Hydrological Uncertainty Research Articles

Stochastic optimization to maximize water supply index in conjunctive use of surface and groundwater

Systematic operation of water resources systems requires rules that consider the uncertainties affecting system performance. Here, stochastic dynamic programming (SDP) is used to structure and solve a full-scale surface and groundwater conjunctive use model to derive a multiparameter operational policy (rule) that maximizes the water supply index (WSI). The model uses a Markovian representation of inflows to account for hydrologic uncertainties. Both the reliability of allocated water and its vulnerability are employed to define the WSI. To overcome the computational burden inherent with SDP, infeasible solutions are identified and removed from the model solution process. Optimal expected values of WSI and surface and groundwater uses are assessed. The system simulation model with embedded rules is executed to assess the performance of the derived rules. The derived rules employ water rationing and account for long-term benefits during periods when the available surface and groundwater resources may suffice to meet demand. The simulation results demonstrate that the derived operational rules produce high WSI values for the long-term operation of the system and ensure sustainable groundwater use.

Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission

ABSTRACTIn the context of global warming, the rise in extreme precipitation events in high‐altitude headwater areas has introduced greater hydrological uncertainty. However, the limited understanding of the physical mechanisms driving extreme precipitation in these areas hinders efforts to mitigate the potential rise in future precipitation risks. This study analysed the extreme precipitation events in the headwater area of the Yellow River (HAYR) from May to September each year from 2015 to 2020 using satellite‐based data from Dual‐frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory and Integrated Multi‐satellite Retrievals for GPM (IMERG). The results show that stratiform precipitation (SP) determines the spatial extent of extreme precipitation events, while convective precipitation (CP) largely affects the rainfall intensity. Statistical analysis from different extreme precipitation events indicates that the rain rate of CP is 2 to 3 times higher than that of SP, thus zones of intense precipitation in the study area are normally dominated by CP. Vertically, the topographic lifting in complex mountainous regions exerts opposite effects on the precipitation rates of SP and CP, weakening the precipitation intensity of SP while enhancing that of CP. The peak precipitation rate in the midstream and downstream regions is observed at approximately 5 km, whereas the upstream region displays a distinctive double‐peaked distribution, with one peak at 8.5 km and another near the surface. This study provides a better understanding of the interior structure evolution process of plateau precipitation, as well as the associated microphysical properties, and highlights some insights to improve microphysical parameterization in the future model developments.

PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration

Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of 20 different PET methods on both time series and gridded datasets. The majority of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with 20 PET methods for station and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity and uncertainty analyses, and hindcasting and forecasting studies (especially in data-scarce regions) is provided.

Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future

Study regionThe three headwaters of Tarim river, south of the Xinjiang Province, China. Study focusTemporal runoff concentration variations are crucial for water resources utilization and management, particularly in arid regions. In this study, the runoff concentration in the Tarim River Basin is studied based on analysis of the trend, abrupt changes, and periodicity, identifying key extreme climate indices influencing the Gini-runoff coefficient and predicting its future changes under three Shared Socioeconomic Pathways (SSPs). New hydrological insights for the regionFrom 1962–2020, the Gini-runoff coefficient decreased by 8.3 %, while annual runoff increased by 14.2 %. This is attributed to temperature changes, escalating hydrological risk uncertainty. The anomaly of low geopotential high fields and Central Asian anticyclones have caused an increase in total cloud cover (TCC) and precipitation, and a decrease in the Daily Temperature Range (DTR). Increasing of Warm nights (TN90), Maximum Tmax (TXx), and Hot nights (TR) have enhanced the warm-season runoff. However, the climatic impact on warm-season runoff is less pronounced than that in the cold season, which led to the runoff concentration decline. Changes in hydro-junction regulation led to decrease of Gini-runoff coefficient by 12.11 % from 2001 to 2020 along the Hotan river. Under the three SSP scenarios, the average Gini-runoff coefficient for the Aksu and Hotan rivers are expected to decrease by 3.3 % and 2.6 %, respectively.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method

Abstract. The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study proposes the copula-based hydrological uncertainty processor BMA (CHUP-BMA) method by introducing a copula-based HUP in the framework of BMA to bypass the need for a normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of eight members (two forecast precipitation inputs; two advanced long short-term memory, LSTM, models; and two objective functions used to calibrate parameters) and is applied to the interval basin between the Xiangjiaba and Three Gorges Reservoir (TGR) dam sites. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168 h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy and that the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width and continuous ranked probability score metrics of the CHUP-BMA method are reduced by a maximum of 28.42 % and 17.86 % within all forecast horizons, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods

