Nash Coefficient Research Articles

Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Abstract Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control. Significance Statement The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network

Floods are among the most dangerous disasters that affect human beings. Timely and accurate flood forecasting can effectively reduce losses to human life and property and improve the utilization of flood resources. In this study, a real-time flood classification and prediction method (RFC-P) was constructed based on factor analysis, the k-means++ clustering algorithm, SSE, a backpropagation neural network (BPNN) and the M-EIES model. Model parameters of different flood types were obtained to forecast floods. The RFC-P method was applied to the Jingle sub-basin in Shanxi Province. The results showed that the RFC-P method can be used for the real-time classification and prediction of floods. The parameters of the flood classification and prediction model were consistent with the characteristics of the flood events. Compared with the results of unclassified predictions, the Nash coefficient increased by 5%–11.62%, the relative error of the average flood peak was reduced by 6.08%–12.7%, the relative error of the average flood volume was reduced by 5.74%–8.07%, and the time difference of the average peak was reduced by 43%–66% based on the proposed approach. The methodology proposed in this study can be used to identify extreme flood events and provide scientific support for flood classification and prediction, flood control and disaster reduction in river basins, and the efficient utilization of water resources.

The Waterlogging Process Model in the Paddy Fields of Flat Irrigation Districts

Flat, low-lying agricultural areas such as irrigation districts in southern China have been increasingly vulnerable to flood inundation disasters because of the increased runoff associated with urbanization and climate change. In this study, we developed a waterlogging process simulation model comprising two parts: runoff generation module and runoff confluence module. An improved tank model and hydrodynamic model based on Saint–Venant equations were adopted in the runoff generation and confluence module, respectively. The results show that the model’s relative error and root mean square error are 2.1% and 0.17 mm/h, and the Nash coefficient of the model is 0.91. The relative error of river level simulation was within 5%, and the Nash coefficient was higher than 0.9. The proposed waterlogging simulation model could be a valuable tool for describing the process of waterlogging generation, accumulation, and confluence in the studied irrigation district or other regions with similar climatic conditions.

Estimation of Surface Runoff to Bahr AL-Najaf

The estimation of the amounts of Surface runoff resulting from rainfall in the water basins is of great importance in water resources management. The study area (Bahr Al-Najaf) is located on the western edge of the plateau and the southwestern part of the city center of Najaf, with an area of 2729.4 (km2). The soil and water assessment tool (SWAT) with ArcGIS software was used to simulate the runoff coming from the three main valleys (Kharr (A and B)), Shoaib Al-Rahimawi, and Maleh), that contribute the flow to the study area. The results of the model showed that the SWAT software was successfully simulating the flow conditions based on the coefficient of determination (R2), the Nash coefficient (NSE), P-factor, and R-factor for calibration (validation) ranged between 0.59-0.62 (0.51-0.59), 0.59-0.66 (0,.60-0.62), 0.57-0.76 (0.62-0.76), and 0.58-0.74 (0.55-0.70) respectively for these valleys. Moreover, the sensitivity results revealed that the most sensitive parameters in (SWAT-CUP) SWAT calibration and uncertainty programs are the curve number (CN2) for the runoff, soil available water capacity (SOL_AWC), and Saturated hydraulic conductivity (Soil_k), according to the calibration results for the main three valleys related the study area. Three hypothesis scenarios were implemented according to the assumed amount of precipitation that would submit a water level of 16,18, and 22 (m.a.m.s.l.) which would result in filling with the bounded lake, the whole study area, and exceeding the boundaries to flood part of the ancient Najaf City.

Data Construction and Spatiotemporal Trend Attribution of Runoff over the African Continent (1981–2016)

AbstractThe availability of streamflow records in Africa has been declining since the 1980s due to malfunctioning gauging stations and data collection failures. Africa also has insufficient hydrological information owing to the allocation of few resources to research efforts. Unreliable runoff datasets and large uncertainties in runoff trends due to climate change patterns and human activities are major challenges to water resource management in Africa. Therefore, this study aimed to improve runoff estimates and to assess runoff trend responses to climate change and human activities in Africa during 1981–2016. Using statistical methods, monthly gridded runoff datasets were generated for the period of 1981–2016 from a modified runoff curve number method calibrated with river discharge data from 535 gauging stations. According to the cross-validation results, the constructed runoff datasets comprised the Nash and Sutcliffe coefficients ranging from 0.5 to 1, coefficients of determination ranging from 0.5 to 1 and percent biases between ±25% for a large number of stations up to 73%, 80% and 91% of the 535 gauged catchments used as references. Analysis of runoff trend responses to climate change and human activities revealed that land cover change contributed more (72%) to the observed net runoff change (0.30%•a−1) than continental climate changes (28%). These contributions were results of cropland expansion rate of 0.46%•a−1 and a precipitation increase of 0.07%•a−1. The performance and simplicity of the statistical methods used in this study could be useful for improving runoff estimations in other regions with limited streamflow data data. The results of the current study could be important to natural resource managers and decision makers in terms of raising awareness of climate change adaptation strategies and agricultural land-use policies in Africa.

