Extended Streamflow Prediction Research Articles

Extended Streamflow Prediction for Russian Rivers

Extended Streamflow Prediction for Russian Rivers

Sub seasonal streamflow forecast assessment at large-scale basins

The sub seasonal forecast horizon, with lead-times up to 7 weeks, has a number of possibilities for operational applications. This sub seasonal time scale is only recently motivated for research on applications, as it is often recognized as being in the gray zone of predictability in both meteorological and hydrological sciences. This work is one of the first assessments of ensemble sub seasonal meteorological inputs to large-scale basin hydrological modelling within a tropical climate – where it could benefit hydropower generation. The quantitative precipitation forecast (QPF) data were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), within the sub seasonal-to-seasonal (S2S) project’s context. To assess the quality of the forecasts, a statistical evaluation was performed, including a comparison with the traditional extended streamflow prediction (ESP) technique. Results allowed for an estimation of the error magnitudes and the potential to the benefit of the ensemble over the deterministic reference forecast and the ESP approach. We showed that the sub seasonal QPF-based forecasts have advantages over the ESP, although, generally their skill deteriorates in lead times after day 30. The evaluation across multiple locations considering drainage area and hydrometeorological conditions, suggests that the statistical performance outcome is also spatio-temporal dependent.

Evaluation of the ECMWF System 4 climate forecasts for streamflow forecasting in the Upper Hanjiang River Basin

AbstractThis paper assesses the potential of the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 forecasts and investigates the post-processing precipitation to enhance the skill of streamflow forecasts. The investigation is based on hydrological modelling and is conducted through the case study of the Upper Hanjiang River Basin (UHRB). A semi-distributed hydrological model, TsingHua Representative Elementary Watershed (THREW), is implemented to simulate the rainfall–runoff processes, with the help of hydrological ensemble prediction system (HEPS) approach. A post-processing method, quantile mapping method, is applied to bias correct the raw precipitation forecasts. Then we evaluate the performance of raw and post-processed streamflow forecasts for the four hydrological stations along the mainstream of Hanjiang River from 2001 to 2008. The results show that the performance of the streamflow forecasts is greatly enhanced with post-processing precipitation forecasts, especially in pre-dry season (November and December), thus providing useful information for water supply management of the central route of South to North Water Diversion Project (SNWDP). The raw streamflow forecasts tend to overpredict and present similarly to forecast accuracy with the extended streamflow prediction (ESP) approach. Streamflow forecast skill is considerably improved when applying post-processing method to bias correct the ECMWF System 4 precipitation forecasts.

Attribution of hydrologic forecast uncertainty within scalable forecast windows

Abstract. Hindcasts based on the extended streamflow prediction (ESP) approach are carried out in a typical rainfall-dominated basin in China, aiming to examine the roles of initial conditions (IC), future atmospheric forcing (FC) and hydrologic model uncertainty (MU) in streamflow forecast skill. The combined effects of IC and FC are explored within the framework of a forecast window. By implementing virtual numerical simulations without the consideration of MU, it is found that the dominance of IC can last up to 90 days in the dry season, while its impact gives way to FC for lead times exceeding 30 days in the wet season. The combined effects of IC and FC on the forecast skill are further investigated by proposing a dimensionless parameter (β) that represents the ratio of the total amount of initial water storage and the incoming rainfall. The forecast skill increases exponentially with β, and varies greatly in different forecast windows. Moreover, the influence of MU on forecast skill is examined by focusing on the uncertainty of model parameters. Two different hydrologic model calibration strategies are carried out. The results indicate that the uncertainty of model parameters exhibits a more significant influence on the forecast skill in the dry season than in the wet season. The ESP approach is more skillful in monthly streamflow forecast during the transition period from wet to dry than otherwise. For the transition period from dry to wet, the low skill of the forecasts could be attributed to the combined effects of IC and FC, but less to the biases in the hydrologic model parameters. For the forecasts in the dry season, the skill of the ESP approach is heavily dependent on the strategy of the model calibration.

