Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Research and application of flood detention modeling for ponds and small reservoirs based on remote sensing data

This paper proposes a method of small reservoir flood detention modeling that utilizes data from the American land resources satellite Landsat TM/ETM+. Precipitation and potential evapotranspiration are taken as the control conditions in this method on the basis of basin terrain classification. The objective of this method is to solve the question of a small-scale water conservancy project’s influence on flood forecasting precision, which can be used in the basin with multitudinous small reservoirs in the upstream region and can help estimate non-runoff data for small reservoir runoff. Taking the 20060826 flood as an example, the flood detention quantity of 19 small reservoirs is modeled. The results show that the absolute error of the total flood detention quantity is 0.2×10 4 m 3 , and the relative error is 0.12%. The flood detention quantity of small reservoirs in the entire basin is then modeled using this method, and the primary flood forecasting model is adjusted. After adjustment, the precision is significantly improved, with the relative error decreasing from 31.8% to 10.1%.

Analysis of Irrigation Systems Employing Comparative Performance Indicators： A Benchmark Study for National Irrigation and Communal Irrigation Systems in Cagayan River Basin

Comparative performance analysis of four irrigation schemes within Cagayan River Basin was assessed using comparative performance indicators between the years 2008 and 2012. The objectives were to establish benchmarks for both productivity and performance of irrigation schemes along the valley and to inquire whether small schemes function better than large schemes. The performance evaluation study of the systems composed of three general performance indicators, based on three domains-（1） system operation performance; （2） agricultural productivity and economics; （3） financial performance. Each indicator was assessed based on the prescribed descriptors used by the International Water Management Institute （IWMI） and Food and Agriculture Organization （FAO）. Analysis showed an overall system performance efficiency of 59%, 55%, 47% and 36% for Magat River Integrated Irrigation System （MARI1S）, Lucban, Garab and Divisoria Communal Irrigation Systems （CIS）, respectively. In terms of annual productivity performance, Lucban CIS dominates the three other systems with 0.35 kg/m3, which was classified as moderately performing system, while the rest were classified with low productivity index. Financial sustainability of the systems were extremely poor with cost recovery ratio of 0, 0.33, 0.41 and 0.49 for Divisoria, Garab, Lucban and MARIIS, respectively, which were exceptionally below the standard value of at least one. Also, analysis of the indicators revealed that on average, large schemes performed similarly to small-scale schemes, but small schemes were more variable, particularly in input-use efficiency. The benchmarking study will provide strategic information to policy makers of agricultural and irrigation agencies on the existing weaknesses of irrigation systems in the country and determine in a more quantifiable terms levels of potential improvement and intervention targets.

Small water bodies in China:Spatial distribution and influencing factors

Inland water bodies,including ponds,lakes,reservoirs,and rivers,provide extensive ecosystem services for human beings.Among these,small water bodies(SWBs),such as ponds and small reservoirs,are more common landscape features and important biogeochemical reactors.SWBs can significantly influence biogeochemical processes and hydrologic cycles.However,due to their small size,SWBs(<10 ha)have been largely ignored in natural resource surveys,leading to a limited understanding of their spatial distribution in China.Insufficient geospatial datasets of SWBs limit the accurate assessments of resource utilization and fluxes of biogenic elements in both aquatic and terrestrial ecosystems.To address this,in this study,we applied a convolutional neural network and a visual interpretation approach to extract SWBs from high-resolution satellite images from Google Earth.The spatial distribution of SWBs in China was mapped,and drivers of the spatial pattern of SWBs were also identified.As a result,a total of 5.18 million water bodies with a surface area larger than 0.1 ha,including ponds,lakes,and reservoirs,were identified.These water bodies(>0.1 ha)cover approximately 179300 km^(2),which is approximately 1.8%of the land area in China.In addition,the combined shoreline length of the water bodies was approximately 2157400 km.Of these water bodies,96.85%were SWBs,accounting for 17.85%of the total water area and 76.4% of the total shoreline length.Precipitation,terrain,and human activity cumulatively explained 45% of the variance in SWB distribution,with precipitation being the strongest climatic explanatory factor.Our results provide important data for determining the roles of SWBs in biogeochemical cycles,habitat protection,and hydrological cycles.