Medium-sized Watersheds Research Articles

ВЛИЯНИЕ ВНЕШНИХ ПАРАМЕТРОВ НА ВЕЛИЧИНУ ЭВАПОТРАНСПИРАЦИИ В МОДЕЛИ ДЕЯТЕЛЬНОГО СЛОЯ СУШИ ИВМ РАН - МГУ

The paper investigates the sensitivity of evapotranspiration in the INM RAS-MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002-2012) from the surface of medium-sized (2-50 × 103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys.

The impact of basin horizontal ecological compensation policies on carbon emissions: A case study of the Yangtze river economic Belt

The issue of global climate change has garnered increasing attention, with carbon emissions emerging as a significant challenge confronting the world today. As an important means of environmental management, river basin ecological compensation must break through the traditional thinking of "water-centric" and move towards the coordinated development of "pollution reduction". Therefore, the study chooses the watershed scale ecological compensation experiment carried out in the Yangtze River Economic Belt as a natural experiment. Based on the prefecture-level city panel data from 2008 to 2021, a double difference model is constructed to examine the impact of the basin horizontal ecological compensation policy on carbon emissions and its mechanism. The study shows that the inter-regional horizontal ecological compensation measures have an obvious inhibitory effect on carbon emissions in the region, and through a series of tests, the conclusion is stable. Mechanism testing shows that policy implementation achieves carbon emission reductions through two channels: improving financial development and promoting scientific and technological innovation. The results of heterogeneity analysis verify that the effect of policy implementation is affected by the dual factors of social economy and innovation and entrepreneurship and that key cities and cities with a high innovation and entrepreneurship environment produce higher carbon emission reduction benefits. The research conclusions provide policy suggestions for promoting watershed ecological compensation policies to achieve carbon emission reduction from three aspects: encouraging small and medium-sized watersheds to implement city-specific policies, promoting innovative technologies and establishing monitoring and evaluation mechanisms, and strengthening policy support and financial investment.

Temporal evolution of suspended solids in the management of medium-sized watersheds in the Metropolitan Region of Paraíba Valley

The demand for water to meet the needs of the population has increased with the rapid growth of urban centers. The urban expansion in Taubaté (23.03º S, 45.55º W) negatively impacts the Una River Basin, a significant tributary of the Paraíba do Sul River, resulting in issues of sedimentation, siltation, and flooding. Systematic monitoring of sediment characteristics in human-influenced areas allows for an understanding of water quality variations, watershed diagnosis, and analysis of impacts on aquatic ecosystems, with total suspended solids (TSS) being a widely used parameter in this context. This study investigated the temporal evolution of the TSS concentration in the Una River, highlighting the importance of sound management practices to reduce sedimentation in urban catchments, through the analysis of biochemical parameters obtained from the Cetesb/Infoaguas platform. From 2008 to 2022, lower TSS concentrations were observed in winter (86.2 ± 35.6 mg L-1), while higher values were obtained in summer (779.7 ± 1024.8 mg L-1). In general, statistically significant correlations were found at the 95% confidence level between TSS and turbidity (Pearson's r = 0.93), dissolved oxygen (Pearson's r = -0.46), and electrical conductivity (Pearson's r = -0.43), while other evaluated parameters (pH, dissolved solids, and precipitation) did not show significant correlations. Among the key findings, it is crucial to study the temporal evolution of TSS and to implement sustainable management strategies in urban catchments, including erosion control measures and stormwater management, to preserve water quality, aquatic ecosystems, and water supply in the urban communities of RMVale.
 Keywords: basin, management strategies, sustainable, total suspended solids.

Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model

The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.

