Role In Water Resources Management Research Articles

Climate Change and Reservoir Impacts on 21st-Century Streamflow and Fluvial Sediment Loads in the Irrawaddy River, Myanmar

Reservoirs play a vital role in water resource management, while also contributing to alterations in downstream flow regimes and sediment load in the river. On the other hand, variations on streamflow and fluvial sediment loads can also result from climate change effects. Here, we assess future changes in streamflow and sediment load due to climate change and planned reservoirs in the Irrawaddy River Basin, Myanmar. The Soil Water Assessment Tool is used to project streamflow and sediment loads during 2046–2065 (mid-century), and 2081–2100 (end-century) periods under the two end-member Representative Concentration Pathways (i.e., RCP 2.6 and RCP 8.5) with and without planned reservoirs. Results show that compared to the baseline period (1991–2005), streamflow and sediment loads are projected to substantially increase during mid- and end-century periods when planned reservoirs are not considered (i.e., with climate change forcing only). Under RCP 2.6 and RCP 8.5, streamflow at the basin outlet is projected to increase by 8–17% and 9–45%, while sediment loads are projected to increase by 13–26% and 18–75%, respectively by the end-century period. When reservoirs are included, while annual streamflow at the basin outlet does not show a significant difference compared to the climate change only case (for any RCP and for both future time periods considered), annual sediment loads at the basin outlet are projected to slightly decrease (compared to the climate change only case) by 4–6% under RCP 8.5 during the end-century period. However, at seasonal time scales, streamflow and sediment loads at the basin outlet are significantly affected by upstream reservoirs. During the monsoon periods, the presence of planned reservoirs is projected to decrease streamflow at the basin outlet by 6–7%, while during non-monsoon periods, the reservoirs result in an increase of 32–38% in the streamflow at the outlet under RCP 8.5 during the end-century period. Similarly, for the same period and RCP 8.5, due to the planned reservoirs, sediment load is projected to decrease by 9–11% and increase by 32–44% in monsoonal and non-monsoonal periods, respectively.

Using geochemistry and environmental tracers to study shallow unconfined aquifer recharge and mineralization processes in the Yinchuan Plain, arid Northwest China

Abstract Irrigation water extracted from the Yellow River plays a key role in water resource management in the Yinchuan Plain (YCP), arid Northwest China. Investigating the soluble matters (ion and gas) of groundwater provides information to explain the unconfined shallow aquifer recharge and groundwater mineralization processes after long-term flood irrigation activity. Environmental tracing with the elements, 2H, 18O, 3H, and CFCs, combining geochemistry using major ions and selected trace elements, was conducted for 43 water samples from September to October 2019 in the YCP. Evaporite and silicate weathering dominate the shallow unconfined groundwater geochemical compositions. Water–rock interactions control the mineralization characteristics regularly along the groundwater flow paths from the southwest toward the northeast. Stable isotopes suggest that Yellow River water and precipitation in winter and/or from Helan Mountainous area are the main recharge sources. The shallow unconfined aquifer mixed young (post-1940) and old (pre-1940) water with young water ratios from 53.1 to 73.5% inferred from the CFC concentrations and 3H activities. Water reinfiltrations extracted from the Yellow River and from the old groundwater are confirmed. Lateral flow recharge for the shallow unconfined aquifer is less indistinctive than that from the water re-infiltration in the plain areas.

Balance hídrico distribuido con funciones de pedotransferencia para estimar eventos extremos.

Los balances hídricos distribuidos tienen un papel muy importante en la gestión del recurso hídrico al contribuir a la simulación y comprensión de la variabilidad espacio - temporal mediante datos hidrológicos y parámetros característicos del suelo. El objetivo de este trabajo es estimar propiedades hidráulicas características en el cantón Valle Hermoso y de esta forma, realizar un balance hídrico distribuido con funciones de pedotransferencia para estimación de eventos extremos.

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Meteo France model had lower skill than the other models.

