Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of ‘interpolation’. However, in many cases environmental models are used for ‘extrapolation’; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models. � 2005 Elsevier Ltd. All rights reserved.

A Framework for Dealing With Uncertainty due to Model Structure Error

Human impacts on the terrestrial water cycle have the potential to influence hazards such as flooding and drought, so understanding the extent of our influence is an important research goal. A study utilizing estimates of evapotranspiration for different types of land cover and a database of changes in use now shows that the extent of land-cover change caused by people is already an important factor affecting the terrestrial water cycle.

The impact of global land-cover change on the terrestrial water cycle

Benefits and limitations of biochar amendment in agricultural soils: A review

Summary The post-real time product of Day-1 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is evaluated over Mainland China from April to December 2014 at the hourly timescale, against data from hourly ground-based observations. In addition, the IMERG product is compared with its predecessor-the Version-7 post-real-time 3B42 (3B42V7) product of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) at its original 3-hourly and then daily timescales for the same period. All the products are cross-evaluated at gridded, regional, and national scales. Results show that: (1) the Day-1 IMERG shows appreciably better performance than 3B42V7 at both sub-daily and daily timescales, and all the three spatial scales. The gap between the two products is more significant at the sub-daily resolution; (2) Out of the six sub-regions of China, IMERG especially performs better than 3B42V7 at the mid- and high-latitudes, as well as relatively dry climate regions; (3) IMERG can better reproduce the probability density function (PDF) in terms of precipitation intensity, particularly in the low ranges; and (4) although IMERG better captures the precipitation diurnal variability, both products have room to further improve their capability, particularly in the dry climate and high-latitude regions. This study is among the earliest evaluation and comparison of IMERG and 3B42V7 products, which could be valuable in providing reference for the development of IMERG algorithms, associated global products, and various applications as well.

First-Year Evaluation of GPM Day-1 IMERG and TMPA Version-7 Legacy Products over Mainland China at Multiple Spatiotemporal Scales

Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

A cloud model-based approach for water quality assessment

Optimization of industry structure based on water environmental carrying capacity under uncertainty of the Huai River Basin within Shandong Province, China

Lachun Wang

Papers

A cloud model-based approach for water quality assessment

Optimization of industry structure based on water environmental carrying capacity under uncertainty of the Huai River Basin within Shandong Province, China

Chaotic dynamics of the flood series in the Huaihe River Basin for the last 500 years

A multidimension cloud model-based approach for water quality assessment

Ecological and health risk assessment of PAHs, OCPs, and PCBs in Taihu Lake basin