WATER RESILIENCE ASSESSMENT DI HULU DAS BATANG ARAU: ANALISIS KESEIMBANGAN SUPPLY – DEMAND BERBASIS PEMODELAN SWAT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Perubahan iklim dan aktivitas antropogenik menyebabkan tekanan signifikan terhadap sumber daya air, mempengaruhi keseimbangan ketersediaan dan kebutuhan air di berbagai daerah aliran sungai. Penelitian ini bertujuan mengevaluasi ketahanan sumber daya air (water resilience) di hulu DAS Batang Arau, Kota Padang dengan menggunakan pendekatan terpadu berbasis pemodelan hidrologi SWAT dan analisis Reliability, Resilience, Vulnerability (RRV). Metode penelitian meliputi: (1) karakterisasi morfometri DAS dengan menganalisis 19 parameter morfometri; (2) pemodelan SWAT dengan kalibrasi-validasi yang menghasilkan nilai performa bervariasi (R² = 0,54-0,97) dan sangat baik (NSE = 0,79-0,89); (3) analisis keseimbangan supply-demand; serta (4) evaluasi ketahanan air dengan pendekatan RRV. Hasil analisis morfometri menunjukkan DAS memiliki bentuk memanjang (Form Factor 0,25) dengan kerapatan drainase sedang (1,40 km/km²). Pemodelan SWAT menghasilkan debit andalan Q80 sebesar 1,51 m³/s yang masih mencukupi total kebutuhan air 0,63 m³/s (domestik 0,0234 m³/s; pertanian 0,4252 m³/s; industri 0,1811 m³/s), dengan Water Availability Ratio (WAR) 1,427. Analisis RRV menghasilkan Indeks Keberlanjutan DAS (IKDAS) sebesar 0,85 (sangat baik), didukung oleh keandalan tinggi (1,0), ketahanan baik (0,84) dengan tutupan hutan 87,9%, namun masih menghadapi kerentanan signifikan (0,65) terutama akibat tingkat erosi tinggi (385,6 ton/ha/tahun) dan area rawan banjir (34,63%). Rekomendasi pengelolaan meliputi teknik konservasi tanah-air, sistem peringatan dini banjir, perluasan zona riparian, dan penguatan kelembagaan pengelolaan kolaboratif untuk mengintegrasikan kepentingan berbagai pemangku kepentingan. Pendekatan RRV terbukti efektif untuk evaluasi komprehensif kondisi DAS dan perumusan prioritas intervensi strategis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it