Daily Runoff Simulation Research Articles

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data

Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying on simplified reservoir operation modules in most hydrological models. To improve the capability of hydrological models to capture flow variability influenced by reservoirs, this study proposes a hybrid hydrological modeling framework, which combines a process-based hydrological model with a machine-learning-based reservoir operation module designed to simulate runoff under reservoir operations. The reservoir operation module employs an ensemble of three machine learning models: random forest, support vector machine, and AutoGluon. These models predict reservoir outflows using precipitation and temperature data as inputs. The Soil and Water Assessment Tool (SWAT) then integrates these outflow predictions to simulate runoff. To evaluate the performance of this hybrid approach, the Xijiang Basin within the Pearl River Basin, China, is used as a case study. The results highlight the superiority of the SWAT model coupled with machine learning-based reservoir operation models compared to alternative modeling approaches. This hybrid model effectively captures peak flows and dry period runoff. The Nash-Sutcliffe Efficiency (NSE) in daily runoff simulations shows substantial improvement, ranging from 0.141 to 0.780, with corresponding enhancements in the coefficient of determination (R2) by 0.098–0.397 when compared to the original reservoir operation modules in SWAT. In comparison to parameterization techniques lacking a dedicated reservoir module, NSE enhancements range from 0.068 to 0.537, and R2 improvements range from 0.027 to 0.139. The proposed hybrid modeling approach effectively characterizes the impact of reservoir operations on river flow dynamics, leading to enhanced accuracy in runoff simulation. These findings offer valuable insights for hydrological forecasting and water resources management in regions influenced by reservoir operations.

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments

Climate change and human activities have a great impact on the environment and have challenged the assumption of the stability of the hydrological time series and the consistency of the observed data. In order to investigate the applicability of machine learning (ML)-based rainfall–runoff (RR) simulation methods under a changing environment scenario, several ML-based RR simulation models implemented in novel continuous and non-real-time correction manners were constructed. The proposed models incorporated categorical boosting (CatBoost), a multi-hidden-layer BP neural network (MBP), and a long short-term memory neural network (LSTM) as the input–output simulators. This study focused on the Dongwan catchment of the Yiluo River Basin to carry out daily RR simulations for the purpose of verifying the model’s applicability. Model performances were evaluated based on statistical indicators such as the deterministic coefficient, peak flow error, and runoff depth error. The research findings indicated that (1) ML-based RR simulation by using a consistency-disrupted dataset exhibited significant bias. During the validation phase for the three models, the R2 index decreased to around 0.6, and the peak flow error increased to over 20%. (2) Identifying data consistency transition points through data analysis and conducting staged RR simulations before and after the transition point can improve simulation accuracy. The R2 values for all three models during both the baseline and change periods were above 0.85, with peak flow and runoff depth errors of less than 20%. Among them, the CatBoost model demonstrated superior phased simulation accuracy and smoother simulation processes and closely matched the measured runoff processes across high, medium, and low water levels, with daily runoff simulation results surpassing those of the BP neural network and LSTM models. (3) When simulating the entire dataset without staged treatment, it is impossible to achieve good simulation results by adopting uniform extraction of the training samples. Under this scenario, the MBP exhibited the strongest generalization capability, highest prediction accuracy, better algorithm stability, and superior simulation accuracy compared to the CatBoost and LSTM simulators. This study offers new ideas and methods for enhancing the runoff simulation capabilities of machine learning models in changing environments.

Application of distributed Xin'anjiang model of melting ice and snow in Bahe River basin

Study regionBahe River basin on the Tibetan Plateau, China. Study focusThe thawing of glaciers and snow in cold regions is critical to the impact of runoff. This study proposes a distributed degree-day Xin'anjiang model (DD-XAJ) considering ice and snow thawing, which added the degree-day module describing snow and ice thawing to the previously distributed Xin'anjiang model. A sensitivity analysis of the main parameters of the improved model was conducted to discuss the effect of snow and glacier melt on the runoff process. The DD-XAJ model was also applied to the Bahe River basin to analyze its runoff change process and identify the composition of runoff components in the basin. New hydrological insightsThe DD-XAJ model was successfully applied to the Bahe River basin, which is located on the Tibetan Plateau, with good daily runoff simulation results. The mean values of the Nash efficiency coefficients were 0.828 and 0.859 in the calibration and validation periods, respectively, and the mean values of the correlation coefficients were 0.917 and 0.941, respectively. The runoff composition of the Bahe River basin is mainly rainfall runoff (76.3 %), snowmelt runoff (16.7 %), and glacier melt runoff (7 %), which indicates that rainfall runoff plays a dominant role in the Bahe River basin. Snowmelt runoff is influenced by rainfall events, while glacier melt runoff is mainly influenced by the seasons. In addition, the contribution of runoff components in different elevation zones differed significantly. The contribution of elevation 4000–5500 m to the total runoff was the largest. This study provides a new understanding of runoff processes and runoff components in the cryosphere basin.