A runoff probability density prediction method based on B-spline quantile regression and kernel density estimation

Exact and dependable runoff forecasting plays a vital role in water resources management and utilization. This paper proposes a B-spline quantile regression probability density prediction method to predict future runoff and quantify the uncertainty of prediction. The method includes three steps. First, the B-spline function is used to perform spline processing on the runoff data. Secondly, the spline-processed training data is input into the quantile regression model to calculate the parameters of the B-spline quantile regression model, and the successfully constructed B-spline quantile regression model is combined with kernel density estimation to construct a probability density prediction method. Finally, the constructed B-spline quantile regression probability density prediction method is used to forecast future runoff flow, and quantitatively analyze the relevant prediction uncertainty. Six evaluation indicators are constructed, among which the root mean square error, the deterministic coefficient, the pass rate are the evaluation metrics of point predictions based on probability mean, median and mode; the prediction interval coverage probability, prediction interval normalized average width and continuous ranked probability score are the evaluation criteria of interval predictions and probabilistic forecasting. The presented method is able to depict the probability density curve of future runoff flow, and obtain more comprehensive information than point forecasting and interval predictions. As a case study, this method is applicable to the Shigu station of the Jinsha River in China. The results show that the results of the proposed method are better than that of existing some state-of-the-art methods. From the perspective of application, the pass rate and the deterministic coefficient of the method have reached the grade A of accuracy. Therefore, the B-spline quantile regression model provides an alternative scheme to runoff prediction.

Uncertainty Analysis of Dominating Hydrological Parameters in Diverse Hydrometeorological Micro-Watersheds in Krishna Basin, India

Hydrological model plays a vital role in water resources management and serves many applications, including water resources planning development and management, agriculture, and flood production. Rainfall-runoff modeling in any watershed is highly influenced by the different hydrological parameters such as Curve Number (CN_2), Groundwater Delay (GW_DEALY), Base flow (ALPHA_BF), ground water Revap (GW_REVAP), Threshold depth of water (GWQM), and ESCO. Therefore, the rainfall-runoff model was simulated using the Algorithm SUFI-2 of SWAT-CUP in the Soil-Water Assessment Tool (SWAT) in two diverse hydro-meteorological watersheds of Marol (area: ~5158 km2), and Talikot (area: ~2370 km2) in Krishna basin of Southern India, where the unregulated flow exit. The results show that the R2 and NSE-values for the calibration of streamflow in Marol watershed are 0.87 and 0.84, respectively, and in the validation, 0.65 and 0.58, respectively. Similarly, for the Talikot watershed, they are 0.90 and 0.52; and 0.83 and 0.74, respectively. The curve number, groundwater delay and water available capacity of the soil are the most sensitive parameters in the Marol watershed, whereas the threshold water level in the shallow aquifer for the base flow is the additional sensitive parameter. The uncertainty ranges of the different sensitivity parameters are observed for both the watersheds. The calculated coefficient of variation shows that, among the different parameters, the GW_DELAY has relatively high uncertainty, but the curve number (CN_2) and threshold depth of water (GWQMN) parameters have relatively low uncertainties.

The Role of Water Resources Management in Reducing the Production Costs of Agricultural Products

In order to achieve the goals of sustainable agricultural and rural development, agricultural education is an issue that should be in base considered and researched according to the importance of the issue and its related challenges must be realistically analyzed, Since water is one of the effective factors in the agricultural sector that plays a vital role in the production of agricultural products. There are several factors for low water productivity in the country, one of which can be considered non-optimal and unplanned use of water. It is clear that by increasing the level of awareness of farmers about the type of management of agricultural water resources it is possible to reduce current expenses and then to achieve high productivity and increasing income from crops and orchards. Therefore, in this study, the optimal management of water and reduction of production costs in the agricultural sector in Zabrkhan county of Neishabour city has been studied. The research method in this article was a descriptive- analytical method and the data was collected by two methods of library and field methods. The results show that the production cost per hectare of all crops in lands under water resources management is less than unmanaged lands. This rate varied between 3.4 to 16.8 percent. If we associate this reduction in production cost with product efficiency and use the cost per kilogram of production as a criterion, the reduction will be 165.2% for irrigated wheat, 150% for irrigated barley, 106.8% fodder corn and 54.4% for garden products. Therefore, according to the research results, the research hypothesis is confirmed.

