Analysis of Drought from Humid,Semi-Arid and Arid Regions of India Using DrinC Model with Different Drought Indices

The study aims at evaluating the various drought indices for the humid, semi-arid and arid regions of India using conventional indices, such as rainfall anomaly index, departure analysis of rainfall and other indices such as Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) that were analyzed using the DrinC software. In SPI, arid region has seven drought years, whereas humid and semi-arid regions have four. In case of RDI, the humid and semi-arid regions have 11 drought years, whereas arid regions have 10 years. The difference in SPI and RDI was due to the fact that RDI considered potential evapotranspiration, and hence, correlation with plants would be better in case of RDI. Humid region showed a decreasing trend in initial value of RDI during the drought as compared to semiarid and arid regions and indicated possible climate change impact in these regions. Among all the indices, RDI was considered as an effective indicator because of implicit severity and high prediction matches with the actual drought years. SPI and RDI were found to be well correlated with respect to 3 months rainfall data and SPI values led to prediction of annual RDI. The results of our study established that this correlation could be used for developing disaster management plan well in advance to combat the drought consequences.

     

A Fast Semi Distributed Rainfall Runoff Model for Engineering Applications in Arid and Semi-Arid Regions

A new GIS based rainfall runoff model is developed for engineering applications, achieving a highly automated watershed analysis process starting from watershed delineation and up to the runoff hydrograph calculation. The model can be classified as a semi-distributed time area model that adopts an improved grid based approach for calculation of watershed response. The model deals with each grid cell in the digital elevation model as an independent hydrologic unit. Travel time through each grid cell is estimated using Manning’s formula and a stream power formula that relates the hydraulic radius at the cell to the characteristics of its upstream watershed area and excess rainfall depth. The watershed response at its outlet is estimated by routing the response of each grid cell using a flow path response function that is defined for that cell. The routed responses of all watershed cells are then convoluted to produce the outflow hydrograph. Model advantages include accuracy improvements due to the incorporation of grid-based routing calculations (both translation and attenuation), fully automated model structure, and fast ability to model many watersheds simultaneously. The combination of these advantages constitutes the novelty of the model that makes it very suitable for engineering design as well as for real-time applications. The model was tested using the data of the experimental watershed, Walnut Gulch, Arizona, USA, gauged by 88 rainfall stations and several discharge recording flumes. The results show that the model can accurately predict the runoff hydrograph where suitable input is available.     

Relevance of Reservoir Morphometry in the Evaporation Process: an Evaporation Model for Semi-Arid Regions

Water Resources Management - Water that evaporates from reservoirs can be considered water lost by the system. To estimate the volume of water evaporated from a reservoir, the climate and reservoir...     

BUCKET模型在降雨径流预报中的应用

BUCKET模型结构简单,原理明确,参数较少,在澳大利亚和新西兰等国家得到了广泛的应用.在介绍模型结构和原理的基础上,为进一步拓宽模型的应用,将其应用于具有遥测资料的伊河流域,对陆浑水库入库旬平均流量过程进行了模拟.结果表明,模型与实测拟合精度较高,能够较好地反映伊河流域以旬为时间尺度的水文过程.将它与新安江三水源模型从原理、结构以及在栾川水文站的应用情况进行了比较,结果表明,率定期和检验期两个模型精度相当.因此,将BUCKET模型应用于陆浑水库入库径流预报中,可以为该水库的预报调度提供可靠的依据.     

BUCKET模型在降雨径流预报中的应用

BUCKET模型结构简单,原理明确,参数较少,在澳大利亚和新西兰等国家得到了广泛的应用。在介绍模型结构和原理的基础上,为进一步拓宽模型的应用,将其应用于具有遥测资料的伊河流域,对陆浑水库入库旬平均流量过程进行了模拟。结果表明,模型与实测拟合精度较高,能够较好地反映伊河流域以旬为时间尺度的水文过程。将它与新安江三水源模型从原理、结构以及在栾川水文站的应用情况进行了比较,结果表明,率定期和检验期两个模型精度相当。因此,将BUCKET模型应用于陆浑水库入库径流预报中,可以为该水库的预报调度提供可靠的依据。     