Confluence simulations based on dynamic channel parameters in the grasslands lacking historical measurements

Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into distributed dynamic process model (DDPM). We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 h factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R2 = 0.947 and 0.932, NSE = 0.945 and 0.905, KGE = 0.971 and 0.680. In the 3-hour flood simulations, the DDPM could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the DDPM more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status, and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions.

Application of a Fractional Instantaneous Unit Hydrograph in the TOPMODEL: A Case Study in Chengcun Basin, China

The movement of water flow usually has history and path dependence. Fractional calculus is very suitable for describing the process with memory and hereditary properties. In this study, the order of the differential equation in the Nash confluence system was extended from integer order to fractional order. On the basis of the Laplace transform, the fractional instantaneous unit hydrograph was obtained, which was used to describe the long-term memory of the basin confluence system. Furthermore, the enhanced TOPMODEL (FTOP) model was obtained by applying the fractional instantaneous unit hydrograph as the surface runoff calculation. Taking Chengcun Basin in China as an example, the FTOP model was used to simulate the daily runoff and 22 floods from 1989 to 1996. The simulation results were compared with two original TOPMODEL models (the NTOP and ITOP models). The results show that in the daily runoff simulation, the Nash–Sutcliffe efficiency (NSE), relative error (RE), and root mean square error (RMSE) of the FTOP model were 0.82, −11.14%, and 15.25 m3/s, respectively, being slightly better than the other two TOPMODEL models. According to the hydrologic frequency curve, the measured daily runoff was divided into different flow levels. It was found that the FTOP model can improve the simulation effect of the medium-flow (frequency between 10% and 50%) and low-flow (frequency more than 50%) sections to a certain extent. In the flood simulation, the average runoff depth relative error (RDRE), peak discharge relative error (PDRE), peak occurrence time error (POTE), and NSE of 22 floods were 1.99%, 14.06%, −1.27, and 0.88, respectively, indicating that the simulation effect had been improved. Especially in NSE, the improvement was more prominent, meaning that the FTOP model can better simulate the flooding process. However, the flood peak and runoff depth simulation effect were not significantly improved. These conclusions indicate that the confluence method using the fractional instantaneous unit hydrograph as the TOPMODEL model can improve the simulation effect.

Simulation and Prediction of the Impact of Climate Change Scenarios on Runoff of Typical Watersheds in Changbai Mountains, China

Simulating the hydrological process of a river basin helps to understand the evolution of water resources in the region and provides scientific guidance for water resources allocation policies between different river basins and water resources management within the river basin. This paper provides a scientific basis for the sustainable development of regional water resources and an accurate grasp of the future change trend of runoff by analyzing the hydrological process response of runoff in typical watersheds in Changbai Mountains, China, to climate change. The applicability of the HEC-HMS (The Hydrologic Engineering Center’s-Hydrologic Modeling System) hydrological model in the watershed is verified by calibrating and verifying the daily rainfall-runoff process in the watershed during the wet season from 2006 to 2017. The daily rainfall data of the two scenarios SSP2-4.5 and SSP5-8.5 under the BCC-CSM2-MR model in the 2021–2050 CMIP6 plan were downscaled and interpolated to in-basin stations to generate future daily precipitation series to predict runoff response to future climate change. The daily rainfall data of the two scenarios were downscaled and interpolated to the stations in the basin to generate future daily rainfall series to predict the runoff response under future climate changes. The average certainty coefficient of the HEC-HMS model for daily runoff simulation reached 0.705; the rainfall in the basin under the two climate scenarios of SSP2-4.5 and SSP5-8.5 in the next 30 years (2021–2050) will generally increase, and rainfall will be more evenly distributed in the future; the outlet flow of the basin will increase during the wet season (June–September) in the next 30 years, but it is lower than the historically measured value; the peak flow of the future will appear at most in August and September. The peak flow current time mostly appears in July and August. The time of peak occurrence has been delayed.

