Seasonal Flow Forecasts Research Articles

Reflections on almost a century of hydrological studies on Africa's largest lake

Abstract. Lake Victoria is the largest lake in Africa and its outflows strongly influence flows in the White Nile, including the availability of water for hydropower generation, irrigation and water supply. Understanding the water balance is a major challenge since the lake is large enough to influence the local climate and its catchment spans several countries. Hydrometeorological monitoring networks are also sparse in some parts of the basin. In this paper, we consider the history of water balance estimates for the lake and how the science has developed as new information and techniques have become available, including in areas such as seasonal flow forecasting and estimating the potential impacts of dam operations and climate change. These findings are placed into a wider context including the challenges arising from a changing climate and evolving ideas from international research programmes, which lead to some suggestions for future research priorities for Lake Victoria and other sub-Saharan/Rift Valley lakes.

Application of regression in seasonal flow forecasting for Upper Indus Basin of Pakistan

Water managers in Pakistan need timely and accurate seasonal flow forecasts like in several areas of the world to allocate water for various kinds of water usages like canal operations for irrigation, reservoir operations, and strategies to respond to extreme cases. Various hydrological models are deployed that include University of British Columbia Watershed Model (UBCWM) and snowmelt runoff model (SRM) to seasonal streamflow forecasts in Pakistan. Here, I assess the approach that employed snow water equivalent (SWE), temperature (T), precipitation (P), and February month flow (Feb) to forecasts Kharif (April–September) season with no need for intensive hydrological modeling in a skillful way for Upper Indus Basin (UIB) at Tarbela Dam. For Indus, I compare this approach results with Indus River System Authority (IRSA), UBCWM, and SRM. This approach uses multiple linear regression (MLR) to develop regression function for forecast seasonal flow volume. This regression approach provides much skillful flow forecasts that is consistent in uncertainty spread for the Indus. The regression forecast accuracy with a mean absolute percentage error (MAPE) is 7.95% with regard to statistical approach used by IRSA, UBCWM, and SRM as 10.25%, 11.05%, and 8.91%, respectively. This approach even allows improvement of 1% (volume ~ 0.6 km3) and is very simple to apply with requirement of four input data that are easy to procure or download and process. With this information in hand, a regression function can generate seasonal forecast for the authorities in Pakistan.

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation–optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR; (2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization.

Development of a framework for an integrated time-varying agrohydrological forecast system for Southern Africa: Initial results for seasonal forecasts

Uncertainty about hydro-climatic conditions in the immediate future (today), as well as the near (up to one week) and more distant futures (up to one season) remains a fundamental problem challenging decision makers in the fields such as water resources, agriculture, and many other water-sensitive sectors in Southern Africa. Currently many institutions, such as the SA Weather Service, provide weather and climate forecasts with lead times ranging from 1 d to one season. However, disconnects exist between the weather / climate forecasts and their links to agrohydrological models, and in the applications of forecast information for targeted agricultural and water-related decision-making. The skills level of the current weather and climate forecasts, and the mismatch in scales between the output from weather / climate models and the spatial scales at which hydrological models are applied, as well as the format of seasonal forecasts in that they cannot be used directly in agrohydrological models, are some of the problems identified in this study and are being addressed. This has necessitated the development of a GIS-based framework in which the 'translation' of weather and climate forecasts into more tangible agrohydrological forecasts such as streamflows, reservoir levels or crop yields is facilitated for all the inter-linked quaternary catchments for enhanced economic, environmental and societal decision making over South Africa in general, and in selected catchments in particular. For monthly and seasonal (i.e. 3-month lead time) forecasts, two methods, viz. the Historical Sequence Method and the Ensemble Re-Ordering Method have been developed to translate the triplet of forecast rainfall probabilities (i.e. above, near and below normal) into daily quantitative values of rainfall for use in hydrological models. The first method was applied together with the daily time step ACRU Model to simulate seasonal flow forecasts in the Mgeni catchment in KwaZulu-Natal, South Africa. In taking account of uncertainty in the seasonal rainfall forecasts through the process of translating these to daily streamflow simulations by the ACRU Model, some skilful initial forecasts of streamflows can be obtained which can assist decision makers to take protective action against the impacts of hydro-climatic variability.