Implementation of hybrid neuro-fuzzy and self-turning predictive model for the prediction of concrete carbonation depth: A soft computing technique

Carbonation is one of the critical problems that affects the durability of reinforced concrete; it is a reaction between CO2 gas and Ca (OH)2 when H2O is available, which forms powdery CaCO3 that alters the microstructure of the concrete by reducing its pH level and initiating corrosion that reduces the structure's service life. This study provides experimental information on the carbonation depths of samples from 10 separate existing reinforced concrete structures, where five are located in the inland area (Nicosia), while the other five are in the coastal area (Kyrenia) of the Turkish Republic of North Cyprus. The study found that the inland buildings have a higher depth of carbonation compared to the coastal buildings. The building structures in North Cyprus exhibit a higher rate of carbonation than the expected threshold within their life span. Constant values of B were yielded, which is useful in predicting carbonation depth. Using AI, the potential Hybrid Neuro-fuzzy model, which is comprised of an Adaptive Neuro-fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Machine (SVM) and a Conventional Multilinear Regression (MLR) model, were employed for the estimation of carbonation depth using experimental data, including age, compressive strength, current density, and carbonation constant. Four different performance indexes were used to verify the modelling accuracy, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash- Coefficient (NSE), and Correlation Coefficient (CC). The results indicated that the AI models (ANFIS, ELM, SVM) performed better than the linear model (MLR) with NSE-values higher than 0.97 in both the testing and training stages. The results also indicated that the prediction skills of ANFIS-M2 increased the performance accuracy of ELM-M2, SVM-M2, and MLR-M2, and the ANFIS-M1 model performed better than ELM-1, SVM-1 and MLR-1 models in terms of prediction accuracy. The final outcomes indicated the capability of the non-linear models (ANFIS, ELM, and SVM) in the prediction of Cd.

Simulation of Lake Water Volume in Ungauged Terminal Lake Basin Based on Multi-Source Remote Sensing

Obtaining the water volume of small- and medium-sized lakes in enclosed watersheds with scarce data is a global focus of research. River flow into a lake is an important factor affecting the water volume. However, most river flow measurement methods involve long cycles, low efficiency, and transdisciplinary expertise, making rapid assessments in ungauged basins impossible. This paper proposes a remote sensing flow estimation method based on multi-source remote sensing data, which quickly assesses river flow and provides important input data for lake water volume simulation. The cross-section flow was estimated by extracting the river width. The calculated results were consistent with the measured data, with accuracy greater than 90%. The results compared with daily data measured at hydrological stations, and the Nash coefficient was greater than 0.9. Additionally, the simulation method for lake area, water volume, and water level was constructed using river inflow input data, greatly reducing the parameters required by the conventional lake water volume simulation method. Based on the remote sensing discharge estimation method, we quickly and conveniently obtained changes in river flow into the lake, simulated lake water volume, and provided the basis for water resource management in terminal lake basins with scarce data.

An evolutionary machine learning approach for municipal solid waste generation estimation utilizing socioeconomic components

Municipal solid waste generation is an important parameter in waste management with significant impacts on environment. There are many components directly influencing solid waste generation (SWG). In this study, the important socioeconomic parameters of the city of Tehran, Iran, were obtained in the period of 1991 to 2013. Important and optimum variables were analyzed and selected using the Pearson correlation analysis, and four variables including income, pop, GDP, and month were selected. In order to find a proper model, three common machine learning (ML) techniques including artificial neural network (ANN), random forest (RF), and multivariate adaptive regression splines (MARS) were used. Five evaluation metrics were used in this study including the correlation coefficient, Nash coefficient, root mean square error (RMSE), mean absolute error (MAE), and ratio of RMSE to the standard deviation of measured data (RSD). The results revealed that the MARS model outperformed all the other models. In addition, in the last step, the crow search algorithm (CSA) was applied to the MARS model to increase the accuracy of the selected model. The hybrid optimized MARS-CSA model resulted in better prediction, where the correlation coefficient increased to 0.82, the Nash coefficient decreased to 0.56, and the errors decreased to RMSE = 10.908, MAE = 9.141, and RSD = 0.657. In practice, for better understanding and efficient management of municipal SWG and reducing the environmental impact of waste, an integrated MARS-CSA model can be suggested for the accurate prediction of the monthly SWG as an essential pre-requirement.