Simulation and Multi-Objective Management of Coastal Aquifers in Semi-Arid Regions

Groundwater is the main water resource in many semi-arid coastal regions and water demand, especially in summer months, can be very high. Groundwater withdrawal for meeting this demand often causes seawater intrusion and degradation of water quality of coastal aquifers. In order to satisfy demand, a combined management plan is proposed and is under consideration for the island of Santorini. The plan involves: (1) desalinization (if needed) of pumped water to a potable level using reverse osmosis and (2) injection into the aquifer of biologically-treated waste water. The management plan is formulated in a multi-objective, optimization framework, where simultaneous minimization of economic and environmental costs is desired, subject to a constraint so that cleaned water satisfies demand. The decision variables concern the well locations and the corresponding pumping and recharging rates. The problem is solved using a computationally efficient, multi-objective, genetic algorithm (NSGAII). The constrained multi-objective, optimization problem is transformed to an unconstrained one using a penalty function proportional to constraint violation. This extends the definition of the objective function outside the domain of feasibility. The impact of prolonged droughts on coastal aquifers is investigated by assuming various scenarios of reduced groundwater recharge. Water flow and quality in the coastal aquifer is simulated using a three-dimensional, variable density, finite difference model (SEAWAT). The method is initially applied to a test aquifer and the trade-off curves (Pareto fronts) are determinedl for each drought scenario. The trade-off curves indicate an increase on the economic and environmental cost as groundwater recharge reduces due to climate change.     

基于SWAT模型的巴勒更河流域降雨-径流关系

研究流域的降雨-径流关系可为水资源管理与水土保持等工作提供依据.根据实测降雨资料和历史实测径流数据构建SWAT模型并用于还原径流序列,采用Kendall秩次检验、滑动平均、M-K突变检验、小波分析、累积距平法以及径流系数,分析巴勒更河流域1970—2015年的降雨-径流关系年际变化特征、变化趋势,并运用累积斜率变化率比...     

Rainfall-Runoff Model Considering Microtopography Simulated in a Laboratory Erosion Flume

A comprehensive process-based rainfall-runoff model for simulating overland flow generated in rills and on interrill areas of a hillslope is evaluated using a laboratory experimental data set. For laboratory experiments, a rainfall simulator has been constructed together with a 6.50 m × 1.36 m erosion flume that can be given adjustable slopes changing between 5 % and 20 % in both longitudinal and lateral directions. The model is calibrated and validated using experimental data of simulated rainfall intensities between 45 and 105 mm/h. Results show that the model is capable of simulating the flow coming from the rill and interrill areas. It is found that most of the flow occurs in the form of rill flow. The hillslope-scale model can be used for better prediction of overland flow at the watershed-scale; it can also be used as a building block for an associated erosion and sediment transport model.     

Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method

Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.

     

Influence of Time Step Synchronization on Urban Rainfall-Runoff Simulation in a Hybrid CPU/GPU 1D-2D Coupled Model

The 1D sewer - 2D surface coupled hydrodynamic model has increasingly become an essential tool for simulating and predicting the flood process and is widely used in the study of urban rainfall-runoff simulation. The current method of using the smaller time step of the sub model in the coupled model as the synchronization time greatly limits the computational efficiency, especially in the case of the large data amount or models executed in different platforms and in various types of codes. To evaluate the impact of time synchronization on the rainfall-runoff process in a coupled hydrodynamic model, a new model that couples the 2D GPU accelerated shallow water model and the 1D SWMM is applied to two urban catchments to simulate the rainfall-runoff-drainage processes, the fixed time step (5 s, 10 s, 30 s, 60 s, 120 s, 180 s and 300 s) is adopted to ensure the calculation efficiency and precision of the model. The results show that the time computational efficiency can be improved by 7.27%–27.37% in different scenarios compared with the method applying 2D model time step as the synchronization time; the surface runoff process is hardly affected as the synchronization time changes; and the relative error of the drainage process is less than 2.5% when the synchronization time is less than 60 s. Therefore, the fixed synchronization time method is recommended in the 1D-2D coupled model to improve the computational efficiency for flood and inundation simulation. Based on the advantage that the fixed synchronization time is easy to realize in the programming of the model and the high efficiency of the fixed synchronization time method concluded above, this work is expected to provide a reference for model coupling applications.

