{"id":80,"date":"2026-10-17T11:31:34","date_gmt":"2026-10-17T11:31:34","guid":{"rendered":"https:\/\/therisk.global\/systems-modelling\/?page_id=80"},"modified":"2026-10-17T11:31:34","modified_gmt":"2026-10-17T11:31:34","slug":"uncertainty-and-validation","status":"publish","type":"page","link":"https:\/\/therisk.global\/systems-modelling\/topics\/uncertainty-and-validation\/","title":{"rendered":"Uncertainty and validation"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/systems-modelling\/topics\/uncertainty-and-validation\/ \u00b7 source authored \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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aKXQs1DonQhbB9gRUK3Q2+iQtv3Dnj1yPw+K4f39YOjocodQTi3hpbBfNQ839na3+Y7j6DmRhfpzeXTlyTshSwfOk7kWC09uc6PRG\/fTykaFKFs9NlqtlTxdWfJWlSyRBSgP03QqeHZ\/v+tsbO3ynbV4167jLG5lDbJv9bEJQR6cH09mJOvPgnfjvunfuG8GxVDeIUTubFXprSBrtrgcpJ44tY\/muFO8GZlw4d0amO8VWwd8G1HSkKj9xwsgTdwsT0Uq7jIOrOWPs+RTnmY8F3duwOLYHpGZUwLfWdOiVpMnQRryP08OD9JJliYcEcPZEDBvfek0rb7VvHH\/SaPEfpcYl8KBgYMqKurlRZojGzpP45jnfVitpTR5EcixGGogdf0XwKFcBgVtZ7Eg3R2+VWGgHAiHlMyLaJyw2AMhh3qUSAoByTVPRMTiwrPSCUgHURMm5\/mnNI6CO89K0pZOshbLhNXMmaOATfftRgohZm565czFJUr3KxoQTuW0aFjNmSsfK8Dg\/lPAiFY1AEo+\/ZnLoQ5Uv1lRM360yuqQuHOjQhJ5M6c\/8wazoYlKiNx25kC7Ib+MGgICtT6FJjNR0odMacEn9gfm3N9bsDCGiKePsDVJZ7bTSl0meu+TfoqrJze7JOBG64QMPIfdAZ1Z3XZRtk2wnwWe2Wh0h3RfWblRI8vNcwBjhWW5WUvg3zgLeZePKsg5YVm1DCdl0RM3ZmLhyE1uEcshtz9XKk3MUr5EC+ZMeNXkEXmukS2Gjy+FfIFzrh9UmhTZH9IDZsOpXyllK5Ro2iHzmIlITEPuzUicJmP5fEskZLOWwLVFMlaIc7ztttqo3RCUCb8tnOVNeuLMC3l70RNfPb0UtBrYghiHduGoi+yJTj0As4\/AV0vDU2s1HLVQjyPNWXyOaz5FoGzJQue7FgEw\/CNNuGf9fnSm9CGOn5pcNS+WQxRQZQWAF3v+yPR1FuQVsGvUtq2mRe2Ux5sE7bXohj9sEXGA85AJ7l0K2ziPW211dxKehCsyOLI\/i5VrOuECVxS33SPruXSs4AYZHJLj4XKlCMzUBnHmPLcrCFiY0CnJlRAwlYqNk5pW44iec9sCGS6K3VvIiAK5YD15HF0U6qDN1krBCwF6DHCI1+UIWHLDoCIU0Srsfab+3tY6BiTJmEvh6q3omy+21Q2tSvT7rCgL6iHSUrPykOLyg\/T4OA15v\/ZmO56WbKtjVM6uMBga9bEIR+\/oV+W4BPANyYmRkkWJhbKqKinmY56EhYXyqioZNMog8ZuoVTjhoN3GYwtkVMaHHMXUIKeqEkGRzXJAavXKXAwx1WuWc3pYH0b\/cM0SCXYR84a4SMM7PcdQQywMyNwfLSPFXlsuaZkOmIR+pkrofIx1JCzrx2Jfcetl5ZzCznZBcwclZviGgLSQA3hL1+k+iCjmheUAyZ8mVv+mD3hYOpL4CqTqJbVkg0azJ4Le9G\/c2UQUnmX5SuSmyqvrM8g5E\/xwwsb8VSQmLKtVKVXY6HxBTmsi4e\/CFkTJABidX3nCvSJR6Ak3ComYFJ5wxaQgE3b73RPAsPpO+K3whMtvBcnZTD5BU5MbYfVlx59BzqwgmSjq30kGqm3CvZErqx9cTLYskk2TCMssZ5DplkWQais8m6+ZZPkddEwsy9F8T2I57iiKBc8roJRUeMkzSi1A8uAWl0xsh0D\/PTkMoDVIxvMohWmRD1A9CVhwySEtZoIXgsg\/8sr+4ZWM1HVd\/YG93GlbwGUlMRs\/XSRmYyxUloEGPybxncfXbjrQHIL5l5ktArpE3hduc+f+Hha7uExzsbY8foU2zRJ54InE5g65ipJwbVH4KBeCpMmDXCyPWAvFGjyUHQRIaJE0Y0Ek1g8ozSynjxOFsiD7sXwO7GY8BogQ9Tv6PZrwfGz13T3PfQF78oLLVeOJyCNeeG0yztNZvTSJU69NLiOvQy5iDgqbRTrNA94CtTFr4R9ymzl+iL\/fB8K9GlIG8l+gpVh5WbKFREk1iZaCRC7loIIEmlkl6Z6WItX34sEl9XxQLm86HsecWs+buoJByofuVfO5NXzuSHE3iJJt8zN5j4I1hFZi6fpESAjKYWOueCwaUkRcXuQgblSDGUuO1XIdl\/xWXZxCXZjCDXgcF3VSACWJoN2jvxJW18bgpY5PQgWCgPt7t9PdITltk5i2SUTbZArgTIo08+ZOj\/JS9SduUlap8uRKIh\/VU7XeD8LHkPJB3twekgAqzZu0UuDJaNvPeqGfwbIEclXjjaTJSsWd3NlISFRPanacjcSBmrqLqeImxUx48UYCuK8XbSQkWDiLMadTvUOmsEPeiv5boZbMU8xp1ZMvQNSXH6XS1E9R92UZ8sVZElEHgpZfB19AZvxTe+5Ltc0eir0LDtfJFwI7yguEC\/3CGwPvCLwb5BOC2grEWhbCwrYJ3WCJNLhF9PfjSEPbuShTgLurnhfGUX703JqAWqrtPQmpNQBNBYBFBT\/XljUBqC4h4WcqFABdW7YOQR9kewqErq13GYY+mtGpYOQyAe2QAtZjIVUbcIWhF6CTLgqrVOj4yOEr1Il6HbUzPq0O+TWvq3yogwyaH2fiodbdNd\/YNpRSgAUcid419yPk\/Z6JQSQ2tof0i6iOeyQ29vSJ3+y0277KBYp1y\/lW5OrqXEqVEKvTGoI609aQTvkAP+0Om\/rxt+FGd2d3UXBXzo6pX2JMHQJCC1RNXOC7FWru8NlZfB98GdKC61ulAPWTU7cALlhFDpAKPgv3R4u7PxTpfQdI5PzKsy5YwS1A8m5ceNSYnnor0T31XuJ86h0QP3jAQ7mo8O4RRxw15BNsJOQT1UbIJ1UT8GK0AK9GA\/CqEUsDOOh8gFKWQCLkEwkXypO+qJhPUjnGmR9y+COvGkEBma8utJAD7STcOG2DLMW9jNq048tbSWJ+g7aU3nB1jXE3Z8lYvXFXmTHp16TXrdGFjHI3aXVIQtskp6zcqjFt+3GPu4kfKyEFd+N0EGsRRF+9wl2G7Q3aQ60wlFBgoek9Lxvwo33Y+61WVVdUrysa7pd1dcq6chqh9lLeMnsu5wN+abLJ1LTAL803maFdiZMHeoxuzhRdLNxMFLUp4EBwMNomicmWzmnbz3tyijW+k2t0J9Zznw9JBNs\/dvPxhUOmNNIAYcuPpYY6Zc2OH7sJnVaiIJAE9aZ+CPWxZtPng2gQbmwNUaKMj83OECTJ8rk7BDQtUXfzxMsJ90KSjy88VY7oMqQssHAWOE3p1TSj3BfAKGE08YWrtiKFeQPbBGqyJ485HE7CSCJHmlOOnWLQm4TEVNc5yHHbxeU5jhc40VDjIIelravlNMpPUt8MsaIOf7E0KbKfpYRMT3hA5YdBOISZJhm1A9Bx4M6GfmjaAag4MEzBB0jpDlsJpuAD9DjrTZ35lGZ+RMNmByZJ9YtOe1vtdj\/y2v7KLn8WleK+pGOV8rxtFRkD\/sTB+hyaVbPMdyHzWRTHJ5yFb9H2NBEKk1g4\/mcB\/OXlu\/QyCkM0sMF7FHPJJMmlSV2WZTwJ0TDC\/gzCt5KxzuA4HAgX5QInoAXWJm3SIR3HPxBAfMan4g6wpnx8YXdIl2w5loI15vd6ZiG15YwUSnkdiVZf6y1K6HMgLzRgCMB1W37Gm1itw4CBiiADVR82gK1dsedxRDBP0LAbpHGaU+uG5XarJfkvVlOApke5oszGBZhkU8FD7C7MkSzp3N8PoP22XO87qQQ6EdSyQMu8TQIu57iSt4OoQlIXc7Bx9OaLBZwe+qCRFOiD8v6LFNohsXyNAaMFi21VXY+0uYnqvj3tC2ZPJR\/Jub\/nzI6cBZ4goNB476g8OUiZicERG\/DhkIJGy4APpYySA25+wX5e4nbBCHc0ODBApLwjLqBmeUvkSrpp86bVspqx4ywA8k6yAR\/SHI7aNR2IAZpiDQk+dPRDVz9s6YdtsGoS7hW\/owPhorUcEcjUIqDye0Vk3S5YXg62h0MlwbivpD+GSv9HVq5UxSuGnXvXfxbwvmid8F6Hb5lMrfKSOIFNHnBasbo4zZXghkP\/nlEK5gJ3lPsTQTGpnCQGs\/395yf7O2GOM\/8+YEM3nkql3MXil0o796Uzf+nmXFzyCQexYjXEOClvu3gqDLgbT4W66LSKpUZPy8sA7DmwZMgnFRUHGrRsCSQzDZITfQcqyMyG7mRIclq9chLTBK52vCXzZqeXuElfpjQ7Qy9xLyNfnoNIK3XGnh03I2ezS0JqqFHhY3GdC3u1Ttp0c2+n3XYcB7W1fD5ggC2fMnsQkpCEQ9KBrQgTyvXm4CWKEtC2HywNM9DDzPSAgiGZ6CFnMNQxOaIdmLWJRrJkiStaJgwylw\/JeZVQagQpfo0DWNGY3g2soGU1r4b39\/iojDWH\/hE979eZQ\/