Simulation Analysis of Vertical Movement of Nitrate Nitrogen in Vadose Zone of Fluvo-aquic Soil Based on HYDRUS-1D

Wastewater reuse has significant social, economic and ecological benefits, and has become an important measure to alleviate the water crisis. In this paper, in the simulation analysis of soil nitrate nitrogen, the method of combining soil column leaching and model simulation is used to test the accuracy of the model. The Pearson correlation coefficient is as high as 0.99 and the Nash coefficient is also over 0.9. It shows that the simulation results of the simulation model are credible. The Hydrus-1D model is used to visually simulate the change process of Tianjin fluvo-aquic soil over time. In the simulation, the process of solute migration in the 6-18 cm of the ground surface is basically the same, and they all reach a stable state about 4 hours, and show a linear increase trend four hours ago, and then the concentration remains stable and unchanged.

Regionalization of flow duration curves in the Amazon with the definition of homogeneous regions via fuzzy C-means.

Data insufficiency is one of the main challenges faced in hydrological studies, including a lack of knowledge regarding flow duration curves (FDCs). Thus, homogeneous regions of streamflow were identified in the Amazon using the Fuzzy C-Means (FCM) method. The PBM index was used to validate the clustering obtained via FCM, in turn, a homogeneity test based on the L-moment was applied to confirm the homogeneity in each defined region. Linear, power, exponential, logarithmic, quadratic and cubic mathematical models were fitted to the FDCs observed in the homogeneous regions. The models are the result of multiple regression analyses involving the parameters of the fitted FDC and the physico-climatic characteristics of the watersheds. The models were validated using the Jack-knife cross-validation method. The validation was satisfactory, with NASH coefficients higher than 0.50. Additionally, the standard deviation (RSR) of observations was less than 0.70, and the averages of the relative mean square error did not exceed 12.26%. These results are relevant for 89.91% of the analyzed watersheds and 73.58% of the study area. Thus, FDCs may be estimated in large parts of the Amazon, thereby making the methodology presented a valuable tool to support projects involving the planning and management of water resources.

Interannual Variation of Transpiration and Its Modeling of a Larch Plantation in Semiarid Northwest China

Quantifying the variation of forest transpiration (T) is important not only for understanding the water and energy budget of forest ecosystems but also for the prediction, evaluation, and management of hydrological effects as well as many other ecosystem services of forests under the changes of climate, vegetation, and anthropological impacts. The accurate prediction of T, a key component of water used by forests, requires mechanism-based models describing the T response to environmental and canopy conditions. The daily T of a larch (Larix principis-rupprechtti) plantation was measured through monitoring the sap flow in the growing season (from May to September) of a dry year (2010), a normal year (2012), and a wet year (2014) at a shady slope in the semi-arid area of Liupan Mountains in northwest China. Meanwhile, the meteorological conditions, soil moisture, and forest canopy leaf area index (LAI) were monitored. To get a simple and easily applicable T model, the numerous influencing parameters were grouped into three factors: the atmospheric evapotranspiration demand indicated by the potential evapotranspiration (PET), the soil water supply ability indicated by the relative extractable soil water content (REW), and the vegetation transpiration capacity indicated by the forest canopy LAI. The T model was established as a continuous multiplication of the T response equations to individual factors, which were determined using the upper boundary lines of measured data. The effect of each factor on the T in a dry year (2010) or normal year (2012) was assessed by comparing the measured T in the baseline of the wet year (2014) and the model predicted T, which was calculated through inputting the actual data of the factor (i.e., PET) to be assessed in the dry or normal year and the measured data of other two factors (i.e., REW, LAI) in the baseline of the wet year. The results showed that the mean daily T was 0.92, 1.05, and 1.02 mm; and the maximum daily T was 1.78, 1.92, and 1.89 mm in 2010, 2012, and 2014, respectively. The T response follows a parabolic equation to PET, but a saturated exponential equation to REW and LAI. The T model parameters were calibrated using measured data in 2010 and 2012 (R2 = 0.89, Nash coefficient = 0.88) and validated using measured data in 2014 satisfactorily (R2 = 0.89, Nash coefficient = 0.79). It showed a T limitation in the dry year 2010 for all factors (18.5 mm by PET, 11.5 mm by REW, and 17.8 mm by LAI); while a promotion for PET (1.4 mm) and a limitation for REW (4.2 mm) and LAI (14.3 mm) in the normal year 2012. The daily T model established in this study can be helpful to assess the individual factor impact on T and improve the daily T prediction under changing environmental and canopy conditions.