A distributed hydrological model for semi-humid watersheds with a thick unsaturated zone under strong anthropogenic impacts: A case study in Haihe River Basin

In the past decades, human activities, including hydraulic structure construction and operation, and groundwater overexploitation have largely changed the land surface conditions and correspondingly altered the natural rainfall-runoff processes. These changes make the traditional operational flood forecasting models without considering these anthropogenic impacts fail to capture these changes, leading to a downgraded forecasting capability. To improve the accuracy of flood forecasting in semi-humid watersheds under strong aboveground and underground anthropogenic impacts, the widely used operational flood forecasting model, the Xin’anjiang model, was chosen as a basis to build a new distributed hydrological model accounting for these anthropogenic impacts. To characterize the runoff generation process under the condition of a thick unsaturated zone caused by groundwater overexploitation, this study developed a runoff generation algorithm based on an idea that conceptualizes the soil free-water storage into two virtual reservoirs. To characterize the impacts of manmade reservoirs on the runoff concentration process, we modified the Muskingum method. In addition, we further developed a priori estimation method for deriving the spatial distribution of the tension water storage capacity by considering the anthropogenic impacts and geographical conditions. Based on the above work, a Grid Xin’anjiang-Haihe model (GXH) was developed. In this study, the Qingshui River, which is a tributary of the Haihe River Basin and a typical semi-humid medium-sized watershed in China with poor flood forecasting accuracy, was selected as the study area to test the effectiveness of the model. The results show that the GXH model can simulate the flood peak flow and peak time with higher accuracy than the current operational flood forecasting models and several other models. In addition, the parameter estimation method developed in this study has also proven to be superior in theory and practice.

Impacts of sampling frequency on the estimation accuracy of exceedance for suspended solids and nitrates in streams in small to medium-sized watersheds

Data from a 389 km2 watershed and one of its 14.5 km2 subbasins were used to assess the effects of sampling frequency on the estimation accuracy of the exceedance frequency (EF) for suspended solids and nitrate-nitrogen in streams. Values of EF estimated from 17 subsampling schemes were compared with the actual EF (EFa) at different threshold concentrations. The coefficient of variation and relative bias were used to measure the estimation accuracy. Results indicated that the EFa of the larger watershed was much lower than that of the smaller watershed for both suspended solids and nitrate-nitrogen. We also found that EFa can be modeled as an exponential function of the threshold concentration. For the EF estimations, the coefficient of variation decreased with increasing sampling frequency and increasing EFa. The relative bias tended to be negative when EFa was low or the threshold concentration was high, reaching -75% in some cases. This result implies that reported EF values based on low-frequency data could be severely underestimated due to the high possibility of missing large events. However, there were also cases with positive relative bias, implying overestimation of EF due to over representation of large events. These findings can be used to determine adequate sampling frequencies for water-quality parameters, avoiding common observed biases (mostly negative) in the estimation of EF for extreme pollution events.

Short-term forecasting of spring freshet peak flow with the Generalized Additive model

• Spring freshet peak flow can be estimated with few variables, easily available. • Generalized Additive Model (GAM) provided a reliable 24-h forecast of the peak flow value. • GAM model forecasts compared favorably to a more complex deterministic hydrological model. • The forecasted flow can be used as a decision support to launch emergency measures. In cold boreal regions, for rivers with small to medium-sized watersheds under natural hydrological regimes, the risk of spring flooding is determined by peak flow intensity rather than flood volume. Nonetheless, short-term forecasting of peak flow intensity is subject to a lot of uncertainty and depends largely on ongoing specific snowmelt conditions. This study proposes a simple operational model based on the Generalized Additive Model (GAM) to forecast short-term spring freshet peak flow. The model uses hydrological and meteorological data publicly available on a daily basis. The model was tested on five rivers in the Province of Québec (Canada) with drainage basins varying between 350 km 2 and 1707 km 2 . The model results (forecasted peak flows) were compared to those obtained using the Generalized Linear Model (GLM) and a distributed deterministic hydrological model (Hydrotel) currently used for flow forecasting of several rivers in the Province. The peak flow was forecasted accurately with relatively few variables, mainly a combination of river flow and rate of flow increase a few days before peak flow, previous air temperature, rain accumulation and snow accumulation. Nonetheless, the best combinations of predictive variables were river-specific. The GAM model, using an automatic fitting and easily accessible daily data, can be implemented by any stakeholder.

