Hydrological Modeling Method Research Articles

Model Diagnostic Analysis in a Cold Basin Influenced by Frozen Soils With the Aid of Stable Isotope

AbstractUnderstanding the hydrological processes on the Tibetan Plateau (TP) under climate change is an important scientific question. The frequent multiphase transfer exacerbates the complexity of hydrological processes on the TP, which brings equifinality problem to hydrological models and causes large uncertainties in quantifying the contributions of runoff components. Tracer‐aided hydrological models are helpful for improving model performances and have been adopted in cryospheric regions, but the influence of frozen soil has yet to be considered. This study adopted the Tracer‐aided Tsinghua Representative Elementary Watershed model (THREW‐T) in a typical cold basin with widespread frozen soil on the TP. The model structure was diagnosed with isotope by identifying the influences of frozen soil. A simplified catchment‐scale frozen soil module was incorporated into the model. Results showed that: (a) The THREW‐T model cannot simultaneously simulate baseflow and stream water isotope well. The imbalance of simulations on two objectives could be attributed to the influence of frozen soil, resulting in seasonal variation of soil‐related parameters, which was not considered in the model. (b) Incorporating the frozen soil module significantly improved the balance of baseflow and isotope simulation, simultaneously producing low baseflow and high contribution of subsurface runoff during wet seasons. (c) The frozen soil had little influence on the annual streamflow, but changed the runoff seasonality by reducing baseflow during dry seasons and increasing subsurface runoff during wet seasons. The frozen soil module was still simplified, and further work is needed to improve the physical representation of soil freeze‐thaw process. This study highlights the value of tracer‐aided hydrological modeling method on diagnosing model structure by identifying the influence of specific processes such as frozen soil.

Impacts of small and medium-sized reservoirs on streamflow in two basins of Southeast China, using a hydrological model to separate influences of multiple drivers

Study regionSoutheast coast of China Study focusThe reservoirs in southeast coast of China are mostly small due to the limitations of terrain. In order to analyze the impact of these densely distributed small and medium-sized reservoirs (SMRs) with lacking observation and operational data on runoff, this paper uses a hydrological model & scenario simulation method to separate the impacts of climate variability (CV), land use change (LUC) and SMRs group change on streamflow variation in two river basins of Southeast China. New hydrological insights for the region(1) The streamflow of two basins has changed greatly, with CV and SMRs change being the main influencing factors, while the impact of LUC was relatively small. The contribution rates of CV to the annual runoff change of three hydrological stations in two river basins were 90.19%, 61.75% and 26.64%; Contribution rates of SMRs changes were 9.34%, 37.24% and 71.04%, correspondingly. (2) The regulation of streamflow by SMRs changes led to a decrease in both annual and monthly runoff, which was due to the fact that the main functions of SMRs in this region are agricultural irrigation and domestic water supply. (3) The three-factor separation method based on hydrological modeling, which separated the cumulative effects of SMRs in the form of SMRs group, would be valuable for the study and management of watersheds with numerous SMRs and lack of observation and operational data.

Kyrgyz transboundary rivers’ runoff assessment (Syr-darya and Amu-darya river basins) in climate change scenarios

The research was carried out as part of the preparation “Water Resources” section of the 4th National Communication of the Kyrgyz Republic to the UNFCCC. The article presents extensive information on the current state and changes over a long period of observations of water resources, climate and glaciation in Kyrgyzstan (Syr-Darya and Amu-Darya river basins). The representative rivers were selected as the main objects, which are transboundary between Kyrgyzstan, Uzbekistan, Kazakhstan and Tajikistan. The study aims to assess the inter-annual and intra-annual dynamics of the flow of the rivers for the period from 2020 to 2080, based on the climate projections CMIP5 (RCP4.5 and RCP8.5) and CMIP6 (SSP2-4.5 and SSP5-8.5) (IPCC WG II Report, 2022). The hydrological modeling method in the HBV light and HBV EHT models and the inertial mean annual flow change method were used to estimate changes in water resources. On the rivers of the northern part of the Fergana Valley, the average annual runoff is expected to increase by 1-19%, in the Amudarya river basin (Kyzyl-Suu river) – by 27-64% of the values for 2006-2019. In the Naryn river, water discharge for this period will remain within the current values for 2006-2019. The method of inertial change in the average annual runoff showed an increase in runoff for 2030 and 2040 for all the studied catchment areas by 6-19% of the current values for 2006-2020. The results of the study are intended for decision-makers on the rational use and long-term planning of water resources under climate change.

