Comprehensive and systematic research linking land-hydrological correlations is lacking in the study of factors driving watershed runoff variations. It quantitatively analyzes the overall watershed hydrological conditions using the range of variability approach (RVA) and applies the Budyko hypothesis to identify driving factors of annual runoff variations. The study also explores the impact of individual land use types on runoff across various timescales using the SWAT model in conjunction with historical and extreme scenarios in the Han River Basin. Results reveal that following abrupt changes, the Han River Basin experiences negative alterations in its hydrological indices and overall conditions. Among the driving factors, the lower cushion surface has the most significant impact on runoff. At an annual scale, runoff increases by 12.57 and 20.4% for cropland and construction land scenarios, while forest and grassland scenarios lead to decreases of 8.45 and 2.32%, respectively. Runoff sensitivity to land use changes is notably higher in the wet season than in the dry season at the quarterly and monthly scales. This study offers valuable insights into the integrated management of land use and water resources in the Han River Basin.

  • Using IHA – RVA analysis of watershed hydrological situation changes.

  • Attribution analysis of runoff before and after 1991 is conducted using the Budyko hypothesis.

  • Integrating five distinct land use scenarios, we constructed a SWAT model to quantify the impact of different land use types on runoff.

Land use changes are the most direct indicators of human activities and natural variations, and they constitute one of the primary driving factors for regional and global ecohydrological changes (Barbarossa et al. 2020). Land use activities, including the conversion of natural landscapes for human use and the changes in human-dominated land management, have greatly transformed the global lower cushion surface (Changming et al. 2012). Land use changes can impact various hydrological processes such as infiltration, groundwater recharge, evapotranspiration, soil moisture, and runoff (Omer et al. 2019; Wen et al. 2020; Alsafadi et al. 2022). Numerous studies have demonstrated that significant human activities can alter land use patterns, subsequently affecting the hydrological cycle and water resources (Welde & Gebremariam 2017; Li et al. 2020). Consequently, land use change and its implications for hydrology have become a prominent topic in the realm of global environmental change (Eekhout et al. 2020; Melo et al. 2022).

Presently, the research on the hydrological impacts of land use change (LUCC) in watersheds is gaining increasing attention (Wang & Stephenson 2018; Sharma & Mondal 2022; Yang et al. 2022). The primary research methods employed include statistical analysis, hydrological feature analysis, and hydrological modeling, with the Soil and Water Assessment Tool (SWAT) model being widely applied at the current stage (Shen et al. 2018; Goździewicz-Biechońska & Brzezińska-Rawa 2022). Existing research has indicated that employing the SWAT model for the quantitative analysis of land use impacts on hydrology is suitable and effective (Devia et al. 2015; Yin et al. 2017). Zhang et al. (2020), using an improved SWAT model, found that in the humid tropical region of eastern Australia, under the same climatic, topographic, and geological conditions, forests produce less runoff compared to grasslands and urban areas, with urban areas demonstrating the most prominent runoff-generating capacity in surface runoff. Khorn et al. (2022) employed the SWAT model and observed that land use changes resulted in an overall increase in annual average surface runoff and water yield while leading to reductions in groundwater recharge and evapotranspiration. Moreover, hydrological models serve as effective tools for quantitatively assessing the hydrological responses to environmental changes. However, many previous studies have primarily focused on the overall impact of basin-wide land use on hydrology while neglecting the effects of extreme land use changes. In reality, rural and urban areas respond differently to extreme land use change scenarios. Chen & Yu (2015) assessed the potential impacts of two land use/land cover change scenarios in the southeastern part of Queensland, revealing that extreme land use/land cover changes can significantly affect rural catchments' flooding while leaving urbanized catchments' flooding unaffected. Hu et al. (2021) employed the SWAT model in the northeastern permafrost region and found that extreme land use scenarios resulted in diverse hydrological responses. The conversion of forest to grassland or water bodies led to a significant increase in runoff, with runoff sensitivity to different land use changes being notably higher during the wet season than in the dry season. Therefore, there is a need for further quantitative differentiation of the impacts of extreme land use scenarios on runoff at multiple temporal scales (annual, seasonal, and monthly) and a detailed understanding of the synergistic coupling between regional water resources and land use types. This will significantly contribute to more precise water resource management and optimization (Zuo et al. 2023).

The Han River, as the largest tributary of the Yangtze River, is a focal point for the implementation of key projects such as the South-to-North Water Diversion Project and the Han River Diversion Project. Its environmental significance is highly significant. With the advancement of socioeconomic development and the execution of ecological projects such as returning farmland to forests (grassland) and the preservation of natural forests, there have been notable changes in land use patterns within the basin. In addition, the recent rapid economic growth in the middle and lower reaches of the Han River Basin, coupled with increased irrigation water demand from agricultural activities, has exacerbated water resource conflicts within the basin. Therefore, there is an urgent need to conduct monitoring and assessment to comprehend the spatiotemporal hydrological characteristics within the basin, along with the underlying driving forces. This understanding is essential for achieving precise and sustainable land use management within the basin. Previous studies have not thoroughly explored the impacts of land use changes within the Han River Basin on hydrology, and their temporal scales have been limited (Zhang et al. 2010; Wang et al. 2015; Li et al. 2022). Furthermore, the mechanisms driving the changes in runoff related to precipitation, evapotranspiration, and land use have not been systematically investigated, and the impacts of extreme land use scenarios on runoff have been overlooked. Therefore, this article combines hydrological methods with distributed hydrological models, providing a research framework for the relationship between hydrological conditions, runoff attribution, and land use change. This framework not only allows for a more detailed land use analysis in the study area but also offers a valuable reference for understanding local hydrological conditions, controlling soil erosion, and planning land structure more effectively.

This study was conducted in the Han River Basin of China as the study area and was divided into the following three main steps: (1) utilizing the indicators of hydrologic alteration (IHA) based on riverine eco-hydrological variations and combining it with the improved range of variability approach (RVA) using the improved Nemerow index to analyze the degree of hydrological changes in the watershed; (2) quantitatively analyzing the contributions of drivers (precipitation, potential evapotranspiration, and human activities) to runoff changes using the Budyko hydro-thermal coupling equilibrium theory; (3) constructing the SWAT model and establishing extreme land use scenarios based on the land use conditions in 1980 to quantitatively reveal the impact of land use changes on watershed hydrological processes. Through this study, valuable scientific insights can be provided for restoring eco-hydrology and rational land use planning.

Study area and data

Study area overview

The Han River is the largest tributary in the middle reaches of the Yangtze River, and the Han River basin is located at latitudes 30°8′–34°11′ N and longitudes 106°12′–114°14′E. The general topography of the Han River basin shows a trend of highs in the northwest and lows in the southeast. Its mainstream flows through Shaanxi and Hubei provinces and joins the Yangtze River at Wuhan City, with a basin area of 210,000 km2 and a total length of 1,577 km (Figure 1). The Han River basin is in the East Asian subtropical monsoon region, with a distinct seasonal climate influenced by the Eurasian cold high pressure in winter and the western Pacific subtropical high pressure in summer (Ban et al. 2018). The Han River basin receives abundant rainfall with an average annual rainfall of 700–1,000 mm, but the rainfall distribution in the Han River basin is uneven, with May–October accounting for about three-fourths of the annual rainfall.
Figure 1

Distribution of water systems and stations in the Han River Basin.

Figure 1

Distribution of water systems and stations in the Han River Basin.

Close modal

Data source and processing

The hydrological information used in this study is the Huangzhuang hydrological station, which is the hydrological station that monitors the changes in the hydrological situation in the middle and lower reaches of the Han River after the outflow of the Danjiang Reservoir and the influx of the Tang, Bai, and Barbary rivers. The day-by-day flow data of the hydrological station from 1965 to 2019 were obtained from the hydrological yearbook of the Yangtze River basin. The daily meteorological data of 16 meteorological stations from 1965 to 2019, such as Taibai and Liuba, were used for meteorological data. It includes precipitation, maximum temperature, minimum temperature, and other indicators, which are used to drive the SWAT hydrological runoff model for simulation studies. The meteorological data were provided by the China Meteorological Data Service Center (http://data.cma.cn/).

The model input data encompass both spatial and hydro-meteorological data. Spatial data include information on soils, digital elevation models (DEMs), and land use. The DEM data with a resolution of 500 × 500 m were sourced from the Chinese Academy of Sciences Resource and Environmental Data Center (http://www.resdc.cn/). Land use data, spanning five periods (1980, 1990, 2000, 2010, and 2020), were provided by the same center with a spatial resolution of 1 × 1 km. The land use types were reclassified based on the First-Level Classification System of Land Use/Cover in the Second National Land Survey, resulting in six land use categories: cropland, forest, grassland, water bodies, urban and rural construction land, and unused land. Soil data were obtained from the Chinese Soil Database, which is part of the World Soil Database (HWSD) constructed by the Food and Agriculture Organization (FAO) of the United Nations and the International Institute for Applied Systems Analysis (IIASA) (http://www.fao.org/). These data were at a scale of 1:1,000,000. By comparing the HWSD_DATA soil attribute data with the soil abbreviations in D_SYMBOL90, 18 soil types within the Han River Basin were identified. Meteorological and hydrological data were selected for 1965–2019.

