Xinanjiang Hydrological Model Research Articles

An Improved Xin’anjiang Hydrological Model for Flood Simulation Coupling Snowmelt Runoff Module in Northwestern China

The Xin’anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM’s effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model’s state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins.

Massively Parallel Monte Carlo Sampling for Xinanjiang Hydrological Model Parameter Optimization Using CPU-GPU Computer Cluster

The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. The MCS method is an internally concurrent algorithm that can be parallelized. It has the potential to execute on massively parallel hardware systems such as multi-node computer clusters equipped with multiple CPUs and GPUs, which are known as heterogeneous hardware systems. To take advantage of this, we parallelize the algorithm and implement it on a multi-node computer cluster that hosts multiple INTEL multi-core CPUs and NVIDIA many-core GPUs by using C++ programming language combined with the MPI, OpenMP, and CUDA parallel programming libraries. The parallel parameter optimization method is coupled with the Xinanjiang hydrological model to test the acceleration efficiency when tackling real-world applications that have a very high computational burden. Numerical experiments indicate, on the one hand, that the computational efficiency of the massively parallel parameter optimization method is significantly improved compared to single-core CPU code, and the multi-GPU code achieves the fastest speed. On the other hand, the scalability property of the proposed method is also satisfactory. In addition, the correctness of the proposed method is also tested using sensitivity and uncertainty analysis of the model parameters. Study results indicate good acceleration efficiency and reliable correctness of the proposed parallel optimization methods, which demonstrates excellent prospects in practical applications.

Evaluating performance dependency of a geomorphologic instantaneous unit hydrograph-based hydrological model on DEM resolution

The digital elevation model (DEM) is a type of model that has been widely used in terrain analysis and hydrological modeling. DEM resolution influences the hydrological and geomorphologic features of delineated catchments and consequently affects hydrological simulations. This study investigated the impacts of DEM resolution on the performance of the XAJ-GIUH hydrological model, a model coupling the widely used Xinanjiang (XAJ) hydrological model with the geomorphologic instantaneous unit hydrograph (GIUH), in flood simulations in small and medium-sized catchments. To test the model performance, the model parameters were calibrated at a fine DEM resolution (30 m) and then directly transferred to the simulation runs using coarser DEMs. Afterwards, model recalibration was conducted at coarser DEM resolutions. In the simulation runs with the model parameters calibrated at the 30-m resolution, the DEM resolution slightly affected the overall shape of the simulated flood hydrographs but presented a greater impact on the simulated peak discharges in the two study catchments. The XAJ-GIUH model consistently underestimated the peak discharges when the DEM resolution became coarser. The qualified ratio of peak simulations decreased by 35% when the DEM resolution changed from 30 m to 600 m. However, model recalibration produced comparable model performances when DEMs with different resolutions were used. This study showed that the impact of DEM resolution on model performance can be mitigated by model recalibration to some extent, if the DEM resolution is not too coarse.

Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure

Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The Encoder-Decoder (ED) that can tackle sequence-to-sequence problems is suitable for multi-step-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTM-EDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model.

A Comparative Evaluation of Gauge-Satellite-Based Merging Products Over Multiregional Complex Terrain Basin

Satellite precipitation products (SPPs) have become a significant data source in hydrometeorology, especially for ungauged or sparsely -gauged basins, and satellite estimates can supplement the lack of precipitation information. However, satellite remote sensing precipitation products have various degrees of uncertainty which may be related to studied area, precipitation type and other factors. In addition, the applicability of the same satellite product may not be consistent in different regions. This study focuses on statistical evaluations of five SPPs (TRMM_3B42v7, TRMM_3B42RT, PERSIANN_CDR, CMORPH_CRT and IMERG_Final) and further improves their accuracy by using the principal component regression-based merging model over two different topography regions. The utilities of all the precipitation sets were then investigated using the Xinanjiang hydrological model to access the influence of the combined product in hydrological response and relative improvements. Results show that the original TRMM_3B42v7, CMORPH_CRT and IMERG_Final products exhibit much better performance than other two products for precipitation estimation, while the accuracy of TRMM 3B42RT varies greatly between two regions. The merged IMERG_Final and CMORPH_CRT significantly improved the results in two basins. In summary, the merging method combines satellite- and ground- based precipitation to produce a finer spatial distribution, while attention should be paid to the effect of merging method with respect to different satellite information within different basins. This study provides quantitative precipitation estimation analysis of SPPs in different regions and the applicability of merging methods for different SPPs and thus will provide useful guidelines for future utilizations of SPPs and merging schemes in regional hydrological applications.