A national-scale seasonal hydrological forecast system: development and evaluation over Britain

Abstract. Skilful winter seasonal predictions for the North Atlantic circulation and northern Europe have now been demonstrated and the potential for seasonal hydrological forecasting in the UK is now being explored. One of the techniques being used combines seasonal rainfall forecasts provided by operational weather forecast systems with hydrological modelling tools to provide estimates of seasonal mean river flows up to a few months ahead. The work presented here shows how spatial information contained in a distributed hydrological model typically requiring high-resolution (daily or better) rainfall data can be used to provide an initial condition for a much simpler forecast model tailored to use low-resolution monthly rainfall forecasts. Rainfall forecasts (hindcasts) from the GloSea5 model (1996 to 2009) are used to provide the first assessment of skill in these national-scale flow forecasts. The skill in the combined modelling system is assessed for different seasons and regions of Britain, and compared to what might be achieved using other approaches such as use of an ensemble of historical rainfall in a hydrological model, or a simple flow persistence forecast. The analysis indicates that only limited forecast skill is achievable for Spring and Summer seasonal hydrological forecasts; however, Autumn and Winter flows can be reasonably well forecast using (ensemble mean) rainfall forecasts based on either GloSea5 forecasts or historical rainfall (the preferred type of forecast depends on the region). Flow forecasts using ensemble mean GloSea5 rainfall perform most consistently well across Britain, and provide the most skilful forecasts overall at the 3-month lead time. Much of the skill (64 %) in the 1-month ahead seasonal flow forecasts can be attributed to the hydrological initial condition (particularly in regions with a significant groundwater contribution to flows), whereas for the 3-month ahead lead time, GloSea5 forecasts account for ∼ 70 % of the forecast skill (mostly in areas of high rainfall to the north and west) and only 30 % of the skill arises from hydrological memory (typically groundwater-dominated areas). Given the high spatial heterogeneity in typical patterns of UK rainfall and evaporation, future development of skilful spatially distributed seasonal forecasts could lead to substantial improvements in seasonal flow forecast capability, potentially benefitting practitioners interested in predicting hydrological extremes, not only in the UK but also across Europe.

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.

An invalidation test for predictive models

The standard means of establishing predictive ability in hydrological models is by finding how well predictions match independent validation data. This matching may not be particularly good in some situations such as seasonal flow forecasting and the question arises as to whether a given model has any predictive capacity. A model-independent significance test of the presence of predictive ability is proposed through random permutations of the predicted values. The null hypothesis of no model predictive ability is accepted if there is a sufficiently high probability that a random reordering of the predicted values will yield a better fit to the validation data. The test can achieve significance even with poor model predictions and its value is for invalidating bad models rather than verifying good models as suitable for application. Some preliminary applications suggest that test outcomes will often be similar at the 0.05 level for standard fit measures using absolute or squared residuals. In addition to hydrological application, the test may also find use as a base quality control measure for predictive models generally.

Considering diversity in precipitation variability when updating seasonal flow forecasts

The Israel Hydrological Service forecasts the flow to Lake Kinneret by use of a hydrometeorological model which relates seasonal flow volumes to predicted annual depths of precipitation at representative stations. Issued forecasts include an array of 10 values assigned with the exceedance probabilities of 5–95%, as well as their average, which is an estimate of the expected value. A naive forecast is issued at the beginning of the precipitation season, and subsequent updating forecasts are issued during the season. Predicted annual depths of precipitation in an updating forecast are composed of the observed depth until the updating date and an array of predicted depths for the subsequent sub-season. No explicit relation was found between corresponding prior and subsequent depths concerning any updating date. But, the distribution of subsequent depths was found related to the category (i.e. low, medium, or high) of the corresponding prior depth. Empirical, parametric and non-parametric tests revealed a number of significant diversities in the distributions of later depths with respect to the corresponding categories of earlier depths. This diversity could result in considerable differences in the forecasted flow volumes, and is particularly important in cases of extreme situations, when management decisions are tied to revisions before the end of the precipitation season.

Value of Seasonal Flow Forecasts in Bayesian Stochastic Programming

This paper presents a Bayesian Stochastic Dynamic Programming (BSDP) model to investigate the value of seasonal flow forecasts in hydropower generation. The proposed BSDP framework generates monthly operating policies for the Skagit Hydropower System (SHS), which supplies energy to the Seattle metropolitan area. The objective function maximizes the total benefits resulting from energy produced by the SHS and its interchange with the Bonneville Power Administration. The BSDP-derived operating policies for the SHS are simulated using historical monthly inflows, as well as seasonal flow forecasts during 60 years from January 1929 through December 1988. Performance of the BSDP model is compared with alternative stochastic dynamic programming models. To illustrate the potential advantage of using the seasonal flow forecasts and other hydrologic information, the sensitivity of SHS operation is evaluated by varying (1) the reservoir capacity; (2) the energy demand; and (3) the energy price. The simulation results demonstrate that including the seasonal forecasts is beneficial to SHS operation.