An Assessment of Ongoing Developments in Water Resources Management Incorporating SWAT Model: Overview and Perspectives

Land and water are the most necessary natural resources because the entire life system depends on them. It requires proper management to achieve maximum utilization. When used in conjunction with Arc GIS, the Soil and Water Assessment Tool (SWAT) is a promising model for simulating the agricultural watershed since it can forecast runoff, sediment and nutrient transport, and erosion under various management scenarios. Furthermore, the model is better at evaluating both the spatial and non-spatial variation of hydrological methods under a very large watershed. This study uses the methodology employed by the SWAT model for the estimation of surface runoff and sediment yield and discusses in detail the setup of the model computer file needed by the model sensitivity analysis parameter and validation area unit. SWAT is a well-known hydrological modeling method used in many hydrologic and environmental simulations. Over 17 years (2005-2021), 212 studies were found from various peer-reviewed scientific publications listed on the SWAT online database (CARD). Applicability studies were divided into five categories: water resources, streamflow, erosion, land-use planning and agricultural settings, climate change scenarios, and model parameterization. Hydrologic phenomena and adaptations in various river basins have been investigated. They mostly examined environmental impacts and preventive techniques to ensure an understanding of effective environmental regulation. Streamflow susceptibility to climatic changes was shown in climate change studies. Modeling streamflow parameters, model modifications, and basin-scale calibrations were investigated. Future simulation aspects such as data sharing and the opportunity for improved future analysis are also discussed. A multimodal approach to future simulations, as well as more efforts to make local data available, are both very good ideas.

Towards reliable uncertainty quantification for hydrologic predictions, part II: Characterizing impacts of uncertain factors through an iterative factorial data assimilation framework

In this study, an iterative factorial data assimilation (IFDA) framework is developed to holistically characterize the individual and interactive effects of various uncertain factors on hydrological predictions. The IFDA framework is flexible and is able to reveal the impacts from different numbers of uncertain factors. An iterative factorial analysis (IFA) approach is proposed in IFDA to diminish the biased variance estimation in traditional multilevel factorial designs and provide more reliable impact characterization for the considered factors. The proposed IFDA framework is applied to quantitatively reveal the individual and interactive effects of hydrological models, data assimilation (DA) methods, and uncertainties in inputs, streamflow observations and sample sizes on the deterministic and probabilistic predictions from data assimilation. The results indicate that the hydrological models, DA methods and their interactions would have the most dominant effects on hydrological predictions. This implies that different hydrological models or DA methods would produce significantly distinguishable results. When the hydrological model and DA method have been specified, uncertainties in streamflow observations would more likely have a visible effect on the accuracy of resulting predictions. Moreover, the inherent randomness, mainly caused by the Monte Carlo sampling procedures in data assimilation, would also have noticeable effects on the DA performances, especially when the hydrological model and DA method have been pre-identified. These results suggest that enhancement of hydrological models and data assimilation methods would be the most efficient pathway to generate reliable hydrological predictions.

Quantitative separation of the local vadose zone water storage changes using the superconductive gravity technique