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

Model coupling approach for daily runoff simulation in Hamp Pandariya catchment of Chhattisgarh state in India

Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1° × 0.1°), newly developed ERA5-Land, and a coarser resolution (0.55° × 0.55°) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37–38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins.

Rainfall-runoff simulation of Bagmati River Basin, Nepal

Runoff simulation is a complex problem in mountain catchments due to high rainfall variability and rugged topography. In the lower parts of Nepal, river flooding is a serious disaster problem in July and August; sometimes it also occurs in September. In this context, Hydro-Informatic Modeling System (HIMS) was used for daily and monthly runoff simulation from the set of daily hydro-meteorological data (Maximum and minimum temperature, rainfall, and discharge) in the time series 1980 to 1989, 1990 to 1999, and 2000 to 2009, respectively. The model performed well for the monthly runoff simulation, whereas the efficiency coefficient and relative coefficient both were found a very good correlation between observed and simulated hydrographs, which varied between 0.883 to 0.940 and 0.889 to 0.945, respectively. However, the efficiency coefficient and relative coefficient both were found a very poor correlation between observed and simulated hydrographs for the daily runoff simulation, which averaged 0.342 and 0.348, respectively. The daily simulation result also might have been improved, if more number of uniformly distributed meteorological station data is available.

An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro‐CH2018‐Runoff ensemble)

AbstractWe present a new ensemble of daily runoff simulations for meso‐scale catchments in Switzerland for the period 1981–2099: The Hydro‐CH2018‐Runoff ensemble. The ensemble contains runoff simulations for 93 catchments in Switzerland covering a wide range of different catchment characteristics governed by pluvial, nival and glacial runoff regimes. The hydrological modelling system PREVAH was thoroughly calibrated and validated for each catchment. The simulations show satisfactory performance with a median Nash‐Sutcliffe efficiency of 0.82 in the calibration and validation period. The calibrated parameters were then used to simulate runoff under climate change for each of the 93 catchments. These simulations were driven by the high‐resolution new Swiss climate change scenarios (CH2018) consisting of 68 GCM‐RCM combinations covering 3 different emission scenarios: RCP2.6, RCP4.5 and RCP8.5. The simulations show good agreement between simulated and observed runoff regimes in the reference period. The Hydro‐CH2018‐Runoff ensemble is publicly available under http://doi.org/10.5281/zenodo.3937485 (Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., and Martius, O. (2020) Hydro‐CH2018‐Runoff ensemble (version v1). Zenodo) and can be used for further impact studies.

Runoff Simulation for Pre-and-Post Hydropower Construction Periods in the Be River Catchment Using LST Model Calibrated by Differential Evolutionary Optimization

Introduction: Hydrological models play an important role in water studies. There have been numerous models developed and applied in river runoff simulation. This study focused on evaluating the runoff simulations using the LST model and the DE optimization for calibrating model parameters in the pre-hydropower construction (PrC) and post-hydropower construction (PoC) periods in the Be river catchment. Methods: The model performance, parameter evolution, and objective function evolution in two periods were evaluated and compared. The duration between 1878 and 1993 was defined as PrC, in which 1978-1988 and 1989-1993 were used for calibration and validation, respectively. The PoC period was from 1994 to 2013, in which 1994-2006 and 2007-2013 were respective calibration and validation durations. Results: The results showed that DE optimization was robust and efficient for calibrating model parameters. The LST model outperformed in natural daily runoff simulation with high NSI values of approximately 0.90 for PrC period. The lower performances were achieved in the PoC period with NSI greater than 0.7. On the other hand, good results were shown for the water balance index, PBIAS, with the obtained values of -0.266% and 6.906% for calibration and validation, respectively, of the PoC period. The obtained values for PrC period were -1.052% and 14.941%, respectively. Using DE optimization, an outperformed parameter set could be found in initial generations for natural runoff simulation of the PrC period. Conclusion: The NSI evolution trend of the PoC period was similar to that of the PrC; however, it gave a lower performance in the initial and the last generations. Parameter evolutions are not the same for all parameters for both periods. Some have constant directions, and others change directions by generations.