Projecting Hydrological Responses to Climate Change Using CMIP6 Climate Scenarios for the Upper Huai River Basin, China

The influence of climate change on the regional hydrological cycle has been an international scientific issue that has attracted more attention in recent decades due to its huge effects on drought and flood. It is essential to investigate the change of regional hydrological characteristics in the context of global warming for developing flood mitigation and water utilization strategies in the future. The purpose of this study is to carry out a comprehensive analysis of changes in future runoff and flood for the upper Huai River basin by combining future climate scenarios, hydrological model, and flood frequency analysis. The daily bias correction (DBC) statistical downscaling method is used to downscale the global climate model (GCM) outputs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and to generate future daily temperature and precipitation series. The Xinanjiang (XAJ) hydrological model is driven to project changes in future seasonal runoff under SSP245 and SSP585 scenarios for two future periods: 2050s (2031–2060) and 2080s (2071–2100) based on model calibration and validation. Finally, the peaks over threshold (POT) method and generalized Pareto (GP) distribution are combined to evaluate the changes of flood frequency for the upper Huai River basin. The results show that 1) GCMs project that there has been an insignificant increasing trend in future precipitation series, while an obvious increasing trend is detected in future temperature series; 2) average monthly runoffs in low-flow season have seen decreasing trends under SSP245 and SSP585 scenarios during the 2050s, while there has been an obvious increasing trend of average monthly runoff in high-flow season during the 2080s; 3) there is a decreasing trend in design floods below the 50-year return period under two future scenarios during the 2050s, while there has been an significant increasing trend in design flood during the 2080s in most cases and the amplitude of increase becomes larger for a larger return period. The study suggests that future flood will probably occur more frequently and an urgent need to develop appropriate adaptation measures to increase social resilience to warming climate over the upper Huai River basin.

Application of an improved distributed Xinanjiang hydrological model for flood prediction in a karst catchment in South‐Western China

AbstractHydrological processes in karst aquifer systems are controlled by highly permeable media, so studying flood processes in karst‐dominated regions is very important; however, it is still a challenge to model the hydrological dynamics in such strongly heterogeneous conditions. This study proposed a distributed Xinanjiang karst hydrological model (DXAJKHM) for simulating the flood processes in karst catchments, which was based on topographical information extracted from a digital elevation model. Considering the dual‐porosity in karst aquifer systems, the DXAJKHM was coupled with a traditional Xinanjiang conceptual hydrological model and utilized two karst reservoirs that simulated both the rapid underground run‐off and the slow underground run‐off in each grid cell. The uncertainty in the model parameters is estimated by the generalized likelihood uncertainty estimation method, and the parameters are determined by the shuffled complex evolution approach optimization algorithm. The simulation results demonstrated that the proposed DXAJKHM satisfactorily simulated the flood processes, and the model has better simulation effects for floods with larger flood peaks. In order to analyse the flood recession error, one run‐off signature index was employed to improve the model runs. This study thus provides a new approach to simulating and predicting floods in karst areas.

Evaluation Method of Multiobjective Functions' Combination and Its Application in Hydrological Model Evaluation.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions' combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions' combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

Parametric uncertainty assessment in hydrological modeling using the generalized polynomial chaos expansion

An integrated framework is proposed for parametric uncertainty analysis in hydrological modeling using a generalized polynomial chaos expansion (PCE) approach. PCE represents model output as a polynomial expression in terms of critical random variables that are determined by parameter uncertainties, thus offers an efficient way of sampling without running the original model, which is appealing to computationally expensive models. To demonstrate the applicability of generalized PCE approach, both second- and third-order PCEs (PCE-2 and PCE-3) are constructed for Xinanjiang hydrological model using three selected uncertain parameters. Uncertainties in streamflow predictions are assessed by sampling the random inputs. Results show that: (1) both PCE-2 and PCE-3 are capable of capturing the uncertainty information in hydrological predictions, generating consistent mean, variance, skewness and kurtosis estimates with the standard Monte Carlo (MC) methodology; (2) Using more collocation points and more polynomial terms, PCE-3 approximation slightly improves the model simulation and provides more matched distribution with that of MC compared to PCE-2; (3) the computational cost using the PCE approach is greatly reduced by 71% (20%) with PCE-2 (PCE-3). In general, PCE-2 is recommended to serve as a good surrogate model for Xinanjiang hydrological modelling in future with much higher computation speed, more efficient sampling, and compatible approximation results.

Searching for an Optimized Single-objective Function Matching Multiple Objectives with Automatic Calibration of Hydrological Models

In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single- or multi-objective functions when utilizing automatic calibration approaches. In most previous studies, there is a general opinion that no single-objective function can represent all important characteristics of even one specific hydrological variable (e.g., streamflow). Thus hydrologists must turn to multi-objective calibration. In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes (i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds. The new objective function was applied to 196 model parameter estimation experiment (MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model. The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively. Results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration. The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed.

