Annual Runoff Research Articles

Comparative Evaluation of Derived and Previously Published Models for Estimating Annual Runoff in the Mountainous Watersheds of Sulaimani Province

Comparative Evaluation of Derived and Previously Published Models for Estimating Annual Runoff in the Mountainous Watersheds of Sulaimani Province

Study on the Annual Runoff Change and Its Relationship with Fractional Vegetation Cover and Climate Change in the Chinese Yellow River Basin

In the context of global climate change and ecological restoration projects, significant changes have been observed in the fractional vegetation cover (FVC) in the Yellow River basin. The increased vegetation growth accelerates water consumption, exacerbating drought and water scarcity issues, thereby heightening regional water resource shortage risks. This study targets the Yellow River basin in China, employing a pixel-based model to convert NDVI into FVC datasets. We establish a pixel-wise mathematical model for annual runoff and environmental factors based on residual analysis and methods like multiple linear regression. Using climate model data from CMIP6 as independent variables, in conjunction with the statistical model, we elucidate the spatiotemporal characteristics of annual runoff in the Yellow River basin under future climate scenarios. Our results indicate that, under four different climate scenarios, the average annual runoff in the Yellow River basin is projected to increase. The increases are quantified as 0.008 mm/a, 0.065 mm/a, 0.25 mm/a, and 0.24 mm/a for SSP126, SSP245, SSP370, and SSP585 scenarios, respectively. From 2022 to 2040, the spatial distribution of the runoff change rates under the SSP245 and SSP370 scenarios show an increasing trend in upstream areas such as the Qinhe and Longmen regions, with rates ranging from 6.00 to 8.61 mm/a. During the period from 2041 to 2060, all four climate scenarios indicate minimal changes in the runoff depth in the northern part of the Yellow River basin. From 2061 to 2080, under the SSP126 and SSP245 scenarios, the spatial distribution of the runoff shows significant increases in the river source area and a decreasing trend in the middle reaches, with rates ranging from 4.52 to 11.39 mm/a. For the period from 2081 to 2100, the runoff change rates vary significantly under the four climate scenarios. These findings provide a detailed understanding of how future climate scenarios could impact water resource distribution in the Yellow River basin, offering critical insights for regional water management and policy making to mitigate potential water scarcity challenges.

Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction

Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity.

Comparison of SWAT and WEPP for Modeling Annual Runoff and Sediment Yield in Agewu - Mariyam Watershed, Northern Ethiopia

The Soil and Water Assessment Tool (SWAT) and the Geographic Water Erosion Prediction Project (Geo-WEPP) were applied to compare modeling of annual runoff and sediment yield in the Agewmariam watershed, eastern Amhara Region, Ethiopia. Spatial and temporal data distributions were required as inputs to run both models. Soil texture and other soil properties were measured in the field and in the laboratory, and soil maps were generated from global digital soil maps. Land use maps were created by manually digitizing Google Earth images. Watersheds were defined using watershed DEMs and gradient maps were created for each runoff event. Runoff samples were collected and analyzed for sediment concentrations in the laboratory; average annual runoff and sediment volumes were estimated using the WEPP and SWAT models. The results were satisfactory compared to the observed values, with R2 values of 0.86 and 0.91 for the SWAT and WEPP models, respectively, and NSE values of 0.54 and 0.71 for the monthly runoff. The estimated annual mean runoff and sediment yield at the watershed outlets were 65.54 mm, 146.14 mm, 43t/ha/yr and 41.7t/ha/yr for the WEPP and SWAT models, respectively. Several sub watersheds were determined to be susceptible to soil erosion and were prioritized, so more attention was given to this area to reduce runoff and soil erosion. Therefore, the SWAT and WEPP models were suitable for estimating annual runoff and sediment volumes. Sediment yields simulated from both models were high and alarming and far exceeded the allowable rate of soil loss.

