Evaluation of MODIS NDVI and LST for indicating soil moisture of forest areas based on SWAT modeling

This study examined the capability of remotely sensed information gained using the terra moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST) to explain forest soil moisture. The soil and water assessment tool (SWAT) was used for the analysis. Nine years (2000–2008) of monthly MODIS NDVI and LST data from a 2,694.4 km² watershed consisting of forest-dominant areas in South Korea were compared with SWAT simulated soil moisture. Before the analysis, the SWAT model was calibrated and verified using 9 years of daily streamflow at three gauging stations and 6 years (2003–2008) of daily measured soil moisture at three locations within the watershed. The average Nash–Sutcliffe model efficiency during the streamflow calibration and validation was 0.72 and 0.70, respectively. The SWAT soil moisture showed a higher correlation with MODIS LST during the forest leaf growing period (March–June) and with MODIS NDVI during the leaf falling period (September–December). Low correlation was observed in the year of frequent rains, regardless of the leaf periods.     

Evaluation of HEC-HMS and WEPP for simulating watershed runoff using remote sensing and geographical information system

Although a variety of rainfall-runoff models are available, selection of a suitable rainfall-runoff model for a given watershed is essential to ensure efficient planning and management of watersheds. Such studies are relatively limited in developing nations, including India. In this study, rainfall-runoff modeling was carried out using HEC-HMS and WEPP hydrologic models, and remote sensing and GIS (geographical information system) techniques in the Upper Baitarani River basin of Eastern India using daily monsoon season (June–October) rainfall and the corresponding streamflow data of 6 years (1999–2005). Other input data such as soil map, land use/land cover map, and slope map were prepared using remote sensing and GIS techniques. The modeling results revealed that both the models under predict streamflow for 1999, 2002, 2004, and 2005 and over predict for 2001 and 2003, whereas HEC-HMS under predicts and WEPP over predicts streamflow for the year 2000. The percent deviation of total runoff volume simulated by HEC-HMS ranges between −2.55 and 31%, while it varies from −13.96 to 13.05% for the WEPP model which suggests that the WEPP model simulates annual flow volumes more accurately than the HEC-HMS model for most years. However, the lower values of root mean square error (RMSE) and RMSE-observation standard deviation ratio coupled with the higher values of Nash–Sutcliffe efficiency, percent deviation and coefficient of determination for HEC-HMS during calibration and validation periods indicated that the streamflow simulated by HEC-HMS is more reliable than that simulated by WEPP. Overall, it is concluded that the HEC-HMS model is superior to the WEPP model for simulating daily streamflow in the Baitarani River basin of Eastern India.     

Comparative effectiveness of different infiltration models in estimation of watershed flood hydrograph

The present study aims to evaluate performance of different infiltration models, namely initial and constant rate, soil conservation service (SCS) curve number and Green–Ampt in simulation of flood hydrographs for the small-sized Amameh Watershed, Iran. To achieve the study purpose, the infiltration rates were measured using rainfall simulator in work units acquired through overlaying topography, land use, drainage network and soil hydrologic group maps. All parameters of the study infiltration models were determined with the help of the Infilt. software package. The performances of the models in simulation of the observed output hydrographs from the entire watershed were ultimately evaluated for 28 rainfall–runoff events in the HEC-HMS environment. The different components of the observed and estimated hydrographs including time to peak, runoff volume, peak discharge, discharge values and peak time deviation were compared using relative error (RE), coefficient of determination (R²), peak-weighted root mean square error (PWRMSE) and Nash–Sutcliffe (NS) criteria. The general performance of estimations was also qualitatively assessed using scatter plot and distribution of study variables around standard lines of 1:1 slope. The results revealed that the SCS infiltration model with PWRMSE = 0.61 m³ s^?1 and NS = 0.53 performed better than initial and constant rate model with PWRMSE = 1.1 m³ s^?1 and NS = 0.54, and Green Ampt model with PWRMSE = 1.35 m³ s^?1 and NS = 0.29 in estimation of flood hydrograph for the Amameh Watershed.     