gmDTz77guvLRNUgb56lTaP+k0ahvbdnR2vI\/XCoZvXdOIm+51+5vJNewI4miPZznDD77c1tnQZ9TrO\/HqNLqB93YLszmb52W13CZSS6Y6zGNO\/uH0nwcAE\/8Aoq6NLrh05iZCCg2NRYsnmq0RtV2058yPq7uw03e2dDaMj1xsddxd1oSdwHfJXPE5nBhKCnhlo5vJmpzdxk\/4EdiG+Dj0oAV14p\/HRdz1q1KKhwRFtLxYLPgjU1hqToxWbCn08KPuBtxpmVGcUBB5zeRht0aNb\/U7bEz260+9seZ1dZ8N+y\/tdd9uT5\/HZL8KZ\/yIAQ4tG9i\/CLdIRmOG85TIZXr23XH0FCK9PPb4pDH\/51Hd3diuaNKFtP+l1d3b9RG\/5fN1iJ61Oe9vZ7HaVBmhcZWx1lnNubzubXGWMaLzf7v8C+wDh950bR8IsEG903I7jeGYWNIU2M7VULp8NElwBzC0vhojkDhxyHDdlFScTLfZpmxzQNnkjFO10SefA2ep0gUHjdfZI4W0vyJuVqX+uSsVa0oR2twxajbFKVlpOzMXm1m6bHGx2dvcMKeIrIcm6oppBKF2NNoW7rlDTx+nBZndTuAVh9EW7xUlCWy\/aTcDqYyYo6yX9tldWlFS1MLwwheNAzjShI4GnO1l1QybVDTkDrOQVAN4oGR\/EEU8E4m6Of1H1t7unNoeE0MKdRaG4dBz\/wMizvZTnkoPHA8fx34hKWVjJhkJ+EwX8U3TL4xOQdN3fd0hXT4BRy8XGGwFsWCPpAJLgwNzKXjyjVBHAt6rJZ5QyZ64+U+7rdMokj\/mHoPY6mdgoDaarpDUgiqkYVAai\/amayove7jZC1R9a7Q+Nq55FxTE7tn+IwdbQMQ1tOP0hgFb5gWYxjGIOeOsgucVBn5ZLfdo\/9ZJi3ibf7Dr+n7ghbMzflLVvdv0\/y30WJfZFBas7BGxpzHfmOBvuC\/+VsP\/Ue0S1I\/qdPa\/TVi2IfqfrdXaxZtGHrQtHc8eDJ1m62livmcnvGVxsdpsjYfPWJe7EjUu3IAeb3aZ9CTW3BKYYOH5eLy5z2bx1sNl1NqHwSNhYVdMWrQuV6CiSDUj7M0DdX6Hx4S08\/gVWozGDTz847ewaJ7hIKvB86RYbEiHqu7\/teOX8vRFEIJOp3\/U67g5OWJcw2lUMfkSY2O33\/g95P2XpzO4S5mx29\/Z6JWrJms1FCaPK9u8qhgW2oO5g6IN6BD7GMy3gq+OpVshHbBoLSxLiFWdalv1ItfJohRhilr5+cIVX1bE2U8jueh9rr95H72M1hpRpDgLJjbaumtam1eT4y\/A3wd+8Knm9quSJwGwg6m1am6p7ZU0o+H1YZ9Ny4Ru\/NeDbNKlqJ7Fc5YjWekum9PMgGvoxG0RD+hdStsAscKcFD+G9lH5M6fyK33kR+eAJIjxOfniM3HkJufVyUgivTaCI9xdoZEZeTJjw2gsfKqdT\/4w3m\/4rZQQ5dapay75eKDHWs1eVpEidxld8iZfKCSsni7tZHrUY\/C60id4t7+31y3r2a2Z9gkJ1l9FISGomZgMkuIB985dw5iGPueCNzyrVP+OtVqUILrU3jJVH5OYWRgcUuUSOhftBctykxiOssfuBCBdO0Q8i3Dsi3FuHzCdpyD0rSPPC+r9WfeRy8vgTqo58WdWR1y3VdL2fkxUN3MJUIDORfoFFwHHmaebIsXd94TJBM56jsVgSaJLzNdyifElPSVVWCLpVZSgB8P8TM3cBRoLOqgpVlg+8\/0D7w+ZkjqyX04wF\/CBNQEUF1d\/QGwuoM0+msYiy+O5lnF0ylb6QHJDFumXBISB6BRNa8oyjyRg5oY8tj3AzFoOd+L9YJ8MKxrg\/nuIE+lwhIz98rpGRH77myf8LNiFzw5zNcFZtHCSwsMgP8gNGE9zgkINbyvRpzNkM5gZmA5kKasg5m2k22UISBuwhaw5qJTlNkD1HYvpj4weJdMVwZWnG1pIxp1Ksj0AG0Iv9CEiNKUDDfBBtbA8Xwh3n\/I5OK\/I0pLDbAxpqFsV+d2enr6vt7Op6vZ9oSdLx0ca2H8g2s+bWsLfXb3vH3A5JPsiG8AP8c\/jTHSIhGbqgX9EX7o9eZYMf0Ci3QxIQTN4CHACIwOiWBotqTqsdEeXyftE8E661+R3JKpECHBIDNhKRKYGqM2MEuZvIMcSDXMoTmjCCksEQDQ3uQNdb\/qAYBR2vjdwOyWP\/IfnqkDClbX\/a++FP9TRNKB+EGz+a0yEZ03gwGd7fS120sSE5h5IZbTE\/61EmtQ3QqxUNmxlkPuop9sHRPv2xVC6AcgGUK1ksV3TaDKDclS53VS8Hmc7p0caP5hVks+NBMjgfQtec3tiZwwud1KywqwUIK64YbHNKcasCRoXSP28inFIwhN\/xIJTXLSqIuAEow0BFvlFRPeNCy+SCW1PLDU8OnpqHdDjHU2Toj8Bpyh+eppgC28D94ZCIyoOCG0RKqBIplWIgwIgH0SAfDhfVKZpS4Aq6HxwC5ym6XVNwOghlQex+NjVOPQdoouHi8tRV0xwoDdoz3qM\/eE2+JkyeMTKfP\/88O\/Qzsp6jkf15wIduIfbBQYKyovmMLG3odB0jMtHHBHG3Vo5\/FhXbQ7M2JVbUP+P7P\/iGu6fUGbW\/AAFsTCjZq2NDkFwiRCYPtshNFLZilHd81or3aVfyySXqaVAHsUQ9NTLaik2r2WQzcsz3fDNyHKQGp\/0p3EWUdk2kLqQ\/NiMtOpmS\/NdoIyQJ\/obD6oho7aY\/HzCppgaTSnti4Jqq6e7tbRo950iu7b9ol3glrygbbtyMuaTiNA2rcrQXywsidSNr3GYso78MWMXkYouKTKmVM45\/bqqO4shIQpHOIjldGgxzSGyyFbZ225s5nDpMC3gU2509SFJgAIkwIMeUmk9dbCDPH1CoIUUCFajTzSkJ5FtTvmUVZf6iTZC6bQK3Z+qQicFe099a8hviCeMHfDq5QewX7VbmbOaOQ46q2iPNoqsyTWSmK1OlxQ7RgBfGfV5LD3S6L7nHS1Pe7JB3VLF1wSMl+Yo+f0TVx467VXGmttwumW6+cJwN+43Y7\/Q7Ltyk5Bse0G+07X\/rXfvfYPW\/ysP+u\/0Xt9+R6\/1O\/9umfQ3c6o4DwAHn+XUJZGplXytIMVCiL1S453Tsz3iPHvkzru9BtMbh9Mq\/4T167t+Uio13jNo3\/Ne4GTu\/xuSU07d9JCQTwsiMkztGShvkG95SjAg1Uc3y04y3YL5xBXHOK237U0XlnwJOLT0s1e69K06BY2KD1XTzhm\/kmm8yJe8ZNRgn0MCMb+QOfEo4zTem5FrQUw40SmVtBypIM9G7Lq1QZ6KpPSR9YfSKN6\/FYCaGGwknnxl9z+R7swMp6KWG9VrKO9AXtn\/RVM+fq+TPbP+guW2M47UY4PmXNXWHzlAuyxdGPjOUYS0vHPTmgNPXYvBNarccVIRwWW1hyJy\/QsbCveDjKPnEwNECbtM\/EpqLDdvda37bcLuGd5g3ANre8F5Zr\/+GN9G9lTtJb\/hZah\/wwRs+bP6REHxqgtObwmV5oL6UyeSPhKjz9+lwo+uoftkmaTxJSvAnkHaqqaBpB2AD7bPSakK20A3iYsCH9\/dKoONo+ZDh2FIz+TH\/hGUDZTbKnWHJBK88H6DuEbxb6Jyzj7Xh49BTmvr4NUENc6tfim5c6fGWMGfJ0UBQA7AA7RKNw+hxan0xeRWCii4uaVyDlBGdS0m2FEjEellKqcSUKhWzQTJU9dcGPHWGhi+YcpNAS9FgOnTm8Au2E\/BXCaorqBACXhL9C0XViIQPFFQpLnOpqhmCJqYca7Bm2\/5uB4TDvipEnl5xnXjngsNX4m7twDfw6\/wVSUV3d2mPV+hcBKoxy3pKGaKE4RCQOzIBXnGGF5EEIa3aW3k0xiCS7GVuoKsbN7WbtSMJizI3GIybnWFzooERI1cmLMIMQ2cDac6x9KBXHqwjcqXdU+HQdNKicIM4LbgcWnmKoAq2vwMVyEmyS7NeRRgIpa5IDCmlu7VjiI7cXcI2f3MctcHOtS+FbT8BAWzS0m7c3lHFnj4fJMNqcF\/NwZ0PEgAGcnDveq2OIlre7V809fPXKvnr\/kGz017aKvV7UTlJQfk21A4eWdz2i43rprvnLK24BEHvyFeSixrY8ev7CrciVLY1dAgHHrXOYa\/YU6u332\/VzqxDszpRS3La6pAYtbnWHvwIDz6ZKlwqLWy+oTq\/CfclHpOorjdUgYPoATgIaTRIhrggnASwteV7moAKGOBVwUbQDDdCqfMVO\/OYZn5OExSWxvtd94Vcn9xAR0ukeCLhKZqMFRrCaPiTDyvYOqlga+l4yptI33TVxjyqkP09lFl0HQPWjavB6X14Rce4y1qcnMPYxrAdmbOxZvLINT3fOG9ebSB9fC1He12ONtd0dbx\/VB+r0ivCwcKlUgzGg7y5BbS19Qn6XvlagQ\/dIUEnTEdfDd85E750tzlzdSn4FRGt1C7D+ofCHcfpBYvRbr+IBP+YcWk1C+glKMLf39c04f1MVfBIybrJS9nNrHZRoaTnv6AE\/l\/B8n25rf4XTL+G1ZJ31ERlydcVTFTJNJESrDVU0hKtVLgRcBxOJ2kqLqNk\/CYBu8lQccj6NTXc\/rOSb6a5XZ7k4KBuntbfFXTs5wLQ\/FyYaP43euV\/AyS\/RDRfC2p\/0yj+jFNEtb9t5OSGg7g9Fxu5r2gBvP5mvMLBT7l5NdxwSLtC5Fs65GaVX7ZvTXendW1QBbmAlHdympMaaZEL8lqQ96ypJYFtD0SNlSL7NZoSjOyE9xO+xAQIEzvhjn8t6KHodzxTL+NrK+EuLFx3u41MzmsB6j2KMkDuRdk8ruNMYH0zEDFXpw6Z8ZT7hcF6xixucENmIHyG3y7+bunfO0ZOObmCf6rp\/bYz\/1PqVC6qlKVGNq5FrZ2EQyOKGV2vdLGol+0YquDsAZpqHFdG8toJUJt9a7ddwgH1pTrUrMqzAimTV17uRz1qMKGnNNoAz1jLOs8r1J0H4XDJrqvEx7ISH5vIDRkg77o5rRCWsbkrg0Gm0ZVsCRebkPEyLoZJsBoc8pkYmcnoOgYVhzrGIRV3HP+Q2TFX9iYK47D4DU\/SMDS9cV1WurOwHJyK\/e02qENttVcgJBAHgbidrTru0llB8jFjXYVeZVwxYSj3V3CcbXJng\/t5jwJHlJY8V1PjBOayJLyF03R3\/HIKFa9Owmdj1l+0Iavur5ExSuwDI2NTZTQsJlrgPK1H4bfs0LSm71I1E+kOAT9wv92f9g40HCi72CZTox8XZIrIdonYlUtyIhd1ee7Rcpy4e0vESIVr71Uj65JLtwC+jsr6Lo0SamGvLZV0wLIy5ZDZrzmR\/TVxTdz1gs4NryOVoxGUd4Lhe3GZCgtk68eib\/jQGhk+qwDYGW5MpOLWOTqZihj8fgV4T36BnwtBW52KDZoBS\/u8xr1Gf2a9WPhCn1Ru2PEPBGjsZEK23NcPAyEdK5EEvk246Ms\/Ol26FKDza5ULIzrIZ8Ro5GOmjH\/ln0J5YvEYmXgJmYBvSa\/0KpWBcNOp+51E+zemN3dJozL0bjZgLvqdGyrp1A75zZFuNSVajspKsQMa3tgUjRe5e02Ze12W9HNX9pxy97rP3euByqsGmbs4GGpDQdOD6zNbl9xvO47kUOTu9aZOBVCUoG45TdxPGgdgiZ24nxxdN6eJ+6b27U35raAJemlbnCtuYG7SMm+Zlg8t6ce0OuWJlMr1\/9SV66FI7x9Qfge3j6UGRsmS3q7amAGow+3mmwCvPEDbrkGs7pEtd7cpNnec1fDMvI2MO2jpBjOgnnFP1TX3lTSigjsoMhW164pS+lVxKOH5l+rxQqrH9GP01B1Lb8k1Ijc0z0YEBstvmX0uBtGwj79utb0d465LQKyYV2JFdcmFOKIM7s8SEtfvuRBx0g3s16TX6iif2xOgy9XzuEoeA12+zG09otM+b3Y9vpLqnoDOdxO4CY8Q3pp4frFTI7jX1FavCewse3g\/G3ccXjZLMHlQBqypgU11k9e7cz0IhlVXFlKnaf3wtpcH90RX0BTDfVHrR8SUrVpEbr0JufPGJPeOmjsLRCLQ7TlDtI+8YlL6RApGtRfrw0TwvOB4dj5eFDyH6+flkqX\/TW6qc0nZuUaeBgW5IAfkjRj6f4lm00dlP78UdIEtiP9W2fIUVPgXlPsHlPmoH3yp1Sojx8d9Raf+pVvQEAbIxRn4dgJ6x+4QaayE8tHlSUetZJ1cGj5dkAPHB9QJVvpt\/04os\/kJeFIH9eU6xndaYXzlWTgdZEOpL58m\/bdaTR6OWantru\/\/CbcnZOIqS2vtMZ9+dy8mffhx00TJO1HAL+sCad6uMwdUYPFwrTHaFHF31qICuybXbZuEm53t1bhAHUvUOwdJmv7b\/ohXZrLOvKCJf0Fz\/4DGcik28k3+08uB1mxo1+VYKxdkAva3YJKGdS8KChaF\/gUFM0L\/gILtIGyNYLA19EuVflD98UHqrTlBfxrSzjEA\/LVbb6njbwT2XP75VztJXiZLOKe8W5bwy0u3gGYZ2MM\/AEa5rNRAY4VDLkxslTut2lcoFcRRZqvdLO+plfrTk1h5dkkZaJNbCg0xnCa+JmC6A3DJOBL9Z8zYxvJwAMUgaeVVe9g03GXKx09eHSHDdvdU264aRyhee4RiEhtHCE5KsuqkQEi1NFdbmAkX\/