Assessing the uncertainty associated with projections of climate change impacts on hydrological processes can be challenging due to multiple sources of uncertainties within and between climate and hydrological models. Here we compare the effects of parameter uncertainty in a hydrological model to inter-model spread from climate projections on hydrological projections of urban streamflow in response to climate change. Four hourly climate model outputs from the RCP8.5 scenario were used as inputs to a distributed hydrologic model (SWMM) calibrated using a Bayesian approach to summarize uncertainty intervals for both model parameters and streamflow predictions. Continuous simulation of 100 years of streamflow generated 90 % prediction intervals for selected exceedance probabilities and flood frequencies prediction intervals from single climate models were compared to the inter climate model spread resulting from a single calibration of the SWMM model. There will be an increase in future flows with exceedance probabilities of 0.5 %-50 % and 2-year floods for all climate projections and all 21st century periods, for the modeled Ohio (USA) watershed. Floods with return periods of ≥ 5 years increase relative to the historical from mid-century (2046–2070) for most climate projections and parameter sets. Across the four climate models, the 90th percentile increase in flows and floods ranges from 17-108 % and 11–63 % respectively. Using multiple calibration parameter sets and climate projections helped capture the most likely hydrologic outcomes, as well as upper and lower bounds of future predictions. For this watershed, hydrological model parameter uncertainty was large relative to inter climate model spread, for near term moderate to high flows and for many flood frequencies. The uncertainty quantification and comparison approach developed here may be helpful in decision-making and design of engineering infrastructure in urban watersheds.

A Retrospective Hydrological Uncertainty Analysis Using Precipitation Estimation Ensembles for a Poorly Gauged Basin in High Mountain Asia

Abstract We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), high mountain Asia (HMA). A process-based hydrological model at 0.5-km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records, and reanalyses over the 2008–15 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilistic processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate. Significance Statement We show how to use different precipitation estimates, like computer simulations of weather or satellite observations, in conjunction with all available ground measurements in regions with generally poor meteorological and flow measurement infrastructure. We demonstrate how it is possible to retrieve past unobserved river flows using these estimates in combination with a hydrological computer model for streamflow simulations. The method can help us to better understand the hydrology of poorly gauged regions that play an important role in the distribution of water resources and can be affected by future changes. We applied the method to a large transboundary river basin in China. This basin holds water needed by large, densely populated regions of India that may become water constrained by warmer climate.

From threat to opportunity: Hydrologic uncertainty regionalization across large domains

Study regionThe study was carried out in Northern Canada (90,000 km2) and Southern Canada (18,000 km2). These basins represent case studies with a larger application target of high latitude regions. Study focusAs model domains become large, ungauged basins are inevitably encountered, and basin heterogeneity increases. This study aims to develop the computationally frugal Model Agnostic Uncertainty Transfer (MAUT) method to regionalize an uncertainty analysis from a set of donor basins to a receiver. The goal is an evaluation of the MAUT method for regionalizing over sufficiently heterogeneous domains to be applicable to high latitude data sparse regions. New hydrological insights for the regionWe propose the Model Agnostic Uncertainty Transfer (MAUT) method, a surrogate method to perform uncertainty analysis in large-domain modelling with a focus on high-latitude regions where data scarcity is especially severe. With the MAUT method, antecedent precipitation and simulated flow are related. Deviation from this relationship is assumed to represent modelling uncertainty and is quantified by quantile regression lines. These lines act as transfer functions requiring only antecedent precipitation to generate uncertainty bounds. The MAUT method requires uncertainty donors and a target receiver model. Key results suggest the method is relatively insensitive to precipitation dataset differences. Simulation length was the most sensitive input, with 15–20 years being ideal for reasonable results. Overall, the MAUT method is viable for high latitude domains.