Heterogeneous parallel computing accelerated generalized likelihood uncertainty estimation (GLUE) method for fast hydrological model uncertainty analysis purpose

The generalized likelihood uncertainty estimation (GLUE) is a famous and widely used sensitivity and uncertainty analysis method. It provides a new way to solve the “equifinality” problem encountered in the hydrological model parameter estimation. In this research, we focused on the computational efficiency issue of the GLUE method. Inspired by the emerging heterogeneous parallel computing technology, we parallelized the GLUE in algorithmic level and then implemented the parallel GLUE algorithm on a multi-core CPU and many-core GPU hybrid heterogeneous hardware system. The parallel GLUE was implemented using OpenMP and CUDA software ecosystems for multi-core CPU and many-core GPU systems, respectively. Application of the parallel GLUE for the Xinanjiang hydrological model parameter sensitivity analysis proved its much better computational efficiency than the traditional serial computing technology, and the correctness was also verified. The heterogeneous parallel computing accelerated GLUE method has very good application prospects for theoretical analysis and real-world applications.

A copula-based analysis of projected climate changes to bivariate flood quantiles

Climate change will lead to great impacts on flood frequency curve and design floods in the future. However, traditional hydrologic approaches often fail to analyze the flood characteristics within a bivariate framework under changing environment. Moreover, previous studies investigating bivariate characteristics of flood usually do not derive the adaptive flood quantiles. This study assesses the implications of climate change for future bivariate quantiles of flood peak and volume in Ganjiang River basin, China. The outputs of two global climate models (BNU-ESM and BCC-CSM1.1) are statistically downscaled by Daily bias correction (DBC) method and used as inputs of the Xinanjiang hydrological model to simulate streamflow during 1966–2099. Projections for future flood (2020–2099) under Representative Concentration Pathway (RCP) 8.5 scenario are divided into two 40-year horizons (2040s, 2080s) and a comparison is made between these time horizons and the baseline (1966–2005). Univariate flood frequency analysis indicates that there is a considerable increase in the magnitude and frequency of flood under the RCP8.5 scenario, especially for the higher return periods. The bivariate quantile curves under different levels of Joint Return Period (JRP) for historical and future periods are derived by copula functions and the most likely realizations are estimated. It is found that climate change has heavier impacts on the future joint bivariate quantiles for larger return periods. Finally the adaptive isolines and most likely flood quantiles under a JRP are derived from analyzing the merged series by non-stationary copula-based models. The results highlight that the joint probability, illustrated by the isoline of a given JRP, varies significantly over time when non-stationary models are applied. This study incorporates the impacts of climate change on bivariate flood quantiles and develops an adaptive quantile estimation approach, which may provide useful information for the references of flood risk assessment and management under changing environment.

Monitoring hydrological drought using long-term satellite-based precipitation data

Long-term (over 30a) satellite-based quantitative rainfall estimate (SRE) products provide an ideal data source for hydrological drought monitoring. This study mainly explores the suitability of the two long-term SREs, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the Climate Hazards Group (CHG) Infrared Precipitation with Stations (CHIRPS), for hydrological drought monitoring. A hydrological drought index called the standardized streamflow index (SSI) was used as an example and the Grid-based Xinanjiang (GXAJ) hydrological model was used for streamflow generation of the SREs. A middle size basin in the humid region of south China was selected as case study. The obtained results show that both SREs present acceptable performances for hydrological modeling, and CHIRPS outperformed PERSIANN-CDR. SSIs calculated by the SRE simulations generally fit well with the trend of observation-based on SSI but apparent deviations in drought intensity were also found. In contrast to hydrological modeling, performance of the SRE-based SSI showed almost no change after model recalibration. Both SREs generally present acceptable classification accuracy but tended to underestimate the levels of drought types. Both SREs accurately captured the beginning, end, and duration of this drought event; however, several deviations were found in severity and intensity estimation of the drought event. In general, both SREs are suitable for hydrological drought monitoring. Although the CHIRPS generally presented better performance, the PERSIANN-CDR is still adequate for hydrological drought monitoring.