Methodology

The specific research framework of this study is shown in Figure 2.
Figure 2

Flowchart of the land-hydrology study.

Figure 2

Flowchart of the land-hydrology study.

Close modal

Trend analysis and mutation test

To analyze the trend and change years of runoff in the Han River Basin, this study employed the Mann–Kendall (M-K) trend test on the annual average runoff data, identifying change years. In addition, trend analyses were conducted for precipitation and potential evapotranspiration. Concerning change point analysis, while this method is effective in detecting data outliers, it can sometimes produce false change points, making it challenging to determine genuine change positions. In terms of trend change analysis, it can help eliminate a few outliers (Pirnia et al. 2018; Sun et al. 2018; Xue et al. 2021). This article utilized three methods, including the M-K test, cumulative departure method, and sliding difference T method, to determine change years that align with real-world conditions. The statistical variables are defined as follows:
formula
(1)
where SK is the cumulative sample sign number and E(SK) is the sample mean and Var(SK) is the sample variance. The variable UBK is calculated from the inverse time series of the series, and the curves formed by the two statistical series are recorded as UF and UB, respectively.

Hydrological situation analysis

The most commonly used indicator in hydrological change analysis is the Indicators of Hydrology Alteration (IHA) proposed by Richter et al. (1997), which consists of 33 parameters (Table 1). The IHA can be used to quantify the impact of human activities on the hydrological regime of the Han River Basin and to analyze it in conjunction with the range of variation (RVA) method, which is the basis of the IHA. In this study, an improved holistic hydrological alteration index (Xue et al. 2017) is employed to evaluate the hydrological condition of the Han River Basin, considering aspects such as flow, timing, frequency, duration, and rate of change. Zero-flow day indicators are not included in this study. The analysis aims to assess the extent of changes in hydrological conditions before and after hydrological regime shifts and determine the impact of human activities on natural river systems. The formula is provided as follows:
formula
(2)
formula
(3)
where Di denotes the hydrological change of the ith index; N0 denotes the actual number of years that the IHA index falls within the RVA target range after the mutation; Ne denotes the predicted number of years that the IHA index value falls within the RVA target range after the mutation; r = 50% in the study; and NT denotes the total number of years in the hydrological series after the mutation.
Table 1

IHA hydrological parameters

IHA panel contentHydrological parameters
Group 1: Monthly average Average monthly flow (precipitation) 
Group 2: Magnitude and duration of annual extremes Monthly average flow (precipitation) Annual average 1, 3, 7, 30, 90 d minimum and maximum flow (precipitation) basic index 
Group 3: Time of year when extremes occur Date of occurrence of the largest and smallest day of the year (Roman day) 
Group 4: Frequency and duration of high and low pulses Average number of high and low pulses per year and the duration of the pulses 
Group 5: Rate and frequency of change Average annual rate of increase or decrease and number of reversals 
IHA panel contentHydrological parameters
Group 1: Monthly average Average monthly flow (precipitation) 
Group 2: Magnitude and duration of annual extremes Monthly average flow (precipitation) Annual average 1, 3, 7, 30, 90 d minimum and maximum flow (precipitation) basic index 
Group 3: Time of year when extremes occur Date of occurrence of the largest and smallest day of the year (Roman day) 
Group 4: Frequency and duration of high and low pulses Average number of high and low pulses per year and the duration of the pulses 
Group 5: Rate and frequency of change Average annual rate of increase or decrease and number of reversals 
The overall hydrologic variability is a macro-level consideration of the extent to which each indicator has changed the hydrologic situation of the river, and is an effective integration of the 32 hydrologic indicators in evaluating the variability of the river. To objectively evaluate the severity of the hydrological alteration of IHA, this article addresses the shortcomings of the overall hydrological alteration calculation method that tends to ignore the height alteration value on the hydrological situation, and combines the advantages of the Nemerow index method that takes into account the influence of the maximum value and has a clear physical concept, and effectively avoids the shortcomings that tend to reduce its influence when evaluating the overall hydrological regime of river runoff. The improved overall hydrological alteration degree D is calculated as follows:
formula
(4)
formula
(5)
formula
(6)
where D denotes the overall hydrological alteration; Djmax denotes the maximum value of individual index alteration Di; and Dw denotes the average value of individual index alteration Di. To quantitatively analyze the degree of change of IHA indicators and set a judgment standard for the degree of hydrological change, Di values from 0 to 33% are classified as low change (L); 33–67% are classified as moderate change (M); and 67–100% are classified as high change (H) (Wang et al. 2023).

The elasticity coefficient method based on the Budyko hypothesis

The Choudhury-Yang formula (Equation (6)) was used to calculate the influencing factors on annual scale runoff variations. The calculation principle involves determining the elasticity coefficients of the respective influencing factors, which are then employed to compute the changes in runoff induced by precipitation, potential evapotranspiration, and land surface characteristics (Roderick & Farquhar 2011).
formula
where represents the multiyear average actual evapotranspiration (mm); stands for the multiyear average annual precipitation (mm); n is the land surface parameter, reflecting the overall conditions of the watershed's vegetation, soil, topography, and land use; and ET0 denotes the multiyear average annual potential evapotranspiration (mm), and these data were obtained from the database used in this study. It is typically calculated using the FAO Penman–Monteith equation, and the specific calculation process is detailed as shown in the study by Guo et al. (2022b).
Given the watershed aridity index , according to the definition of elasticity coefficients, the elasticity coefficients corresponding to land surface, precipitation, and potential evapotranspiration can be calculated using the full differential form of the water-energy coupled balance equation:
formula
(8)
formula
(9)
formula
(10)
By using εp、, and εn, the changes in runoff caused by the respective factors can be calculated as follows:
formula
(11)

In the equation, Δn, ΔET0, and ΔP represent the changes in the lower cushion surface, potential evapotranspiration, and precipitation, respectively, before and after the abrupt change. Among these, the combined effects of precipitation and potential evapotranspiration represent the changes in runoff caused by climate change. The changes in runoff due to variations in the lower cushion surface signify the impact of human activities on runoff.

SWAT model

The SWAT model (Gassman et al. 2007), developed by the US Department of Agriculture, can simulate the dynamics of watershed hydrological processes at annual, monthly, and daily scales, taking into account physical differences and spatial heterogeneity in the subsurface of different watersheds. It can simulate the impact of land use on water, sediment, and water quality in large and complex watersheds. It is widely used for long time series simulation and has become one of the most widely used distributed hydrological models in the world. In the SWAT model run, the water balance is the original driving force in the model so hydrological processes occur, and the equation for its water balance is as follows:
formula
(12)
where SWt is the water content on day n, mm; SW0 is the initial soil water content on day n, mm; t is time, d; Rn is the total rainfall on day n, mm; Qn is the total surface runoff on day n, mm; En is the evapotranspiration on day n, mm; Wn is the infiltration of the soil profile on day n, mm; and Tn is the groundwater return flow on day n, mm.

The SWAT model divides the watershed into many sub-basins based on DEW data, soil type, and land use data, and each sub-basin is then divided into many hydrological response units (HRUs) by the minimum threshold ratio of land use, soil area, and slope, and the yield of each subbasin is obtained by superimposing the material yield calculated for each HRU. The results of the simulations are then obtained by performing slope and network confluence (Malagò et al. 2015).

To further validate the model's applicability, this study divided the entire period into specific segments based on the determination of abrupt years: the warm-up period from 1965 to 1966, the calibration period from 1966 to 1990, and the validation period from 1991 to 2019. To improve the simulation speed of the SWAT model, parameter adjustment was conducted using the SUFI-2 algorithm of SWAT-CUP (Fu et al. 2019). Following sensitivity analysis of the model parameters and in conjunction with the relevant literature (Thavhana et al. 2018; Bhattacharya et al. 2020), the parameters that significantly influenced the research results were identified (Table 2). Sensitivity analysis results were measured using the t-statistic (t-value) and significance indicator (p-value), where a higher absolute t-value and a lower p-value indicate stronger parameter sensitivity (Arnold et al. 2012).

