Simulation of evolution characteristics of latent heat budgets of a heavy rain event in October 2011 over Haikou, China

Jiangnan Li*, Kailu Wu, Fangzhou Li and Yangjie Zhao

In this study, the WRF model and the WRF-3DVAR system were used to simulate an exceptionally heavy rain event over Haikou that occurred in October 2011. Simulation results of precipitation of five frequently used microphysical parameterizations schemes (WDM5, WDM6, WSM6, Lin and Morrison) were evaluated. Verification results indicated that using the WDM6 scheme provided better statistical scores for heavy precipitation. It is essential that the simulation environmental fields of the WDM scheme are consistent with the actual situation, especially in the vicinity of Hainan Island. Using the WDM6 scheme simulation results, the evolution characteristics of hydrometeors and latent heat in the cloud microphysical processes were investigated. From the development to the mature stage of rainstorm, cloud water, rainwater, graupel increased while snow decreased.

The release or absorption of latent heat in all microphysical processes increased. From the mature to the dissipation stage, all of the hydrometeors decreased and all of the heating (cooling) rates weakened. From the development to the dissipation stage, the top four heating processes were the condensation of water vapor into cloud water, water vapor condensed into rain water, rainwater collected by graupel and graupel deposition growth. The top four cooling processes during the development and dissipation stage were the evaporation of rainwater, the melting of graupel, evaporation of cloud water into water vapor and the enhanced melting of graupel. But in the mature stage, the top four cooling processes were the evaporation of rainwater, the melting of graupel, evaporation of cloud water into water vapor and the enhanced melting of graupel.

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Dynamic Simulation of Macroscopic Vulnerability for the Lifeline System of Beijing-Tianjin-Hebei Region

Wang Wei, Liu Chang* and Liu Xiao Ran

In order to study the dynamic changes of the macro vulnerability of the lifeline system in Beijing-Tianjin-Hebei region, we constructed an index system from two aspects to describe the spatial difference of the macro vulnerability of lifeline system in Beijing-Tianjin-Hebei region; one is sensitivity, the other is response - resilience ability. In this study, we evaluated the risk assessment of the lifeline system for the study area by the Maximum Information Entropy (MIE) model. The highest and higher risk areas in Beijing-Tianjin-Hebei region reach 50% and the medium or medium above reach 92%.

Meanwhile, we analyzed the space-time characteristics of the macro vulnerability to lifeline system of Beijing-Tianjin-Hebei region. The spatial changes of the vulnerability are different on the forms, extent and main driving factors and the cities of Beijing, Tianjin and Tang are more vulnerable. The vulnerability of Beijing floats severely and that of Tianjin, Tangshan and Langfang are above the regional average level. The vulnerability of Shijiazhuang, Zhangjiakou, Chengde, Qinhuangdao and Xingtai had a downward trend while that of the other cities floated around the regional average level.

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Identification of Underground River Flow Pattern using Self Potential (SP) and Resistivity Methods for Drought Mitigation at Druju, Sumbermanjing Wetan, Indonesia

Hasan Muhammad F.R., Susilo Adi* and Sunaryo

The drought has become one of the major problems of society; it can be overcome when an area has groundwater resources. This research has been taken at the Karst area of Druju Village, Sumbermanjing Wetan Subdistrict, Malang District. The purpose of this research is to identify the underground river flow pattern and the relation between the Wonorejo well and the spring water at Kaligoro River. The method used is Self Potential (SP), Leap Frog configuration with 104 measurement points and resistivity method, dipole-dipole configuration with seven measurement lines.

The contour maps indicate that the value of the SP ranged from 0 to 13.5 mV, assuming a low potential value (0-1 mV) is an indication of underground water. In addition, the resistivity value ranges from 1.7 to 29485 Ωm with the rocks as clay stone (1.7-112 Ωm), sandy marl (113-1818 Ωm) and carbonate/limestone rock (1819-29482 Ωm). The indication of the presence of groundwater (underground river) is indicated by the depth of the carbonate rock suspected to be the scouring process by water and this is also supported by the low potential (SP) value of the area. The existence of underground rivers is detected on line 1 and 2 at depths of 23.6 and 35 meters and line 6 on the surface. The underground river flows from line 2 to line 1, or the North-West direction and it is possibly end up to the Lesti River in the Northern area of the study site while the existence of spring that appears around the Kaligoro River does not have correlation with Wonorejo well.

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Flood Frequency Analysis of River Jhelum at Three Main Gauging Stations

Bashir Suhail* and Hameed Mehvish

The present study represents the flood frequency analysis for river Jhelum at three main gauging stations. The annual maximum series of discharge or flow data for 40 years (1975-2014) at three flow gauging stations namely; Asham, Ram Munshi Bagh and Sangam, were each fitted with 3 probability distribution models viz; Log Normal, Extreme Value TYPE-I (Gumbel) and Log Pearson Type III and subjected to two specific measures of errors in prediction i.e. R Square and Standard Error in order to select the best probability distribution model that fits the observed flow data at the stations. For any model to be best fit, R-square value should be maximum and the Standard Error Minimum. Therefore, for Ram Munshibagh station, Log Pearson type III best fits the observed data as it has the maximum value of R-square among the three i.e. 0.832962687 and the minimum value for standard error i.e. 1713.248299.

Similarly for Asham, Extreme Value I or Gumbel Distribution fits the data better than the other two models as it has the maximum value of R-square among the three i.e. 0.855940558 and the minimum value for standard error i.e. 1665.136373 and for the third station, Sangam, again Log Pearson type III is the best fit model than the other two models as it has the maximum value of R-square among the three i.e. 0.822241201 and the minimum value for standard error i.e. 1961.310317. The best fit distribution model at each station was then utilized to predict return period floods for each station for return periods of 2, 5, 10, 25, 50 and 100 years. The best fit probability distribution models obtained for the stations Sangam and Ram Munshibagh came out to be Log Pearson Type III and for Asham station came out to be Gumbel Model.

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Impact of Climate Change on the Mahanadi Basin – A Case Study

Samantaray Sandeep*, Sahoo Abinash and Rath Ashutosh

The Mahanadi River rising from the state of Chhattisgarh is an important river especially in the state of Orissa. Upstream of the basin (Chhattisgarh plain) experiences periodic droughts in contrast with the regular flood events in downstream (delta region of Orissa). Frequent occurrence of these events indicates a shift in the hydrological response of the basin attributed to anthropogenic activities causing significant Landover changes. The rate at which the global surface temperature is increasing, is going to have significant impact on local hydrological regimes and thus on water resources. Main parameters that are closely related to the climate change are temperature, precipitation and runoff.

The present work intends to determine the climate change impact in the Mahanadi basin through statistical analysis of historical climate trends using Mann-Kendall test and Sen’s Slope test. The test has been used to find out the magnitude of the trend over 19 years due to the increase in temperature and carbon dioxide emissions. Non-uniformity in rainfall has been found from the present study which is causing floods and droughts in many parts of the basin. The months of July and August are experiencing heavy rain with respect to the other two monsoon months. The climate is also getting impacted due to the reduction in forest cover which leads to soil erosion and siltation of the river.

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