Study on the Annual Runoff Forecast Model of the Main Stream of Nanxi River Based on PSO-ANFIS

In 2021, Wenzhou adopted measures to restrict the use of electricity, and the shortage of electricity became an important factor affecting the production and life of Wenzhou. Nanxi River is one of the main rivers in Wenzhou City, and its water resources are very rich. According to the statistics of the water conservancy planning of the Nanxi River basin, there are 96 hydropower stations in the Nanxi River basin, with a total installed capacity of 152100 kW, accounting for 57% of the installed capacity. The development and utilization of the Nanxi River water resources can alleviate the power shortage in Wenzhou power grid to a certain extent. The development and utilization of hydropower are closely related to the runoff of the basin. The river runoff is mainly determined by rainfall, underlying surface and upstream inflow. River runoff is affected by many factors in the process of formation, so it is difficult to improve its prediction accuracy. In order to improve the prediction accuracy of the runoff of the main stream of the Nanxi River, this paper introduces the runoff prediction model of particle swarm optimization adaptive fuzzy inference system (PSO-ANFIS). ANFIS model has the advantages of applying fuzzy rules and the nonlinear approximation ability of neural network, but the antecedent parameters of ANFIS model are prone to fall into local optimization. In order to improve the generalization ability of the antecedent parameters of ANFIS model, the PSO algorithm of global optimization is introduced to optimize the antecedent parameters of ANFIS. Through the application of the example, it is found that the decision coefficient of PSO-ANFIS model in the simulation stage is 0.987, and the decision coefficient in the prediction stage is 0.856. This model can be applied in the annual runoff forecast. Through comparison with ANFIS model, it is found that PSO-ANFIS model has better prediction effect.

Environmental Flow Assessment (EFA) of Tawi River Discharge at the Jammu Location Using the Global Environmental Flow Calculator (GEFC)

The water, food, and energy demands are the basic requirements of society. These demands are increasing daily due to an increase in population or lifestyle changes. To fulfill these ever-increasing demands, several water resource projects have come up which require the storage or diversion of river water. These interventions have caused widespread degradation of aquatic ecosystems. Due to the degradation of the aquatic ecosystem, several programs all around the globe began. In this series, Brisbane Declaration (2007) provided a more holistic definition of Environmental Flows (EFs) as the quantity, timing, duration, frequency, and quality of flows required to sustain freshwater, estuarine and near-shore ecosystems and the human livelihoods and well-being that depend on them. The present study was envisaged to assess for environmental flows of the Tawi river with a major objective of assessing the environmental flows of the Tawi river using the Global Environmental Flow Calculator developed by IWMI. The method provides E-Flows for different Environmental Management Classes. For the western Himalayan region, the river stretches in Environmental Management Class ‘B’ and ‘C’. The assessment provides E-Flows in two ways: (i) the percentage of Mean Annual Runoff and (ii) average monthly environmental flows. E-Flows were estimated as 42.34% to 56.96% of Mean Annual Runoff and varied from 5.73 cumecs during November to 68.23 during August.

Climate Elasticity of Annual Runoff: Observation in Fifteen Forested Catchments on a Latitudinal Gradient in East Asia

In order to overview the impact of climate change on runoff from forested catchments over Asian countries, we collected water balance data from fifteen long-term catchment monitoring stations (total monitoring period 1975–2018, not continuous), spanning from Sabah, Malaysia (our southernmost site), to Hokkaido, Japan (our northernmost site). We then employed an elasticity analysis to the dataset to examine how the annual runoff from each catchment responded to inter-annual fluctuations in annual rainfall and annual mean air temperature. As a result, we found that (1) the annual runoff was sensitive to annual rainfall for all the catchments examined. In addition, (2) the annual runoff from seven of the fifteen catchments was sensitive to inter-annual changes in the mean air temperature, which was likely due to changes in forest evapotranspiration. Three catchments, however, exhibited an increased runoff in a hot year. Finally, (3) the annual rainfall from the previous year (carry-over soil moisture) was important in explaining the variation in annual runoff in two tropical montane forest catchments. This study may serve as one of the pilot studies toward a comprehensive understanding of the climate elasticity of runoff in countries over Asia, because the examined catchments are unevenly and sparsely distributed over the area.

Applying SWAT Model to Estimate the Annual Runoff of Wadi Al-Mohammadi Basin, Western Iraq