Table 2

Description of the parameter and fitted values

Input parameterst-Satp-ValueRank of sensitivityFitted value
R__SOL_K. sol 2.289 0.024 −0.05 
R__SOL_Z. sol 1.787 0.077 2.42 
R__SOL_BD 1.566 0.121 0.16 
V__SFTMP. bsn 1.506 0.135 4.12 
V__SURLAG. bsn 1.247 0.215 12.06 
V__CANMX. hru −1.222 0.225 26.22 
V__SMFMN. bsn 1.183 0.240 15.08 
V__ALPHA_BF.gw 0.977 0.331 0.69 
Input parameterst-Satp-ValueRank of sensitivityFitted value
R__SOL_K. sol 2.289 0.024 −0.05 
R__SOL_Z. sol 1.787 0.077 2.42 
R__SOL_BD 1.566 0.121 0.16 
V__SFTMP. bsn 1.506 0.135 4.12 
V__SURLAG. bsn 1.247 0.215 12.06 
V__CANMX. hru −1.222 0.225 26.22 
V__SMFMN. bsn 1.183 0.240 15.08 
V__ALPHA_BF.gw 0.977 0.331 0.69 

Calibration and validation of the model

In this study, the Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and the refined index of agreement (dr) were selected as evaluation metrics for the monthly runoff at the main hydrological stations within the watershed during the calibration. In the calculation of the refined index of agreement, this study set the value of c as 3, and the specific computation process for these evaluation metrics is as presented in the studies by Willmott et al. (2012) and Hu et al. (2021). According to SWAT-CUP and the relevant literature (Moriasi et al. 2015), typically, the model is considered reliable when the correlation coefficient R2 is greater than 0.6, NSE is greater than 0.5, and dr is greater than 0.5. When R2, NSE, and dr are all greater than or equal to 0.65, the simulation results are considered to be very good.

By comparing with the observed results from the Huangzhuang hydrological station, it was found that the R2, NSE, and dr values for both the calibration and validation periods at the hydrological station were all above 0.65. During the calibration period, the NSE, R2, and dr values for monthly runoff were 0.83, 0.82, and 0.79, respectively. In the validation period, the NSE, R2, and dr values for monthly runoff were 0.76, 0.76, and 0.7, respectively. The fitting of observed runoff to simulated values during the calibration and validation periods is shown in Figure 3. The simulation results were better before the change point year compared to after the change point year. However, the simulation during both calibration and validation periods can be described as ‘very good,’ indicating that the SWAT model is suitable for simulating runoff in the Han River Basin.
Figure 3

Comparison between measured and simulated monthly runoff data at Huangzhuang Station.

Figure 3

Comparison between measured and simulated monthly runoff data at Huangzhuang Station.

Close modal

Multiscenario settings

First, based on landuse data from 1980, 2000, and 2020, combined with meteorological data from 1965 to 2019, this study simulated monthly runoff variations during the period of 1965–2019. This allowed for a quantitative exploration of the impact of land use and land cover change (LUCC) on runoff. Second, to further investigate the impact of changes in individual land-use types on runoff, this study examined landuse transition matrices from 1980 to 2020 (as shown in Table 3). It was observed that forests, croplands, grasslands, and construction land were the main landuse types in the Han River Basin, collectively occupying more than 90% of the total basin area. Between 1980 and 2020, cropland and forest land showed a declining trend, with reductions of 3.44 and 4.50%, corresponding to area decreases of 7,358.35 and 9,613.22 km2, respectively. On the other hand, grassland area increased by 3.66%, equivalent to an area increase of 7,824.32 km2, and urban land increased by 2.97%, with a growth in area of 6,348.86 km2.

Table 3

Land use transfer matrix, 1980–2020

1980\2020CroplandForestGrasslandWetlandConstruction landBarren landTotal
Cropland 49,523.58 11,541.08 8,781.42 3,444.92 5,675.27 48.77 79,015.04 
Forest 13,643.03 61,408.41 21,529.89 441.48 433.62 45.48 97,501.91 
Grassland 7,754.72 14,507.40 11,384.52 251.05 140.40 1.11 34,039.19 
Wetland 679.73 356.41 80.26 1,621.39 126.46 4.39 2,868.64 
Construction land 14.10 8.67 2.22 3.69 102.02 0.60 131.30 
Barren land 41.53 66.72 85.20 0.61 2.40 196.45 
Total 71,656.69 87,888.69 41,863.51 5,763.14 6,480.16 100.35 213,752.54 
1980\2020CroplandForestGrasslandWetlandConstruction landBarren landTotal
Cropland 49,523.58 11,541.08 8,781.42 3,444.92 5,675.27 48.77 79,015.04 
Forest 13,643.03 61,408.41 21,529.89 441.48 433.62 45.48 97,501.91 
Grassland 7,754.72 14,507.40 11,384.52 251.05 140.40 1.11 34,039.19 
Wetland 679.73 356.41 80.26 1,621.39 126.46 4.39 2,868.64 
Construction land 14.10 8.67 2.22 3.69 102.02 0.60 131.30 
Barren land 41.53 66.72 85.20 0.61 2.40 196.45 
Total 71,656.69 87,888.69 41,863.51 5,763.14 6,480.16 100.35 213,752.54 

Finally, based on the characteristics of landuse changes described earlier, this study considered two main factors and established four scenarios (Baltzer et al. 2014; Stone et al. 2019). On the one hand, to eliminate the influence of external factors such as topography and underlying surface conditions, this study conducted comparative analyses using extreme land-use scenarios. On the other hand, due to recent policies in the Han River Basin, including reforestation, ecological conservation, and economic development, the conversion between the four main landuse types (forests, grasslands, croplands, and urban areas) in the basin has become more frequent. Therefore, this study used the landuse status in 1980 as the baseline and established four extreme landuse scenarios, namely, the forestland scenario, grassland scenario, cropland scenario, and urban land scenario, as outlined in Table 4. While keeping other conditions constant, these scenarios were incorporated into the calibrated SWAT model. In this study, a calibrated SWAT model was employed to simulate runoff from 1965 to 2019 under different scenarios, using fixed meteorological and soil conditions. The study employed a controlled variable approach to conduct comparative analyses, quantifying the impact of land use types on runoff.

Table 4

Land use type changes under different scenarios (km2)

Land use typeScenario 1 (S1)Scenario 2 (S2)Scenario 3 (S3)Scenario 4 (S4)Scenario 5 (S5)
Cropland 79,942 210,847 
Forest 97,213 210,847 
Grassland 33,962 210,847 
Wetland 3,037 3,037 3,037 3,037 3,037 
Construction land 154 154 154 154 211,001 
Barren land 217 217 217 217 217 
Land use typeScenario 1 (S1)Scenario 2 (S2)Scenario 3 (S3)Scenario 4 (S4)Scenario 5 (S5)
Cropland 79,942 210,847 
Forest 97,213 210,847 
Grassland 33,962 210,847 
Wetland 3,037 3,037 3,037 3,037 3,037 
Construction land 154 154 154 154 211,001 
Barren land 217 217 217 217 217 

Combining five land use scenarios, using 1980 land use type as the baseline scenario, the SWAT model based on rate-determined parameters simulates the process of runoff from 1980 to 2019 under different scenarios, and the rate of change of runoff equation is expressed as follows:
formula
(13)
where Qi is the simulation result of runoff under extreme land use scenario, m3/s; and Ql is the simulation result of runoff under 1980 land use, m3/s.

Time-varying characteristics of runoff

The Mann–Kendall test was applied to assess the occurrence of abrupt changes in the annual runoff data of the Han River Basin, as illustrated in Figure 4(a). The results indicate that the Mann–Kendall statistic is −1.82, with an absolute value less than 1.96, meaning that it did not pass the 95% significance test. Consequently, it can be inferred that there were no significant abrupt changes in runoff during the years 1994, 1997, and 2004. However, through further validation using the cumulative distance level method and the mean difference t-test, insights from Figure 4(b) suggest that runoff may have undergone abrupt changes in the years 1979, 1991, and 2011. Subsequently, when subjecting the results to a mean difference t-test, the statistical value t for the year 1991 is 2.74, which exceeds the threshold of 2.58, successfully passing the 99% significance test. Therefore, the Han River Basin experienced a genuine abrupt change in 1991, and this year is selected as the year of the abrupt change in this study. The results are summarized in Table 5. This is generally consistent with the results of Ban et al. (2018) for the abrupt change analysis of Han River runoff and is largely consistent with the construction time of water conservancy projects such as water conservancy hubs in the basin from 1991 to 2019. Therefore, 1991 is designated as the abrupt change year for Han River runoff. The long-term runoff series of the Han River Basin is defined in this study as the natural period from 1965 to 1991, during which it was solely influenced by climate variability. The period from 1992 to 2019 is considered as the variability period, during which it was influenced by both climate variability and human activities.
Table 5

Results of sudden change test of Han river runoff

Hydrological stationMutation point
Mutation year
M-K testCumulative distance levelt-test
Huangzhuang station 1994, 1997, 2002 1974, 1991, 2011 1991 1991 
Hydrological stationMutation point
Mutation year
M-K testCumulative distance levelt-test
Huangzhuang station 1994, 1997, 2002 1974, 1991, 2011 1991 1991 
Figure 4

Mutant point test: (a) cumulative distance leveling method and (b) M-K test.