Runoff forecasting model based on variational mode decomposition and artificial neural networks.

Accurate runoff forecasting plays a vital role in water resource management. Therefore, various forecasting models have been proposed in the literature. Among them, the decomposition-based models have proved their superiority in runoff series forecasting. However, most of the models simulate each decomposition sub-signals separately without considering the potential correlation information. A neoteric hybrid runoff forecasting model based on variational mode decomposition (VMD), convolution neural networks (CNN), and long short-term memory (LSTM) called VMD-CNN-LSTM, is proposed to improve the runoff forecasting performance further. The two-dimensional matrix containing both the time delay and correlation information among sub-signals decomposing by VMD is firstly applied to the CNN. The feature of the input matrix is then extracted by CNN and delivered to LSTM with more potential information. The experiment performed on monthly runoff data investigated from Huaxian and Xianyang hydrological stations at Wei River, China, demonstrates the VMD-superiority of CNN-LSTM to the baseline models, and robustness and stability of the forecasting of the VMD-CNN-LSTM for different leading times.

Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling

Hydrological modeling has experienced rapid development and played a significant role in water resource management in recent decades. However, modeling uncertainties, which are propagated throughout model runs, may affect the credibility of simulation results and mislead management decisions. Therefore, analyzing and reducing uncertainty is of significant importance in providing greater confidence in hydrological simulations. To reduce and quantify parameter uncertainty, in this study, we attempted to introduce additional remotely sensed data (such as evapotranspiration (ET)) into a common parameter estimation procedure that uses observed streamflow only. We undertook a case study of an application of the Soil Water Assessment Tool in the Guijiang River Basin (GRB) in China. We also compared the effects of different combinations of parameter estimation algorithms (e.g., Sequential Uncertainty Fitting version 2, particle swarm optimization) on reduction in parameter uncertainty and improvement in modeling precision improvement. The results indicated that combining Sequential Uncertainty Fitting version 2 (SUFI-2) and particle swarm optimization (PSO) can substantially reduce the modeling uncertainty (reduction in the R-factor from 0.9 to 0.1) in terms of the convergence of parameter ranges and the aggregation of parameters, in addition to iterative optimization. Furthermore, the combined approaches ensured the rationality of the parameters’ physical meanings and reduced the complexity of the model calibration procedure. We also found the simulation accuracy of ET improved substantially after adding remotely sensed ET data. The parameter ranges and optimal parameter sets obtained by multi-objective calibration (using streamflow plus ET) were more reasonable and the Nash–Sutcliffe coefficient (NSE) improved more rapidly using multiple objectives, indicating a more efficient parameter optimization procedure. Overall, the selected combined approach with multiple objectives can help reduce modeling uncertainty and attain a reliable hydrological simulation. The presented procedure can be applied to any hydrological model.

Multi-objective optimization of groundwater monitoring network using a probability Pareto genetic algorithm and entropy method (case study: Silakhor plain)

Abstract Optimal groundwater monitoring networks have an important role in water resources management. For this purpose, two scenarios were presented. The first scenario designs a monitoring network and the second scenario chooses optimal wells from the existing ones in the study area of the monitoring network. At the first step, a database including groundwater elevation in potential wells was produced using the Kriging method. The optimal monitoring network in the first scenario was determined by preset conventions and found by the non-dominated sorting genetic algorithm (NSGA-II). In the second scenario, the optimal monitoring network was determined by entropy theory through calculating entropy for each of the 29 observation wells. Finally, the first scenario obtained a network with 12 observation stations showing root mean square error (RMSE) value given as 0.61 m. Comparison between entropy of rainfall and groundwater level time series in the first scenario had the same variation. The optimal monitoring network in the first scenario has been able to reduce the number of monitoring stations by 60% in comparison with the existing observation network. The second scenario used entropy theory and calculated the energy of each of the 29 observation wells which obtained a monitoring network with 11 stations.

