WATER RESILIENCE ASSESSMENT DI HULU DAS BATANG ARAU: ANALISIS KESEIMBANGAN SUPPLY – DEMAND BERBASIS PEMODELAN SWAT
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".