The quantitative study of vadose zone water (i.e., the water contained in the rock formation between the ground surface and the groundwater level) is of vital importance for understanding the groundwater flow mechanism, mass exchange, and circulation. However, the vadose zone is closely related to the saturated zone, and it is difficult to directly observe the vadose zone water, causing the quantitative separation of the vadose zone water storage to be a challenging problem. Considering the insufficient spatial resolution of Gravity Recovery and Climate Experiment (GRACE), we draw on the practice of converting the observed groundwater level changes into the water storage changes in the study of groundwater storage changes using GRACE data, and propose a new method for implementing the quantitative separation of vadose zone water storage changes based on a single superconducting gravimeter and groundwater level observation data. This new method is validated with measurement data collected using a superconducting gravimeter (GWR-C032) at the National Geodetic Observation Station in Wuhan, China (Wuhan SG Station) and groundwater level observation data obtained during the same period. The calculation results show that the changes in the vadose zone water storage obtained using the superconductive gravity technique are in good agreement with that obtained using the local hydrological modeling method, indicating that the quantitative separation of the changes in the local vadose zone water storage can be achieved using superconductive gravity and groundwater level observations. During the study period from May 2008 to April 2010, the peak-to-peak changes of the vadose zone water storage at Wuhan SG Station reached 1580 mm, and the seasonal variations were the inverse of the variations of the total groundwater storage changes and the saturated zone water storage changes, indicating that the vadose zone plays a pronounced role in slowing down the total groundwater storage changes.

Identifying rainfall threshold of flash flood using entropy decision approach and hydrological model method

Flash flood disaster, with strong suddenness and tremendous destructiveness, is one of the most severe natural disasters in China that seriously threaten the lives and property safety of people and social development. Owing to the complex terrain and limited rainfall and runoff monitoring gauges, it is arduous to effectively prevent and control flash flood disasters in small-sized and medium-sized hilly watersheds. Identifying rainfall threshold, critical discharge and warning periods for flash flood, is critical in disaster prevention in such regions. This study adopted two approaches, the entropy-based decision approach and the hydrological model approach in calculating rainfall thresholds under different antecedent moisture condition (AMC). In particular, the entropy-based decision approach was improved, by using the Frank copula to calculate the multivariate joint distribution of the cumulative rainfall and the corresponding peak discharge. These two approaches were validated in a typical basin, located in the Pearl River Delta in South China that is characterized by frequently heavy rainfall and floods in the monsoon. Results showed that both approaches have the ability to quantify rainfall thresholds. Relatively, the Bayesian method exhibited higher rainfall thresholds, comparing to the other methods. In particular, for AMC I, the utility-entropy risk function method (with λ = 1) exhibited the best-applied practicable forecast lead time of 2.0 h; as for AMC II, the two methods showed the same forecast lead time of 1.5 h, and for AMC III, the hydrological model-based approach showed an optimal forecast lead time of 6.0 h. Meanwhile, the entropy-based approach exhibited the same performance as hydrological modeling approaches, indicating that this method is practicable in flood forecasting. The results would be helpful for floods prevention and mitigation in such small-sized and medium-sized hilly watersheds.

Impact assessment of climate change and human activities on streamflow signatures in the Yellow River Basin using the Budyko hypothesis and derived differential equation

Streamflow has been complexly altered by climate change and human activities in major rivers worldwide. The traditional Darwinian approach for impact assessment of climate change and human activities mainly focuses on variations in annual streamflow, but ignores other critical streamflow characteristics (e.g., high- and low-flow signatures). In our study, seven streamflow metrics were adopted to characterize the main streamflow signatures including average, low and high flows, and the Budyko-based method was extended based on the derived differential equation of streamflow signature change to separate individual impacts of climate change and human activities. The Yellow River Basin, China was selected as the study area because of its high intensity of human activities, and daily runoff observation series from 1960 to 2015 at four stations from the source region to the downstream region were used for the assessment. The assessment results were also compared with those estimated by the hydrological modeling method for validation. The results show that all streamflow signatures at the four stations have evidently declined after the 1980s, particularly for high-flow magnitudes. Both climate change and human activities decrease most of streamflow signatures, and human activities play major roles, which are gradually enhanced from the source region to the downstream region. The contributions of human activities are from −66.85% to −56.68% in the Tangnaihai controlled catchment, from −96.93% to −54.59% in the Qingtongxia controlled catchment, from −83.61% to −57.49% in the Longmen controlled catchment, and from −91.02% to −57.29% in the Lijin controlled catchment. The most impacted signature is the average flow in the non-flood season, followed by the annual maximum flow. The estimated contributions of climate change and human activities match well with those estimated by the hydrological modeling method, particularly for the high- and average-flow signatures with contribution deviations less than 10%. However, the estimated contributions to the low-flow signatures are quite different likely due to large uncertainty of low flow estimation. This study is expected to provide insights into the streamflow variations basin wide and provide scientific foundations for ecological protection and high-quality development of the Yellow River Basin, China.

