Estimation of drought characteristics such as probabilities and return periods of droughts of various lengths is of major importance in drought forecast and management and in solving water resources problems related to water quality and navigation. This study aims at applying first- and second-order Markov chain models to dry and wet periods of annual streamflow series to reproduce the stochastic structure of hydrological droughts. Statistical evaluation of drought duration and intensity is usually carried out using runs analysis. First-order Markov chain model (MC1) for dry and wet periods is not adequate when autocorrelation of the original hydrological series is high. A second-order Markov chain model (MC2) is proposed to estimate the probabilities and return periods of droughts. Results of these models are compared with those of a simulation study assuming a lag-1 autoregressive [AR(1)] process widely used to model annual streamflows. Probability distribution and return periods of droughts of various lengths are estimated and compared with the results of MC1 and MC2 models using efficacy evaluation statistics. It is found that the MC2 model in general gives results that are in better agreement with simulation results as compared with the MC1 model. Skewness is found to have little effect on return periods except when autocorrelation is very high. MC1 and MC2 models are applied to droughts observed in some annual streamflow series, with the result that the MC2 model has a relatively good agreement considering the limited duration of the records.
Interpretations of state and trends in lake water quality are generally based on measurements from one or more stations that are considered representative of the response of the lake ecosystem. The objective of this study is to examine how these interpretations may be influenced by station location in a large lake. We addressed this by analyzing trends in water quality variables collected monthly from eight monitoring stations along a transect from the central lake to the north in Lake Taihu (area about 2,338 km(2)), China, from October 1991 to December 2011. The parameters examined included chlorophyll a (Chl a), total nitrogen (TN), and total phosphorus (TP) concentrations, and Secchi disk depth (SD). The individual variables were increasingly poorly correlated among stations along the transect from the central lake to the north, particularly for Chl a and TP. The timing of peaks in individual variables was also dependent on station location, with spectral analysis revealing a peak at annual frequency for the central lake station but absence of, or much reduced signal, at this frequency for the near-shore northern station. Percentage annual change values for each of the four variables also varied with station and indicated general improvement in water quality at northern stations, particularly for TN, but little change or decline at central lake stations. Sediment resuspension and tributary nutrient loads were considered to be responsible for some of the variability among stations. Our results indicate that temporal trends in water quality may be station specific in large lakes and that calculated whole-lake trophic status trends or responses to management actions may be specific to the station(s) selected for monitoring and analysis. These results have important implications for efficient design of monitoring programs that are intended to integrate the natural spatial variability of large lakes.
Controlling and reducing the watershed's erosion and sedimentation is essential to ensure the continuity of projects implemented to develop land and water resources and improve sustainability, performance, and longevity. Sediment control is also critical in managing the river basin in limiting the transport of solids, improving water quality, sustaining aquatic life, and preventing damage to downstream aquatic environments and ecosystems. Estimating the potential effects of land-use changes on surface runoff and soil erosion requires distributed hydrological modeling methods. In addition to naturally occurring sediments, changes in land-use types for different applications can be a primary cause for the increase in sediment rates in the watershed. This study used the Soil and Water Assessment Tool (SWAT), a rainfall-runoff model, to evaluate land use/cover changes (i.e., deforestation) and their impact on sediment load under different scenarios. For the baseline (no changes) scenario, the watershed is calibrated using the flow and sediment data measured from the rain gauge station during the time step to estimate the post-deforestation changes at the subcatchment scale of the study area. The study results indicated that the total surface runoff and sediment yield for the selected sub-catchment in the deforestation scenario with the highest spatial distribution, due to the high erosivity (24% increase) of excessive surface runoff after deforestation, sediment yield increased 3.5-fold. In contrast, due to the removal of trees and vegetation's canopy, the evapotranspiration, leaf area index, and dissolved oxygen transported into reach showed the inverse ratios, and the values decreased by 5%, 24, and 17%, respectively, in compared with the baseline scenario. In terms of watershed management, therefore, the application of hydrological models such as SWAT rainfall-runoff and erosion models can be a helpful method for decision-makers to apply for the protection of forests from intensive impacts such as deforestation and limiting their socio-environmental effects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.