     

ARIMA-SVR组合方法在水质预测中的应用

针对复杂水域水质变化机理难以掌握、水质预测建模困难且预测精度低的问题,将时间序列分析方法与机器学习方法引入水质预测领域,提出了基于差分自回归移动平均(ARIMA)与支持向量回归(SVR)组合模型的水质预测方法。数据经过预处理后先由ARIMA模型对其进行线性拟合,然后通过SVR模型预测残差以补偿其中的非线性变化。选择巢湖水域2004—2015年间的pH和溶解氧监测数据作为试验样本,通过Hodrick-Prescott(HP)滤波方法分析,结果表明2组数据具有不同的趋势特性和波动特性。根据精度评价指标对比分析模型的预测效果,结果表明组合模型预测精度显著提高,pH和溶解氧预测值与观测值间的相关系数均达到了0.99,均方根误差分别为0.20和0.61,平均绝对百分比误差分别为2.2%和6.6%。本研究所建立的组合预测方法具有较高的预测精度和较强的泛化能力,适用于复杂水域的水质预测。     

Long Term Streamflow Forecasting Using a Hybrid Entropy Model

In this paper, the development and evaluation of an entropy based hybrid data driven model coupled with input selection approach and wavelet transformation is investigated for long-term streamflow forecasting with 10 years lead time. To develop and test the models, data including 45 years of monthly streamflow time series from Taleghan basin, located in northwest of Tehran, are employed. For this purpose, first the performance of a maximum entropy forecasting model is evaluated. To boost the accuracy, an auto-correlation method with %95 confidence levels was carried out to determine the optimum order of the entropy model. Nevertheless, the basic entropy model, as expected, was only able to reach Nash-Sutcliffe efficiency (NSE) index of 0.35 during the test period. On the other hand, data driven models such as artificial neural networks (ANN) have shown to yield good accuracy in modeling complicated and nonlinear systems. Thus, to improve the performance of the maximum entropy model, an entropy-based hybrid model using evolutionary ANN (ENN) was proposed for further investigation. The proposed model with seasonality index substantially improved the test NSE to 0.51 and provided more accurate results than the basic entropy model. Moreover, when wavelet transform was applied to preprocess the input data, the model shows a slight improvement (NSE?=?0.54).     

Identification of the SCS-CN Parameter Spatial Distribution Using Rainfall-Runoff Data in Heterogeneous Watersheds

The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff volume for a given rainfall event. However, previous results indicated that when the CN value is determined from measured rainfall-runoff data in a natural watershed it is not possible to attribute a single CN value to the watershed, but actually the calculated CN values vary systematically with the rainfall depth. In a previous study, the authors investigated the hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of the inevitable presence of soil-cover complex spatial variability along watersheds. In this study, a method to determine SCS-CN parameter values from rainfall-runoff data in heterogeneous watersheds is proposed. This method exploits the observed correlation between the calculated CN values and the rainfall depths in order to identify the spatial distribution of CN values along the watershed taking in to account the specific characteristics of the watershed. The proposed method utilizes the available rainfall-runoff data, remote sensing data and GIS techniques in order to provide information on spatial watershed characteristics that drive hydrological behavior. Furthermore, it allows the estimation of CN values for specific soil-land cover complexes in more complex watersheds. The proposed method was tested in a small experimental watershed in Greece. The watershed is equipped with a dense hydro-meteorological network, which together with a detailed land cover and soil survey using remote sensing and GIS techniques provided the detailed data required for this analysis.     

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R ² ), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R ² , NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R ² , NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R ² , NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region.     

Correction to: Spatiotemporal Analysis of Droughts Over Different Climate Regions Using Hybrid Clustering Method

Water Resources Management -     

海河流域平原区降雨产流模型研究

通过对海河流域平原区降雨产流机理进行研究,得出耕地灌溉率提高、地下水埋深增加是影响平原区产流的主要下垫面要素.根据下垫面要素的变化特点,建立了模拟地下水埋深变化和灌溉耕地产流特点的蓄满-超渗产流水文模型.并通过在海河平原的大清河山前平原、大清河中部平原、黑龙港中部平原3个类型区7个田块的灌溉入渗实验,分析给出了产流模型的重要参数——耕作层下层稳定入渗率.模型在牤牛河等7个典型流域进行应用,均得到较高的模拟精度.     