Assimilation of surface soil moisture jointly retrieved by multiple microwave satellites into the WRF-Hydro model in ungauged regions: Towards a robust flood simulation and forecasting

This study investigates how assimilation of surface soil moisture jointly retrieved by multiple microwave satellites affects flood simulation and forecasting based on the experiments of simulation (Sim), Open Loop (OL), and Ensemble Kalman Filter (EnKF) in small and medium-sized watersheds without gauged soil moisture. We developed a framework for data assimilation (DA) of satellite soil moisture into the WRF-Hydro model based on the EnKF algorithm. Three statistical metrics to evaluate the impacts of DA, including net error reduction, normalized error reduction, and effectiveness criterion, are all positive values (>6.0%), indicating that DA gains reduced errors. Meanwhile, the deterministic coefficients of the EnKF experiment are also greater than those of the OL experiment. It is obvious that multi-satellite retrieved soil moisture and DA technology can improve the accuracy of flood simulation and forecasting in ungauged regions and play an important and positive role in hydrological forecasting.

Application of the TOPKAPI Model in Flood Forecasting of the Upstream of the Zhenjiang River in China

The lumped hydrological model and empirical model have the problems of low accuracy and short forecasting period in real-time flood forecasting of small- and medium-sized rivers in a mountainous watershed. The sharing of underlying surface data such as high-resolution DEM, land use data, soil data, and the popularization and application of the Internet of Things, big data, cloud computing, and intelligent calculation methods makes distributed hydrological model an effective method for real-time runoff simulation and prediction. The topographic, kinematic, approximation, and integration (TOPKAPI) model is a distributed hydrological model whose physical mechanism developed gradually in the late 20th century. It has great advantages in real-time flood forecasting in small- and medium-sized watersheds. Based on the data required by the TOPKAPI model, in this study, 26 selected flood events were simulated from 2000 to 2013 at the outlet section of the upper reach of the Zhenjiang River in Guangdong Province, and the effect of application of the model in flood forecasting of small- and medium-sized rivers was evaluated. The results show that the pass rate (considering the peak discharge as the evaluation item) of 18 flood events in the calibration period was 66.67%, and that of 8 flood events in the validation period was 75%, while the mean Nash efficiency coefficient of the selected 26 flood events was 0.789. According to the simulation results, real-time flood forecasting should be closely combined with the dispatching of the small- and medium-sized reservoirs in the basin. The application of the TOPKAPI model can make a scientific and rapid analysis of the flood control situation in the whole basin and provide accurate information and maximum convenience for flood forecasting consultation and decision making. Additionally, it can improve the efficiency of disaster prevention and mitigation work in small- and medium-sized river basins, and has a major significance in enhancing the modernization level of flood forecasting.

Differences analysis of flood simulations and response evaluation of impact factors in semi-arid areas

中国北方半干旱地区的降水与下垫面条件具有明显的时空异质性，如何完整准确地描述该类区域的水文过程是当代水文学研究的难点之一.选择半干旱地区水文实验区域——绥德流域和曹坪流域，通过构建不同时空规律的降水场，并结合3种不同产流机制的水文模型，进行大型数值模拟实验，去探究时间、空间、产流机制等因素对半干旱地区洪水模拟的影响，为该类地区水文模型的研制工作提供借鉴.结果表明：1）半干旱地区中小流域的产流对降雨强度较为敏感，因此降水输入的时间步长对洪水模拟效果的影响程度较大；相比之下，流域雨量站数量的增减，仅体现在降雨分布场的暴雨中心缺失以及面平均降雨量的微小差别，对洪水模拟效果的影响程度较小.2）水文模型能否准确描述主导水文过程是半干旱地区洪水模拟效果优良的关键，流域的尺度效应及其下垫面条件的空间异质性是半干旱地区不同水文模型研制和调整应当优先考虑的问题，无论时间步长、雨量站数量怎么组合，产流结构适宜的模型其模拟效果总是趋于较好的结果.;The precipitation and surface conditions in the semi-arid areas of northern China have significant spatial and temporal heterogeneity. How to completely and accurately describe the hydrological process in such areas is one of the challenges in contemporary hydrological research. Spatiotemporal precipitation schemes were constructed with different time steps and numbers of rainfall stations in Suide and Caoping Watersheds. To provide a reference for the development of hydrological models in semi-arid regions, large-scale numerical simulation experiments were conducted to explore the influence of precipitation spatiotemporal heterogeneity and runoff generation mechanisms on flood simulation. The results show that:1) The runoff generation in semi-arid areas is strongly influenced by the rainfall intensity in small and medium-sized watersheds, so the time step of precipitation has a great influence on the flood simulation accuracy. In contrast, the impact of the increase or decrease in the number of rainfall stations on rainfall is mainly the absence of storm recentres and small differences in the average rainfall, which has a small effect on the flood simulation. 2) The ability of the hydrological model to accurately describe the dominant hydrological processes is the key to good flood simulations in semi-arid areas. The scale and spatial heterogeneity of the watershed is a priority for model development in such areas. Regardless of the combination of time step and rainfall station density, the model simulations with suitable runoff generation structures always tend to be better.

