Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components

نویسندگان

  • Bahareh Kamali
  • Karim C. Abbaspour
  • Hong Yang
چکیده

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Identification of Multiple Input-multiple Output Non-linear System Cement Rotary Kiln using Stochastic Gradient-based Rough-neural Network

Because of the existing interactions among the variables of a multiple input-multiple output (MIMO) nonlinear system, its identification is a difficult task, particularly in the presence of uncertainties. Cement rotary kiln (CRK) is a MIMO nonlinear system in the cement factory with a complicated mechanism and uncertain disturbances. The identification of CRK is very important for different pur...

متن کامل

تخمین

Temporal and spatial distribution of water components in watersheds, estimation of water quality, and uncertaintiesassociated with these estimations are important issues in freshwater studies. In this study, Soil and Water AssessmentTool (SWAT) model was used to estimate components of freshwater availability: blue water (surface runoff plus deepaquifer recharge), green water flow (actual evapot...

متن کامل

A Novel Type-2 Adaptive Neuro Fuzzy Inference System Classifier for Modelling Uncertainty in Prediction of Air Pollution Disaster (RESEARCH NOTE)

Type-2 fuzzy set theory is one of the most powerful tools for dealing with the uncertainty and imperfection in dynamic and complex environments. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Predict...

متن کامل

پیش‌بینی دمای سطح آب خلیج فارس با استفاده از رگرسیون چندگانه و تحلیل مؤلفه‌های اصلی

Since the fluctuations of the Persian Gulf Sea Surface Temperature (PGSST) have a significant effect on the winter precipitation and water resources and agricultural productions of the south western parts of Iran, the possibility of the Winter SST prediction was evaluated by multiple regression model. The time series of PGSSTs for all seasons, during 1947-1992, were considered as predictors, an...

متن کامل

Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran

Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined u...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017