scholarly journals Moving beyond the cost-loss ratio: Economic assessment of streamflow forecasts for a risk-averse decision maker

2016 ◽  
Author(s):  
Simon Matte ◽  
Marie-Amélie Boucher ◽  
Vincent Boucher ◽  
Thomas-Charles Fortier Filion
Keyword(s):  
Utility Function ◽  
Decision Maker ◽  
Economic Value ◽  
Economic Assessment ◽  
Risk Averse ◽  
Loss Ratio ◽  
Ensemble Forecasts ◽  
South Central ◽  
Streamflow Forecasts ◽  
Cara Utility

Abstract. A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority, if not all, of existing hydro-economic studies rely on a cost-loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known CARA utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses rather on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exists many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles and a variant of the latter that also includes an estimation of variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in south central Quebec, Canada. The assessment of forecasts is performed for lead times of one to five days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution.

scholarly journals Moving beyond the cost–loss ratio: economic assessment of streamflow forecasts for a risk-averse decision maker

2017 ◽  
Vol 21 (6) ◽  
pp. 2967-2986 ◽  
Author(s):  
Simon Matte ◽  
Marie-Amélie Boucher ◽  
Vincent Boucher ◽  
Thomas-Charles Fortier Filion
Keyword(s):  
Risk Aversion ◽  
Utility Function ◽  
Decision Maker ◽  
Economic Value ◽  
Economic Assessment ◽  
Risk Averse ◽  
Loss Ratio ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Cara Utility

Abstract. A large effort has been made over the past 10 years to promote the operational use of probabilistic or ensemble streamflow forecasts. Numerous studies have shown that ensemble forecasts are of higher quality than deterministic ones. Many studies also conclude that decisions based on ensemble rather than deterministic forecasts lead to better decisions in the context of flood mitigation. Hence, it is believed that ensemble forecasts possess a greater economic and social value for both decision makers and the general population. However, the vast majority of, if not all, existing hydro-economic studies rely on a cost–loss ratio framework that assumes a risk-neutral decision maker. To overcome this important flaw, this study borrows from economics and evaluates the economic value of early warning flood systems using the well-known Constant Absolute Risk Aversion (CARA) utility function, which explicitly accounts for the level of risk aversion of the decision maker. This new framework allows for the full exploitation of the information related to a forecasts' uncertainty, making it especially suited for the economic assessment of ensemble or probabilistic forecasts. Rather than comparing deterministic and ensemble forecasts, this study focuses on comparing different types of ensemble forecasts. There are multiple ways of assessing and representing forecast uncertainty. Consequently, there exist many different means of building an ensemble forecasting system for future streamflow. One such possibility is to dress deterministic forecasts using the statistics of past error forecasts. Such dressing methods are popular among operational agencies because of their simplicity and intuitiveness. Another approach is the use of ensemble meteorological forecasts for precipitation and temperature, which are then provided as inputs to one or many hydrological model(s). In this study, three concurrent ensemble streamflow forecasting systems are compared: simple statistically dressed deterministic forecasts, forecasts based on meteorological ensembles, and a variant of the latter that also includes an estimation of state variable uncertainty. This comparison takes place for the Montmorency River, a small flood-prone watershed in southern central Quebec, Canada. The assessment of forecasts is performed for lead times of 1 to 5 days, both in terms of forecasts' quality (relative to the corresponding record of observations) and in terms of economic value, using the new proposed framework based on the CARA utility function. It is found that the economic value of a forecast for a risk-averse decision maker is closely linked to the forecast reliability in predicting the upper tail of the streamflow distribution. Hence, post-processing forecasts to avoid over-forecasting could help improve both the quality and the value of forecasts.

scholarly journals The Stochastic Cash Balance Problem with Fixed Costs: The Risk-averse Case

2014 ◽  
Vol 2 (6) ◽  
pp. 520-531
Author(s):  
Shuren Liu ◽  
Pei Tang
Keyword(s):  
Utility Function ◽  
Decision Maker ◽  
Optimal Policy ◽  
Ambiguity Aversion ◽  
Exponential Utility ◽  
Risk Averse ◽  
Fixed Costs ◽  
Cash Balance ◽  
Balance Problem ◽  
Exponential Utility Function

AbstractThis paper discusses multi-period stochastic cash balance problem with fixed costs when the decision maker is risk averse. By using the consumption model introduced by Chen et al, we characterize the structure of the optimal policy for the stochastic cash balance problem under the general increasing concave utility function and exponential utility function, respectively. We show that the structure of the optimal policy for a decision maker with exponential utility function is almost identical to the structure of the optimal risk-neutral operations policy. Furthermore, we extend the results for the exponential utility function to the ambiguity aversion case.