Response of Riverine Nitrogen Exports to Land Use Pattern and Hydrological Regime in the Jiulong River Watershed

Watershed land use patterns combined with hydrological regimes affect riverine nitrogen (N) sources, transformation pathways, and exports, which can affect watershed health and freshwater ecosystem service supply. Understanding how land use and hydrological regimes affect riverine N exports is therefore useful for developing sustainable watershed management strategies. Based on in-situ observations during the period 2010-2017, watershed modeling, geospatial technology, and statistical analysis were coupled in this study to explore the responses of riverine nitrogen exports to watershed land use pattern and hydrological regime in a medium-sized watershed. Results showed that nitrate was the major form of dissolved inorganic N in the Jiulong River watershed; agricultural and urban watersheds had higher N exports and greater temporal variability than those in natural watershed. The seasonal fluctuation for watershed N concentrations and exports was obvious in wet years compared with dry years. Compared with the hydrological regime, the land use pattern had significant effects on N concentrations and exports. This study demonstrated that spatiotemporal variations of riverine nitrogen exports were mainly contributed by the coupled effects of watershed land use pattern and hydrological regime.

A stream prediction model based on attention-LSTM

The small- and medium-sized watersheds have complex and varied hydrogeological features, boundary conditions, and human activities. There are nonlinear interactions between these factors, which leads to great challenges in predicting the stream of the river. Since not all factors are positively correlated with flood forecasting, and irrelevant factors tend to bring a lot of noise, it is necessary to give more attention to the absolute action factors. In this paper, we forecast the flow values over the next 12 hours, using an Attention-LSTM prediction model with an attention mechanism based on long-term and short-term memory networks that consider past stream data, past weather data, and weather forecasts data. We use data from Tunxi watershed, China, and evaluate the model with root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2). The forecast results of the Attention-LSTM model are compared with the prediction results of two traditional machine learning models and an LSTM model. The experimental results show that the Attention-LSTM model has a higher score, and provided a new method for flood forecasting.

Flood Forecasting Method of Small and Medium-sized Watershed Based on Convolutional Neural Network

A data-driven flood forecasting model based on a convolution neural network is proposed for the small and medium-sized watershed. Pearson correlation coefficient analysis was used to determine the time length of the input data. The historical rainfall and discharge were used to create the two-dimensional input data matrix. NAS was used to determine the structure of the model. The experiment results in Tunxi watershed in Anhui Province show that the accuracy is high under the 1˜3h forecast period, with the forecast period increased, accuracy would decrease.

SDGs-Based River Health Assessment for Small- and Medium-Sized Watersheds

A river health assessment index system was established, focusing on the realistic needs of county sustainable development and the refined management of small- and medium-sized watersheds. The index system takes into consideration the United Nations’ Sustainable Development Goals (SDGs) and the vulnerability characteristics of small- and medium-sized watershed ecosystems and consists of 15 indicators in four areas: clean water, sanitation, the present status of biodiversity and threats to biodiversity. This paper uses the minimum discrimination information principle to construct a dynamic combination-weighting technology composed of a subjective weighting method (document frequency method) and an objective weighting method (entropy weight method). Using the fuzzy matter-element analysis theory, a comprehensive river health assessment technology system was constructed. Baoxing County was chosen as the research area and the results reveal that: (1) Key indicators are the biodiversity index of fish, water use intensity, endemic or indicative species retention, and chemical oxygen demand (COD) emissions. (2) The Euclid approach degree of Baoxing County indicates that the entire river is in a moderate state of health. In the future, towns must take targeted measures to coordinate the relationship between the ecological environment and socio-economic development, and enhancement and releasing must be prioritised.