Assessment of AquaCrop Model in Simulating Sugar Beet Canopy Cover,Biomass and Root Yield under Different Irrigation and Field Management Practices in Semi-Arid Regions of Pakistan

The AquaCrop model was analyzed for simulating sugar beet crop production under four irrigation regimes, three mulching conditions and three furrow irrigation systems in semi-arid region of Pakistan. Irrigation regimes were full irrigation (FI), 20% deficit irrigation (DI₂₀), 40% deficit irrigation (DI₄₀) and 60% deficit irrigation (DI₆₀). The mulching practices were No-mulch (NM), black film mulch (BFM) and straw mulch (SM). The furrow irrigation systems were conventional ridge-furrow (CRF) system, medium raised-bed (MRB) system and wide raised-bed (WRB) system. The model was calibrated and validated using the independent data sets of full irrigation and deficit irrigation regimes collected during 2011–12 cropping season. The model performance was evaluated by using different statistical indicators such as Root Mean Square Error (RMSE), index of agreement (d_index), and Nash–Sutcliffe Efficiency (NSE). These indicators showed that the model fairly simulated sugar beet canopy cover for all treatments with 3.00 ≤ RMSE ≤ 16.89, 0.84 ≤ d_index ≤ 0.97, and 0.76 ≤ NSE ≤ 0.99. For biomass and root yield, the model performance was excellent under all full irrigation (FI) and mild deficit irrigation (DI₂₀) treatments with RMSE ranged between 0.07 and 1.17, d_index between 0.48 and 0.84, and NSE between 0.42 and 0.86, respectively. However the low values of d_index (0.10 and 0.13) and NSE (?69.32 and ?30.63) showed that the model overestimated both the biomass and root yield when 20% deficit irrigation was applied without mulch in WRB system. The model also over estimated the yield and biomass when 40% deficit irrigation was applied in CRF system. The highest overestimation (d_index: 0.10 to 0.11; NSE: ?50.92 to ?70.55) was observed when highest stress level (DI₆₀) was applied in the presence of BFM in CRF system. Based on the model’s overall performance, the AquaCrop application is recommended for developing efficient farm water management strategies in the semi-arid regions.     

Runoff Prediction Using a Novel Hybrid ANFIS Model Based on Variable Screening

Water Resources Management - The accurate and reliable prediction of future runoff is important to guarantee for strengthening water resource optimization and management. The novel contribution of...     

A Hybrid Model to Predict Monthly Streamflow Using Neighboring Rivers Annual Flows

Water Resources Management - The issue of predicting monthly streamflow data is an important issue in water resources engineering. In this paper, a hybrid model was proposed to generate monthly...     

A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

Over the past few decades, many numerical streamflow prediction techniques using observed time series (TS) have been developed and widely used in water resources planning and management. Recent advances in quantitative rainfall forecasting by numerical weather prediction (NWP) models have made it possible to produce improved streamflow forecasts using continuous rainfall-runoff (RR) models. In the absence of a suitable integrated system of NWP, RR and river system models, river operators in Australia mostly use spreadsheet-based tools to forecast streamflow using gauged records. The eWater Cooperative Research Centre of Australia has recently developed a new generation software package called eWater Source, which allows a seamless integration of continuous RR and river system models for operational and planning purposes. This paper presents the outcomes of a study that was carried out using Source for a comparative evaluation of streamflow forecasting by several well-known TS based linear techniques and RR models in two selected sub-basins in the upper Murray river system of the Murray-Darling Basin in Australia. The results were compared with the actual forecasts made by the Murray River operators and the observed data. The results show that while streamflow forecasts by the river operators were reasonably accurate up to day 3 and traditional TS based approaches were reasonably accurate up to 2?days. Well calibrated RR models can provide better forecasts for longer periods when using high quality quantitative precipitation forecasts. The river operators tended to underestimate large magnitude flows.