributaries in the Three Gorges Reservoir Region (TGRR) is important for the prevention and control measures. In this study, the Environmental Fluid Dynamics Code (EFDC) was used to extend in situ monitoring data, followed by the application of hydrogen and oxygen isotope data and scoring methods to predict the nutritional status in the backwater area of the Daning River. The results indicated that the nutritional status-sensitive period mainly ranged from May-October and was distributed at the end of the backwater area and tributary bay. The water temperature and intermediate density flow were identified as the key factors inducing deterioration in the nutritional status at the end of the backwater area and tributary bay, respectively. Nutrient reduction in the mainstream and tributary areas significantly affected the nutritional status in the backwater area. Notably, hydrodynamic adjustment under reservoir objectives is indispensable for improving the water environment in tributary bay after reaching the water quality standard. Our findings highlight the necessity of focusing on the upper reaches of the tributaries and reservoir for nutrient control and emphasize the importance of combining reservoir operation strategies to mitigate the deterioration in the nutritional status.

Reservoir Operation based Machine Learning Models: Comprehensive Review for Limitations, Research Gap, and Possible Future Research Direction

The operation of dams and reservoirs is critical for water resource management, including flood control, irrigation, hydropower generation, and environmental conservation. Traditional optimization techniques like Dynamic Programming (DP), Linear Programming (LP), and Nonlinear Programming (NLP) have been foundational in managing these operations. However, they often fall short in addressing the complexities of modern water management challenges posed by climate variability and increasing water demands. Machine learning (ML) techniques have emerged as powerful tools to enhance the efficiency and accuracy of dam and reservoir operations. This paper provides a comprehensive review of various ML models, including Neural Networks, Genetic Algorithms, Decision Trees, and Ensemble Methods, highlighting their applications in predicting reservoir inflows, optimizing water release schedules, and improving flood risk management. Notably, ML models like Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) have shown significant improvements in forecasting accuracy and operational decision-making. Despite these advancements, several limitations and research gaps persist, including the need for real-time data integration, adaptive learning mechanisms, and models that consider socio-economic and climatic factors. This review underscores the importance of addressing these gaps to develop more robust and generalizable ML models. Future research directions are suggested to focus on hybrid models combining ML with traditional optimization techniques, comprehensive validation across diverse conditions, and the integration of ecological and economic considerations. By systematically identifying and addressing these limitations, this research aims to pave the way for more effective and sustainable dam and reservoir management practices. Leading towards suggesting that enhancing real-time data integration and developing adaptive learning mechanisms are in order to improve model responsiveness.

Climate Change Impacts for Multipurpose Reservoir Planning: Basnagoda Reservoir, Sri Lanka

Climate change via rainfall and temperature changes poses threats to water resources. Traditionally, these variations are rarely considered in irrigation infrastructure planning. Integrating climate impacts into early irrigation planning ensures resilience and sustainability.This study aims to assess the climate change impacts on Basnagoda reservoir using a reservoir operation study, system designing and sizing, water use strategies and making recommendations as mitigation and adaptation strategies.This study employed reservoir operation model recommended in the Irrigation guideline, Sri Lanka to assess four climatic scenarios derived from predicted temperature and rainfall changes. Sensitivity analysis included reservoir sizing for the most critical scenario. Mitigation and adaptation methodologies such as 1) Reservoir capacity enhancement 2) Seasonal shifting 3) Conveyance enhancement were analyzed. Reservoir operations currently meet 100% of domestic demand, irrigating total 950 ha annually (380 ha Maha, 570 ha Yala) with environmental flow. Design capacity fulfils 78% of domestic demand, with environmental flow for projected period. The maximum cultivable area at the design stage while providing 25% of design domestic demand is 544 ha in Maha and 680 ha in Yala season. With a 20% rainfall reduction posing the worst scenario, domestic water fulfilment reduced to 20% (Maha) and 19% (Yala). Optimal reservoir capacity enhancement is 15%, expanding cultivable area to 390 ha, while providing 25% of design domestic demand. Conveyance enhancement increases cultivable area from reservoir water by 15%. This study is unique for integrating climate change impacts into reservoir infrastructure planning and design stages.

A framework for evaluating the combined effects of water transfer and storage strategies on water stress alleviation

Water transfer and storage are two effective anthropogenic management strategies to alleviate the contradictions between water supply and demand. However, the trade-off and synergistic impacts of management strategies in alleviating water stress remain unclear at the national level. Therefore, this study proposes a framework that integrates the fraction of runoff being withdrawn (scarcity coefficient) and the variation of runoff weighted by reservoir (variability coefficient) to evaluate the multifaceted impacts of management strategies on water stress mitigation. The proposed framework evaluates the changes in both the water supply–demand balance and the historical variability of runoff by considering physical water transfers, virtual water flows, and reservoir operations. This study applied the framework to evaluate the spatiotemporal patterns of water stress and used principal component analysis to estimate the relative contributions of management strategies across ten first-order basins in China for the period of 2014–2018. Results show that water-resource scarcity coefficient varied between −37.15% and 13.28% at the basin scale (the national average varied −5%) and water-resource variability coefficient varied between −100% and −19.26% at the basin scale (the national average varied −61.11%). Management strategies, incorporating water transfer and storage strategies, shifted the distribution patterns of national water stress. The attribution analysis revealed that reservoir storage capacity was the largest contributor to the first principal component representing infrastructure element, whereas the second component representing economic element was affected by the net virtual water inflows. Overall, through exploring the outcomes of combined effects among management strategies, this proposed framework provides a comprehensive perspective for investigating how human activity alleviates regional water stress.

Evaluating the Effects of Proppant Flowback on Fracture Conductivity in Tight Reservoirs: A Combined Analytical Modeling and Simulation Study

This work establishes an analytical model for determining the critical velocity for proppant flowback, and evaluates how proppant flowback affects fracture conductivity for tight reservoirs. The multiphase effects are considered for determining the critical velocity for proppant flowback before and after fracture closure, respectively. The model’s performance is demonstrated by comparing the results against previous models. A finite-element model is built to simulate the proppant flowback process for a hydraulic-fractured well completed in the Ordos Basin. The change in fracture conductivity caused by proppant flowback for several scenarios with varying saturation and net pressure in fractures is further quantitatively assessed. Our results highlight the importance of multiphase effects in determining the critical velocity for proppant flowback at relatively low water saturation in fractures. The critical velocity generally increases with increasing water saturation in fractures and net pressure in fractures. At a flowback velocity higher than the critical value, the loss in fracture conductivity becomes relatively more pronounced at a lower water saturation in fractures and a lower net pressure in fractures. The findings of this work are expected to provide insights into the mechanisms of proppant flowback and flowback drawdown management for field operations in tight reservoirs.