scholarly journals Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

2016 ◽  
Author(s):  
Louise Crochemore ◽  
M.-H. Ramos ◽  
Florian Pappenberger
Keyword(s):  
Bias Correction ◽  
Linear Scaling ◽  
Drought Risk ◽  
Seasonal Precipitation ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Streamflow Forecasts ◽  
Distribution Mapping ◽  
Seasonal Streamflow

Abstract. Meteorological centres make sustained efforts to provide seasonal forecasts that are increasingly skilful, which has the potential to benefit streamflow forecasting. Seasonal streamflow forecasts can help to take anticipatory measures for a range of applications, such as water supply or hydropower reservoir operation and drought risk management. This study assesses the skill of seasonal precipitation and streamflow forecasts in France to provide insights into the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. We apply eight variants of bias correction approaches to the precipitation forecasts prior to generating the streamflow forecasts. The approaches are based on the linear scaling and the distribution mapping methods. A daily hydrological model is applied at the catchment scale to transform precipitation into streamflow. We then evaluate the skill of raw (without bias correction) and bias corrected precipitation and streamflow ensemble forecasts in sixteen catchments in France. The skill of the ensemble forecasts is assessed in reliability, sharpness, accuracy, and overall performance. A reference prediction system, based on historical observed precipitation and catchment initial conditions at the time of forecast (i.e., ESP method), is used as benchmark in the computation of the skill. The results show that, in most catchments, raw seasonal precipitation and streamflow forecasts are often more skilful than the conventional ESP method in terms of sharpness. However, they are not significantly better in terms of reliability. Forecast skill is generally improved when applying bias correction. Two bias correction methods show the best performance for the studied catchments, each method being more successful in improving specific attributes of the forecasts: the simple linear scaling of monthly values contribute mainly to increasing forecast sharpness and accuracy, while the empirical distribution mapping of daily values is successful in improving forecast reliability.

scholarly journals Support of decision-making under risk by a risk-averse decision-maker

2020 ◽  
Vol 13 ◽  
Author(s):  
Andrzej Łodziński
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Maker ◽  
The State ◽  
Decision Making Process ◽  
Discrete Problem ◽  
Risk Averse ◽  
Control Parameters ◽  
Decision Making Under Risk ◽  
State Of The Environment

The paper presents the decision support under risk by the risk averse decision maker. Decision making under risk occurs when the result of the decision is not unequivocal and depends on the state of the environment. The decision making process is modeled with the use of multi-criteria optimization. The decision is made by solving the problem with the control parameters that determine the decision maker's aspirations and the evaluation of the solutions received. The decision maker asks the parameter for which the solution is determined. Then, evaluate the solution received accepting or rejecting it. In the second case, the decision maker gives a new parameter value and the problem is solved again for the new parameter. The work includes an simple discrete problem of decision support under risk

scholarly journals Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

2017 ◽  
Vol 145 (12) ◽  
pp. 4911-4936 ◽  
Author(s):  
Jonathan Labriola ◽  
Nathan Snook ◽  
Youngsun Jung ◽  
Bryan Putnam ◽  
Ming Xue
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
South Central ◽  
Single Moment ◽  
Double Moment ◽  
Horizontal Grid ◽  
Severe Weather Event ◽  
Numerical Weather Prediction Models

Explicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size of hail (MESH), hydrometeor classification algorithm (HCA) output, and hail size discrimination algorithm (HSDA) output. Resulting hail forecasts show at most marginal skill, with the level of skill dependent on the forecast initialization time and microphysical scheme used. Forecasts using the double-moment scheme predict many small hailstones aloft, while the single-moment members predict larger hailstones. Near the surface, double-moment members predict larger hailstone sizes than their single-member counterparts. Hail in the forecasts is found to melt too quickly near the surface for members using either of the microphysics schemes examined. Analysis of microphysical budgets in both schemes indicates that both schemes suboptimally represent hail processes, adversely impacting the skill of surface hail forecasts.

Information in Fleeting Opportunities

2013 ◽  
pp. 110-153
Author(s):  
Ibrahim Almojel ◽  
Jim Matheson ◽  
Pelin Canbolat
Keyword(s):  
Utility Function ◽  
Decision Maker ◽  
Optimal Policy ◽  
Value Of Information ◽  
Risk Preference ◽  
Dynamic Program ◽  
Exponential Utility ◽  
Venture Capitalists ◽  
Exponential Utility Function ◽  
Over Time

This paper focuses on the study of information in fleeting opportunities. An application example is the evaluation of business proposals by venture capitalists. The authors formulate the generic problem as a dynamic program where the decision maker can either accept a given deal directly, reject it directly, or seek further information on its potential and then decide whether to accept it or not. Results show well behaved characteristics of the optimal policy, deal flow value, and the value of information over time and capacity. It is presumed that the risk preference of the decision maker follows a linear or an exponential utility function. This approach is illustrated through several examples.

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