The applicability of ASCS_LSTM_ATT model for water level prediction in small- and medium-sized basins in China

Abstract Water level prediction of small- and medium-sized rivers plays an important role in water resource management and flood control. Such a prediction is concentrated in the flood season because of the frequent occurrence of flood disasters in the plain area. Moreover, the flood in mountainous areas suddenly rises and falls, and the slope is steep. Thus, establishing a hydrological prediction model for small- and medium-sized rivers with high accuracy and different topographic features, that is, plains and mountains, is an urgent problem. A prediction method based on ASCS_LSTM_ATT is proposed to solve this problem. First, the important parameters are optimized by improving the cuckoo search algorithm. Second, different methods are used to determine the forecast factors according to various topographic features. Finally, the model is combined with the self-attention mechanism to extract significant information. Experiments demonstrate that the proposed model has the ability to effectively improve the water level prediction accuracy and parameter optimization efficiency.

Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the current study examines the prediction capability of three well-known machine learning algorithms (Support Vector Regression (SVR), Artificial Neural Network with backpropagation (ANN-BP), and Extreme Learning Machine (ELM)) for the monthly and daily streamflows of four rivers in the United States. For model development, three main predictor variables (P, Tmax, and Tmin) and their antecedent values were considered. The SVM-RFE feature selection method was used to select the most appropriate predictor variable.The performance of the developed models was tested using four evaluation statistics. The results indicate that (1) except some improvements, the accuracy of all models decreases at the daily scale compared to that at the monthly scale; (2) the SVR has the best performance among the three models at the monthly and daily scales, while the ANN-BP model has the worse performance; (3) the ELM has better generalization performance than the ANN-BP for streamflow simulation at the monthly and daily scales; and (4) all models fail to predict the streamflow for the Carson River as a snowmelt-dominated basin. Generally, findings of the current study indicate that the SVR model produces better results than the ELM and ANN-BP for streamflow simulation at the monthly and daily scales.

Rainfall variability over the Indus, Ganga, and Brahmaputra river basins: A spatio-temporal characterisation

Understanding of rainfall variability and its changing pattern play a significant role in water resource management. This study examined the seasonal and annual rainfall extent and its linkage with water availability over the Indus, Ganga, and Brahmaputra river basins (IGB) using Tropical Rainfall Measuring Mission (TRMM) from 1998 to 2017. The monsoonal and annual rainfall indicates a significant decreasing trend in the eastern while increasing in the western part of the IGB. Therefore, the Indus basin observed a positive rainfall trend as compared to the Ganga and Brahmaputra basins. However, the precipitation concentration index (PCI) shows irregular distribution with an insignificant trend. Therefore, we have analyzed the changing pattern of climatic variables (air temperature and rainfall) and their influence on terrestrial water storage (TWS) over the study area during 2003–2015. Results indicate a significant increase in mean annual temperature (0.02 °C yr−1) while a decrease in TWS (−12.6 mm yr−1) over the region. The rainfall shows a positive and negative correlation with TWS (r = 15.8) and air temperature (r = −38.8), respectively. Therefore, the changing pattern of rainfall and air temperature can be attributed to the reduction of TWS in the IGB.

Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.

Short-Term Hydrological Drought Forecasting Based on Different Nature-Inspired Optimization Algorithms Hybridized With Artificial Neural Networks

Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.81 and RMSE = 0.45 and SHDI6 with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.82 and RMSE = 0.40.

Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model.

River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

The key role of water resources management in the Middle East dust events

Dust events in the Middle East have resulted in serious environmental problems in many countries in the area. The dust activities in the Middle East were intensified in recent years due to many factors including climate change, drought, and poor water management strategies. This paper investigates the dust activity during 1980–2010 in the Lower Mesopotamia and southwestern Iran and demonstrates that the dust activity in the study area has increased after 1999. This study investigates the role of surface temperature, precipitation, water resources, vegetation, and surface wind velocity on the development of dust events during the study period. This investigation is performed using the correlation analysis of the normalized value of the 5 and 10 year moving average of the mentioned parameters to evaluate the role of each parameter on the increase of dust activity in the last decade of the study period. The correlation analysis demonstrates that the dust activity has the highest correlation and relationship to the water resources of the Tigris and Euphrates rivers and indicates the key role of water resources management on the dust activity in the study area. It is shown, The large scarcity of the Tigris and Euphrates rivers water resources has occurred when there was not a sharp reduction in the average precipitation in the study area. Hence, the excessive water withdrawal through the dam construction projects could be considered as the main reason of large reduction of the Tigris and Euphrates rivers water resources and high dust activity in the Middle East during the last decades.