Figure 4

Mutant point test: (a) cumulative distance leveling method and (b) M-K test.

Close modal

Runoff and climate change characteristics

To reveal the trends of annual mean runoff depth, annual rainfall, and potential evapotranspiration in the Han River, the Mann–Kendall method trend test was used to calculate the trends of annual runoff depth and precipitation within the control flow area at each hydrological station in the Han River, as shown in Figure 5. Their Mann–Kendall statistics were −1.82, −0.37, and −1.05, indicating that the annual mean runoff depth, annual rainfall, and potential evapotranspiration decreased throughout the whole period (1965–2019), and the annual mean runoff depth passed the 90% significance test. However, due to the combined effects of land surface characteristics and climate change, the trend in runoff depth was increasing before 1991, with a slope of 2.99. After the abrupt change year, it shifted to a decreasing trend with a slope of −2.74. The average runoff depth for multiple years before and after the abrupt change was 304.21 and 242.28 mm, respectively, indicating a decrease of 61.93 mm in the runoff depth. The linear trend of potential evapotranspiration changed from −5.49 to −0.04 after the abrupt change year, indicating a slowing of the decline in rainfall. Precipitation, which previously exhibited an increasing trend (2.28), shifted to a decreasing trend (−2.74). These findings suggest that, under the combined influence of human activities and climate change, significant alterations occurred in runoff and climatic characteristics after the abrupt change year.
Figure 5

Changes in runoff depth, rainfall, and potential evapotranspiration in the Han River Basin before 1991 and after 1991.

Figure 5

Changes in runoff depth, rainfall, and potential evapotranspiration in the Han River Basin before 1991 and after 1991.

Close modal

Hydrological status of the Han River Basin

The aforementioned analysis finally identified 1991 as the abrupt change year of Huangzhuang station, and the abrupt change of the annual mean flow at Huangzhuang station in the lower Han River. The daily flow data of Huangzhuang hydrological station were divided into two stages, before (1965–1991) and after (1992–2019), and combined with the hydrological index method and range of variation (RVA) method to analyze the overall change of hydrological situation and the degree of change of each hydrological index (Figure 6).
Figure 6

Schematic diagram of hydrological alteration of Han River.

Figure 6

Schematic diagram of hydrological alteration of Han River.

Close modal

The IHA-RVA method was used to analyze the degree of hydrological changes in the Han River Basin before and after the abrupt change. Among the 32 flow hydrological indicators in the Han River Basin, 9 were highly altered, 12 were moderately altered, and 11 were slightly altered, as shown in Figure 5; among them, the large changes in each group of IHA indicators in the Han River Basin are median flow in March and September (86.21 and 68.97%, respectively), annual minimum continuous 3, 7, 30, and 90 days average flow (75.86, 86.21, 100, and 100%), and the annual maximum continuous 3 and 30 days average flow (68.97 and 68.97%); the fall rates (86.21%) of these eight indicators are highly altered, or even reach 100% change. The hydrological indicators that reach a moderate degree of change include February, June, July, August, and October median flow (65.52, 48.28, 34.48, 57.68, and 58.62%, respectively), annual maximum flow 1 and 7 days average flow (58.62 and 58.62%, respectively), annual maximum continuous 90 days average flow (58.62%), date of the maximum (65.52%), low and high pulse count (37.93 and 53.45%, respectively), and number of reversals (44.14%).

By substituting the changes in the 32 hydrological indices from Table 6 into Equations (4) and (5), the overall hydrological change for the Han River basin is calculated to be 62.81%, indicating a moderate level of change. Among the various hydrological indices after the abrupt change, the annual extreme flow exhibits the most significant change, with a magnitude of 75.49%, signifying a high degree of change. Subsequently, alterations of moderate magnitude are observed in the changes of monthly average water volume, timing of annual extreme hydrological conditions, frequency of high/low flow pulses, and the rate and frequency of hydrological condition changes, with values of 52.4, 51.51, 35.65, and 56.77%, respectively. Among the five sets of overall hydrological indicator changes, no alterations of low magnitude were observed. The overall hydrological alteration extent in the Han River Basin has undergone distinct modification, with an alteration magnitude of 62.81%, representing a moderate degree of change.

Table 6

Statistical table of IHA indicators before and after the alteration of the Han River Basin

IHA indicatorsMean
Change Di (%)
Pre-1991Post-1991
Ⅰ January 714 800 13.79 (L) 
February 800.5 795 65.52 (M) 
March 690 837 86.21 (H) 
April 1,075 917.5 6.89 (L) 
May 1,190 1,080 3.45 (L) 
June 1,350 1,040 48.28 (M) 
July 2,050 1,590 34.48 (M) 
August 1,800 1,580 57.68 (M) 
September 1,765 1,140 68.97 (H) 
October 1,230 914 58.62 (M) 
November 984 812 14.66 (L) 
December 775 743 13.79 (L) 
Ⅱ 1-day minimum 384 469 30.34 (M) 
3-day minimum 519.3 511.7 75.86 (H) 
7-day minimum 538.6 528.1 86.21 (H) 
30-day minimum 636 582.4 100 (H) 
90-day minimum 747.1 629.8 100 (H) 
1-day maximum 9,400 4,540 58.62 (M) 
3-day maximum 8,550 3,867 68.97 (H) 
7-day maximum 7,197 3,027 58.62 (M) 
30-day maximum 4,085 2,205 68.97 (H) 
90-day maximum 2,576 1,685 58.62 (M) 
Base index 0.3457 0.4702 27.59 (L) 
Ⅲ Date of the minimum 63 345 27.59 (L) 
Date of the maximum 207 209 65.52 (M) 
Ⅳ Low pulse count 37.93 (M) 
Low pulse duration 5.75 3.5 0.86 (L) 
High pulse count 53.45 (M) 
High pulse duration 4.5 4.5 23.82 (L) 
Ⅴ Rise rate 47 40 6.89 (L) 
Fall rate −40 −43 86.21 (H) 
Number of reversals 120 149 44.14 (M) 
IHA indicatorsMean
Change Di (%)
Pre-1991Post-1991
Ⅰ January 714 800 13.79 (L) 
February 800.5 795 65.52 (M) 
March 690 837 86.21 (H) 
April 1,075 917.5 6.89 (L) 
May 1,190 1,080 3.45 (L) 
June 1,350 1,040 48.28 (M) 
July 2,050 1,590 34.48 (M) 
August 1,800 1,580 57.68 (M) 
September 1,765 1,140 68.97 (H) 
October 1,230 914 58.62 (M) 
November 984 812 14.66 (L) 
December 775 743 13.79 (L) 
Ⅱ 1-day minimum 384 469 30.34 (M) 
3-day minimum 519.3 511.7 75.86 (H) 
7-day minimum 538.6 528.1 86.21 (H) 
30-day minimum 636 582.4 100 (H) 
90-day minimum 747.1 629.8 100 (H) 
1-day maximum 9,400 4,540 58.62 (M) 
3-day maximum 8,550 3,867 68.97 (H) 
7-day maximum 7,197 3,027 58.62 (M) 
30-day maximum 4,085 2,205 68.97 (H) 
90-day maximum 2,576 1,685 58.62 (M) 
Base index 0.3457 0.4702 27.59 (L) 
Ⅲ Date of the minimum 63 345 27.59 (L) 
Date of the maximum 207 209 65.52 (M) 
Ⅳ Low pulse count 37.93 (M) 
Low pulse duration 5.75 3.5 0.86 (L) 
High pulse count 53.45 (M) 
High pulse duration 4.5 4.5 23.82 (L) 
Ⅴ Rise rate 47 40 6.89 (L) 
Fall rate −40 −43 86.21 (H) 
Number of reversals 120 149 44.14 (M) 

Note: H, high change; M, moderate change; L, low change.

Analysis of runoff attribution

The calculation results are presented in Table 7, indicating that during the change period in the Han River basin, P has slightly decreased compared to the reference period, with a decrease rate of 3.06%. ET0, on the other hand, has shown a slight increase, with a growth rate of 0.54%. Consequently, the aridity index (ET0/P) has risen during this period. Specifically, in the change period, the elasticity coefficients (εp,, and εn) for various influencing factors on runoff are 2.18, −1.14, and −0.99, respectively. This indicates that for each 1% increase in P, the runoff depth increases by 2.18%, while each 1% increase in ET0 results in a 1.14% decrease, and each 1% increase in the lower cushion surface parameter n leads to a 0.99% decrease in streamflow depth. Hence, it can be concluded that the change in streamflow depth in the Han River is positively correlated with precipitation and negatively correlated with potential evapotranspiration. The variation in streamflow in the Han River Basin is most sensitive to changes in precipitation and least sensitive to changes in the lower cushion surface.