近红外反射光谱技术预测花生种子含水量

选取116份大花生为实验材料,应用近红外反射光谱技术,结合偏最小二乘法,采用交叉检验建立了大花生含水量的近红外模型。优化结果表明,原始光谱不经过预处理,光谱范围为4597．7～11988C1TI-1,维数12,此时建立的模型校正结果最佳。模型决定系数（形）为93．62,根均方差（RMSECV）为1．17。用该模型对20个未参与建模的实验材料进行预测,偏差为-1．781～1．902,相对误差为0．122Voo～2．855％。结果表明含水量模型具有很好的预测准确性,可用于鲜食花生种子水分含量快速检测。     

A grid-based rainfall-runoff model for flood simulation including paddy fields

A grid-based, KIneMatic wave STOrm Runoff Model (KIMSTORM) is described. The model adopts the single flow-path algorithm and routes the water balance during the storm period. Manning’s roughness coefficient adjustment function of the paddy cell was applied to simulate the flood mitigation effect of the paddy fields for the grid-based, distributed rainfall-runoff modeling. The model was tested in 2296 km² dam watershed in South Korea using six typhoon storm events occurring between 2000 and 2007 with 500 m spatial resolution, and the results were tested through the automatic model evaluation functions in the model. The average values of the Nash–Sutcliffe model efficiency (ME), the volume conservation index (VCI), the relative error of peak runoff rate (EQ_p), and the absolute error of peak runoff (ET_p) were 0.974, 1.016, 0.019, and 0.45 h for calibrated storm events and 0.975, 0.951, 0.029, and 0.50 h for verified storm events, respectively. In the simulation of the flood mitigation effect of the paddy fields, the average values of the percentage changes for peak runoff, total runoff volume, and time to peak runoff were only −1.95, −0.93, and 0.19%, respectively.     

Rapid quantitative determination of oleuropein in olive leaves (Olea europaea) using mid-infrared spectroscopy combined with chemometric analyses

Oleuropein, the major active compound in olive leaf, is well known for its benefits for human health. Oleuropein is classically quantified by HPLC, which is time and chemical consuming, laborious and expensive. The aim of this work was to examine the potential of mid-infrared spectroscopy, as a rapid tool, to predict oleuropein content in olive leaf from five Tunisian cultivars (Chemlali, Chetoui, Meski, Sayali and Zarrazi) and one French cultivar (Bouteillan). The reference data of oleuropein content were obtained by the HPLC method. Hundred five samples were analyzed by HPLC and mid-infrared spectroscopy. Samples were randomly divided in a calibration set (73 samples) and in a validation set (32 samples). The spectral data sets were correlated with reference data of oleuropein content by using partial least squares (PLS) regression algorithm. The results showed that the PLS model gave satisfactory model for quantitative prediction of oleuropein content in olive leaf (relative error of prediction = 8.5%). The correlation coefficient was 0.91 and 0.74 for calibration set and validation set, respectively. It can be concluded that mid-infrared spectroscopy constitutes a promising technique for rapid quantification of oleuropein in olive leaf.     

基于近红外光谱技术建立沉香含油量预测模型

为建立沉香(Aquilaria sp.)含油量的近红外光谱预测模型,在950~1 650 nm的光谱范围内,使用DA7200 NIRS分析仪收集了64个沉香样本的光谱数据,采用偏最小二乘法(PLS)建立回归模型,并选择最佳预处理方法和最佳主成分数,建立沉香含油量近红外光谱模型。结果表明,采用卷积平滑法(S-G)对光谱进行预处理且当最佳主成分数为7时,可达到最优模型,其校正集相关系数(RC)和校正集均方根误差(RMSEC)分别为0.980 9和0.958 9,交互验证集相关系数(RV)和交互验证集均方根误差(RMSEV)分别为0.697 4、1.029 0。说明预测值与测量值具有显著的相关性,该模型的预测准确度较高,可以满足对沉香结香品质进行快速预测的要求。     