Impact of the spatial density of weather stations on the performance of distributed and lumped hydrological models

This study aimed to quantify the ability of distributed and lumped hydrological models to use high-resolution precipitation and temperature data to improve streamflow simulation at watershed outlets. To that end, a 40-year, high-resolution, spatially distributed, meteorological dataset was extracted from a 15-km resolution regional climate model simulation (from the Canadian Regional Climate Model – CRCM v.4.2.4 driven by ERA40 reanalysis). This dataset was used to feed one distributed and four lumped hydrological models. The five models were calibrated on 192 watersheds located in the province of Quebec (Canada) using five different meteorological network densities of pseudo-stations. These densities ranged from one single station (located at the centre of gravity of the watershed) up to the maximum grid density of 1 station per 225 km2 (15 km × 15 km which corresponds to the CRCM spatial resolution). No significant decrease in validation performance for both types of hydrological models was observed when using any of the tested station densities. Similar results were also obtained when investigating the subsets of 54 smaller (≤2,500 km2) and 84 medium-sized (2,500 < area <10,000 km2) watersheds. However, for the 54 larger watersheds (≥10,000 km2), the decrease in performance was statistically significant for the distributed model when using one single station. While all lumped models showed a noticeable drop in performance only when using a single station, the distributed model was the only model to show a gradual decrease in performance as the network density decreased. These results indicate that when dealing with large watersheds, distributed models could benefit up to some extent from a larger meteorological network density. These conclusions are likely to be relevant to Canadian watersheds with similar physiographic characteristics and hydroclimatic conditions as the ones included in the Quebec database that was studied.

Similarity Analysis of Small- and Medium-Sized Watersheds Based on Clustering Ensemble Model

Similarity analysis of small- and medium-sized watersheds mainly depends on manual work, and there is no complete automated analysis method. In order to solve this problem, we propose a similarity analysis method based on clustering ensemble model. First, the iterative clustering ensemble construction algorithm with weighted random sampling (WRS-CCE) is proposed to get great clustering collectives. Then, we combine spectral clustering with the fuzzy C-means method to design a consensus function for small- and medium-sized watershed data sets. Finally, the similarity analysis of small- and medium-sized watersheds is carried out according to the clustering results. Experiments show that the proposed clustering ensemble model can effectively find more potential similar watersheds and can output the similarity of these watersheds.

Research on Ecological Risk Assessment Model Based on Genetic Algorithm

In this study, Daxia river, a typical watershed in northwest China, was selected as the research area. The heavy metal content of the soil in the basin is affected by topography, meteorological factors and vegetation. The use of traditional methods to sample heavy metals in the basin soil consumes manpower and material resources, and is affected by geographical environment and weather conditions, resulting in inefficiency. In order to more accurately screen the factors related to soil water content, the characteristics of the global optimization of genetic algorithm are used, and the key factors are screened by controlling the iteration times to accelerate convergence. Furthermore, using the method of multiple regression, using the data from 2015 to 2017, a prediction model for soil heavy metal content was established. The conformity of this model is more than 80%, which can effectively characterize the soil heavy metal content in the study area. The establishment of this model provides a good theoretical and practical basis for the heavy metal content of soil in small and medium-sized watersheds in the future.