In arid and semi-arid regions, with unexpected periods of floods or droughts as a result of extreme variability in rainfall rates, runoff occurs in separate periods after clear occasional rainstorms. People living in these areas are greatly affected by water shortages during droughts and often have unsafe livelihoods. Wadi Al-Mohammadi is one of the main valleys in the Western Desert flows into the Euphrates River. It is considered an important area, due to many characteristics, including its relatively large area, the amount of water drained through, and has significant storage of groundwater, which is used mainly in agriculture and watering livestock, in addition to industrial purposes such as the production of washed sand, gravel that scattered on both sides of the valley. The Soil and Water Assessment Tool (SWAT) model is proven to be effective as a working platform in the hydrological cycle and to clarify its components and factors affecting the hydrological situation in a region. By applying the SWAT model using the required data set (LULC, slope, and soil types), Wadi Al-Mohammadi is subdivided into 199 HRU (hydrologic response units). The annual average flow discharge was 0.138 m3/s with an average annual water volume about 4.366 Mm3, while the average annual water volume is calculated from runoff which was of about 4.651 Mm3. There is a difference in the average annual volume of water calculated from surface runoff and discharge, due to a little slope, and not all the surface runoff water reaches the mainstream, or it ends in secondary valleys or collecting in pits, and there for the discharge is less than the surface runoff. Peak runoff is observed in Wadi Al-Mohammadi basin mostly during the winter season, from November to March.

Attribution of changes in the water balance of a basin to land-use changes through combined modelling of basin hydrology and land-use dynamics

Abstract Land use and land cover change (LULCC) is considered one of the major drivers of climate change, although climate change can also foster direct or indirect influences leading to LULCC. The objective of the presented study is to offer a strategic observation frame as the land use and land cover (LULC) transitions are grouped to define the cover flows (CFs). The Küçük Menderes River Basin (KMRB), which is located in the west of Turkey was examined as the case study. Through LULCC modelling via the employment of multi-layer perceptron (MLP), cellular automata (CA), and Markov Chain methods, future LULC maps were projected up to the horizon of 2050. Hydrologic responses of the basin to LULCC were determined by the developed hydrologic model, which is generated by the Soil and Water Assessment Tool (SWAT). The superimposed impacts of the examined effects of LULCC have been investigated by the CF types. This way, the individual impacts of the CFs have been assessed. In the case of the KMRB, projected annual runoffs for the year 2050 cover map represent a 9.06% reduction and the major responsible CF type for this reduction is the conversion from forest to non-irrigated agricultural land cover by 22.90%.

The Use of GAMLSS Framework for a Non-Stationary Frequency Analysis of Annual Runoff Data over a Mediterranean Area

Climate change affects all the components of the hydrological cycle. Starting from precipitation distribution, climate alterations have direct effects on both surface water and groundwater in terms of their quantity and quality. These effects lead to modifications in water availability for agriculture, ecology and other social uses. Change in rainfall patterns also affects the runoff of natural rivers. For this reason, studying runoff data according to classical hydrological approaches, i.e., statistical inference methods that exploit stationary probability distributions, might result in missing important information relevant to climate change. From this point of view, a new approach has to be found in the study of this type of data that allows for non-stationary analysis. In this study, the statistical framework known as Generalized Additive Models for Location, Scale and Shape (GAMLSS), which can be used to carry out non-stationary statistical analyses, was applied in a non-stationary frequency analysis of runoff data collected by four gauges widely distributed across Sicily (Italy) in the period 1916–1998. A classical stationary frequency analysis of these runoff data was followed by a different non-stationary frequency analysis; while the first was made using annual rainfall as a covariate, with the aim of understanding how certain statistical parameters of runoff distribution vary with changes in rainfall, the second derived information about the temporal variability of runoff frequencies by considering time as a covariate. A comparison between stationary and non-stationary approaches was carried out using the Akaike information criterion as a performance metric. After analyzing four different probability distributions, the non-stationary model with annual rainfall as a covariate was found to be the best among all those examined, and the three-parameter lognormal the most frequently preferred distribution.

Estimation of Annual Runoff Volume in Hydrological Region No. 10 (Sinaloa), Mexico, through Regional Frequency Analysis

Estimation of Annual Runoff Volume in Hydrological Region No. 10 (Sinaloa), Mexico, through Regional Frequency Analysis

Environmental flow estimation through a hydrology-based method in the Tamas River at Bakiya Barrage, Madhya Pradesh, India