基于等距拆分和随机森林算法的皖北小麦始花期气象预报

为探讨基于等距拆分和随机森林算法用于皖北小麦始花期气象预报的可行性,利用1980-2019年皖北地区7个农业气象观测站的冬小麦始花期原位观测物候数据和平行观测的气象数据,采用相关系数法,筛选影响始花期早迟的特征变量,采用有序等距离抽样法,拆分出训练集和测试集。基于随机森林算法（RF）,从4月10日到4月15日,每日训练1个预报模型,实现小麦始花期逐日滚动气象预报,并与基于类神经网络（ANN）、线性支撑向量机（LSVM）、多元回归（RG）和支持向量机（SVM）4种算法训练的预报模型进行比较。结果表明,由平均气温、最高气温、日照时数3类气象要素构成的40个关键气象因子与小麦始花期早迟密切相关;训练出的6个始花期逐日气象预报模型中,4月10-14日5个模型入选特征变量均为40个,4月15日模型入选特征变量为39个;6个气象预报模型训练集与测试集的平均正确率分别为93.3%和80.4%,平均均方根误差（RMSE）分别为1.860～1.960和2.510～2.709,平均决定系数分别为0.944和0.841;基于RF算法训练的预报模型3项检验指标均优于ANN、LSVM、RG和SVM算法训练的预报模型;利用RF算法模型在2020年和2021年进行预报,提前7～9 d准确预报出当年始花期。由此可见,采用有序等距离抽样拆分出训练集,再基于RF算法构建的皖北地区小麦花期气象预报模型,能够以较高精度对小麦始花期进行预报。     

Modification of SWAT auto-calibration for accurate flow estimation at all flow regimes

To secure accuracy in the Soil and Water Assessment Tool (SWAT) simulation for various hydrology and water quality studies, calibration and validation should be performed. When calibrating and validating the SWAT model with measured data, the Nash–Sutcliffe efficiency (NSE) is widely used, and is also used as a goal function of auto-calibration in the current SWAT model (SWAT ver. 2009). However, the NSE value has been known to be influenced by high values within a given dataset, at the cost of the accuracy in estimated lower flow values. Furthermore, the NSE is unable to consider direct runoff and baseflow separately. In this study, the existing SWAT auto-calibration was modified with direct runoff separation and flow clustering calibration, and current and modified SWAT auto-calibration were applied to the Soyanggang-dam watershed in South Korea. As a result, the NSE values for total streamflow, high flow, and low flow groups in direct runoff, and baseflow estimated through modified SWAT auto-calibration were 0.84, 0.34, 0.09, and 0.90, respectively. The NSE values of current SWAT auto-calibration were 0.83, 0.47, ?0.14, and 0.90, respectively. As shown in this study, the modified SWAT auto-calibration shows better calibration results than current SWAT auto-calibration. With these capabilities, the SWAT-estimated flow matched the measured flow data well for the entire flow regime. The modified SWAT auto-calibration module developed in this study will provide a very efficient tool for the accurate simulation of hydrology, sediment transport, and water quality with no additional input datasets.     

Effect of surface cover on the reduction of runoff and agricultural NPS pollution from upland fields

In this study, two types of simulations were performed. First, indoor rainfall simulation revealed that runoff ratio (0–63.3 %) decreased dramatically with surface cover, compared with no surface cover condition (55–85.3 %), and sediment load and concentration also decreased. With additions of PAM, sawdust, and rice hull to rice straw mat, the runoff ratio decreased to 52.8, 36.6, and 53.2 %, compared with only rice straw mat condition (runoff ratio of 63.3 %). When gypsum was added, no runoff was observed in case of rainfall intensity of 30 mm/h. Under 60 mm/h rainfall condition, 50 % or more runoff reduction was observed. These could be explained in that surface cover reduces detachment of soil particles and keeps infiltration rate by reducing surface sealing with detached soil particle which could happen under non-surface cover condition. Second, when rice straw mat was applied to soybean field, no runoff was observed until rainfall intensity of 5.8 mm/h or greater, while runoff was observed with rainfall intensity of 1.5 mm/h at no surface covered soybean field. In addition, 89.7–99.4 % of pollutant reductions were observed with rice straw mat at the soybean field. When rice straw mat with additions of wood shaves was applied to Chinese cabbage and radish fields, 4.3–75.8 % of runoff reductions and 28–80.8 % of pollutant reductions were observed. In case of Chinese cabbage, 122.1 % yield increase was observed and 153.4 % yield increase in case of radish.     

