https://www.irpanchumaedi.com/tag/gpm-imerg/GPM IMERGWowchemy (https://wowchemy.com)en-usSun, 10 May 2026 00:00:00 +0000https://www.irpanchumaedi.com/images/icon_huc8b49dbecede763dad003ee9bbdb929f_244914_512x512_fill_lanczos_center_2.pnghttps://www.irpanchumaedi.com/tag/gpm-imerg/https://www.irpanchumaedi.com/post/materi-keterbatasan-data-hujan/Sun, 10 May 2026 00:00:00 +0000https://www.irpanchumaedi.com/post/materi-keterbatasan-data-hujan/<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.9.4/leaflet.min.css"/> <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.9.4/leaflet.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/plotly.js/2.27.0/plotly.min.js"></script> <p>Ketersediaan data curah hujan yang memadai merupakan fondasi utama dalam analisis hidrologi. Namun di Indonesia, data pos hujan konvensional menghadapi berbagai keterbatasan — terutama di wilayah luar Pulau Jawa.</p> <hr> <h2 id="1-sebaran-stasiun-hujan-bmkg">1. Sebaran Stasiun Hujan BMKG</h2> <p>Berdasarkan inventarisasi BMKG, terdapat <strong>6.487 stasiun hujan</strong> aktif di seluruh Indonesia — terdiri dari Pos Hujan Kerjasama (PHK), Automatic AWS (AAWS), dan UPT.</p> <div id="peta-bmkg" style="height:420px;border-radius:8px;margin:1rem 0;border:1px solid rgba(0,0,0,0.1);"></div> <p style="font-size:0.8rem;color:#888;text-align:center;"> 💡 Zoom masuk untuk melihat nama stasiun &nbsp;·&nbsp; Klik titik untuk detail &nbsp;·&nbsp; Biru = PHK · Merah = AAWS · Hijau = UPT </p> <script> function initPetaBMKG() { if (typeof L === 'undefined') { setTimeout(initPetaBMKG, 100); return; } (function() { var data = [{"n":"Genteng I","j":"Pos Hujan Kerjasama","p":"Jawa Timur","e": 171,"lo":113.94690,"la": -8.34778},{"n":"Huta Bagasan","j":"Pos Hujan Kerjasama","p":"Sumatera Utara","e":null,"lo": 99.32000,"la": 2.75000},{"n":"Surat/Mojo","j":"Pos Hujan Kerjasama","p":"Jawa Timur","e": 123,"lo":111.95030,"la": -7.88194},{"n":"Niniari","j":"Pos Hujan 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Kerapatan Stasiun vs Standar WMO</h2> <p>Standar WMO (2008) merekomendasikan minimal <strong>1 pos per 250 km²</strong> untuk kawasan pegunungan tropis.</p> <table> <thead> <tr> <th>Wilayah</th> <th>Luas (km²)</th> <th>Stasiun</th> <th>Kerapatan (km²/pos)</th> <th>Status WMO</th> </tr> </thead> <tbody> <tr> <td>Jawa</td> <td>128.297</td> <td>2.765</td> <td>46</td> <td>✅ Memenuhi</td> </tr> <tr> <td>Sumatera</td> <td>473.481</td> <td>1.321</td> <td>358</td> <td>⚠️ Di bawah standar</td> </tr> <tr> <td>Kalimantan</td> <td>748.168</td> <td>842</td> <td>889</td> <td>❌ Jauh di bawah</td> </tr> <tr> <td>Sulawesi</td> <td>188.522</td> <td>1.023</td> <td>184</td> <td>✅ Memenuhi</td> </tr> <tr> <td>Maluku</td> <td>31.982</td> <td>213</td> <td>150</td> <td>✅ Memenuhi</td> </tr> <tr> <td>Papua</td> <td>421.981</td> <td>323</td> <td>1.306</td> <td>❌ Jauh di bawah</td> </tr> </tbody> </table> <blockquote> <p>Kalimantan dan Papua memiliki kerapatan stasiun yang sangat rendah — DAS-DAS besar di sana hampir tidak terpantau secara langsung.</p> </blockquote> <hr> <h2 id="3-gpm-imerg-sebagai-alternatif">3. GPM IMERG sebagai Alternatif</h2> <p><strong>GPM IMERG</strong> adalah produk curah hujan berbasis satelit dari NASA dan JAXA, tersedia gratis sejak Juni 2000.