tailieunhanh - Modeling Hydrologic Change: Statistical Methods - Chapter 10

Phân tích số liệu thủy văn được giả định là có khả năng phát hiện những ảnh hưởng của đầu nguồn thay đổi. Phương pháp đồ họa và kiểm tra thống kê có thể được sử dụng để hỗ trợ giả thuyết phát triển trên cơ sở thông tin độc lập. Tuy nhiên, phát hiện là hiếm khi là mục tiêu cuối cùng. Thay vào đó, mục đích phát hiện nói chung là mô hình ảnh hưởng của sự thay đổi đầu nguồn để có hiệu lực có thể được ngoại suy cho tương lai một số nhà nước. Ví dụ, nếu thiết kế nghiên. | 10 Modeling Change INTRODUCTION The analysis of hydrologic data is assumed to be capable of detecting the effects of watershed change. Graphical methods and statistical tests can be used to support hypotheses developed on the basis of independent information. However detection is rarely the ultimate goal. Instead the purpose of detection is generally to model the effect of the watershed change so that the effect can be extrapolated to some future state. For example if the study design requires the specification of ultimate watershed development knowing the hydrologic effect of partial development in a watershed may enable more accurate forecasts of the effects that ultimate development will have. Detecting hydrologic effects of watershed change is the first step. Modeling the effect is necessary in order to forecast the effects of future change. Therefore one graph or one statistical test will probably be inadequate to detect hydrologic change. Several statistical tests may be required in order to understand the true nature of the effect. Is the change in the distribution of the hydrologic variable due to a change in the function itself or to a change in one or more of the moments Does a statistically significant serial correlation reflect a lack of independence or is it due to an unidentified secular trend in the data Questions such as these emphasize the need for a thorough effort in detecting the true effect. An inadequate effort may ultimately lead to an incorrect assessment of the hydrologic effect and therefore an incorrect forecast. Assuming that the correct hydrologic effect has been detected the next step is to properly model the effect. Given the substantial amount of natural variation in hydrologic data it is important to use the best process and tools to model the hydrologic effect of change. Using a simple model is best only when it is accurate. With the availability of computer-aided modeling tools the ability to model non-stationary hydrologic .