중장기 유량예측 향상을 위한 국내 기후정보의 이용

중장기 기후예보는 기후역학모형의 비약적인 발전과 ENSO등의 기후현상에 대한 규명으로, 전세계적으로 정확성이 크게 향상되고 있어 중장기 유량예측의 중요한 실마리가 되고 있다. 본 연구에서는 우선 중장기 유량예측 향상을 위하여 국내에서 사용 가능한 기후정보, 즉 월간산업기상정보와 GDAPS(Global Data Assimilation and Prediction System)를 조사하고 그 정확성을 평가하였다. 월간산업기상정보와 GDAPS의 순별 예보에서 모두 초보예측보다 정확하였고 특히 갈수기보다는 홍수기에 정확성이 더 높게 나와 이 기간에는 기후예보로서 유효함을 확인하였다. 다음으로 기후예보를 이용하여 충주댐 유역에 대하여 유량예측을 수행하였다. 월간산업기상정보에서는 전체 시나리오, 교집합 시나리오, 합집합 시나리오로 나누어 유량예측에 적용하였다. 세 경우 모두 초보예측보다 평균예측점수가 높아 예측으로서 유효하였으며, 특히 홍수기에 교집합 및 합집합 시나리오의 평균예측점수가 전체 시나리오보다 높게 나타났다. GDAPS를 이용한 순별 유량예측의 경우에도 역시 갈수기보다 홍소기에 더 높은 정확성이 나타났다. 따라서 본 연구에서는 홍수기에 보다 정확한 기후예보를 사용하여 기상학적 불확실성을 줄인다면 월 유량예측의 정확성을 향상시킬 수 있음을 증명하였다. Since the accuracy of climate forecast information has improved from better understanding of the climatic system, particularly, from the better understanding of ENSO and the improvement in meteorological models, the forecasted climate information is becoming the important clue for streamflow prediction. This study investigated the available climate forecast information to improve the extended streamflow prediction in Korea, such as MIMI(Monthly Industrial Meteorological Information) and GDAPS(Global Data Assimilation and Prediction) and measured their accuracies. Both MIMI and the 10-day forecast of GDAPS were superior to a naive forecasts and peformed better for the flood season than for the dry season, thus it was proved that such climate forecasts would be valuable for the flood season. This study then forecasted the monthly inflows to Chungju Dam by using MIMI and GDAPS. For MIMI, we compared three cases: All, Intersection, Union. The accuracies of all three cases are better than the naive forecast and especially, Extended Streamflow Predictions(ESPs) with the Intersection and with Union scenarios were superior to that with the All scenarios for the flood season. For GDAPS, the 10-day ahead streamflow prediction also has the better accuracy for the flood season than for the dry season. Therefore, this study proved that using the climate information such as MIMI and GDAPS to reduce the meteorologic uncertainty can improve the accuracy of the extended streamflow prediction for the flood season.

Long-range climate forecasting and its use for water management in the Pacific Northwest region of North America

Ongoing research by the Climate Impacts Group at the University of Washington focuses on the use of recent advances in climate research to improve streamflow forecasts at seasonal-to-interannual, decadal, and longer time scales. Seasonal-to-interannual climate forecasting capabilities have advanced significantly in the past several years, primarily because of improvements in the understanding of, and an ability to forecast, El Niño/Southern Oscillation (ENSO) at seasonal/interannual time scales, and because of better understanding of longer time scale climate phenomena like the Pacific Decadal Oscillation (PDO). These phenomena exert strong controls on climate variability along the Pacific Coast of North America. The streamflow forecasting techniques we have developed for Pacific Northwest (PNW) rivers are based on climate forecasts that facilitate longer lead times (as much as a year) than the methods that are traditionally used for water management (maximum forecast lead times of a few months). At interannual time scales, the simplest of these techniques involves resampling meteorological data from previous years identified to be in similar climate categories as are forecast for the coming year. These data are then used to drive a hydrology model, which produces an ensemble of streamflow forecasts that are analogous to those that result from the well-known Extended Streamflow Prediction (ESP) method. This technique is a relatively simple, but effective, way of incorporating long-lead climate information into streamflow forecasts. It faithfully captures the history of observed climate variability. Its main limitation is that the sample size of observed events for some climate categories is small because of the length of the historic record. Furthermore, it is unable to capture important aspects of global change, which may interact with shorter term variations through changes in climate phenomena like ENSO and PDO. An alternative to the resampling method is to use nested regional climate models to produce the long-lead climate forecasts. Success using this approach has been hindered to some degree by the bias that is inherent in climate models, even when downscaled using regional nested modeling approaches. Adjustment or correction for this bias is central to the use of climate model output for hydrologic forecasting purposes. Approaches for dealing with climate model bias in the context of global and meso-scale are presently an area of active research. We illustrate an experimental application of the nested climate modeling approach for the Columbia River Basin, and compare it with the simpler resampling method. At much longer time scales, changes in Columbia River flows that might be associated with global climate change are of considerable concern in the PNW, given recent Endangered Species Act listing of certain salmonid species, and the increase in water demand that is expected to follow increases in human population in the region. Many of the same general challenges associated with the spatial downscaling of climate forecasts are present in these long-range investigations. Additional uncertainties exist in the ability of climate models to predict the effects of changing greenhouse gas concentrations. These uncertainties tend to dominate the results, and lead us to use relatively simplemethods of downscaling seasonal temperature and precipitation to interpret the implications of alternative climate scenarios on PNW water resources.

Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals

A simple method has been devised to incorporate the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate signals into the well-known extended streamflow prediction forecasting approach. Forecasts of ENSO are currently available up to a year or more in advance, which facilitates forecasting of the streamflow response to this climate signal at interannual forecast lead times. The biomodal phase of the PDO can be identified in real time using a combination of assumed persistence of the existing phase and the tracking of extreme events to identify transitions. The technique makes use of a gridded meteorological data set to drive a macroscale hydrology model at 1° spatial resolution over the Columbia River Basin above The Dalles. A streamflow forecast ensemble is created by resampling from the historical meteorological data according to six predefined PDO/ENSO categories. Given a forecast of the ENSO climate signal for the coming water year and the existing phase of the PDO, these meteorological ensembles are then used to drive the hydrology model based on the initial soil and snow conditions as of the forecast date. To evaluate the technique, a retrospective forecast of the historic record was prepared (1989–1998), using October–September as the forecast period, as well as an ensemble forecast for water years 1999 and 2000 that were prepared on June 1, 1998 and May 10, 1999, respectively. The results demonstrate the increase in lead time and forecast specificity over climatology that can be achieved by using PDO and ENSO climate information to condition the forecast ensembles.

Assessing Risk in Operational Decisions Using Great Lakes Probabilistic Water Level Forecasts

/ A method adapted from the National Weather Service's Extended Streamflow Prediction technique is applied retrospectively to three Great Lakes case studies to show how risk assessment using probabilistic monthly water level forecasts could have contributed to the decision-mak-ing process. The first case study examines the 1985 International Joint Commission (IJC) decision to store water in Lake Superior to reduce high levels on the downstream lakes. Probabilistic forecasts are generated for Lake Superior and Lakes Michigan-Huron and used with riparian inundation value functions to assess the relative impacts of the IJC's decision on riparian interests for both lakes. The second case study evaluates the risk of flooding at Milwaukee, Wisconsin, and the need to implement flood-control projects if Lake Michigan levels were to continue to rise above the October 1986 record. The third case study quantifies the risks of impaired municipal water works operation during the 1964-1965 period of extreme low water levels on Lakes Huron, St. Clair, Erie, and Ontario. Further refinements and other potential applications of the probabilistic forecast technique are discussed.KEY WORDS: Great Lakes; Water levels; Forecasting; Risk; Decision making

Nonparametric Framework for Long‐range Streamflow Forecasting

The extended streamflow prediction (ESP) procedure of the National Weather Service River Forecast System (NWSRFS) produces long‐range forecasts of streamflow through the use of hydrologic models and historical hydrologic data. An important element of the ESP procedure is converting hydrologic‐model output to estimates of a forecast random variable. In this paper, nonparametric statistical procedures are developed for combining hydrologic models and historical hydrologic data into long‐range streamflow forecasts. Although these procedures are developed for use within the ESP system, they should be broadly applicable to problems of long‐range streamflow forecasting. Two notable features of the procedures developed in this paper are: (1) Climate information is easily incorporated; and (2) hydrologic‐model errors can be accommodated. Results are presented for a test implementation of ESP for a basin in the southeastern United States during the severe drought period of 1988. The relative importance of climate information and soil moisture information for long‐range streamflow forecasting is compared and contrasted.

Technological developments in real-time operational hydrologic forecasting in the United States

The hydrologic forecasting service of the United States spans applications and scales ranging from those associated with the issuance of flood and flash warnings to those pertaining to seasonal water supply forecasts. New technological developments (underway in or planned by the National Weather Service (NWS) in support of the Hydrologic Program) are carried out as combined efforts by NWS headquarters and field personnel in cooperation with other organizations. These developments fall into two categories: hardware and software systems technology, and hydrometeorological analysis and prediction technology. Research, development, and operational implementation in progress in both of these areas are discussed. Cornerstones of an overall NWS modernization effort include implementation of state-of-the-art data acquisition systems (including the Next Generation Weather Radar) and communications and computer processing systems. The NWS Hydrologic Service will capitalize on these systems and will incorporate results from specific hydrologic projects including collection and processing of multivariate data sets, conceptual hydrologic modeling systems, integrated hydrologic modeling systems with meteorological interfaces and automatic updating of model states, and extended streamflow prediction techniques. The salient aspects of ongoing work in these areas are highlighted in this paper, providing some perspective on the future U.S. hydrologic forecasting service and its transitional period into the 1990s.

Extended Streamflow Forecasting Using NWSRFS

Extended forecasting using the National Weather Service River Forecast System (NWSRFS) is done with the NWS Extended Streamflow Prediction (ESP) program. This paper examines the theory, capabilities, and potential applications of the ESP procedure. ESP uses conceptual hydrologic/hydraulic models to forecast future streamflow using. the current snow, soil moisture, river, and reservoir conditions with historical meteorological data. The ESP procedure assumes that meteorological events that occurred in the past are representative of events that may occur in the future. Each year of historical meteorological data is assumed to be a possible representation of the future and is used to simulate a streamflow trace. The simulated streamflow traces can be scanned for maximum flow, minimum flow, volume of flow, reservoir stage, etc., for any period in the future. ESP produces a probabilistic forecast for each streamflow variable and period of interest. The procedure was originally developed for water supply forecasting in snowmelt areas, but it can also be used to produce spring flood outlooks, forecasts for navigation, inflow hydrographs for reservoir operation, and time series needed for risk analysis during droughts.