Table 7

Hydrometeorological characteristic values of the Han River Basin

PeriodsP/mmE0/mmR/mmnE0/Pεpεn
1965–1991 889.92 946.87 304.21 1.55 1.064 1.92 −0.92 −0.86 
1992–2019 862.66 952.50 242.28 1.82 1.104 2.18 −1.14 −0.99 
PeriodsP/mmE0/mmR/mmnE0/Pεpεn
1965–1991 889.92 946.87 304.21 1.55 1.064 1.92 −0.92 −0.86 
1992–2019 862.66 952.50 242.28 1.82 1.104 2.18 −1.14 −0.99 

Table 8 presents the contribution rates of various driving factors to runoff, and from this table, it can be observed that the multi-year average runoff during the change period decreased by 62.78 mm compared to the reference period. The decrease in P by 34.25 mm resulted in a runoff reduction of 21.15 mm, contributing 33.69% to the change. Increased ET0 by 1.02 mm led to a runoff reduction of 3.39 mm, contributing 5.4%. A decrease in the lower cushion surface by 0.28 resulted in a runoff reduction of 38.24 mm, contributing 60.91% to the change. Therefore, the runoff change due to climate variation is 24.89 mm, contributing 39.09%. The lower cushion surface parameter is the primary factor responsible for runoff change, with a contribution rate of 60.91%. However, it is worth noting that runoff change is least sensitive to the lower cushion surface. Despite this, land surface contributes the most to the changes in runoff. Hence, a more detailed examination of the lower cushion surface on runoff is necessary.

Table 8

Contribution of each influencing factor to runoff in the Han River

ParameterR (mm)P (mm)ET0 (mm)n
Variable quantity −62.78 −34.25 1.02 0.28 
Driven runoff variation – −21.15 −3.39 −38.24 
Contribution rate η/% – 33.69% 5.4% 60.91% 
ParameterR (mm)P (mm)ET0 (mm)n
Variable quantity −62.78 −34.25 1.02 0.28 
Driven runoff variation – −21.15 −3.39 −38.24 
Contribution rate η/% – 33.69% 5.4% 60.91% 

The impact of different land use types on runoff across multiple timescales

Based on four land use types (cropland, forest, grassland, and construction land), extreme land use scenarios were constructed and compared with the baseline scenario. By simulating with the SWAT model, the trends in annual average runoff under different land use scenarios were obtained (as shown in Figure 7). The multiyear average runoff volumes under different scenarios are ranked below: S5 (construction land scenario) > S2 (cropland scenario) > S1 (baseline scenario) > S4 (grassland scenario) > S3 (forest scenario). According to this order, their multiyear average runoff is as follows: 1,598.11, 1,494.24, 1,327.35, 1,296.59, and 1,215.14 m3/s, respectively. From the multiyear average runoff, it can be observed that in S2, cropland can increase runoff to some extent, with an increase rate of 12.57%. In S5, construction land has the largest increasing effect compared to other extreme land use changes, with an increase of 20.4%. In S3 and S4, forest and grassland have a certain decreasing effect on runoff, with reduction rates of −8.45 and −2.32%, respectively. In addition, at the annual scale, different land use scenarios have a significant impact on high (low) runoff in different years. As shown in Figure 7, in years with relatively high (low) runoff such as 1983, 1987, 1990, 1997, and 2005, the changes in runoff under different scenarios are not significant, ranging from 0.67 to 8.51%. Conversely, in the years 1968–1976, 1976–1980, 1994–1996, 2001–2005, and 2006–2017, when runoff is not particularly high or low, runoff responds significantly to different scenarios, with changes ranging from −18.74 to 36.85%.
Figure 7

Annual average runoff volumes and variations under different scenarios. Note: Multiyear average runoff is the average of runoff for different years from 1966 to 2019.

Figure 7

Annual average runoff volumes and variations under different scenarios. Note: Multiyear average runoff is the average of runoff for different years from 1966 to 2019.

Close modal
On a quarterly scale (Figure 8), only S2 (cropland scenario) shows an increase in runoff compared to S1 (baseline scenarios), with increases of 11.49, 17.53, 7.03, and 20.17% in different quarters. For S3 (forest scenario), runoff is decreasing compared to S1 (baseline scenarios), by 6.05, 13.62, 8.43, and 3.84%, respectively. For S4 (grassland scenario) and S5 (construction land scenario), the situation is special, and in S4 (grassland scenario), the runoff increases in the third quarter with an increase of 5.45% and the runoff is lower than normal S1 in the other three quarters. The S5 (construction land scenario) runoff only shows a decrease in the first quarter with a 27.98% decrease and an increase in all other quarters.
Figure 8

Runoff change rates for different quarters under different scenarios.

Figure 8

Runoff change rates for different quarters under different scenarios.

Close modal
In the analysis of monthly scales (e.g., Figure 9), we find that the response of land use to runoff under different scenarios is similar to that under quarterly scales. S2 (cropland scenario) has an increase in runoff throughout the year, with the largest change in October, up 21.66%, and the largest increase in the same month, up 353.13 m3/s; S3 (forest scenario) has a decrease in runoff throughout the year, with the largest rate of change in June, down 15.59%, and the largest decrease in September, down 249.35 m3/s. S4 (grassland scenario) has an increasing trend in runoff from June to August, and a decreasing trend in runoff in all other months, with the largest rate of change in March, down 15.08%, and the largest change in July, up 240.3 m3/s. S5 (construction land scenario) is increasing in April to November and decreasing in January to March and December, with the largest rate of change in January, down 45.9%, and the largest change in September, up 782.17 m3/s.
Figure 9

Rate of change of monthly runoff under different scenarios.

Figure 9

Rate of change of monthly runoff under different scenarios.

Close modal

The impact of hydrological regime changes on ecology

The dynamic balance of water resources contributes positively to regional economic development and promotes a beneficial cycle in the natural environment, addressing the conflicts between water supply and demand (Thompson et al. 2021; Gong et al. 2023). In this article, in the study on the ecohydrological changes to the Han River Basin, it was found that the runoff volume was reduced by 20.28% after the mutation, and the five groups of ecohydrological indexes in the Han River Basin changed significantly, reaching a moderate change (62.81%) in general. The changes in the frequency and duration of high- and low-flow pulses will have an impact on the food chain of aquatic and riparian organisms in the Han River Basin (Shan et al. 2021; Hung et al. 2022). Therefore, it is necessary to discuss the current ecological responses in this context.

After the hydrological indicators in the Han River Basin changed, a significant transformation occurred in the river, which is an important natural breeding ground for the four major fish species in the Yangtze River Basin. The abundance of zooplankton and algae in the Han River sharply decreased, while the influx of organic matter led to an increase in certain algal species and biomass, resulting in a reduction of aquatic habitat range. The hydrological conditions necessary for the four major fish species to stimulate their spawning behavior experienced a moderate to high degree of alteration, making it difficult for the hydrological conditions to meet the requirements for spawning. As a result, the area available for fish spawning gradually diminished (Chen et al. 2020). The impoundment of water by hydraulic engineering projects further impairs the flow characteristics of the Han River, reduces its self-purification capacity, and increases the likelihood of algal blooms (Shen et al. 2021).

Major drivers of runoff change

According to the attribution analysis based on the Budyko hypothesis, it is observed that the sensitivity of runoff to the lower cushion surface is the lowest. However, the lower cushion surface is the primary factor contributing to the runoff changes, with a contribution rate of 60.91%. Climate factors are one of the reasons for runoff variations, where precipitation serves as the main source of runoff. Runoff is highly sensitive to precipitation, with changes directly affecting runoff; precipitation contributes to 33.69% of runoff variations. On the other hand, the correlation between runoff and potential evapotranspiration is relatively weak, contributing only 5.4% to runoff variations. In summary, climate change has a relatively minor impact on runoff, but considering the lowest sensitivity of runoff changes to the lower cushion surface and the largest contribution of the lower cushion surface to runoff, quantifying the impact of individual land cover types on runoff is indispensable. Zhou et al. (2022) used a modified Wetspa model to simulate future runoff trends and found that changes in rainfall were positively correlated with changes in runoff, and that changes in runoff in the Han River basin were mainly due to the influence of human activities. Gong et al. (2023) employed the SWAT to simulate the future scenarios of the Ganjiang River and found that runoff sensitivity to climate variations exceeded that to land use/cover changes, with insignificant synergistic effects. Shrestha et al. (2021) conducted a study on land use changes in Xiamen using the geographic information system (GIS)-based SCS-CN method and revealed a significant impact of rapid urbanization on surface runoff in the area, with a substantial increase in surface runoff observed from 1980 to 2015. However, some studies have presented contrasting findings. In this study, a comparative analysis with the research conducted by Wang et al. (2013) revealed significant differences in the topography between the Baima River Basin and the Han River Basin. The Baima River Basin exhibits a rugged topography with substantial elevation differences, wherein forested areas are situated at higher elevations above the ground level. These factors collectively contribute to a promotive effect of forests on runoff generation, while cultivated lands exhibit suppressive characteristics. In this regard, it is evident that the topography within the basin can also influence the results. However, the fact that the majority of the Hanjiang River Basin is situated in flat terrain implies that elevation differences have a minimal impact on the results presented in this study. Furthermore, aligning with previous research on an annual scale reinforces the reliability of the findings in this study. In addition, this research goes further to quantitatively assess the disparities in the influence of land use types on runoff at the quarterly and monthly scales.