AbstractThe River Tamas is a right-bank tributary of the River Ganga flowing through the states of Madhya Pradesh and Uttar Pradesh, India. It originates in Jhukehi village in the Satna district of Madhya Pradesh and after 268 km it joins with the River Ganga at Sirsa, Prayagraj, Uttar Pradesh. The river has a diverse habitat and harbours a variety of flora and fauna. More than 70 fish species reside in the river, along with other biotic communities. A barrage, named Tons and situated at Bakiya village, was commissioned in 1992 for hydro-electric purposes. The continuity and volume of water in the river have been disturbed due to the construction of this barrage and it has changed the ecology and biodiversity. Keeping the ecosystem intact requires that environmental flow calculations be made of the river at the barrage. An attempt has been made to estimate the environmental flow requirement using a desktop hydrological method. Based on the flow duration curve (a hydrological method of flow estimation), the environmental flow for the River Tamas has been calculated using Global Environmental Flow Calculator software. Our calculation are based on flow discharge data received from the Tons Barrage Authority at Bakiya for the period January 2006 to December 2017. Average annual discharge was calculated from the recorded maximum (151.46 cumecs in 2016) and the minimum, 13.53 cumecs in 2007. Mean Annual Runoff was calculated as 1607 Million Cubic Meter with Coefficient of Variation =0.799. To maintain the river in the “slightly modified” class of Environmental Management Class, 27.8 % of Mean Annual Runoff discharge should be released into the river to largely maintain the biodiversity and habitat, while about 57% of Mean Annual Runoff discharge is required to maintain this river in pristine condition. This is a preliminary study to determine the water requirements for healthy habitat and biodiversity of the Tamas river.

Synchronism of Variations in the Annual Runoff of the Largest Rivers in the Asian Part of Russia

Synchronism of Variations in the Annual Runoff of the Largest Rivers in the Asian Part of Russia

Frequency Analysis of the Nonstationary Annual Runoff Series Using the Mechanism-Based Reconstruction Method

Due to climate change and human activities, the statistical characteristics of annual runoff series of many rivers around the world exhibit complex nonstationary changes, which seriously impact the frequency analysis of annual runoff and are thus becoming a hotspot of research. A variety of nonstationary frequency analysis methods has been proposed by many scholars, but their reliability and accuracy in practical application are still controversial. The recently proposed Mechanism-based Reconstruction (Me-RS) method is a method to deal with nonstationary changes in hydrological series, which solves the frequency analysis problem of the nonstationary hydrological series by transforming nonstationary series into stationary Me-RS series. Based on the Me-RS method, a calculation method of design annual runoff under the nonstationary conditions is proposed in this paper and applied to the Jialu River Basin (JRB) in northern Shaanxi, China. From the aspects of rationality and uncertainty, the calculated design value of annual runoff is analyzed and evaluated. Then, compared with the design values calculated by traditional frequency analysis method regardless of whether the sample series is stationary, the correctness of the Me-RS theory and its application reliability is demonstrated. The results show that calculation of design annual runoff based on the Me-RS method is not only scientific in theory, but also the obtained design values are relatively consistent with the characteristics of the river basin, and the uncertainty is obviously smaller. Therefore, the Me-RS provides an effective tool for annual runoff frequency analysis under nonstationary conditions.

Changes in specific runoff in river catchments of Western Pomerania versus climate change

This paper examines specific runoffs in the catchments of the rivers Ina, Rega, Parsęta, Radew and Wieprza in the hydrological years 1981 through 2019. The magnitude of specific runoff is an indirect measure of water resources in a given region. Except for the Radew catchment, mean annual specific runoffs have diminished in all the analyzed catchments through the study period. In some or all of the catchments, runoffs from April through July have also diminished. The largest changes have been observed for June in the Ina and Parsęta catchments. These changes are basically due to the increase in air temperature.

An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

Accurate and consistent annual runoff prediction in a region is a hot topic in management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, wavelet packet decomposition (WPD) is adopted to further decompose the IMF with the maximum SE into several appropriate components. Then long short-term memory (LSTM) model, a deep learning algorithm based recurrent approach, is employed to predict all components. Finally, forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to seven annual series from different areas in China, is evaluated based on four evaluation indexes (R, MAE, MAPE and RMSE). Results indicate that ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four evaluation indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, rendering it an efficient instrument for scientific management and planning of water resources.

A Two-Field Geostatistical Model Combining Point and Areal Observations—A Case Study of Annual Runoff Predictions in the Voss Area

AbstractWe estimate annual runoff by using a Bayesian geostatistical model for interpolation of hydrological data of different spatial support: streamflow observations from catchments (areal data), and precipitation and evaporation data (point data). The model contains one climatic spatial effect that is common for all years under study, and 1 year specific spatial effect. Hence, the framework enables a quantification of the spatial variability caused by long-term weather patterns and processes. This can contribute to a better understanding of biases and uncertainties in environmental modelling. The suggested model is evaluated by predicting annual runoff for catchments around Voss in Norway and through a simulation study. We find that on average we benefit from combining point and areal data compared to using only one of the data types, and that the interaction between nested areal data and point data gives a spatial model that takes us beyond smoothing. Another finding is that when climatic effects dominate over annual effects, systematic under- and overestimation of runoff can be expected over time. However, a dominating climatic spatial effect also implies that short records of runoff from an otherwise ungauged catchment can lead to large improvements in the predictive performance.