Precipitation Trends and Alteration in Wei River Basin: Implication for Water Resources Management in the Transitional Zone between Plain and Loess Plateau, China

Precipitation plays a critical role in water resources management, and trend changes and alterations thereof are crucial to regional or basin water security, disaster prevention, and ecological restoration under a changing environment. In order to explore the implications of precipitation variation for water resources management, taking the Wei River Basin (a transitional zone between the Guanzhong Plain and Loess Plateau) as an example, this paper proposes an index system, namely the index of precipitation alteration (IPA), to evaluate changes in precipitation and investigate their potential influence on water resources management. The system includes 17 indicators gained from observed daily rainfall, involving some structural precipitation indicators describing the precipitation patterns and some functional precipitation indicators influencing utilization of watershed water resources. Non-parametric Mann-Kendall (MK) statistical test is employed to identify the IPA trend change, and range of variability approach is used to evaluate the variation of IPA. The analysis results in Wei River Basin show that IPA varies with different spatial and temporal distributions. Overall, although the annual total precipitation declined in the study area, the frequency of extreme events was increased during 1955–2012. In the face of severe climate change patterns, it is necessary to establish the precipitation index to evaluate the change of precipitation and to provide useful information for future precipitation assessments.

Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model

Large-scale reservoirs play an essential role in water resources management for agriculture irrigation, water supply and flood controls. However, we need robust reservoir operation systems under both normal flow and extreme flow conditions. In this study, we applied recurrent neural networks (RNN) to simulate the operation of three multi-purpose reservoirs located in the upper Chao Phraya River basin. Two reservoirs have the function of multiannual flow regulation and one has the function of incomplete annual regulation. The goal of this study is to explore the applicability of RNN models for operation of reservoirs with multiannual flow regulation under different flow regimes, especially under extreme floods and droughts. We used three RNNs, namely nonlinear autoregressive models with exogenous input (NARX), long short-term memory (LSTM) and genetic algorithm based NAXR (GA-NAXR) for reservoir operation based on historical data. For real-time water resources management, an accurate inflow forecast is required to provide a real-time reservoir outflow, and thus we also carried out a real-time reservoir operation using the RNN and the inflow forecast by a distributed hydrological model. Results show that (1) GA-NARX has the highest accuracy among three RNNs and is more stable than the original NARX by optimizing the initial conditions, although it takes longer training time than NARX and LSTM; (2) GA-NARX-based operation model is effective under extreme floods and droughts; and (3) the real-time operation system combining the GA-NARX and the distributed hydrological model has reasonable accuracy in both wet season and dry season. RNN-based operation model developed in this study has potential applicability in practical water management, and the model combining the hydrological prediction is specially useful for real-time reservoir operation.

A hybrid wavelet neural network (HWNN) for forecasting rainfall using temperature and climate indices

Rainfall forecasting plays an important role in water resources management and also for controlling the unusual events related to the rainfall. This study aims to forecast monthly rainfall from antecedent monthly rainfall, temperature and climate indices using a hybrid wavelet neural network (HWNN) model. The discrete wavelet transform is used incorporation with a conventional ANN model. The skilfulness of the proposed model is compared with the observed rainfall and the ANN model. The results show that the HWNN model provides a good fit with the observed rainfall data particularly in facing the extreme rainfall. The decomposed sub-series obtained by wavelet transform can extract invaluable information which is enormously useful for future rainfall prediction. The results confirm that the hybrid model considerably improves the neural network ability to predict future rainfall.