</p> <table> <thead> <tr> <th>Parameter</th> <th>Nilai</th> </tr> </thead> <tbody> <tr> <td>Resolusi spasial</td> <td>0.1° × 0.1° (~11 km)</td> </tr> <tr> <td>Resolusi temporal</td> <td>30 menit</td> </tr> <tr> <td>Ketersediaan</td> <td>Juni 2000 – sekarang</td> </tr> <tr> <td>Varian terbaik</td> <td>Final Run (koreksi dengan data pos)</td> </tr> <tr> <td>Akses</td> <td>Gratis via NASA Earthdata</td> </tr> </tbody> </table> <hr> <h2 id="4-validasi-gpm-imerg--interaktif">4. Validasi GPM IMERG — Interaktif</h2> <p>Geser slider untuk mensimulasikan <strong>koreksi bias</strong> pada data GPM IMERG. Metrik validasi (NSE, KGE, PBIAS, RMSE) berubah otomatis.</p> <div style="margin:1.2rem 0 0.5rem;"> <label style="font-size:0.9rem;font-weight:600;"> Faktor Koreksi Bias: <span id="bias-label" style="color:#1565C0;font-family:monospace;">1.00×</span> </label><br> <input type="range" id="bias-slider" min="0.7" max="1.3" step="0.01" value="1.0" style="width:100%;max-width:480px;margin-top:6px;accent-color:#1565C0;"> </div> <div style="display:grid;grid-template-columns:1fr 290px;gap:14px;margin-top:0.5rem;"> <div> <div id="plot-hidrograf" style="height:290px;"></div> <p style="font-size:0.78rem;color:#888;text-align:center;margin-top:2px;"> DAS Akelamo, Pulau Obi, 2020–2022 · Hover untuk nilai per bulan </p> </div> <div> <table style="width:100%;border-collapse:collapse;font-size:0.85rem;"> <thead> <tr style="background:#1A3A5C;color:white;"> <th style="padding:6px 9px;text-align:left;">Metrik</th> <th style="padding:6px 9px;text-align:center;">Nilai</th> <th style="padding:6px 9px;text-align:left;">Status</th> </tr> </thead> <tbody> <tr><td style="padding:5px 9px;font-weight:600;">NSE</td> <td id="val-nse" style="padding:5px 9px;text-align:center;font-family:monospace;font-weight:700;"></td> <td id="sta-nse" style="padding:5px 9px;font-size:0.8rem;"></td></tr> <tr style="background:#f5f5f5;"> <td style="padding:5px 9px;font-weight:600;">KGE</td> <td id="val-kge" style="padding:5px 9px;text-align:center;font-family:monospace;font-weight:700;"></td> <td id="sta-kge" style="padding:5px 9px;font-size:0.8rem;"></td></tr> <tr><td style="padding:5px 9px;font-weight:600;">PBIAS (%)</td> <td id="val-pbias" style="padding:5px 9px;text-align:center;font-family:monospace;font-weight:700;"></td> <td id="sta-pbias" style="padding:5px 9px;font-size:0.8rem;"></td></tr> <tr style="background:#f5f5f5;"> <td style="padding:5px 9px;font-weight:600;">RMSE (mm)</td> <td id="val-rmse" style="padding:5px 9px;text-align:center;font-family:monospace;font-weight:700;"></td> <td id="sta-rmse" style="padding:5px 9px;">—</td></tr> </tbody> </table> <div style="margin-top:8px;font-size:0.77rem;color:#444;background:#EFF6FF; border-left:3px solid #1565C0;padding:7px 9px;border-radius:0 5px 5px 0;"> <strong>Kriteria Moriasi et al. (2007):</strong><br> NSE ≥ 0.65 → Memuaskan<br>KGE ≥ 0.50 → Baik<br>|PBIAS| ≤ 15% → Memuaskan </div> </div> </div> <script> (function() { var obs = [210,185,230,160,90,45,20,15,30,110,185,230, 190,210,250,140,80,35,18,20,40,120,200,215, 220,195,240,150,85,50,22,18,35,115,190,225]; var imerg = [195,200,215,150,85,40,18,18,28,105,175,215, 180,195,235,135,75,32,16,18,38,115,190,200, 205,185,225,142,80,46,20,16,32,108,180,210]; var bln = ["Jan'20","Feb'20","Mar'20","Apr'20","Mei'20","Jun'20","Jul'20","Ags'20","Sep'20","Okt'20","Nov'20","Des'20", "Jan'21","Feb'21","Mar'21","Apr'21","Mei'21","Jun'21","Jul'21","Ags'21","Sep'21","Okt'21","Nov'21","Des'21", "Jan'22","Feb'22","Mar'22","Apr'22","Mei'22","Jun'22","Jul'22","Ags'22","Sep'22","Okt'22","Nov'22","Des'22"]; function hitung(sim) { var n=obs.length, mo=0, ms=0; for(var i=0;i<n;i++){mo+=obs[i];ms+=sim[i];} mo/=n; ms/=n; var ssTot=0,ssRes=0,sumObs=0,sumSim=0,d1=0,d2=0,num=0; for(var i=0;i<n;i++){ ssTot+=Math.pow(obs[i]-mo,2); ssRes+=Math.pow(obs[i]-sim[i],2); sumObs+=obs[i]; sumSim+=sim[i]; d1+=Math.pow(obs[i]-mo,2); d2+=Math.pow(sim[i]-ms,2); num+=(obs[i]-mo)*(sim[i]-ms); } var nse=1-ssRes/ssTot; var pbias=(sumSim-sumObs)/sumObs*100; var rmse=Math.sqrt(ssRes/n); var r=num/(Math.sqrt(d1)*Math.sqrt(d2)); var kge=1-Math.sqrt(Math.pow(r-1,2)+Math.pow(Math.sqrt(d2/n)/Math.sqrt(d1/n)-1,2)+Math.pow(ms/mo-1,2)); return {nse:nse,kge:kge,pbias:pbias,rmse:rmse}; } function sta(v,t) { if(t==="nse") return v>=0.75?"