According to Figure 10, a significant increase in urban land area and a substantial decrease in forest land area are evident. This reflects the rapid economic development in the Han River Basin and the further degradation of the ecological environment. From 1980 to 1990, there was a 2.43% reduction in arable land area, with the converted land mainly transitioning to grassland and urban land, which increased by 3.96 and 2.78%, respectively. Forest land predominantly converted to grassland, showing a declining trend of 5.6%. This period witnessed the most frequent land use changes. However, from 1990 to 2020, the increase in the area of construction land slowed down, while the area of grassland and forest slowly increased, which shows that the policy of ‘returning farmland to forest and grass’ has had an effect and that people are aware of the importance of the ecological environment. In addition, we can see that cropland is the type of land that is most frequently converted to other land use types. As economic development and ecological environment are mutually regulated, it is important to analyze the impact of landuse change on the hydrological situation in the Han River basin. This study utilized the SWAT model and integrated four extreme land use scenarios to dissect the impact of land use types on runoff at different temporal scales. When considering multiyear average runoff, scenarios S2 and S5 showed an increase in runoff, while scenarios S3 and S4 exhibited a decrease. On an annual scale, the impact of land use changes was significant in years with moderate runoff. At the quarterly scale, scenario S2 resulted in increased runoff in different quarters, whereas scenario S3 led to decreased runoff in all quarters. Scenario S4 generally led to reduced runoff except in the third quarter, and scenario S5 generally increased runoff except in the first quarter. At the monthly scale, the effects of different scenarios on runoff were similar. This is due to the rapid expansion of construction land, resulting in surface hardening, making it difficult for the surface to absorb rainfall, thus promoting increased surface runoff. However, during periods of lower rainfall, surface hardening reduces water infiltration through the soil, leading to a decrease in groundwater recharge. Cropland has a relatively low surface runoff retention capacity, resulting in more surface runoff during periods of higher rainfall. During periods of lower precipitation, water can replenish runoff through the soil, contributing to the increased runoff from cropland. Forest and grassland, on the other hand, have strong water retention capacities, allowing them to intercept rainwater, increase soil moisture content, and suppress increases in runoff (Xiong et al. 2019). Overall, while an increase in the area of cultivated and developed land generally promotes increased runoff, an increase in forest and grassland areas tends to inhibit excessive runoff. However, at the monthly scale, during periods of heavy rainfall, the corresponding land types may not exhibit the expected response of inhibiting or increasing runoff. Therefore, it is possible to optimize the response of land use types at different monthly scales through controlled land use conversions. This approach can fully utilize the water storage capacity of land during the flood season to prevent flood disasters (Gosling et al. 2017; Duong et al. 2021). During the nonflood season, land plays a role in replenishing runoff to ensure the production and domestic water supply for downstream residents (Ali et al. 2011; Al-Hameedi et al. 2022). This study holds significant implications for optimizing watershed land use structure and water resource planning (Zhang et al. 2022).
Figure 10

Transfer matrix of land use types in Han River 1980–2020.

Figure 10

Transfer matrix of land use types in Han River 1980–2020.

Close modal

Limitations and uncertainties

This study has certain limitations that need to be addressed in future research. First, the land use data used in this study has a spatial resolution of 1 km, which may be relatively coarse for identifying land use conversions and detecting hydrological responses. In this context, the issue of land use data could introduce uncertainties in the assessment of land use changes and their hydrological consequences. Mukundan et al. (2010) found that different data resolutions can influence the delineation of hydrologic response units (HRUs) using SWAT and result in prediction biases in hydrological simulations. On the other hand, similar to other applications of the SWAT model, this study encounters challenges in accurately simulating peak flows, as observed in studies by Zhang et al. (2020) and Brouziyne et al. (2021). Furthermore, other hydrological models also face difficulties in accurately simulating peak flows during both low and high flow conditions (Guo et al. 2022a; Ji et al. 2022). The theoretical foundation and calibration standards of hydrological models pose challenges in accurately capturing peak flows and low flow conditions. These aspects warrant further investigation in future research.

This study constructed a hydrological regime-runoff attribution-land use change research framework based on the mutability test, IHA-RVA, Budyko hypothesis, and the SWAT model and conducted an integrated timescale study of the Han River basin. The results showed that the Han River basin underwent a sudden change in 1991. The overall hydrological regime of the Han River changed moderately after the mutation, with a degree of change of 62.81%. This not only had adverse impacts on local biodiversity and survival but also posed challenges to the recovery and development of the basin. Utilizing the Budyko hypothesis, this study quantitatively delineated the contributions of climate components (precipitation and potential evapotranspiration) and the lower cushion surface to changes in runoff. It was revealed that runoff is most sensitive to precipitation, with the lower cushion surface acting as the primary driver of runoff variations, contributing to 60.91% of the total change. In addition, this study investigated the spatial heterogeneity of land-use changes in the Han River Basin and examined the influence of different land-use types on runoff. The analysis revealed that the SWAT model was suitable for the Han River Basin, with acceptable NSE, R2, and dr values. The land-use change analysis showed that forests, croplands, and grasslands were the predominant land-use types in the basin. Over the period from 1980 to 2020, land-use changes transitioned from frequent to gradual and then back to frequent. In the overall assessment, grasslands and urban land areas increased, expanding by 7,824.32 and 6,348.86 km2, respectively, while cropland and forested areas decreased, contracting by 7,358.35 and 9,613.22 km2, respectively.

By using SWAT and setting four extreme land use scenarios while excluding other influencing factors, this study simulated runoff variations from 1965 to 2019. It quantitatively assessed the impact of land use types on runoff across multiple timescales, including yearly, quarterly, and monthly scales. The results indicate that compared to S1 (baseline scenario), both S2 (cropland scenario) and S5 (urban scenario) have a promoting effect on runoff, resulting in an increase of 12.57 and 20.4% in runoff, respectively. On the other hand, S3 (forest scenario) and S4 (grassland scenario) hindered runoff formation, causing a reduction of 8.45 and 2.32% in runoff, respectively. At the annual scale, land-use changes had a more significant impact on years with moderate runoff but a lesser impact on years with extreme runoff values. When examining the data at the quarterly and monthly scales, it becomes evident that rainfall had a notable influence on the different land types. During the third quarter (July, August, and September), characterized by higher rainfall, runoff under S4 showed an increasing trend, with the maximum increase reaching 240.3 m3/s. Conversely, in the first quarter (January, February, and March), characterized by lower rainfall, runoff decreased under S5, with the maximum reduction being 45.9%. This study holds significant importance for understanding the impact of landuse changes on complex small watersheds and provides valuable insights for soil and water conservation research in the Han River Basin.

Weiqi Yuan conceived the study and wrote the first draft, Weiqi Yuan and Xiangyu Bai collected the data and performed the analysis, Wenxiong Chen and Fengtian Hong analyzed the methodology, Wenxian Guo and Hongxiang Wang supervised the paper, and all authors provided comments and assistance on the first few versions of the manuscript. All authors read and approved the final manuscript.

The authors thank their brothers at North China University of Water Resources and Electric Power for their comments and help with this study.