Evaluation of hydrological utility of IMERG Final run V05 and TMPA 3B42V7 satellite precipitation products in the Yellow River source region, China

Global satellite precipitation products, such as Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with a critical precipitation data source for hydrological applications in data-sparse or ungauged basins. This study conducts a comparative analysis on the quality of the TRMM Multi-satellite Precipitation Analysis 3B42V7 and the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run version 05 precipitation products and their hydrological utilities in the Yellow River source region (YRSR), a mountainous Alpine region in northwestern China, from April 2014 to December 2016. Results indicate that when evaluated against the ground precipitation observations, IMERG generally improves the daily precipitation estimates relative to its predecessor 3B42V7. In addition, and the correlation coefficients (CCs) of IMERG (0.328 and 0.527) are significantly higher at grid and basin scales than those of 3B42V7 (0.287 and 0.458). However, the 3-hourly precipitation estimates from both products poorly correlate with the ground observations at grid and basin scales. By using the grid-based Xinanjiang (GXAJ) hydrological model calibrated with the gauge-based precipitation for daily streamflow simulations, the 3B42V7-driven model run shows acceptable hydrological simulation skill with regard to the Nash-Sutcliffe model efficiency coefficient (NSE = 0.729), whereas IMERG demonstrates improved performance (NSE = 0.810), which is comparable with the gauge-based simulation (NSE = 0.807). Input-specific model recalibration effectively enhances the hydrological performance of both satellite products (NSE = 0.856 for IMERG and NSE = 0.840 for 3B42V7). Additionally, the gauge-benchmarked GXAJ model with 3B42V7 has limited hydrological skill in simulating three historical flood events at 3-hourly time intervals (NSE = −0.070 to 0.702), while IMERG has a slightly better performance (NSE = 0.266–0.792). Model recalibration also significantly improves the simulations of two out of three flood events, and the NSE skill cores of IMERG (0.581–0.901) and 3B42V7 (−0.795 to 0.852) are relatively lower than that of the gauge-based simulation (0.753–0.969) but comparable. Overall, the IMERG and 3B43V7 satellite precipitation products can be adopted as reliable precipitation sources for hydrological simulations at daily and sub-daily time scales in the study area, with IMERG better suited than 3B42V7. Considering that the low CC values exist in both IMERG and 3B42V7 products, in particular, at a sub-daily temporal scale, the GPM research community should further improve the calibration algorithms and enhance the quality of IMERG products in YRSR. Performing bias-correction of satellite precipitation products is also necessary for hydrological modelers to effectively improve their hydrological utilities.

Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters’ optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the e-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

An improved multi-cores parallel artificial Bee colony optimization algorithm for parameters calibration of hydrological model

Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations’ parallel operation to guarantee the population’s diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash–Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution’s quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters’ optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.

WRF model for precipitation simulation and its application in real-time flood forecasting in the Jinshajiang River Basin, China

An accurate flood forecasting with long lead time can be of great value for flood prevention and utilization. This paper develops a one-way coupled hydro-meteorological modeling system consisting of the mesoscale numerical weather model Weather Research and Forecasting (WRF) model and the Chinese Xinanjiang hydrological model to extend flood forecasting lead time in the Jinshajiang River Basin, which is the largest hydropower base in China. Focusing on four typical precipitation events includes: first, the combinations and mode structures of parameterization schemes of WRF suitable for simulating precipitation in the Jinshajiang River Basin were investigated. Then, the Xinanjiang model was established after calibration and validation to make up the hydro-meteorological system. It was found that the selection of the cloud microphysics scheme and boundary layer scheme has a great impact on precipitation simulation, and only a proper combination of the two schemes could yield accurate simulation effects in the Jinshajiang River Basin and the hydro-meteorological system can provide instructive flood forecasts with long lead time. On the whole, the one-way coupled hydro-meteorological model could be used for precipitation simulation and flood prediction in the Jinshajiang River Basin because of its relatively high precision and long lead time.

Multi-source hydrological soil moisture state estimation using data fusion optimisation

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.

Model Parameter Optimization Method Research in Heihe River Open Modeling Environment (HOME)

How to make the existing models from different disciplines effectively interoperate and integrate is one of the primary challenges for scientists and decision-makers. Heihe river Open Modeling Environment (HOME) provides a convenient model coupling platform that enables researchers concentrate on the theory and applications of ecological and hydrological watershed models. The model parameter optimization is an important component and key step that links models and simulation of watershed. In this paper, through integration modules of existing models, an improved ABC algorithm (ORABC) based on optimization strategy and reservation strategy of the best individuals was introduced into HOME as a hydrological model parameter optimization module, and coupled with the Xinanjiang hydrological model to complete automatically task of model parameter optimization. The runoff simulation experiments in Heihe river watershed were taken to verify the parameter optimization in HOME, and the simulation results testified the efficiency and effectiveness of the method. It can significantly improve simulation accuracy and efficiency of hydrological and ecological models, and promote the scientific researches for watershed issues.