Flood forecasting by reach
Ensemble weather forecasts drive hydrological and hydraulic models to give the probability of exceeding warning thresholds at each gauge, by lead time.
GCRI · Water Nexus
Forecasting turns observations into lead time: hours for flash floods, days for river floods, weeks to a season for drought. The value is in the lead time and the stated skill, not in a single number.
Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support peringatan dini and planning across hazards, markets and health. In water, climate and hazards and infrastruktur kritis, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected sistem. The canonical description is on STEM Commons.
Ensemble weather forecasts drive hydrological and hydraulic models to give the probability of exceeding warning thresholds at each gauge, by lead time.
Sub-seasonal and seasonal climate forecasts are combined with current storage and soil moisture to state the odds of deficit categories for the coming months.
Operators receive inflow distributions, not point forecasts, so release rules can be set against the risk of both spill and shortage.
Hujan, genangan banjir, dan kelembapan tanah zona akar di Lembah Sungai Nil, dari NASA GIBS. Setiap lapisan memuat tanggalnya sendiri.
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Terbaru yang tersedia
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © kontributor OpenStreetMap (ODbL) · Natural Earth
Memuat citra NASA terbaru.
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Air saat ini, dibingkai pada Lembah Sungai Nil.Produk satelit dan model sebagaimana diterbitkan NASA, masing-masing dengan tanggalnya sendiri. Produk ini adalah indikator, bukan penilaian atas tempat mana pun.
Lapisan dan tanggal
Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in register sumber.
| Data | Peran | Label |
|---|---|---|
| Ensemble numerical weather prediction | Forcing | PRAKIRAAN |
| Seasonal climate forecasts | Outlook forcing | PRAKIRAAN |
| Real-time gauge telemetry | Asimilasi data | TERAMATI |
| Snow and soil-moisture state | Initial conditions | DIMODELKAN |
Layar yang akan digunakan sebuah tim untuk mengikuti metode ini: ukuran, status, dan keputusannya. Nama adalah contoh dan nilai bersifat ilustratif.
Water · Hydrological forecasting · Run R-0460
held-out period
| T+2 | 0.33 | 0.29 | 0.19 | Skilful |
| T+4 | 0.20 | 0.26 | 0.33 | Skilful |
| T+6 | 0.26 | 0.09 | 0.39 | Marginal |
| T+8 | 0.33 | 0.30 | 0.28 | Marginal |
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
| Pemeriksaan | Cara pelaksanaannya |
|---|---|
| Hindcast verification | Skill is measured on past forecasts against what happened, by lead time and threshold |
| Reliability diagrams | A forecast of thirty percent must come true about thirty percent of the time |
| Warning performance | Hits, misses and false alarms are reported for every threshold |
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
Rain becomes runoff by the curve number method, drains each sub-catchment through a linear reservoir and travels down the main stem by Muskingum routing; play it to watch the flood wave move and flatten. Teaching model with stated illustrative values, not a prediction. The map on this page shows The Nile Basin; the model itself is not tied to a place.
Kendali
Pembacaan
| Hour | Outlet (m³/s) | Unrouted (m³/s) | Upper reach (m³/s) |
|---|---|---|---|
| 2.4 | 0.0 | 678.9 | 0.0 |
| 12.0 | 308.3 | 1,151.4 | 181.8 |
| 21.6 | 452.4 | 35.1 | 73.7 |
| 31.2 | 363.3 | 1.1 | 11.1 |
| 40.8 | 198.8 | 0.0 | 1.5 |
| 50.4 | 79.3 | 0.0 | 0.2 |
| 60.0 | 25.4 | 0.0 | 0.0 |
| 69.6 | 6.9 | 0.0 | 0.0 |
| 79.2 | 1.7 | 0.0 | 0.0 |
| 88.8 | 0.4 | 0.0 | 0.0 |
| 98.4 | 0.1 | 0.0 | 0.0 |
| 108.0 | 0.0 | 0.0 | 0.0 |
| 117.6 | 0.0 | 0.0 | 0.0 |
| 127.2 | 0.0 | 0.0 | 0.0 |
| 136.8 | 0.0 | 0.0 | 0.0 |
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
Di seluruh enam pilar, dipimpin oleh Nexus Consortium
Disusun dari profil bidang Nexus Standards profil area Standar risiko air: bahaya yang disebutkannya, sistem dan sektor yang menanggung kerugian, kendali yang meredamnya, dan data yang mengamatinya. Pilih komponen, tentukan bahaya pemicu, dan telusuri kaskade langkah demi langkah. Tampilan Peta menempatkan proses pada contoh geografi dengan data terbuka. Setiap bobot keterkaitan adalah asumsi yang dinyatakan dan dapat Anda ubah. Model pengajaran, bukan prakiraan, penilaian, atau sertifikasi. Untuk memodelkan tempat pilihan Anda sendiri, buka System Builder.
Bahaya
Sistem dan keterkaitan
Kendali
NS-0 Anggaran dasar dan prinsipNS-1 Observatory UNI dan katalog artefak (tata bahasa v0.2: segmen domain, sektor, wilayah, dan lingkungan)NS-3 UNI catatan risiko (tata bahasa, pemetaan ke sistem lama, kewenangan alokasi)NS-3b Profil metadata dataset dan produk (STAC, DCAT, PROV-O)NS-9 UNI kontekstual (penerapan, instans, instans program)NS-10 Kebijakan dan klausul (kewajiban yang dapat dibaca mesin: definisi, pengikatan, evaluasi, dan penegakan)NS-11 Verifikasi dan pemantauan (bukti, atestasi, pengamatan kesesuaian zero trust)NRS-U28 Semesta : Water NexusNRS-H61 Kelompok bahaya: Kegagalan berantai (NHS-1 sys.cascade)NRS-K42 Profil paket domain: Daerah aliran sungai lintas batas (DOMAIN-042)NRS-N1 Risiko dan ketahanan airNRS-N7 Keterkaitan nexus air, energi, pangan, kesehatan, keanekaragaman hayati, dan infrastrukturNRS-H62 Keluarga bahaya: Peristiwa majemuk dan multibahaya (NHS-1 sys.compound)Pengamatan
Contoh geografi
Iklim dan Bahaya stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.
The sectors hydrological forecasting touches and how strongly they depend on each other: what a study has to observe and model together.
Tanda tangan
Air dan mitranya
Air memberi irigasi bagi pangan (0,8), air pendingin dan tenaga air bagi energi (0,6), dan aliran lingkungan bagi keanekaragaman hayati (0,6).
W · E · I · F · H · B
Model, asumsi, data, dan ketidakpastiannya, dalam bentuk yang akan diperiksa ilmuwan atau insinyur. Strukturnya mengikuti metode yang dipublikasikan. Nilai yang digambar bersifat ilustratif, bukan temuan.
The couplings between the systems in hydrological forecasting: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Laporan is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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