Contemporary and relic waters strongly decoupled in arid alpine environments

Deciphering the dominant controls on the connections between groundwater, surface water, and climate is critical to understanding water cycles in arid environments. Yet, persistent uncertainties in the fundamental hydrology of these systems remain. The growing demand for critical minerals such as lithium and associated water demands in the arid environments in which they often occur has amplified the urgency to address these uncertainties. We present an integrated hydrological analysis of the Dry Andes region utilizing a uniquely comprehensive set of tracer data (3H, 18O/2H) for these environments, paired directly with physical hydrological observations. We find two strongly decoupled hydrological systems that interact only under specific hydrogeological conditions where preferential conduits exist. The primary conditions creating these conduits are laterally extensive fine-grained evaporite and/or lacustrine units and perennial flowing streams connected with regional groundwater discharge sites. The efficient capture and transport of modern or “contemporary” water (weeks to years old) within these conduits is the primary control of the interplay between modern hydroclimate variations and groundwater aquifers in these environments. Modern waters account for a small portion of basin budgets but are critical to sustaining surface waters due to the existence of these conduits. As a result, surface waters near basin floors are disproportionally sensitive to short-term climate and anthropogenic perturbations. The framework we present describes a new understanding of the dominant controls on natural water cycles intrinsic to these arid high-elevation systems that will improve our ability to manage critical water resources.

Quantifying Uncertainty in Hydrological Drought Index Using Calibrated SWAT Model

The hydrological model requires precise parameter calibration to reflect the basin characteristicsproperly. The optimum parameter values depend highly on the runoff characteristics; thus, the simulated runoff data affects the uncertainty in hydrological drought assessment using hydrological models. This study quantified the hydrological drought uncertainty based on all available calibrated parameters for 12 different observation periods. The Soil and water assessment Tool was used for hydrological modelling, and the uncertainty was analyzed using Bayesian model averaging (BMA). The hydrological drought index using the simulated runoff was analyzed to vary significantly with the optimized hydrological parameter cases. The drought index calculation using the estimated runoff showed that the number of droughts occurred frequently as the duration increased, But, it was observed that severe droughts of 21-month duration occur most frequently. The uncertainty analysis conducted with BMA revealed that for durations shorter than 18 months, the uncertainties in drought index calculations were higher than those in runoff simulations. As the duration increased, uncertainties decreased, with values of 57%, 54%, 50%, 23%, 23%, 19%, 16% and 16% for 3, 6, 9, 12, 15, 18, 21 and 24-month durations, respectively. The average uncertainty was larger for runoff estimation than for drought calculation. Still, the uncertainty variation by each PC was larger for drought calculation, ranging from 0 to 94% as the duration increased to 24 months. Therefore,the results of this study confirm that hydrological drought analysis using hydrological models contains uncertainties depending on the hydrological model parameter cases.

Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations.

Hydrological modeling and uncertainty analysis for a snow-covered mountainous river basin

Hydrological modeling and uncertainty analysis for a snow-covered mountainous river basin

Using a scenario-neutral approach to assess the impacts of climate change on flooding in the Ba River Basin, Viet Nam

Abstract Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.

Assessment of the structural uncertainty of hydrological models and its impact on flood inundation mapping

ABSTRACTFloods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty.

Partitioning model uncertainty in multi-model ensemble river flow projections

Floods are the largest natural disaster currently facing the UK, whilst the incidents of droughts have increased in recent years. Floods and droughts can have devastating consequences on society, resulting in significant financial damage to the economy. Climate models suggest that precipitation and temperature changes will exacerbate future hydrological extremes (i.e., floods and droughts). Such events are likely to become more frequent and intense in the future; thus to develop adaptation plans climate model projections feed hydrological models to provide future water resource projections. ‘eFLaG’ is one set of future river flow projections produced for the UK driven by UKCP18 climate projections from the UK Met Office. The UKCP18-derived eFLaG dataset provides state-of-the-art projections for a single GCM driven by RCP 8.5 across the entire UK. A QE-ANOVA approach has been used to partition contributing sources of uncertainty for two flow quantiles (Q5 high flows and Q95 low flows), at near and far future time scales, for each of the 186 GB catchments in the eFLaG dataset. Results suggest a larger hydrological model uncertainty associated with low flows and greater regional climate model uncertainty for high flows which remains stationary between flow indicators. Total uncertainty increases from near to far future and highly uncertain catchments have been identified with a high concentration in South-East England.