Changes in runoff and sediment discharge and influencing factors analysis in the Xiliugou Basin, China

Based on the observed runoff, sediment discharge and precipitation data of the Xiliugou Basin from 1960 to 2015, the changes in runoff and sediment discharge were analyzed, and the attribution of precipitation and human activities to runoff and sediment discharge changes in the Xiliugou Basin was quantitatively assessed by using the hydrological modeling method. The results showed that, the runoff and sediment discharge in the Xiliugou Basin showed a significant decreasing trend from 1960 to 2015, and the change point for runoff and sediment discharge was around 2005. Human activities was the main driving factor for the runoff and sediment discharge change for the period of 2006-2015, which accounted for 74.6% and 88.5% of the runoff and sediment discharge reduction, respectively. Precipitation was responsible for 25.4% and 11.5% of the runoff and sediment discharge reduction, respectively.

Simulation and evaluation of actual evapotranspiration based on inverse hydrological modeling at a basin scale

Reliable estimation of actual evapotranspiration (AET) is essential for hydrological, ecological, and land-surface models. The water balance method is a useful tool for estimating AET. However, it usually neglects soil moisture (SM), which is a dominant factor in AET simulation. Based on field observations, an inverse hydrological modeling method was proposed for regional AET simulation. The method can be applied to estimate AET in other basins, where observations of SM are available. A nonlinear relationship between AET and potential evapotranspiration, a soil water constraint factor, was developed and calibrated for the studied basin. Results of the AET evaluation indicated this relationship was able to estimate AET accurately for both monthly and two-week series, as the Nash and Sutcliffe efficiency coefficient (NSE) was 0.903 and 0.613, respectively. The meteorologically based complementary relationship (CR) method was also able to estimate monthly AET accurately, in particular the revised CR method. But it showed poor performance when estimating AET at a shorter timescale. The performance of the revised CR method with a calibrated parameter was better than that of the initial CR method, as the NSE improved from 0.52 to 0.70. Overall, the performance of the nonlinear relationship with calibrated parameters was better than that of the CR method. In conclusion, monthly AET can be accurately estimated using the CR method based only on meteorological data.

Impact of catchment classification on streamflow regionalization in ungauged catchments

Hydrological parameter transfer between dissimilar catchments is bound to be associated with errors. As a result of that, hydrological prediction at ungauged sites gets affected. If the catchments are similar to each other in some way, then it may reduce the error in prediction. The logic mentioned above is applied in the current study, where three regionalization approaches, i.e., Inverse Distance Weighted, Kriging and global mean based on spatial proximity between gauged and ungauged catchments, were applied on 32 catchments in India. Before application of regionalization techniques, 32 catchments were categorized into four homogeneous groups using the self-organizing map method. The improvement in streamflow regionalization was checked for both calibration (1995–2006) and validation (2007–2011) while moving from unclassified to classified catchments. Results suggest that prior classification of catchments into homogeneous groups helps in improving the regionalization output. Fifty per cent of the total catchments displayed 10% improvement, while 30% of the catchments showed more than 10% improvement. The results imply that an appropriate combination of a hydrological model (Soil and Water Assessment Tool), regionalization technique and classification method will yield better results for ungauged catchments in streamflow prediction.

