Seasonal prediction of lake inflows and rainfall in a hydro-electricity catchment, Waitaki river, New Zealand

The Waitaki River is located in the centre of the South Island of New Zealand, and hydro-electricity generated on the river accounts for 35-40% of New Zealand's electricity. Low inflows in 1992 and 2001 resulted in the threat of power blackouts. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro-generation on a seasonal basis. Researchers have stated that two key directions in the fields of seasonal rainfall and streamflow forecasting are to a) decrease the spatial scale of forecast products, and b) tailor forecast products to end-user needs, so as to provide more relevant and targeted forecasts. Several season-ahead lake inflow and rainfall forecast models were calibrated for the Waitaki river catchment using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error and randomization techniques used to establish the significance of the skill of the models. Many of the models calibrated predict rainfall and inflows better than random chance and better than the long-term mean as a predictor. When compared to the range of all probable inflow seasonal totals (based on the 80-year recorded history in the catchment), 95% confidence limits around most model predictions offer significant skill. These models explain up to 19% of the variance in season-ahead rainfall and inflows in this catchment. Seasonal rainfall and inflow forecasting on a single catchment scale and focussed to end-user needs is possible with some skill in the South Island of New Zealand

Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information

The complex temporal heterogeneity of rainfall coupled with mountainous physiographic context makes a great challenge in the development of accurate short-term rainfall forecasts. This study aims to explore the effectiveness of multiple rainfall sources (gauge measurement, and radar and satellite products) for assimilation-based multi-sensor precipitation estimates and make multi-step-ahead rainfall forecasts based on the assimilated precipitation. Bias correction procedures for both radar and satellite precipitation products were first built, and the radar and satellite precipitation products were generated through the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), respectively. Next, the synthesized assimilated precipitation was obtained by merging three precipitation sources (gauges, radars and satellites) according to their individual weighting factors optimized by nonlinear search methods. Finally, the multi-step-ahead rainfall forecasting was carried out by using the adaptive network-based fuzzy inference system (ANFIS). The Shihmen Reservoir watershed in northern Taiwan was the study area, where 641 hourly data sets of thirteen historical typhoon events were collected. Results revealed that the bias adjustments in QPESUMS and PERSIANN-CCS products did improve the accuracy of these precipitation products (in particular, 30-60% improvement rates for the QPESUMS, in terms of RMSE), and the adjusted PERSIANN-CCS and QPESUMS individually provided about 10% and 24% contribution accordingly to the assimilated precipitation. As far as rainfall forecasting is concerned, the results demonstrated that the ANFIS fed with the assimilated precipitation provided reliable and stable forecasts with the correlation coefficients higher than 0.85 and 0.72 for one- and two-hour-ahead rainfall forecasting, respectively. The obtained forecasting results are very valuable information for the flood warning in the study watershed during typhoon periods. © 2013 Elsevier B.V

Merging multiple precipitation sources for flash flood forecasting

We investigated the effectiveness of combining gauge observations and satellite-derived precipitation on flood forecasting. Two data merging processes were proposed: the first one assumes that the individual precipitation measurement is non-bias, while the second process assumes that each precipitation source is biased and both weighting factor and bias parameters are to be calculated. Best weighting factors as well as the bias parameters were calculated by minimizing the error of hourly runoff prediction over Wu-Tu watershed in Taiwan. To simulate the hydrologic response from various sources of rainfall sequences, in our experiment, a recurrent neural network (RNN) model was used. The results demonstrate that the merged method used in this study can efficiently combine the information from both rainfall sources to improve the accuracy of flood forecasting during typhoon periods. The contribution of satellite-based rainfall, being represented by the weighting factor, to the merging product, however, is highly related to the effectiveness of ground-based rainfall observation provided gauged. As the number of gauge observations in the basin is increased, the effectiveness of satellite-based observation to the merged rainfall is reduced. This is because the gauge measurements provide sufficient information for flood forecasting; as a result the improvements added on satellite-based rainfall are limited. This study provides a potential advantage for extending satellite-derived precipitation to those watersheds where gauge observations are limited. © 2007 Elsevier B.V. All rights reserved

Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan

Multi-step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3-hours warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context makes the development of real-time rainfall-runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3-hours. In this paper we develop a novel semi-distributed, data-driven, rainfall-runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network-based Fuzzy Inference System solutions is created using various combinations of auto-regressive, spatially-lumped radar and point-based rain gauge predictors. Different levels of spatially-aggregated radar-derived rainfall data are used to generate 4, 8 and 12 sub-catchment input drivers. In general, the semi-distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead-times greater than 3-hours. Performance is found to be optimal when spatial aggregation is restricted to 4 sub-catchments, with up to 30% improvements in the performance over lumped and point-based models being evident at 5-hour lead times. The potential benefits of applying semi-distributed, data-driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, is thus demonstrated

Evaluation of Machine Learning Techniques for Inflow Prediction in Lake Como, Italy

Abstract Accurate streamflow prediction is a fundamental task for integrated water resources management and flood risk mitigation. The purpose of this study is to forecast the water inflow to lake Como, (Italy) using different machine learning algorithms. The forecast is done for different days ranging from one day to three days. These models are evaluated by three statistical measures including Mean Absolute Error, Root Mean Squared Error, and the Nash-Sutcliffe Efficiency Coefficient. The experimental results show that Neural Network performs better for streamflow estimation with MAE and RMSE followed by Support Vector Regression and Random Forest

Complex Hydrological System Inflow Prediction using Artificial Neural Network

Artificial neural networks have been successfully used to model and predict water flows for a few decades. Different network types have proven to work better in different cases and additional tools and algorithms have been implemented to improve those neural models. However, some problems still occur in certain cases. This paper deals with the limitation of complex hydrological system inflow prediction using artificial neural network and inflow time series. This limitation is called the prediction lag and it disables the model from giving accurate predictions. To eliminate the prediction lag and to extend prediction horizon an alternative input variable named forecasted precipitation frequency is proposed in addition to antecedent inflow time-series. Simulation results prove the efficiency of the proposed solution that enables time series neural network model for 7th-day inflow prediction. This represents important information in operational planning of the hydrological system, used for short-term optimization of the system, e.g. optimization of the hydroelectric power plant operation

MetZoom: A CNN/LSTM hybrid based model for water reservoir inflow prediction

Hydropower reservoir volumes fluctuate as water levels increase or decrease according to precipitation, valve output and inflow through water retained in the surrounding area. Predicting these fluctuations with machine learning is possible through the use of an Artificial Neural Network (ANN) architecture proposed in this thesis. The neural network model aims to fore- cast the changes in relative water level for a reservoir managed by Saudefaldene, a hydropower company in Rogaland, Norway. The predictions are made through the use of radar images reflecting the precipitation rate, and a dataset provided by Saudefaldene. The provided dataset contains the precipitation history, valve-opening records and relative water levels across 2014- 2021. Such a forecast can have various impacts on hydropower reservoir management, which lay the foundation for the thesis. The architecture proposed in this thesis, namely MetZoom, contains a Convolutional Neural Network (CNN) architecture which predicts future precipitation rates in the form of radar image replications and precipitation i up to 12 hours ahead. The use of radar images is motivated by the intent to forecast precipitation as a tool for predicting changes in the relative water level. The predictions made by the CNN are forwarded to a Recurrent Neural Network (RNN) in the form of a Long Short-Term Memory (LSTM) network to learn the fluctuations of reservoir water levels. The architecture of Met- Zoom is a result of several tested CNN and RNN models and a combination of these.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Modeling of Reservoir Inflow for Hydropower Dams Using Artificial Neural Network

The stream flow at the three hydropower reservoirs in Nigeria were modeled using hydro-meteorological parameters and Artificial Neural Network (ANN). The model revealed positive relationship between the observed and the modeled reservoir inflow with values of correlation coefficient of 0.57, 0.84 and 0.92 for Kainji, Jebba and Shiroro hydropower reservoir respectively. The established model was used to predict 20 years stream-flow for each of the hydropower reservoirs which were found to have similar statistics with the observed values.  The predicted reservoir inflow were subjected to trend analysis which revealed an upward trend with percentage increase of 4.58%, 6.34% and 5.42% for Kainji, Jebba and Shiroro hydropower reservoirs respectively. The upward trend is an indication of increase in water availability for hydropower generation at the three stations given other constraints are brought under control.http://dx.doi.org/10.4314/njt.v34i1.