Trend Analysis of Annual Runoff in Yong’an Creek Basin of Zhejiang

Yong’an Creek is the source of the Lingjiang River, one of the eight major river systems in Zhejiang, and is of great significance to support the economic and social development of the Zhejiang region. Based on the runoff data of Baizhi’ao Hydrological Station from 1985 to 2020, this paper uses linear regression, Mann-Kendall method and Sen’s slope estimator to analyze the change trend of the annual runoff of Yong’an Creek, and diagnoses abrupt changes. The results show: The runoff in the Yong’an Creek basin has large interannual variability, and the linear regression and M-K trend test results show that the runoff in the Yongan River Basin has a downward trend. There is no real mutation point in the precipitation in the Yong’an Creek basin from 1985 to 2020. The results of Sen’s slope show that the rate of change of runoff is small. The results of the study will provide necessary guidance for sustainable water resources management and disaster risk reduction of the Yong’an Creek basin.

Estimation of Annual Runoff of Galal Badra Transboundary Watershed Using Arc Swat Model, Wasit, Eastern of Iraq

Optimal investment of natural water resources in an area is an effective way to provide significant amounts of water that can contribute to reduce the negative impacts of climate extremism. Proper assessment of the components of any hydrological system is a priority in watersheds studying. SWAT (Soil and Water Assessment Tool) model was used within ArcGIS, to assess the hydrological situation in general, and surface runoff in particular for Galal Badra Watershed GBW (Wasit Governorate, eastern Iraq). GBW has an area of 2,655 square kilometers (89% of which is in Iran and the rest 11% within Iraq). The data set for SWAT model running were digital elevation model, slope map, soil map, LULC map, and climatic data (precipitation, relative humidity, wind speed, solar radiation, minimum/ maximum air temperature). SWAT simulation concluded that the annual average surface runoff in GBW was 244x106 cubic meters (with an average discharge of 7.8 M3 / s), which accounts for about 25.7% of the total precipitation. This ratio can be used in preliminary forecasting of surface runoff resulting from different amounts of precipitation. The model was not calibrated due to insufficient data available to complete the calibration process. However, the results provided by the SWAT model regarding the water balance elements in the watershed, make the SWAT model an effective tool for hydrological assessments, especially in cases where the necessary data are scarce for various reasons. Also, SWAT results can be considered as a preliminary assessment, which gives an overview of the hydrological situation of the area, contributes to building an initial perception of the water system, determining the most important elements in it, and anticipating the factors most influencing it. This enables policymakers, decision-makers, and stakeholders to adopt future plans at the level of research and implementation that will develop the reality of water investment in the region under conditions of climate extremism.

An Attempt to Decompose the Impact of Land Use and Climate Change on Annual Runoff in a Small Agricultural Catchment

The aims of this study are: i) to better understand the coupled interactions between land use changes, climate change and the aquatic ecosystem in a small agricultural catchment (<100 km2) with a long observation history (1963–2018) and a known land use history, and ii) to test available approaches to separate land use and climate change impacts on water resources in such a small catchment. The pre- and post-change periods have been separated based on change points and the known land use history. Next, conceptual and analytical approaches were applied to quantify and to distinguish between the impacts of climate and land use changes on annual runoff for these two periods. Over the observation period, both land use changes (increase in forest areas) as well as climate change (a temperature rise and a decrease in annual precipitation) occurred. These changes contributed to a decrease in the average annual runoff by 51.9 mm (49% of the long-term average) during the observation period. The quantified contributions of climate and land use changes to the decrease in the mean annual runoff amount to between 60% and 80% and between 40% and 20%, respectively. (i) The results obtained from different methods were consistent - a change in runoff was primarily caused by shifts in climatic variables. (ii) However, the quantified contributions varied depending on the method applied and the form of the Budyko curve. (iii) Thus, special care should be taken in relation to the selection of the Budyko curve for quantifying these changes. (iv) Knowledge of the water deficit sources can result in better planning of water resources management in such small catchments.