Long-term evaluation of the BMPs scenarios in reducing nutrient surface loads from paddy rice cultivation in Korea using the CREAMS-PADDY model

Curbing nutrient loads from rice cultivation has been an issue for the water quality management of surface water bodies in the Asian monsoon region. The objectives of this study were to develop paddy BMP scenarios and to evaluate their effectiveness on nutrient loads reduction using long-term model simulation. Totally five BMP scenarios were developed based on the three paddy farming factors of drainage outlet height, fertilizer type, and application amount and were compared with conventional practices. CREAMS-PADDY model was chosen for the paddy nutrient simulation, and two-year field experimental data were used for the model calibration and validation. The validated model was used to evaluate the developed BMP scenarios for the 46 years of simulation period. The observed nutrient loads were 15.2 and 1.45 kg/ha for nitrogen and phosphorus, respectively, and mainly occurred by early season drainage and rainfall runoff in summer. The long-term simulation showed that the soil test-based fertilization and drainage outlet raising practice were the two most effective methods in nutrient loads reduction. The combination of these two resulted in the greatest loads reduction by 29 and 37 % for T-N and T-P, respectively (p value < 0.001). Overall the effectiveness of the BMP scenarios was decreased in the wet season. As the conclusion, outlet height control and soil nutrient-based fertilization were suggested as the effective practices in paddy loads reduction and their combination can be a practicable BMP scenario for the paddy nutrient management.     

The use of hybrid algorithms to improve the performance of yarn parameters prediction models

Although gradient based Backpropagation (BP) training algorithms have been widely used in Artificial Neural Networks (ANN) models for the prediction of yarn quality properties, they still suffer from some drawbacks which include tendency to converge to local minima. One strategy of improving ANN models trained using gradient based BP algorithms is the use of hybrid training algorithms made of global based algorithms and local based BP algorithms. The aim of this paper was to improve the performance of Levenberg-Marquardt Backpropagation (LMBP) training algorithm, which is a local based BP algorithm by using a hybrid algorithm. The hybrid algorithms combined Differential Evolution (DE) and LMBP algorithms. The yarn quality prediction models trained using the hybrid algorithms performed better and exhibited better generalization when compared to the models trained using the LM algorithms.     

Reduction of runoff and soil loss over steep slopes by using vetiver hedgerow systems

Vetiver hedgerow system has potential for reducing runoff and soil loss especially on steep slope areas, but the dynamics of these reductions are not fully understood. This research was conducted to determine reduction in runoff and soil loss by vetiver hedgerow system. Vetiver hedgerow systems with three vertical intervals of hedgerow were tested on three land slopes and compared with the case without hedgerow for six simulated rainfall amounts. The vetiver hedgerows reduce runoff volume and soil loss by 31–69 and 62–86 %, respectively compared to the case without vetiver hedgerow. Runoff volume increases with rainfall amount, and hence increases soil loss. Therefore soil loss increases with land slope, runoff volume, rainfall amount, and vertical hedge interval. Two final equations for estimating soil loss are presented in this study. The first equation contains parameters of runoff volume, land slope, and vertical hedge interval, while the second equation contains rainfall amount instead of runoff volume. The correlation coefficients between estimated soil losses and the experimental data in this study and in the literatures were found to be 0.94 and 0.90 for the first and second equations, respectively.     

Estimation of pollutant loads in an estuarine reservoir considering pollution source characteristics and seasonal variation

The Total Maximum Daily Load (TMDL) program is an integrated process of watershed assessment and management to address surface water quality impairment. The management of organic contaminants and nutrients is a primary concern in conserving surface water bodies. Watershed-scale pollutant loads simulation can assist stakeholders and watershed planners in making decisions on immediate and long-term land use schemes to improve water quality. However, the behavior of contaminants in a watershed needs to be characterized prior to such model applications. The objectives of this study were to characterize point and nonpoint pollutants runoff at a watershed scale and to develop a Pollutant Load Calculation Model (PLCM), which facilitates the estimation of pollutant delivery to a watershed outlet. The developed model was applied for the six sub-watersheds of the Saemangeum estuarine watershed in Korea, where a large tidal reclamation project has been underway. Two years stream flow and water quality data were used for the model calibration, while 1 year data were utilized for the model validation. The model calibration resulted in the R ² values of 0.58, 0.53, and 0.35 for BOD, TN, and TP, respectively. Overall performance for the validation period was similar with that for the calibration period although the R ² values were slightly decreased. The PLCM tends to substantially under or overestimate delivery pollutants loads during the summer rainy seasons when most rainfall events occur. This is probably because once-a-month-measured water quality data, which might not represent appropriately monthly water quality, particularly, for rainy seasons, were used for the loads calculation. Thus, more frequently monitored water quality data should be used for the delivery loads estimation at least for a rainy season in order to improve the PLCM performance. Nevertheless, the developed model took the pollutant reduction process into account, which is not allowed with the conventional unit loading method, and furthermore temporal variations of pollutant loads based on stream flows were also incorporated into the pollutant loads estimation. The developed PLCM can be a useful tool to assess pollutants delivery loads at a watershed scale and thus assist decision makers in developing watershed pollution management schemes.     