Quantitative analysis of the driving factors for groundwater resource changes in arid irrigated areas

AbstractHow to quantify the impact of climate change and human activities on groundwater is not only a hot topic of current research but also a key point of water resource management in arid irrigated areas. Therefore, this paper analyzes the changes in the trends of land use, climate, and groundwater extraction in the Yanqi Basin in recent years and uses the distributed hydrological model MIKE‐SHE to quantitatively analyze the impacts of these three factors on groundwater resources. The results show that: 1. The Nash coefficients of the simulated and observed groundwater levels during the verification period are 0.84, 0.79 and 0.76; the correlation coefficient between the simulated and observed soil moisture is 0.86. Although there are some uncertainties in the simulation, the results prove that the model can be used to simulate arid irrigated areas. 2. The effects of these three factors on groundwater levels are 5, 12.5 and 82.5%, respectively, and have caused the regional average groundwater level to decrease by a maximum of 0.07, 0.23 and 1.79 m, respectively. The effects of these three factors on the interactions between surface water and groundwater were 7.04, 3.63 and 89.33%. Groundwater extraction has become the main influencing factor of regional groundwater resources changes due to its more direct influence. 3. The influence of groundwater extraction has a strong spatial distribution characteristic and 10% of the study area has been greatly impacted by the groundwater extraction. Base on the above results, integrating multidisciplinary knowledge to establish the relationship between ecological environment and groundwater changes can provide strategies for the sustainable development of groundwater.

A simultaneous calibration and data assimilation (C/DA) to improve NRLMSISE00 using thermospheric neutral density (TND) from space-borne accelerometer measurements

SUMMARY Improving thermospheric neutral density (TND) estimates is important for computing drag forces acting on low-Earth-orbit (LEO) satellites and debris. Empirical thermospheric models are often used to compute TNDs for the precise orbit determination experiments. However, it is known that simulating TNDs are of limited accuracy due to simplification of model structure, coarse sampling of model inputs and dependencies to the calibration period. Here, we apply TND estimates from accelerometer measurements of the Challenging Minisatellite Payload (CHAMP) and the Gravity Recovery and Climate Experiment (GRACE) missions as observations to improve the NRLMSISE-00 model, which belongs to the mass spectrometer and incoherent scatter family of models. For this, a novel simultaneous calibration and data assimilation (C/DA) technique is implemented that uses the ensemble Kalman filter and the ensemble square-root Kalman filter as merger. The application of C/DA is unique because it modifies both model-derived TNDs, as well as the selected model parameters. The calibrated parameters derived from C/DA are then used to predict TNDs in locations that are not covered by CHAMP and GRACE orbits, and forecasting TNDs of the next day. The C/DA is implemented using daily CHAMP- and/or GRACE-TNDs, for which compared to the original model, we find 27 per cent and 62 per cent reduction of misfit between model and observations in terms of root mean square error and Nash coefficient, respectively. These validations are performed using the observations along the orbital track of the other satellite that is not used in the C/DA during 2003 with various solar activity. Comparisons with another empirical model, that is, Jacchia-Bowman, indicate that the C/DA results improve these quality measurements on an average range of 50 per cent and 60 per cent, respectively.

