Watershed classification by remote sensing indices： A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index(SPI) was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the Principal Components Analysis(PCA) method.The Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.

Precipitation forecasting by large-scale climate indices and machine learning techniques

Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment.The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production,to rising sea levels that deteriorates the risk of catastrophic flooding.Among all aspects related to global warming,there is a growing concern on water resource management.This field is targeted at preventing future water crisis threatening human beings.The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions.Numerous prediction models,methods and tools,in this case,have been developed and applied so far.In line with trend,the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron(MLP)network to explore any meaningful connection between large-scale climate indices(LSCIs)and precipitation in the capital of Iran,a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming.This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran.Therefore,the current and future environmental conditions of this city especially its water supply conditions are of great importance.To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed.To this end,the present study investigates three training methods namely backpropagation(BP),genetic algorithms(GAs),and particle swarm optimization(PSO)algorithms on a MLP platform.Two frameworks distinguished by their input compositions are denoted in this study:Concurrent Model Framework(CMF)and Integrated Model Framework(IMF).Through these two frameworks,13 cases are generated:12 cases within CMF,each of which contains all selected LSCIs in the same lead-times,and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time.Following the evaluation of all model performances through related statistical tests,Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms,the best of which is found to be MLP-PSO in IMF.

森林生态蓄水功能的经济评价(个案研究:伊朗扎格罗斯森林)(英文)

森林生态服务直接或间接地造福人类。本文利用模拟模型和地理信息系统作为工具,分析生态因子对生态系统服务功能的作用,评估了伊朗西部扎格罗斯森林对Bazoft河流域水源保护的经济价值。应用HEC-HMS模型中的曲线数字法对降雨量-径流进行了模拟。该模型需要输入土地覆被、土壤、短期降雨和流出数据。用观测数据并分阶段校对对拟合模型的功效进行了修订。测定了森林在4种设定的土地覆盖方案中保水性和减少地表径流量的作用以及土地用途改变对该区降雨量-径流行为的影响。结果表明:方案一假定该流域全被森林覆盖,总的流出量将最小,降雨初期损失将增加。用重置成本法估算了森林水文功能之保水性经济价值。评估结果表明,每公顷Bazoft河流域的森林可以蓄存84.8立方米水,年产值0.5 US$/m3。因此,每公顷BazoR河流域的森林保水性经济评价值是43US$。说明覆被森林有利于该流域经济发展,该研究有助于决策者选择适当的、经济可行的发展策略。

Homogeneity analysis of streamflow records in arid and semi-arid regions of northwestern Iran

Homogeneity analysis of the streamflow time series is essential to hydrological modeling, water resources management and climate change studies. In this study, five absolute homogeneity tests and one clustering approach were used to determine the homogeneity status of the streamflow time series （over the period 1960-2010） in 14 hydrometric stations of three important basins （i.e., Aras River Basin, Urmia Lake Basin and Sefid-Roud Basin） in northwestern Iran. Results of the Buishand range test, yon Neumann ratio test, cumulative deviation test, standard normal homogeneity test and Pettitt test for monthly streamflow time series detected that about 42.26%, 38.09%, 33.33%, 39.28% and 68.45% of the streamflow time series were inhomogeneous at the 0.01 significance level, respectively. Streamflow time series of the stations located in the eastern parts of the study area or within the Urmia Lake Basin were mostly homogeneous. In contrast, streamflow time series in the stations of the Aras River Basin and Sefied-Roud Basin showed inhomogeneity at annual scales. Based on the overall classification for the monthly and annual streamflow series, we determined that about 45.60%, 11.53% and 42.85% of the time series were categorized into the ＇useful＇, ＇doubtful＇ and ＇suspect＇ classes according to the five absolute homogeneity tests. We also found the homogeneity patterns of the streamflow time series by using the clustering approach. The results suggested the effectiveness of the clustering approach for homogeneity analysis of the streamflow time series in addition to the absolute homogeneity tests. Moreover, results of the absolute homogeneity tests and clustering approach indicated obvious decreasing change points of the streamflow time series in the 1990s over the three basins, which were mostly related to the hydrological droughts.