基于多源遥感数据和机器学习算法的冬小麦产量预测研究

为探讨基于多源遥感数据和机器学习算法预测冬小麦产量的可行性,利用中麦175/轮选987重组自交系F₇代群体中70个家系开展田间试验,通过无人机遥感平台和地面表型车平台及手持式冠层鉴定平台,获取冬小麦灌浆期光谱数据,分别用4种机器学习方法和集成方法建立产量预测模型。结果表明,在61个光谱指数中,除MCARI、DSI、PVI外,其余指数均与产量显著相关或极显著相关,700 nm和800 nm组合的高光谱指数能够比较准确地预测产量。相对于高光谱和多光谱,RGB传感器预测产量精度最高,平均决定系数（r²）为0.74,平均均方根误差（RMSE）为517.78 kg·hm_-2。相对于决策树（DT）、随机森林（RF）、支持向量机（SVM）三种传统机器学习算法,岭回归（RR）算法预测产量的精度最高,平均r²为0.73,平均RMSE为516.1 kg·hm_-2。与单一的传统机器学习算法相比,DT、RF、SVM、RR结合集成算法的预测精度高且稳定,r²高达0.77,RMSE也较低。SVM 、RF、DT、RR四种机器学习算法和RGB、ASD、UAV、UGV四个传感器构成的算法-传感器集成方法的预测精度提升,r²为0.79,RMSE降至469.98 kg·hm_-2。因此,利用Stacking集成方法将不同算法、传感器进行结合,能够有效地提高冬小麦产量预测精度。     

Testing the CERES-Maize simulation model in a semi-arid tropical environment

Crop simulation models are proposed as tools for agricultural risk analysis in order to explore potential cropping locations and appropriate farming systems in the semi-arid tropics. This study takes the initial step of independently validating the STANDARD CERES-Maize simulation model in the semi-arid tropics, and reports some modifications made to improve its performance. The CERES-Maize model did not accurately predict grain-yield of cultivar Dekalb XL82 which was grown over a range of sowing dates and water regimes at Katherine, N.T. Experimental yields (at 15.5% moisture) ranged from 0 to 9840 kg ha⁻¹. Calibration of CERES-Maize reduced the root mean square deviation ( ) for observed grain-yields from 3480 to 2015 kg ha⁻¹. Functions describing phenology, leaf growth and senescence, assimilate production and grain growth were revised and validated against field data. The revisions to CERES-Maize not only provide a model more applicable to the semi-arid tropics but also identify the parameters that may require calibration for other maize genotypes and locations in this climatic zone. Further validations of the functions describing nitrogen cycling and rainfall infiltration and runoff are required to increase the model's applicability to risk-analysis studies.     

Runoff simulation considering the effects of hydraulic structures on agricultural water uses

For the efficient management of water resources in the target basin, this study proposed a method to improve the reliability of a long-term hydrological simulation model by applying to the model agricultural water more approximate to actual water uses (than planned water demands) through their adjustment based on the effects of small-scale hydraulic structures. To verify agricultural water uses estimated using the proposed method, they were applied to a basin management model. And then, simulated runoff at main station points was compared with measured runoff. As a result, there occurred errors with large differences from measured data, mainly, at station points where their dependency on river water was high. To verify simulated return rate, return rate for a test zone was estimated, and then compared with the simulated return rate. Correlations between annual rainfall and runoff errors were analyzed. As a result, it was found that those errors were enlarged in dry years. Long-term runoff simulation analysis showed that simulated runoff came to be negative when a farming season began. This could be significantly improved using water uses adjusted to consider the effects of small-scale hydraulic structures. Also, correlation analysis quantitatively confirmed that simulated runoff after adjustment was more correlated with measured runoff than before adjustment. Finally, fitness tests for runoff simulations before and after adjustment were carried out through a residual analysis to analyze residual normality and independence. As a result, the fitness of runoff simulation after adjustment was significantly improved.     