Intelligent approach to predict future groundwater level based on artificial neural networks (ANN)

To depict hydrogeological variables and understand the physical processes taking place in a complex hydrogeological system, artificial neural networks (ANNs) are widely used as a good alternative approach to tedious numerical models. The aim of this study was to predict the dynamic fluctuations in piezometric levels in Nebhana aquifers (NE Tunisia) using ANNs. A correlation analysis carried out between piezometry, evapotranspiration and rainfall during the period 2000 to 2018 revealed that piezometric levels were influenced by monthly rainfall, evapotranspiration and initial water table level. These informative variables were used as input variables to train the ANN to predict future monthly water table levels for four hydrogeological systems. The minimal and maximal computed relative errors were 0.01 and 19.00%, respectively; root mean square error (RMSE) varied between 0.41 and 2.06; the determination coefficient (R2) ranged between 0.93 and 0.99; and the Nash–Sutcliffe (NASH) efficiency coefficient ranged from 85.32 to 97.82%. To test the generalization capacity of the developed ANN models, we used the ANNs to predict monthly piezometric levels for the period September 2016 to August 2018. The results were satisfactory for all piezometers. Indeed, the minimal and maximal computed RE were − 12.00 and 0.03%, respectively; RMSE was between 0.44 and 1.74; R2 varied between 0.95 and 0.98; the NASH coefficient ranged from 60.00 to 98.99%. These models developed in this study can be adopted for future groundwater level prediction to accurately estimate trends in piezometric levels as well as water pumping costs.

Irrigation Water Management for Wheat Using Aquacrop Model إدارة میاه الری الحقلی للقمح باستخدام نموذج AquaCrop

The study aimed to evaluate the performance of the Food and Agriculture Organization AquaCrop model (version 6.1) in simulating the productivity and biomass of wheat crops in old lands under the surface irrigation system. Field data for the period from 2013- 2016 were used to calibrate the model's through matching productivity and biomass observed using root mean square error (RMSE) 0.05 and 0.2 ton/ha and Nash coefficient values of 0.9 and 0.8, resp. A calibrated model was simulated the grain yield to generate an irrigation schedule with the aim of developing an appropriate irrigation management strategy for wheat. Results showed that the highest value of wheat water production was achieved through the application of five irrigations when applying fixed net application (80 mm) with a total of 400 mm for the season and different interval between irrigation ranges between 30-39 days depended on depilation of the 80% of Readily Available Water (RAW) threshold while taking into rainy mind. This sequence was superior to the normal used irrigation sequence conducted from 2013 to 2016 (fixed net application 80,96mm and fixed interval 27,33 days on six and five irrigations application resp. with a total of 480 mm per season). 16.7% less water use with increasing water productivity is one of the important things at the present time. The results will help determine an irrigation management option appropriate to the prevailing weather conditions and farm resources, and thus this model can be used as a decision support tool in increasing water productivity.

Event-Based Rainfall Interception Modeling in a Cerrado Riparian Forest—Central Brazil: An Alternative Approach to the IS Method for Parameterization of the Gash Model

Although being the second largest biome in South America, the Cerrado biome is understudied in terms of hydrological modeling of the interception processes of its different vegetation types. To better understand its net precipitation components, high-temporal-resolution throughfall data were used to parameterize the Gash Analytical Model on a per storm basis. Two different methods (Single Event Regression—SI, and Particle Swarm Optimization—PSO) for parametrizing the Gash model were tested. Simulated throughfall from 35 events showed the Nash coefficient ranging from 0.779 to 0.989, confirming the good phenomenological approach of the Gash model. One of the most important results presented in this work was the analysis of the variability of the parameters. Considering the PSO method, we observed a mean free throughfall (ρ) of 0.17, with a confidence interval ranging from 0.16 to 0.18. The storage (S) confidence intervals ranged from 1.11 mm to 1.62 mm, with a mean value of 1.37 mm. The observed saturation depth (P′G) mean value was 1.66 mm, with a confidence interval from 1.35 mm to 1.97 mm. Finally, the mean evaporation precipitation rate (E/R) was of 0.20, with confidence bands from 0.15 to 0.24. Overall, there were observed higher losses from the Cerrado compared to other Brazilian biomes such as the Amazon and Atlantic Forest. These findings can contribute to the parameterization of hydrological models applied to the Brazilian Cerrado.

Study on water cycle simulation model of multi-sources and multi-functional irrigation area based on SWAT model (II): Application of Wanyao irrigation area

Aiming at the water cycle simulation in multi-sources and multi-functional irrigation area, the improved SWAT model was constructed and applied to Wanyao irrigation area, which is located in Jiangshan county, Zhejiang province. The measured data from 1990 to 2006 of the watershed outlet observation stations named Jiangshan and Daqiao were used to calibrate the model parameters, which measured data from 2007 to 2017 were used to validate the model. The correlation coefficient of verification period of monthly runoff simulation was 0.91, Nash coefficient was 0.90, relative errors of simulated results and measured data of 16 monitoring facilities in irrigation area were within 10%. The improved SWAT model simulation results were used to construct the statistical models of irrigation water consumption in Wanyao irrigation area on a monthly scale and an annual scale respectively. The mean relative error of the monthly scale model is 4.5% and the mean relative error of the annual scale model is 1.2%, which satisfy the needs of accurate water resources management.