This study was supported by Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (23ZX012) and the National Natural Science Foundation of China (Grant No. 51779094).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Al-Hameedi
W. M. M.
,
Chen
J.
,
Faichia
C.
,
Nath
B.
,
Al-Shaibah
B.
&
Al-Aizari
A.
2022
Geospatial analysis of land use/cover change and land surface temperature for landscape risk pattern change evaluation of Baghdad City, Iraq, using CA–Markov and ANN models
.
Sustainability
14
(
14
),
8568
.
http://doi:10.3390/su14148568
.
Ali
M.
,
Khan
S. J.
,
Aslam
I.
&
Khan
Z.
2011
Simulation of the impacts of land-use change on surface runoff of Lai Nullah Basin in Islamabad, Pakistan
.
Landscape and Urban Planning
102
(
4
),
271
279
.
http://doi:10.1016/j.landurbplan.2011.05.006
.
Alsafadi
K.
,
Al-Ansari
N.
,
Mokhtar
A.
,
Mohammed
S.
,
Elbeltagi
A.
,
Sammen
S. S.
&
Bi
S.
2022
An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East
.
Environmental Research
17
,
014051
.
http://doi10.1088/1748-9326/ac4765
.
Arnold
J. G.
,
Moriasi
D. N.
,
Gassman
P. W.
,
Abbaspour
K. C.
&
Jha
M. K.
2012
SWAT: Model use, calibration, and validation
.
Transactions of the Asabe
55
(
4
),
1345
1352
.
http://doi:10.13031/2013.42256
.
Baltzer
J. L.
,
Veness
T.
,
Chasmer
L. E.
,
Sniderhan
A. E.
&
Quinton
W. L.
2014
Forests on thawing permafrost: Fragmentation, edge effects, and net forest loss
.
Glob. Change Biol.
20
(
3
),
824
834
.
http://doi:10.1111/gcb.12349
.
Ban
X.
,
Zhu
B.
,
Shu
P.
,
Du
H.
&
Lv
X.
2018
Trend and driving force of climate and hydrological process in Hanjiang Basin
.
Resources and Environment in the Yangtze Basin
27
(
12
),
13
.
http://CNKI:SUN:CJLY.0.2018-12-019
.
Barbarossa
V.
,
Schmitt
R.
,
Huijbregts
M.
,
Zarfl
C.
&
Schipper
A. M.
2020
Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide
.
Proceedings of the National Academy of Sciences
117
(
7
),
3648
3655
.
http://doi:10.1073/pnas.1912776117
.
Bhattacharya
R. K.
,
Chatterjee
N. D.
&
Das
K.
2020
Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models
.
Science of the Total Environment
734
,
139474
.
http://doi:10.1016/j.scitotenv.2020.139474
.
Brouziyne
Y.
,
Girolamo
A. D.
,
Aboubdillah
A.
,
Benaabidate
L.
,
Bouchaou
L.
&
Chehbouni
A.
2021
Modeling alterations in flow regimes under changing climate in a Mediterranean watershed: An analysis of ecologically-relevant hydrological indicators
.
Ecological Informatics
61
,
101219
.
Changming
L.
,
Dan
Z.
,
Xiaomang
L.
&
Zhao
C.
2012
Spatial and temporal change in the potential evapotranspiration sensitivity to meteorological factors in China (1960–2007)
.
Journal of Geographical Sciences
22
(
1
),
3
14
.
Chen
Y.-R.
&
Yu
B.
2015
Impact assessment of climatic and land-use changes on flood runoff in southeast Queensland
.
Hydrological Sciences Journal
60
(
10
),
1759
1769
.
http://doi:10.1080/02626667.2014.945938
.
Devia
G. K.
,
Ganasri
B. P.
&
Dwarakish
G. S.
2015
A review on hydrological models
.
Aquatic Procedia.
4
,
1001
1007
.
https://doi.org/10.1016/j.aqpro.2015.02.126
.
Duong
V. H. T.
,
Duc
T. D.
,
Andreas
S.
,
Phuc
N. C.
,
Long
V. H.
,
Peter
O.
,
Cong
T. V.
&
Franz
N.
2021
Land use change in the Vietnamese Mekong Delta: New evidence from remote sensing
.
The Science of the Total Environment
813
,
0048
9697
.
http://doi:10.1016/j.scitotenv.2021.151918
.
Eekhout
J. P. C.
,
Boix-Fayos
C.
,
Pérez-Cutillas
P.
&
Vente
J. D.
2020
The impact of reservoir construction and changes in land use and climate on ecosystem services in a large Mediterranean catchment
.
Journal of Hydrology
590
,
125208
.
Fu
Q.
,
Shi
R.
,
Li
T.
,
Sun
Y.
,
Liu
D.
,
Cui
S.
&
Hou
R.
2019
Effects of land-use change and climate variability on streamflow in the Woken River basin in Northeast China
.
River Research and Applications
35
(
2
).
http://doi:10.1002/rra.3397
.
Gassman
P. W.
,
Reyes
M. R.
,
Green
C. H.
&
Arnold
J. G.
2007
The soil and water assessment tool: Historical development, applications, and future research directions
.
Transactions of the ASABE
50
(
4
),
0001
2351
.
http://doi:10.13031/2013.23637
.
Gong
L.
,
Zhang
X.
,
Pan
G.
,
Zhao
J.
&
Zhao
Y.
2023
Hydrological responses to co-impacts of climate change and land use/cover change based on CMIP6 in the Ganjiang River, Poyang Lake basin
.
Anthropocene
41
.
http://doi:10.1016/j.ancene.2023.100368
.
Gosling
S. N.
,
Zaherpour
J. J.
,
Mount
N. J.
,
Hattermann
F. F.
,
Dankers
R.
,
Arheimer
B.
,
Breuer
L.
,
Ding
J.
,
Haddeland
I.
&
Kumar
R.
2017
A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1, 2 and 3 °C
.
Climatic Change
141
,
577
595
.
http://doi:10.1007/s10584-016-1773-3
.
Goździewicz-Biechońska
J.
&
Brzezińska-Rawa
A.
2022
Protecting ecosystem services of urban agriculture against land-use change using market-based instruments. A Polish perspective
.
Land Use Policy
120
,
106296
.
https://doi.org/10.1016/j.landusepol.2022.106296
.
Guo
W.
,
Hong
F.
,
Yang
H.
,
Huang
L.
,
Ma
Y.
,
Zhou
H.
&
Wang
H.
2022a
Quantitative evaluation of runoff variation and its driving forces based on multi-scale separation framework
.
Journal of Hydrology: Regional Studies
43
.
http://doi:10.1016/j.ejrh.2022.101183
.
Guo
W.
,
Zhou
H.
,
Jiao
X.
,
Huang
L.
&
Wang
H.
2022b
Analysis of alterations of the hydrological situation and causes of river runoff in the Min River, China
.
Water
14
(
7
).
http://doi:10.3390/w14071093
.
Hu
P.
,
Cai
T.
,
Sui
F.
,
Duan
L.
,
Man
X.
&
Cui
X.
2021
Response of runoff to extreme land use change in the permafrost region of northeastern China
.
Forests
12
(
8
).
http://doi:10.3390/f12081021
.
Hung
H.-J.
,
Lo
W.-C.
,
Chen
C.-N.
&
Tsai
C.-H.
2022
Fish’ habitat area and habitat transition in a river under ordinary and flood flow
.
Ecological Engineering
179
,
106606
.
http:// org/10.1016/j.ecoleng.2022.106606
.
Ji
H.
,
Peng
D.
,
Gu
Y.
,
Luo
X.
,
Pang
B.
&
Zhu
Z.
2022
Snowmelt runoff in the Yarlung Zangbo River basin and runoff change in the future
.
Remote Sensing
15
(
1
),
55
.
http://doi:10.3390/rs15010055
.
Khorn
N.
,
Ismail
M. H.
,
Nurhidayu
S.
,
Kamarudin
N.
&
Sulaiman
M. S.
2022
Land use/land cover changes and its impact on runoff using SWAT model in the upper Prek Thnot watershed in Cambodia
.
Environmental Earth Sciences
81
(
19
),
1
14
.
Li
J.
,
Cai
C.
&
Zhang
F.
2020
Assessment of ecological efficiency and environmental sustainability of the Minjiang-source in China
.
Sustainability
12
(
11
).
http://doi:10.3390/su12114783
.
Li
Y.
,
Deng
J.
,
Zang
C.
,
Kong
M.
&
Zhao
J.
2022
Spatial and temporal evolution characteristics of water resources in the Hanjiang River Basin of China over 50 years under a changing environment
.
Frontiers in Environmental Science
10
,
http://doi:10.3389/fenvs.2022.968693
.
Malagò
A.
,
Pagliero
L.
,
Bouraoui
F.
&
Franchini
M.
2015
Comparing calibrated parameter sets of the SWAT model for the Scandinavian and Iberian peninsulas
.
Hydrological Sciences Journal
60
(
5–6
),
949
967
.
http://doi:10.1080/02626667.2014.978332
.
Melo
P. A.
,
Alvarenga
L. A.
,
Tomasella
J.
,
De Mello
C. R.
,
Martins
M. A.
&
Coelho
G.
2022