Non-destructive prediction of chemical composition in sunflower seeds by near infrared spectroscopy

Near infrared reflectance spectroscopy (NIRS) was explored as a technique to predict moisture (M), oil and crude protein (CP) content on intact sunflower seeds (Helianthus annuus L.). Three hundred samples were scanned intact in a monochromator instrument NIRS 6500 (NIRSystems, Silver Spring, MD, USA). Calibration equations were developed using modified partial least square regression (MPLS) with internal cross validation. Samples were split in two sets, one set used as calibration (n=250) where the remaining samples (n=50) were used as validation set. Two mathematical treatments (first and second derivative), none (log 1/R) and standard normal variate and detrend (SNVD) as scatter corrections were explored. The coefficient of determination in calibration (R_cal²) and the standard error in cross validation (SECV) were 0.95 (SECV: 3.3) for M; 0.96 (SECV: 13.1) for CP and 0.90 (SECV: 22.3) for oil in g kg⁻¹ on a dry weight basis (second derivative, 400–2500 nm). Prediction models accounted for less than 65, 70 and 72% of the total variation for oil, M and CP, respectively. However, it was concluded that NIRS is a suitable technique to be used as a tool for rapid pre-screening of quality characteristics on breeding programs.     

Improvement of the agricultural effective rainfall for irrigating rice using the optimal clustering model of rainfall station network

In this study, the clustering method was applied to improve the usage of effective rainfall (ER) for irrigating rice paddy in the region managed by the TaoYuan Irrigation Association (TIA) of Taiwan. A total of 16 rainfall stations and rainfall data from 1981 to 2000 were used. A traditional area-weighted method (Thiessen polygons method) and an optimal clustering model of ER were evaluated and compared. The optimal clustering model of ER comprised self-organizing map (SOM), k-means (KM), and fuzzy c-means (FCM) clustering algorithms. To obtain optimal clustering data of ER, the clustering groups from two to five of SOM, KM, and FCM algorithms were determined using root-mean-squared-error. The results show that three algorithms with group numbers from two to five are adopted for the monthly optimal clustering model in different months. However, for the annual optimal model, 12 sub-models are assessed and then compared. The results show that the SOM clustering with groups three was the optimal model for annual ER. The optimal clustering model of ER provides a new procedure step in preparation of the irrigation scheduling for the TIA, and the amount irrigation water waste can be reduced from 770.1 to 22.3 mm/year. The planned ER using the optimal clustering model significantly improves the irrigation water use efficient in agricultural water management.     

Simulation of rainfall runoff and pollutant load for Chikugo River basin in Japan using a GIS-based distributed parameter model

In watershed management, the determination of peak and total runoff due to rainfall and prediction of pollutant load are very important. Measurement of rainfall runoff and pollutant load is always the best approach but is not always possible at the desired time and location. In practice, diffuse pollution has a complex natural dependence on various land-use activities such as agriculture, livestock breeding, and forestry. Estimation of pollutant load is therefore essential for watershed management and water pollution control. In this study, a model of rainfall runoff and pollutant load, which uses a geographical information system (GIS) database, is a convenient and powerful tool for resolving the abovementioned complexities. This technology was applied in order to simulate the runoff discharge and the pollutant load of total nitrogen (TN) and total phosphorus (TP) in the Chikugo River basin of Kyushu Island, Japan. First, a hydrologic modeling system (HEC-HMS) and GIS software extension tool were used for simulations of elevation, drainage line definition, watershed delineation, drainage feature characterization, and geometric network generation. The spatial distributions of land cover, soil classes, rainfall, and evaporation were then analyzed in order to simulate the daily runoff discharge at the Chikugo Barrage from April 2005 to December 2007. An important point in this approach is that a new development for data input processing with HEC-HMS was introduced for optimizing parameters of the model. Next, the water quality indicators TN and TP were examined, and an efficient approach was investigated for estimating monthly pollutant loads directly from unit load and ground-observed hydrological data. Both nonpoint and point sources of pollutants were considered, including different land-cover categories, sewers, factories, and livestock farms. The observed and simulated results for the runoff discharges and pollutant loads were in good agreement and totally consistent, indicating that the proposed model is applicable to simulation of rainfall runoff and pollutant load in the Chikugo River basin. Further, this model will be able to provide managers with a useful tool for optimizing the water surface management of this river basin.