Modeling spatial distribution of rainfall infiltration amounts in South China using cellular automata and its relationship with the occurrence of collapsing gullies

Collapsing gullies involve considerable erosion and extreme landform changes and frequently occur in South China. The amount of rainfall infiltration has a direct influence on the occurrence of collapsing gullies. However, the mechanistic effect of infiltration or rainfall on the development of collapsing gullies still needs to be explored, as it is difficult to spatially describe infiltration amounts of event rainfall using existing models (e.g., SWAT and WEPP). A model based on cellular automata (CA) was developed for the spatial simulation of infiltration amounts after event rainfall by analyzing water allocation within and between the cells of the model. The spatial distributions of infiltration amounts after event rainfalls with differing intensities and durations were then predicted and spatially compared with the distribution of collapsing gullies. The results demonstrate that the model performs with good accuracy, as indicated by the Nash coefficient of 0.63. The variations among different rainfall infiltration amounts demonstrate that both rainfall intensity and duration promote spatial variability in infiltration amounts and reduce regional infiltration efficiency. The spatial comparison between rainfall infiltration amounts and collapsing gullies shows that rainfall infiltration promotes the occurrence of collapsing gullies, but steep topographical conditions are also required to collapse the hillside soil. The comparison also reveals that a lower-intensity rainfall event contributes more to the development of collapsing gullies than a higher-intensity rainfall event, mainly due to the soil absorbing more rainfall under lower intensity, increasing in soil weight to induce collapse. This CA-based model for spatially simulating infiltration amounts provides an improved and effective method for understanding the spatial and temporal variations in rainfall infiltration and to better capture the trends in the occurrence of collapsing gullies from hydrological effects. The model can also be customized for convenient and practical application in other watersheds.

Bias correction of ensemble precipitation forecasts in the improvement of summer streamflow prediction skill

In recent years, ensemble precipitation forecasting has been able to provide more uncertainty information and plays an increasingly important role in basin-scale hydrologic predictions. The Global Ensemble Forecast System (GEFS) reforecast data released by the National Centers for Environmental Prediction (NCEP) has a long-term data archive and relatively stable systematic error, which shows great potential in hydrologic applications. This paper aims to bias correct the GEFS reforecast precipitation forecasts and analyze the impact of bias-corrected ensemble precipitation forecasts on the improvement of summer streamflow prediction skill. Driven by the GEFS reforecast ensemble precipitation forecasts, the Variable Infiltration Capacity (VIC) distributed hydrological model is applied to simulate the 2000–2010 summer streamflow over the Huaihe River basin.The results show that the GEFS reforecast data generally underestimates the precipitation amount with the increasing leadtime and the streamflow simulation tends to underestimate the peak due to the spatial precipitation error. In this study, both the frequency matching method (FMM) and the analog method are applied to calibrate the GEFS reforecast ensemble precipitation forecasts. The searching criteria of the analog method has been improved based on the basin-scale areal precipitation.After bias correction, the FMM method mainly improves the forecast skill of light rain and moderate rain for the 1–8 d leadtime. By contrast, the analog method improves probabilistic precipitation forecasts and increases ensemble spread to alleviate the underdispersion of raw ensemble forecasts for the 1–8 d leadtime. The analog method can also improve spatial distributions with the downscaling information from the observed precipitation, producing better probabilistic precipitation forecasts. The streamflow prediction using bias-corrected precipitation forecasts better resembles the observed streamflow. The analog-corrected precipitation forecasts can effectively improve the streamflow simulations for the 1–5 d leadtime, with the maximum improvements at the 2–3 d leadtime. The FMM method only improves the streamflow predictions for the 2 d leadtime.For the heavy flood events caused by extreme rainstorms in the summer of 2003, the ensemble mean forecasts have limited forecast skill, but probabilistic precipitation forecasts show great potential in predicting the magnitude of extreme rainfall events. Meanwhile, the ensemble streamflow predictions using the analog-corrected precipitation forecasts have larger ensemble spread and a higher Nash coefficient of extreme streamflow.The encouraging results suggest that the reforecast ensemble dataset exhibits a great value to improve hydrometeorological predictions for operational applications.