Evaluating two conceptual hydrological models: LST and NAM

Rainfall-runoff models are essential applications for water resources researches. A large number of models have been proposed and developed. Comparisons among models are helpful for researchers to have better choices for their studies of catchment water researches. This study focused on performance evaluations, and comparisons between the LST and NAM lumped conceptual rainfall-runoff models. Two models were applied to simulate runoffs of three catchments in the upper Dong Nai river basin: Phuoc Hoa, Ta Pao, and Phu Hiep. We used the data from 1984-1988 and 1989-1991 to calibrate and validate the Phuoc Hoa catchment, respectively. Those for Ta Pao and Phu Hiep catchments were 1988-1996 and 1997-1999. The results showed that both models performed well in the catchment monthly runoff simulation with NSE and R2 values greater than 0.90 for Phuoc Hoa and Phu Hiep stations and greater than 0.87 for Ta Pao station. The NSE and R2 by daily runoff simulations for Phuoc Hoa and Phu Hiep were approximate 0.90 except for the Phu Hiep runoff by the NAM model. That for Ta Pao was approximate 0.80. The obtained RSR and PBIAS are respectively lower than 0.50 and 10.00% in almost all simulations. Significantly, the LST model resulted in relatively better statistical performances than the NAM model in almost all statistical fitness values. The study would provide a noteworthy introduction of a good runoff simulation tool to hydrological studies in the future.

Assessing glacier retreat and its impact on water resources in a headwater of Yangtze River based on CMIP6 projections

Glacier retreat caused by global warming alters the hydrological regime and poses far-reaching challenges to water resources and nature conservation of the headwater of Yangtze River, and its vast downstream regions with dense population. However, there is still lack of a robust modeling framework of the “climate-glacier-streamflow” in this water tower region, to project the future changes of glacier mass balance, glacier geometry, and the consequent impacts on runoff. Moreover, it is imperative to use the state-of-the-art sixth phase Coupled Model Intercomparison Project (CMIP6) to assess glacio-hydrology variations in future. In this study, we coupled a glacio-hydrological model (FLEXG) with a glacier retreat method (Δh-parameterization) to simulate glacio-hydrological processes in the Dongkemadi Glacier (over 5155 m.a.s.l), which has the longest continuous glacio-hydrology observation on the headwater of Yangtze River. The FLEXG-Δh model was forced with in-situ observed meteorological data, radar ice thickness, remote sensing topography and land cover data, and validated by measured runoff. The results showed that the model was capable to simulate hydrological processes in this glacierized basin, with Kling-Gupta efficiency (IKGE) of daily runoff simulation 0.88 in calibration and 0.70 in validation. Then, forcing by the bias-corrected meteorological forcing from the eight latest CMIP6 Earth system models under two climate scenarios (RCP2.6 and RCP8.5), we assessed the impact of future climate change on glacier response and its hydrological effects. The results showed that, to the end of simulation in 2100, the volume of the Dongkemadi Glacier would continuously retreat. For the RCP2.6 and RCP8.5 scenarios, the glacier volume will decrease by 8.7 × 108 m3 (74%) and 10.8 × 108 m3 (92%) respectively in 2100. The glacier runoff will increase and reach to peak water around 2060 to 2085, after this tipping point water resources will likely decrease.

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

Classifying floods by quantifying driver contributions in the Eastern Monsoon Region of China

Understanding the generation mechanisms of floods is important to estimating future flood hazards by improving flood frequency analysis and flood trend interpretation. From the hydrological perspective of flood classification, flood types are determined by the seasonality or magnitudes of hydrological drivers (e.g., rainfall, snowmelt, catchment wetness). Few studies attribute floods to the causative drivers by quantifying the contributions of each driver to the flood peaks. Therefore, we propose the Quantification of Driver Contributions for Flood Classification (QDC-FC) method, which combines a data-driven hydrological model, the Shapley value, which quantifies driver contributions to flood peaks, and a classification tree for dividing floods into five types: long-rain floods (>1 day), rain-on-snow floods, snowmelt floods, short-rain floods (≤1 day), and antecedent-wet floods. In the Eastern Monsoon Region (EMR) of China, the method is applied in 225 catchments where the data-driven model has high Nash–Sutcliffe efficiency (NSE > 0.6) in daily runoff simulation and a high R-squared value (R2>0.6) in flood peak simulation. Under the impact of the eastern monsoon, short-rain floods and long-rain floods are the most frequent types in 143 and 69 catchments, respectively. In 213 catchments, all extreme floods (with return periods larger than 10 years) are caused by short rain or long rain. Antecedent wetness causes more than 20% of floods in 26 out of 66 catchments in northeastern China and northern China with cumulative catchment wetness in summer. Although snowmelt-induced floods occur in April in some catchments of northeastern China, only one catchment has more than 20% of floods caused by snowmelt. As a new method for flood classification, the QDC-FC sheds light on the mixed generation mechanisms of floods and possible drivers of flood trends in the EMR of China.