Quantitative evaluation of the impact of hydrological forecasting uncertainty on reservoir real-time optimal operation

Quantitative evaluation of the impact of hydrological forecasting uncertainty on reservoir real-time optimal operation

Effect of soil spatial aggregation caused by the calculation unit division on runoff and sediment load simulation in the SWAT model

Distributed hydrological model divides watersheds into distinct calculation units to simulate hydrological processes. Within the model, different calculation unit division schemes induce input elements, such as soil and land use, to certain degrees of generalization, thus affecting the simulation result. Analyzing the effect of the calculation unit division on the simulation result is an important methodology to reduce uncertainty and improve the simulation accuracy of the model. Previous researchers have focused on the comprehensive effect of underlying surface generalization (such as soil, land use, and terrain generalization) caused by the calculation unit division on the simulation results, while it did not separate the effect of the generalization of the individual input factor on the simulation result. In this paper, the Dongjiang headwater region in southern China was selected as the study area. The single type of land use and slope were input into the Soil and Water Assessment Tool (SWAT) model to fix the effects of land use and terrain caused by calculation unit division on the simulation results. Eleven calculation unit division schemes were used to analyze the impact of soil spatial aggregation on runoff and sediment load simulations. The calculation unit division schemes included 11 sub-watershed division schemes and their hydrological response unit (HRU) thresholds were set at 8 %. The drainage area thresholds of the 11 sub-watershed division schemes were 30 ha, 60 ha, 120 ha, 240 ha, 480 ha, 1000 ha, 1600 ha, 2200 ha, 2800 ha, 3400 ha and 4000 ha. The results demonstrated that the soil spatial distribution showed an obvious aggregation with an increase in the drainage area threshold in the SWAT model. Of these, the Krasnozem soil type which covers the largest area in the watershed, increased significantly with an increase in the drainage area threshold. However, the areas of all the other soil types decreased significantly. The Lioth soil type covers the smallest area in the watershed decreased and even disappeared when the threshold in the SWAT model was greater than 1000 ha. Furthermore, soil spatial aggregation caused a significant increase in the annual runoff and sediment load (P < 0.05), with the sediment load (Re = 29.78 %) increasing at a greater rate compared to runoff (Re = 1.74 %). Increased soil aggregation significantly increased the sediment loads in the maximum 1-day, the continuous maximum 5-day, the continuous maximum 7-day, and the sediment load in flood processes (P < 0.001). Refining the calculation unit division scheme will cause a significant increase in the number of calculation units. Due to the computer processing power limitations, we cannot further refine the calculation unit division scheme. Based on our current operational calculation unit division scheme, we did not identify the optimal division scheme in the study region. These results can provide reference information for future research into hydrological model uncertainty and can improve the SWAT model simulation accuracy.

Hydrological uncertainty analysis using Monte Carlo simulations to determine power purchasing agreements for small hydroelectric powerplants

The energy generated by Small Hydroelectric Plants (SHPs) is sold in Brazil using a Physical Energy Guarantee, also known as a Power Purchase Agreement (PPA), the maximum amount of energy that a generator can sell. The PPA is calculated based on monthly average flow series data, however, studies have shown that this can overestimate the PPA and that daily average flow series data are more accurate in reflecting the amount of generated electric energy. Unlike other hydrological evaluations, the uncertainty levels associated with the data used to calculate the PPA at SHPs are neither considered nor quantified. This study investigates the impact of hydrological uncertainties on SHP PPAs. The Monte Carlo method was used to analyze the uncertainty levels for an SHP case study. It has been elaborated 2 models, where the results show that the models have on average an upper deviation of 1.30 MW from current PPA and a lower deviation of 1.19 MW from the current PPA. Dispersion analysis revealed that SHPs with smaller installed capacities and lower PPAs faced more business risks than SHPs with higher PPAs, which are generally more thoroughly evaluated by investors.

Integrated model for optimal scheduling and allocation of water resources considering fairness and efficiency: A case study of the Yellow River Basin

Water is an important natural resource with economic and social attributes. The most important aspects of water resource regulations are fairness and efficiency. Achieving a balance between fairness and efficiency is a challenging but popular topic in related research. In this study, three optimal water-resource allocation models were constructed: efficiency priority (E-P), fairness priority (F-P), and stratified water supply (SWS). The three models were applied to the Yellow River Basin (YRB), and the results show that, compared with the E-P and F-P models, the SWS model is a more equilibrium model with significant advantages. Considering the impact of reservoirs on water allocation, an optimal operation model for cascade reservoirs groups, considering fairness and efficiency, was constructed based on the principle of welfare economics and coupled with the SWS model. Proxy modeling was used in the coupling process to enhance the computational efficiency of the integrated model. Compared with the SWS model, the integrated model can better satisfy the water supply demand outside a river while ensuring the ecological environment and sand flushing demand inside the river; its fairness and efficiency are also significantly improved. In addition, integrated models can effectively address hydrological uncertainty and reduce losses. The model proposed in this study has significant flexibility and application potential and can provide a reference for other governments and water-resource management institutions worldwide.