Frequency analyses of peak discharge and suspended sediment concentration in the United States

Compound extreme events with concurrent peak discharges and suspended sediment concentrations have substantial impacts such as floods, infrastructure failure, pollution, increased water treatment costs, and threats to aquatic life. It is thus essential to understand the spatiotemporal dynamics of water and sediment transport during such extreme flood events. Copulas are used to conduct multivariate flood frequency analyses and provide relationships between design variables and recurrence intervals that are useful in hydrology and water resource management. We used hydrological data from 20 gauging stations on important rivers across the United States with daily records spanning at least the last 30 years. We extracted annual maximum discharges (Qmax) and corresponding suspended sediment concentrations (SSCmax) at these 20 stations. We analyzed the temporal trends of Qmax and SSCmax by using the Mann–Kendall non-parametric statistical test. Then, we investigated the interdependency of Qmax and SSCmax through various correlation coefficients and used the Multivariate Copula Analysis Toolbox (MvCAT) for flood frequency analyses. We found Qmax decreased significantly for one river, whereas SSCmax decreased significantly for 10 of the 20 rivers over the analysis period. Five of rivers selected as statistically suitable for copula analysis were of the smallest size and with the fewest dams. Seven of rivers showed significantly decreasing SSCmax trends were of the largest size and with the most dams. The remaining eight rivers were of moderate size. In larger watersheds, the number of dams more strongly affected sediment transport dynamics. Soil condition and forest cover exerted stronger influences on SSCmax in smaller than in medium-sized watersheds, and soil condition was better correlated with the spatial SSCmax distribution than forest cover. Using MvCAT, we computed the optimal continuous marginal distribution functions for the five suitable rivers, and the optimal copula was different in each case. Our results indicate reduced sediment transport during extreme flood events. Copula functions are very useful in such flood frequency analyses, and measures should be applied according to local conditions to achieve optimal water resource management and planning.

Development and application of a spatially distributed snowmelt runoff model for limited data condition

Two main approaches are used to calculate snowmelt, namely, energy balance (physically based) and temperature index (conceptual). Energy balance requires comprehensive data to solve physical equations. In this study, a distributed conceptual model, “spatially distributed snowmelt runoff model”, based on modified temperature index approach was developed to estimate snow-related parameters like snow density and degree day factor in a distributed manner over space and time to meet the hydrological demands of data-scarce snow-dominated mountainous catchment. The model needs only daily temperature, precipitation, snow cover, and albedo as basic inputs. It gives pixel-wise output of snow density, snow depth, snow water equivalent, degree day, net radiation, snowmelt depth, rain-induced runoff, snowmelt runoff, actual evaporation, and infiltration, while routing the flow up to the catchment outlet to estimate discharge as time series along with contribution breakup between snowmelt and rain-induced runoff. The developed model was tested for a small-sized watershed Nuranang (58 km2) as well as for a medium-sized watershed Mago basin (844 km2) both located in the Tawang district of Arunachal Pradesh, India, and was successfully calibrated for 2004–2005 and validated for 2008–2009 for Nuranang watershed. In the case of Mago basin, a single year (2007) was used for calibration and validation was also performed for a single year (2009) as well due to the unavailability of data. Performance of the model was found satisfactory for both the basins with modelling efficiency (ME) greater than 0.6 and coefficient of residual mass (CRM) in between − 0.2 and 0.2 for both calibration and validation years in these data-scarce eastern Himalayan watersheds. Considering the satisfactory performance of the model in both the small- and medium-sized watersheds, the model can be very useful for any un-instrumented mountainous basins for pixel-wise snowmelt runoff computation and gridded output generation using degree day approach, where observed snow density data is not available.

Design of Intelligent Environmental Management and Decision-Making Platform for Small- and Medium-Sized Hydropower-Intensive Watersheds

Watershed management includes the management of water resources, water environment, and water ecology. The use of intelligent management methods has become a new development trend. Facing the current situation of river basin management in China and the latest management requirements, the positioning of intellectualization in the top-level design of watershed management is clarified. Based on the vulnerability and complexity of ecosystem in small- and medium-sized hydropower-intensive watersheds, the overall framework of intelligent management and decision-making platform is designed, which covers water resource allocation, pollution source analysis, risk early warning, ecological monitoring, industrial access, ecological compensation, public participation, and other functional modules and forms a government–enterprise–society multi-management system.