Distributed hydrological modelling at multiple hydrological stations in the upper reach of Han River based on SWAT model

Using distributed hydrological model to study runoff simulation in sub-watershed zones is important to understand the hydrological process in a river basin. In order to identify the sensitive parameters in different regions, in this paper, the SWAT model was constructed based in the upstream area of Shiquan hydrological station in the upper reach of the Han River. The SUFI-2 algorithm is used to analyze the parameter sensitivity of the five hydrological stations in Hanzhong, Yangxian, Shiquan, Youshuijie and Lianghekou, and the monthly runoff process of five stations and the daily runoff process at Shiquan hydrological station were calibrated in 2004-2009 and validated in 2010-2014, respectively. And the simulated results are evaluated at each station. The results show that, the parameter sensitivity of the hydrological stations is different. The value of NSE and R2 of the monthly runoff of each hydrological station were greater than 0.73 in calibration and validation period. The values of relative error are less than 18% except Yangxian station. The daily runoff simulation of Shiquan hydrological station also meets the accuracy requirement of the model. SWAT model has a good applicability in the upper area of Shiquan hydrological station, which provides a basis for the study of the impact of climate change and human activities on runoff in the upper reach of the Han River.

Bayesian evaluation of meteorological datasets for modeling snowmelt runoff in Tizinafu watershed in Western China

The Tizinafu watershed is characterized by extremely scarce precipitation and low temperature, and snow and glacier melting which dominate the main water resource of this area. Thus, assessing the snowmelt water resource has great significance to local residents and ecological systems. Based on snowmelt runoff model (SRM) and Markov chain Monte Carlo (MCMC) simulation, four combinations of meteorological data consisting of two daily temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the variable infiltration capacity (VIC) model and two daily precipitation data from Tropical Rainfall Measuring Mission (TRMM) and VIC, respectively, are evaluated for this ungauged basin in Bayesian uncertainty analysis. By comparing the performances of the four meteorological data combinations based on SRM (SRMMODIS + TRMM, SRMMODIS + VIC, SRMVIC + TRMM and SRMVIC + VIC, the former and latter subscripts denote temperature and precipitation data, respectively), results show that the four SRMs are both capable of reproducing the runoff process of the Tizinafu watershed on daily runoff simulation, and SRMVIC + VIC has the best performance. In addition, the impact of climate change on the water resources of the Tizinafu watershed is also evaluated in Bayesian uncertainty analysis, and four climate change scenarios under the condition of Representative Concentration Pathway 8.5 of CMIP5 projection are considered. The results demonstrate that the mean annual runoff prediction of the 2090s increased by 17.2%, 20.2%, 24.1%, and 16.2% compared to that of base year for the four scenarios, respectively. In addition, with the increase of the cryosphere area, snow cover area will have an increasing impact on the mean annual runoff of the study area in the 2090s. The runoff components of snowpack melt and new snow are both sensitive to the cryosphere area, while the runoff component of rainfall is not sensitive to the cryosphere area.

Application Research of Parallel Optimization Technology in Hydrological Model

Hydrological model parameters are generally considered to be a simplified representation that characterizes hydrologic processes, As hydrological models continue to deepen the application of hydrological processes in the basin, they face enormous calculations. Meanwhile, in pursuit calibrating the model parameters by optimal algorithms for higher accuracy, the computation burden of optical techniques has become much heavier. Therefore, in order to solve this problem of low efficiency of hydrological model calculation, this paper uses parallel PSO algorithm to calibrate the TOPMODEL model parameters, and then uses parallel computing to process the flow generation in each sub-basin. The results show that the daily runoff simulation value of tangnaihai hydrological station fits well with the measured hydrological process; Whether PSO or sub-basin all can improve computational efficiency by using parallel optimization techniques, the former and the latter increased by 3.22 and 2.57 times, respectively. The results provide a reference for further understanding the application of parallel computing in hydrological models.