🟢 Sangat baik":v>=0.65?"🟡 Baik":v>=0.5?"🟠 Memuaskan":"🔴 Tidak memuaskan"; if(t==="kge") return v>=0.75?"🟢 Sangat baik":v>=0.5?"🟡 Baik":"🔴 Perlu koreksi"; if(t==="pbias") return Math.abs(v)<=10?"🟢 Sangat baik":Math.abs(v)<=15?"🟡 Baik":Math.abs(v)<=25?"🟠 Memuaskan":"🔴 Tidak memuaskan"; return "—"; } // Init Plotly Plotly.newPlot('plot-hidrograf', [ {x:bln,y:obs,name:'Observasi',type:'scatter',mode:'lines', fill:'tozeroy',fillcolor:'rgba(21,101,192,0.12)', line:{color:'#1565C0',width:2},hovertemplate:'%{x}<br>Observasi: <b>%{y} mm</b><extra></extra>'}, {x:bln,y:imerg,name:'GPM IMERG',type:'scatter',mode:'lines', line:{color:'#C62828',width:2,dash:'dash'},hovertemplate:'%{x}<br>GPM IMERG: <b>%{y} mm</b><extra></extra>'} ], { margin:{t:10,b:50,l:52,r:10}, yaxis:{title:'mm/bulan',gridcolor:'#eee'}, xaxis:{tickangle:-45,tickfont:{size:9}}, legend:{orientation:'h',y:-0.3}, paper_bgcolor:'rgba(0,0,0,0)', plot_bgcolor:'rgba(0,0,0,0)', font:{size:11} }, {displayModeBar:false,responsive:true}); function update(bias) { var sim = imerg.map(function(v){return Math.round(v*bias*10)/10;}); // Plotly.react: update seluruh data chart - lebih reliable dari restyle Plotly.react('plot-hidrograf', [ {x:bln,y:obs,name:'Observasi',type:'scatter',mode:'lines', fill:'tozeroy',fillcolor:'rgba(21,101,192,0.12)', line:{color:'#1565C0',width:2}, hovertemplate:'%{x}<br>Observasi: <b>%{y} mm</b><extra></extra>'}, {x:bln,y:sim,name:'GPM IMERG',type:'scatter',mode:'lines', line:{color:'#C62828',width:2,dash:'dash'}, hovertemplate:'%{x}<br>GPM IMERG: <b>%{y} mm</b><extra></extra>'} ], { margin:{t:10,b:50,l:52,r:10}, yaxis:{title:'mm/bulan',gridcolor:'#eee'}, xaxis:{tickangle:-45,tickfont:{size:9}}, legend:{orientation:'h',y:-0.3}, paper_bgcolor:'rgba(0,0,0,0)', plot_bgcolor:'rgba(0,0,0,0)', font:{size:11} }, {displayModeBar:false,responsive:true}); var m = hitung(sim); document.getElementById('val-nse').textContent = m.nse.toFixed(3); document.getElementById('val-kge').textContent = m.kge.toFixed(3); document.getElementById('val-pbias').textContent = m.pbias.toFixed(1); document.getElementById('val-rmse').textContent = m.rmse.toFixed(1); document.getElementById('sta-nse').textContent = sta(m.nse,'nse'); document.getElementById('sta-kge').textContent = sta(m.kge,'kge'); document.getElementById('sta-pbias').textContent = sta(m.pbias,'pbias'); } update(1.0); document.getElementById('bias-slider').addEventListener('input',function(){ var b=parseFloat(this.value); document.getElementById('bias-label').textContent=b.toFixed(2)+'×'; update(b); }); })(); </script> <hr> <h2 id="kesimpulan">Kesimpulan</h2> <ol> <li>Kerapatan pos hujan Indonesia tidak merata — Kalimantan dan Papua jauh di bawah standar WMO</li> <li>GPM IMERG Final Run merupakan alternatif andal untuk DAS tanpa pos hujan memadai</li> <li>Validasi dengan NSE, KGE, dan PBIAS wajib dilakukan sebelum digunakan dalam pemodelan</li> <li>Koreksi bias disarankan jika |PBIAS| &gt; 15%</li> </ol> <p><strong>Referensi:</strong> Moriasi et al. (2007) · Huffman et al. (2020) · WMO (2008)</p>