Hydrological complexities in irrigated agro-ecosystems with fragmented land cover types and shallow groundwater: Insights from a distributed hydrological modeling method

Hydrological effects caused by fragmented land cover types in human-dominated agro-ecosystems are poorly understood. In this study, based on visual interpreted high-resolution land cover map, an agro-hydrological model (HYDRUS-dualKc) was used in a distributed manner to investigate the hydrological complexities caused by fragmented land cover types and shallow groundwater. An irrigation system located in the upper Yellow River basin was chosen as a case study area, where field observations were conducted in 2012 and 2013. Combined effects of vegetation, soil, irrigation and groundwater were considered. Simulations of soil moisture and soil salinity fitted well with field observations; evapotranspiration (ETa) was also comparable with remote sensing data. Results showed that the growing season ETa varied from 162 to 567 mm among the different vegetation covers with the values for crop fields usually higher than those for natural lands. The soil evaporation was obviously larger in natural land and the natural vegetation growth was seriously stressed. Through lateral groundwater exchange, the cropland functioned as a stable groundwater recharge zone while the natural land functioned as a discharge zone during the growing season. As a result, 21% of the total water diverted to Jiyuan migrated to natural lands through groundwater due to field percolation and canal seepage, along with 50–75% of the total salt introduced. The fragmented land covers and complex hydrological processes bring many challenges to remote sensing and model simulation. Future land planning and water resources management should consider the hydrological effects caused by the fragmented land cover types and the associated management strategies.

Changes in Different Classes of Precipitation and the Impacts on Sediment Yield in the Hekouzhen-Longmen Region of the Yellow River Basin, China

The sediment yield of the Yellow River Basin has obviously decreased since the 1980s, and the impacts of precipitation on sediment yield changes have become increasingly important with the global climate change. The spatial and temporal variations in annual precipitation and different classes of precipitation in the Hekouzhen-Longmen region (HLR) in the middle reaches of the Yellow River Basin were investigated using data collected from 301 rainfall stations from 1966 to 2016. The impacts of precipitation variation on sediment yield were evaluated, and the hydrological modeling method was used to quantitatively assess the attribution of precipitation and other factors to sediment yield changes in the HLR. The results show that the annual precipitation and P10 increased from the northwest to the southeast of the HLR, suggesting it was drier in the northwest region of the HLR. P25 and P50 were mainly concentrated in the northwestern and southwestern parts of the HLR, reflecting that heavy rain was more likely to occur in these regions of the HLR. All of the annual precipitation and different classes of precipitation had no significant changing trends from 1966 to 2016, and the relationship between rainfall and sediment yield obviously changed in 2006. Compared with the average annual mean values from 1966 to 2016, both the annual precipitation and the different classes of precipitation were higher in the HLR during 2007–2016. The sediment yield decrease during 1990–1999 was mainly influenced by precipitation, while other factors were the main driving factor for the sediment yield decrease in the periods of 1980–1989, 2000–2009, and 2010–2016, and other factors have become the dominant driving factors of the sediment yield change in the HLR since 2000.

Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

Impact of natural climate variability on runoff based on Monte Carlo method

Abstract Studying the impact of climate variability is important for the rational utilization of water resources, especially in the case of intensified global climate variability. Climate variability can be caused by natural climate variability or human-caused climate variability. The analysis of Jinghe River Basin (JRB) may not be comprehensive because few studies have concentrated on natural climate variability. Therefore, the primary goal is to explore the impact of natural climate variability on runoff. A modified Mann–Kendall test method was adopted to analyze the aberrance point to determine the natural condition period during which runoff was only influenced by natural climate variability. Then, the Monte Carlo method was employed to extract segments of monthly runoff in the natural condition period and combine them to construct a long series to reduce the instability. Results indicate that the percentage of runoff variability affected by natural climate variability is 30.52% at a confidence level of 95%. Next, a topography-based hydrological model and climate elasticity method were used to simulate runoff after the aberrance point without considering the impact caused by local interference. Through a comparison of the measured and simulated runoff, we discovered that local interference has the greatest impact on runoff in the JRB.