Analysis of hydrological impacts caused by climatic and anthropogenic changes in Upper Grande River Basin, Brazil
.
Environmental Earth Sciences
81
(
21
),
1
15
.
Moriasi
D. N.
,
Gitau
M. W.
,
Pai
N.
&
Daggupati
P.
2015
Hydrologic and water quality models: Performance measures and evaluation criteria
.
Transactions of the Asabe
58
(
6
),
2151
0032
.
http://doi:10.13031/trans.58.10715
.
Mukundan
R.
,
Radcliffe
D. E.
&
Risse
L. M.
2010
Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment
.
Journal of Soil and Water Conservation
65
(
2
),
92
104
.
Omer
A.
,
Zhuguo
M.
,
Zheng
Z.
&
Saleem
F.
2019
Natural and anthropogenic influences on the recent droughts in Yellow River Basin, China
.
Science of the Total Environment
704
(
1758–6798
),
135428
.
Pirnia
A.
,
Golshan
M.
,
Darabi
H.
,
Adamowski
J.
&
Rozbeh
S.
2018
Using the Mann–Kendall test and double mass curve method to explore stream flow changes in response to climate and human activities
.
Journal of Water and Climate Change
jwc2018162
.
http://doi:10.2166/wcc.2018.162
.
Richter
B.
,
Baumgartner
J.
,
Wigington
R.
&
Braun
D.
1997
How much water does a river need?
Freshwater Biology
37
(
1
),
231
249
.
http://doi:10.1046/j.1365-2427.1997.00153.x
.
Roderick
M. L.
&
Farquhar
G. D.
2011
A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties
.
Water Resources Research
47
(
12
),
W00G07.01
W00G07.11
.
http://doi:10.1029/2010WR009826
.
Shan
C.
,
Dong
Z.
,
Lu
D.
,
Xu
C.
&
Liu
Q.
2021
Study on river health assessment based on a fuzzy matter-element extension model
.
Ecological Indicators
127
(
10
),
107742
.
Shen
M.
,
Chen
J.
,
Zhuan
M.
,
Chen
H.
,
Xu
C. Y.
&
Xiong
L.
2018
Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology
.
Journal of Hydrology
S0022169417307588
.
http://doi:10.1016/j.jhydrol.2017.11.004
.
Shen
L.
,
Dou
M.
,
Xia
R.
,
Li
G.
&
Yang
B.
2021
Effects of hydrological change on the risk of riverine algal blooms: Case study in the mid-downstream of the Han River in China
.
Environmental Science and Pollution Research
28
(
16
),
0944
1344
.
http://doi:10.1007/s11356-020-11756-2
.
Shrestha
S.
,
Cui
S.
,
Xu
L.
,
Wang
L.
,
Manandhar
B.
&
Ding
S.
2021
Impact of land use change due to urbanisation on surface runoff using GIS-Based SCS–CN method: A case study of Xiamen City, China
.
Land
10
(
8
),
839
.
https://doi.org/10.3390/land10080839
.
Stone
L. E.
,
Fang
X.
,
Haynes
K. M.
,
Helbig
M.
&
Quinton
W. L.
2019
Modelling the effects of permafrost loss on discharge from a wetland-dominated, discontinuous permafrost basin
.
Hydrological Processes
33
(
20
).
http://doi:10.1002/hyp.13546
.
Sun
P.
,
Sun
Y.
,
Zhang
Q.
&
Wen
Q.
2018
Temporal and spatial variation characteristics of runoff processes and its causes in Huaihe Basin
.
Journal of Lake Sciences
30
(
2
),
497
508
.
http://doi:10.18307/2018.0221
.
Thavhana
M. P.
,
Savage
M. J.
&
Moeletsi
M. E.
2018
SWAT model uncertainty analysis, calibration and validation for runoff simulation in the Luvuvhu River catchment, South Africa
.
Physics and Chemistry of the Earth
105
.
http://0.1016/j.pce.2018.03.012
.
Thompson
J. R.
,
Gosling
S. N.
,
Zaherpour
J.
&
Laizé
C. L. R.
2021
Increasing risk of ecological change to major rivers of the world with global warming
.
Earth's Future
9
(
11
),
2
20
.
https://doi.org/10.1029/2021EF002048
.
Wang
H.
&
Stephenson
S. R.
2018
Quantifying the impacts of climate change and land use/cover change on runoff in the lower Connecticut River Basin
.
Hydrological Processes
32
(
9
),
1301
1312
.
http://doi:10.1002/hyp.11509
.
Wang
H.
,
Ma
Y.
,
Hong
F.
,
Yang
H.
,
Huang
L.
,
Jiao
X.
&
Guo
W.
2023
Evolution of water–sediment situation and attribution analysis in the Upper Yangtze River, China
.
Water
15
(
3
).
http://doi:10.3390/w15030574
.
Wang
X.
,
Zhang
Z. l.
&
Ning
J. C.
2013
Runoff response to land use change in Baimahe basin of China based on SWAT model
.
Chinese Journal of Ecology
32
(
1
),
186
194
.
http://doi:10.13292/j.1000-4890.2013.0073
.
Wang, Y., Wang, D. & Wu, J. 2015 Assessing the impact of Danjiangkou reservoir on ecohydrological conditions in Hanjiang river, China. Ecological Engineering 81, 41–52. http://doi:10.1016/j.ecoleng.2015.04.006.
Wen
S.
,
Su
B.
,
Wang
Y.
,
Zhai
J.
,
Sun
H.
,
Chen
Z.
,
Huang
J.
,
Wang
A.
&
Jiang
T.
2020
Comprehensive evaluation of hydrological models for climate change impact assessment in the Upper Yangtze River Basin, China
.
Climatic Change
163
(
3
),
1573
1480
.
http://doi:10.1007/s10584-020-02929-6
.
Willmott
C. J.
,
Robeson
S. M.
&
Matsuura
K.
2012
A refined index of model performance
.
International Journal of Climatology
32
(
13
),
2088
2094
.
http://doi:10.1002/joc.2419
.
Xiong
B.
,
Xiong
L.
,
Xia
J.
,
Xu
C.-Y.
,
Jiang
C.
&
Du
T.
2019
Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects
.
Hydrology and Earth System Sciences
23
(
11
),
4453
4470
.
https://doi.org/10.5194/hess-23-4453-2019
.
Xue
L.
,
Zhang
H.
,
Zhang
L.
&
Chi
Y.
2017
Impact of water conservancy projects on eco-hydrological regime of Tarim River based on improved RVA method
.
Journal of Hohai University (Natural Sciences)
45
(
3
),
189
196
.
http://doi:10.3876/j.issn.10001980.2017.03.001
.
Xue
D.
,
Zhou
J.
,
Zhao
X.
,
Liu
C.
&
Zhao
Y.
2021
Impacts of climate change and human activities on runoff change in a typical arid watershed, NW China
.
Ecological Indicators
121
,
107013
.
http://doi:10.1016/j.ecolind.2020.107013
.
Yang
L.
,
Zhao
G.
,
Tian
P.
,
Mu
X.
,
Tian
X.
,
Feng
J.
&
Bai
Y.
2022
Runoff changes in the major river basins of China and their responses to potential driving forces
.
Journal of Hydrology
607
,
127536
.
Yin
J.
,
He
F.
,
Xiong
Y. J.
&
Qiu
G. Y.
2017
Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China
.
Hydrology and Earth System Sciences
21
(
1
),
183
196
.
http://doi:10.5194/hess-21-183-2017
.
Zhang
L.
,
Qin
L.
,
Hu
Z.
&
Zeng
S.
2010
Simulated hydrologic responses to climate change of water source area in the middle route of south-to-north water transfer project
.
Journal of Hydraulic Engineering
4
(
11
),
11
.
http//:CNKI:SUN:SLXB.0.2010-11-004
.
Zhang
H.
,
Wang
B.
,
Liu
D. L.
,
Zhang
M.
,
Leslie
L. M.
&
Yu
Q.
2020
Using an improved SWAT model to simulate hydrological responses to land use change: A case study of a catchment in tropical Australia
.
Journal of Hydrology
585
.
http://doi:10.1016/j.jhydrol.2020.124822
.
Zhang
X.
,
Chen
X.
,
Zhang
W.
,
Peng
H.
,
Xu
G.
,
Zhao
Y.
&
Shen
Z.
2022
Impact of land use changes on the surface runoff and nutrient load in the three Gorges reservoir area, China
.
Sustainability
14
(
4
).
http://doi:10.3390/su14042023
.
Zhou
X.
,
Chen
W.
,
Liu
Q.
,
Shen
H.
,
Cai
S.
&
Lei
X.
2022
Future runoff forecast in Hanjiang River Basin based on Wetspa model and CMIP6 model
.
Frontiers in Environmental Science
10
.
http://doi:10.3389/fenvs.2022.980949
.
Zuo
D.
,
Chen
G.
,
Wang
G.
,
Xu
Z.
,
Han
Y.
,
Peng
D.
,
Pang
B.
,
Abbaspour
K. C.
&
Yang
H.
2023
Assessment of changes in water conservation capacity under land degradation neutrality effects in a typical watershed of Yellow River Basin, China
.
Ecological Indicators
148
.
doi:10.1016/j.ecolind.2023.110145
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).