Development of a Watershed Characteristic Database: Investigating Improvements in Low Streamflow Prediction

2001 ◽  
Author(s):  
Chuck Kroll ◽  
Joana Luz ◽  
Richard Vogel
Keyword(s):  
Streamflow Prediction ◽  
Watershed Characteristic

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

Quantitative estimation on contribution of climate changes and watershed characteristic changes to decreasing streamflow in the Huangshui Basin, China

2021 ◽  
Author(s):  
Xizhi Lv ◽  
Shaopeng Li ◽  
Yongxin Ni ◽  
Qiufen Zhang ◽  
Li Ma
Keyword(s):  
Water Resources ◽  
Climate Changes ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Quantitative Estimation ◽  
Meteorological Data ◽  
Characteristic Parameter ◽  
Research Area ◽  
Watershed Characteristic ◽  
Streamflow Change

<p>In the past 60 years, climate changes and underlying surface of the watershed have affected the structure and characteristics of water resources to a different degree It is of great significance to investigate main drivers of streamflow change for development, utilization and planning management of water resources in river basins. In this study, the Huangshui Basin, a typical tributary of the upper Yellow River, is used as the research area. Based on the Budyko hypothesis, streamflow and meteorological data from 1958-2017 are used to quantitatively assess the relative contributions of changes in climate and watershed characteristic to streamflow change in research area. The results show that: the streamflow of Huangshui Basin shows an insignificant decreasing trend; the sensitivity coefficients of streamflow to precipitation, potential evapotranspiration and watershed characteristic parameter are 0.5502, -0.1055, and 183.2007, respectively. That is, an increase in precipitation by 1 unit will induce an increase of 0.5502 units in streamflow, and an increase in potential evapotranspiration by 1 unit will induce a decrease of 0.1055 units in streamflow, and an increase in the watershed characteristic parameter by 1 unit will induce a decrease of 183.2007 units in streamflow. Compared with the reference period (1958-1993), the streamflow decreased by 20.48mm (13.59%) during the change period (1994-2017), which can be attribution to watershed characteristic changes (accounting for 73.64%) and climate change (accounting for 24.48%). Watershed characteristic changes exert a dominant influence upon the reduction of streamflow in the Huangshui Basin.</p>

A robust method for non-stationary streamflow prediction based on improved EMD-SVM model

2019 ◽  
Vol 568 ◽  
pp. 462-478 ◽  
Author(s):  
Erhao Meng ◽  
Shengzhi Huang ◽  
Qiang Huang ◽  
Wei Fang ◽  
Lianzhou Wu ◽  
...  
Keyword(s):  
Robust Method ◽  
Streamflow Prediction ◽  
Svm Model

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

Parallel computation for streamflow prediction with distributed hydrologic models

1997 ◽  
Vol 197 (1-4) ◽  
pp. 1-24 ◽  
Author(s):  
Theodore K. Apostolopoulos ◽  
Konstantine P. Georgakakos
Keyword(s):  
Parallel Computation ◽  
Streamflow Prediction ◽  
Hydrologic Models

Multi-objective optimization of empirical hydrological model for streamflow prediction

2014 ◽  
Vol 511 ◽  
pp. 242-253 ◽  
Author(s):  
Jun Guo ◽  
Jianzhong Zhou ◽  
Jiazheng Lu ◽  
Qiang Zou ◽  
Huajie Zhang ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Multi Objective Optimization ◽  
Streamflow Prediction ◽  
Multi Objective

scholarly journals Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

2017 ◽  
Author(s):  
Diana Lucatero ◽  
Henrik Madsen ◽  
Jens C. Refsgaard ◽  
Jacob Kidmose ◽  
Karsten H. Jensen
Keyword(s):  
Hydrological Model ◽  
Predictive Distribution ◽  
Low Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Initial State ◽  
Lower Accuracy ◽  
Spatially Distributed ◽  
Streamflow Forecasts ◽  
Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

scholarly journals Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK

2020 ◽  
Vol 24 (12) ◽  
pp. 6059-6073
Author(s):  
Andres Peñuela ◽  
Christopher Hutton ◽  
Francesca Pianosi
Keyword(s):  
Decision Maker ◽  
Water Resource ◽  
Water Resource Management ◽  
Forecast Skill ◽  
Weather Data ◽  
Ensemble Size ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Hydrological Conditions ◽  
The Uk

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

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