Assessing the impact of climate variability and human activities on streamflow variation

Abstract. Water resources in river systems have been changing under the impact of both climate variability and human activities. Assessing the respective impact on decadal streamflow variation is important for water resource management. By using an elasticity-based method and calibrated TOPMODEL and VIC hydrological models, we quantitatively isolated the relative contributions that human activities and climate variability made to decadal streamflow changes in the Jinghe basin, located in the northwest of China. This is an important watershed of the Shaanxi province that supplies drinking water for a population of over 6 million people. The results showed that the maximum value of the moisture index (E0∕P) was 1.91 and appeared in 1991–2000, and the decreased speed of streamflow was higher since 1990 compared with 1960–1990. The average annual streamflow from 1990 to 2010 was reduced by 26.96 % compared with the multiyear average value (from 1960 to 2010). The estimates of the impacts of climate variability and human activities on streamflow decreases from the hydrological models were similar to those from the elasticity-based method. The maximum contribution value of human activities was 99 % when averaged over the three methods, and appeared in 1981–1990 due to the effects of soil and water conservation measures and irrigation water withdrawal. Climate variability made the greatest contribution to streamflow reduction in 1991–2000, the values of which was 40.4 %. We emphasized various source of errors and uncertainties that may occur in the hydrological model (parameter and structural uncertainty) and elasticity-based method (model parameter) in climate change impact studies.

生态恢复对流域水沙演变趋势的影响及其程度分析——以北洛河上游为例

The Loess Plateau in China is characterized by a heavily dissected landscape and severe soil erosion. To fully understand the impacts of soil conservation measures and vegetation restoration on streamflow,sediment load and runoffsediment behaviors,hydrologic data over 47 years(1963 to 2009) in the upper reaches of the Beiluo River( drainage area of 3408 km2) are assessed. The Beiluo is a secondary tributary of the Yellow River,and has had substantial vegetation restorative effects on the Loess Plateau. Based on the Mann-Kendall trend test,Pettit statistical test and flow duration curve(FDC),the daily streamflow and sediment load at Wuqi hydrological station,which is the maximum control station in theupper reaches of the Beiluo River,was chosen to investigate temporal change and evolution characteristics. Precipitation data and rainfall erosion force were interpolated using tessellation polygons from five representative meteorological stations on the Loess Plateau. A dual mass curve and empirically- based hydrological modeling method were also used to estimate the response of streamflow and sediment load to climate change and human activities. The results indicate that:(1) In the past50 years,under a background of precipitation with no significant trend,both streamflow and sediment load had significant negative trends( P 0. 01),with average annual rates of- 0. 28 mm km- 2a- 1and- 180 t km- 2a- 1,respectively. Under the influence of soil conservation measures that have been applied on the Loess Plateau since the 1960 s,there were change points in 1979 and 2002. Thus,the entire study period was divided into a period 1 of base(1963—1979),period 2 of comprehensive harness of soil and water conservation(1980—2002),and period 3 of the Grain for Green project(2003—2009).(2) The FDC showed that,compared with period 1,streamflow in period 2 for 5% and 50% decreased by 17. 0%and 5. 2%,respectively,while it increased by 94. 2% for 95%. Suspended sediment load decreased more significantly,with 34. 6% and 40. 1% for 5% and 50%,respectively,and no suspended sediment days remained almost constant for95%. Period 3 saw more remarkable changes in both streamflow and suspended sediment load. For 5% and 50%,streamflow decreased by 40. 2% and 26. 8%,respectively,while suspended sediment load decreased by 95. 7% and 96.8%,respectively. For 95%,streamflow rose by 128. 1%,and no sediment days increased. The reduction of sediment load was greater than that of streamflow,with the periods altered and negative changes of sediment load.( 3) Compared with period 1,streamflow and sediment load decreased by 0. 10 × 108m3(3 mm) and 0. 04 × 108t(1408 t km- 2a- 1) in period2. It was estimated that human activities accounted for 38. 2% and 51. 4% of the changes of streamflow and sediment load,respectively. In period 3,there was a greater reduction in both streamflow and sediment load,with 0. 52 × 108m3(15. 2mm) and 0. 33 × 108t(9683. 1t km- 2a- 1),respectively,with effects reaching 74. 7% and 86. 7%. Great improvement in the ecological environment of the Loess Plateau has already demonstrated the advantages of biological treatments on a regional scale in reducing flood volumes,and compensating streamflow.

