BIMBINGAN TEKNIS PERENCANAAN TANDON AIR BAWAH TANAH DI SMP NEGERI 22 KOTA MADYA MALANG

With the vision of "Realizing Students Who Are Excellent in Achievement, Skilled, Praised in Character and Cultured in an Environment Based on Faith and Faith" SMP Negeri 22 Malang is located in the eastern area of Malang City, bordering the Malang Regency area, precisely in the hilly area of Villa Gunung Buring, Kelurahan Cemoro, the enclosure of Kedungkandang District, Malang City. This school was founded on October 20, 1999, with a land area of 5,597 m2 with a total area of 2,160 m2 consisting of 36 rooms, the number of study groups is 20 classes, each 6-7 study groups, the total number of students is 610 people and is supported by 35 educators. person. As we know above, with a large enough number of teachers, employees and students, it needs quite a lot of clean water. Whereas the water source at the school is obtained from the distribution of water from the housing whose discharge is very small, so it requires a water reservoir to accommodate water, currently the school only has a water reservoir above with a small volume capacity with a small pressure so that the water is unable to meet even though it has been assisted by a pump due to small water discharge.For this reason, the school has a plan to make an underground water reservoir with the aim of storing water which is then raised to the upper reservoir with the help of a pump, that the water reservoir planning is carried by the ground.

Height–Area–Storage Functional Models for Evaporation-Loss Inclusion in Reservoir-Planning Analysis

Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height–area–storage (H–A–S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H–A–S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H–A–S models underestimate the exposed surface area of the Pong reservoir by up to 11.19%; the bias at Bhakra was much less. The GRanD H–A–S model performed very poorly at both reservoirs, producing overprediction in exposed reservoir area of up to 100% and 415% at the Pong and Bhakra reservoirs, respectively. Analyses also showed that up to 29% increase in reservoir capacity is required to compensate for the effect of net evaporation loss at low demand levels. As demand increases, the required evaporation-correction capacity decreases in proportional terms and is indistinguishable for all H–A–S models. Finally, recommendations are made on using the results for evaporation adjustment at nongauged sites in the region.

Multiple Regression Model Using Performance Indices for Storage Capacity of a Reservoir System in Johor Catchment

Storage–yield–reliability (S–Y–R) relationship is useful in many areas of hydrology and water resources. With the availability of such a relationship, reservoir planning analysis can be implemented much more rapidly and at a much shorter time needed for sequential analysis applying time series data. Existing relationships have been developed mostly for over-year capacity without considering both reliability and vulnerability performance indices. Hence, in this study, streamflow data from Johor river was used to develop a predictive relationship for total (i.e. within-year plus over-year) storage capacity involving both reliability and vulnerability performance indices as for use during reservoir planning. The reservoir was analyzed using 1000 sequences of synthetic data having the same length as historical data involving both time-based reliability and vulnerability performance indices by modified SPA. The model was then calibrated based on the mean of 1000 simulation results. Subsequently, the performance of the model was observed by comparing the model’s results with simulation outcomes for study systems. It was found that the performance of the model was very good in reproducing, the total storage capacity.