Exploring the spatial variability of contributions from climate variation and change in catchment properties to streamflow decrease in a mesoscale basin by three different methods

The hydrological response to environmental changes has attracted a lot of attention and a couple of methods have been used to quantify the relative contributions of climate variability and change in catchment properties to streamflow change at basin scale. However, few studies have been carried out to explore the spatial variability of the results at sub-basin scale. The aim of this study is to explore the spatial variability of relative contributions from climate variability and change in catchment properties to streamflow change within a mesoscale basin using three methods, namely elasticity and decomposition methods based on the Budyko framework, and the dynamic hydrological modeling method. The Upper Hanjiang River Basin (UHRB) is chosen as the study area, which presents a significantly decreasing trend of annual streamflow since 1990. We partitioned change in catchment properties into vegetation-induced change and non-vegetation-induced change in the hydrological modeling method, and climate conditions into precipitation and potential evaporation in the elasticity method. The results of the three methods suggest that climate variability is a greater contributor to streamflow decrease than change in catchment properties for the UHRB, whereas the relative contribution from change in catchment properties increases from 17% in the upper parts to 54% in the lower parts, which is likely linked to the population growth. The relative climate contribution estimated from the hydrological model is greater than these from the two Budyko framework based methods, and the estimated relative climate contribution from the decomposition method is the smallest in the three methods.

Impacts of climate variability and human activities on decrease in streamflow in the Qinhe River, China

Under the impacts of climate variability and human activities, there are statistically significant decreasing trends for streamflow in the Yellow River basin, China. Therefore, it is crucial to separate the impacts of climate variability and human activities on streamflow decrease for better water resources planning and management. In this study, the Qinhe River basin (QRB), a typical sub-basin in the middle reach of the Yellow River, was chosen as the study area to assess the impacts of climate variability and human activities on streamflow decrease. The trend and breakpoint of observed annual streamflow from 1956 to 2010 were identified by the nonparametric Mann–Kendall test. The results showed that the observed annual streamflow decreased significantly (P < 0.05) and a breakpoint around 1973 was detected. Therefore, the time series was divided into two periods: “natural period” (before the breakpoint) and “impacted period” (after the breakpoint). The observed annual streamflow decreased by 68.1 mm from 102.3 to 34.2 mm in the two periods. The climate elasticity method and hydrological model were employed to separate the impacts of climate variability and human activities on streamflow decrease. The results indicated that climate variability was responsible for 54.1 % of the streamflow decrease estimated by the climate elasticity method and 59.3 % estimated by the hydrological modeling method. Therefore, the climate variability was the main driving factor for streamflow decrease in the QRB. Among these driving factors of natural and anthropogenic, decrease in precipitation and increase in water diversion were the two major contributions of streamflow reduction. The finding in this study can serve as a reference for regional water resources management and planning.

Impacts of climate variability and human activity on streamflow decrease in a sediment concentrated region in the Middle Yellow River

The Kuye River is the primary tributary located in the sediment concentrated regions in the Middle Yellow River in China. Significant decrease in streamflow has been observed in the Kuye River. The non-parametric Mann-Kendall test was applied to detect the change in annual streamflow for the period of 1960 to 2006. Mean annual streamflow in the Kuye River was 84.9 mm from 1960 to 1979 (period I), while it decreased to 58.2 mm from 1980 to 1998 (period II) and 20.5 mm from 1999 to 2006 (period III), respectively. The climate elasticity method and the hydrological modeling method were individually employed to assess the impact of climate variability and human activities on the decrease in streamflow . The results showed that climate variability was responsible for 29.6 and 27.1 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 70.4 and 72.9 % of the streamflow decrease in period II. In period III, climate variability contributed 40.9 and 39.3 % of the streamflow decrease from the climate elasticity method and the hydrological modeling method, respectively; while human activities accounted for 59.1 and 60.7 % of the streamflow decrease. Therefore, human activities were the main reason of the streamflow decrease. Soil conservation measures (planting trees, improving pastures, building terraces and sediment-trapping dams) and coal mining led to the streamflow reduction in the Kuye River.