dFKlhMSU8KZAxqA4FeUsqwzSKq20pmChlDHn9yZv66xXW9hV70ysjwrmOzKy2d13EXpXZgGayNhvzG4\/YTRN7hlcI9otCJOx\/Ybt0ANalTk0awKJeZ3VSyYMp2tSU+cfbfd7mp7EuRDuN3tCrhK\/ATElRLINinbyOEAbsie8NvMTuCrkOwBvNlkaRyGAs04Bjy3b9wC+YyyI2fMmY\/VdIvleXwtVVXQki6McnFXHuKFupZwcl8xnG18\/z2xb2to45FySVUz8xM\/r9ci0MzvzYANKWiTLBavhP1GWhTKERwK58nxvjaJt4zXNc1yhqrBJH\/ExOSNWyCNKs9FAmbTNpdK9DmJaILO5JlUtc\/9I2HPCy9HpzAxWsNEC0mqfZT2Tkec1h34kffUNJD5UxiO4C6F\/cE+4k5LKC+A1VB+aJwcVK\/QoUqp466U2yVTsbKmO0NLulKRC78C1PnUObIMJXRZmanbPu5jP8p2WpioeuTp5NWhDy4Ng8v\/Rdkeh6n0xpAPXVqRQde1Jn4fs3nG7TIztEw3\/88nu+\/Zf4dN57XTt\/vemX79A1\/x8cjpO\/1fNl1+ywNboB1L2VTftpvgcAGU6TY629vtJrx35fuufNvCN2djl297Qk9rf6sjp0knfbP6W7u7Ks3w8H5nqBMZPAzTE6RwNnf5dtWpXrdv\/TOFkK3pzPJ4r9Nt93nTakyipMHGKSR19\/aUi3RZHd\/cRdHEpcxQ+wLDgm\/g5RI\/G5r2RudgXtXeAfhYLpnBWqhWT+vTg5ivWtLqsR7zovQpYPM+89qO6ZaW7W9t8CZOWxMbGOPcy3lEEG5sOW4YqGiFxGemMxUwELGUgyzPspR2gK03ZB8HrK63vgqJWAgWc8ht6jU9OD5\/gYJ1E32D8Wr05eeXzObmOVGCo3+wVNW+jsLYvMtNv+e8ik3wvf\/LJgEH6A5EcoQWVQtXQnagUlRjlW4n+P5RDIW3S9qbaknnKhd94CGoUhHHDHUHsb4uJtXwV9ujVHYp2iKlDf9pKxQyn3BxKV3ziaJv\/fHm5WvLs35\/c2aR\/1h9XyuQQ9RscNU25sK2ZEBN9ILJAjFlsXR9Vt4VrJx9y223IU6qJdW8jfX0teOTEmjmS9Ay9\/X2p3PhrQXSyQMI3c+9GB1jxYvFkv\/WVV5anxnAVPnbqlvQwbao9YVj7bXtuVh8BaOXGyFtMkwtfvgwr7P8SjTSYPydSn7fJfgcJMmyR1CI5lm5IwPntuCMjJde7+p+ZpIampoM8qEZCxOMFDEGgDP\/JmofqmP1AQfbYDcsikF2ZnlXwv6ICNCZRnzL3G0TPqpr+IP90XE25VL6Z6s8j9yAfwqAyEQJxN+3YuexvNBTizzZR20jfLbOh2oZC90CBYLHs0GEdLm7QZtHOQaRhhbY3833G512u2n9ai20dZfME\/OReJBF8bHSFZFay7UtBM+GRrhW6U4gXOFJIBwEw3Ue2ATPLMfHHGFUSOknWr4nMIl9PYleZrjJrFLLOZZ0z8ce\/VPY7x0t8tEFnDk2UKz30GasFvqX8Sys0ZKkzyfuzD\/x2g48ymu49VEl1Tl94M60MlRjLvgcx8urhNbWKRM8jiPBG9LJX4PlvJGORg3wXXMZFY0MHCI37WPRt\/wGSiUamr\/YgJiNGBH5YhrFYUOLBxrpqGE1r8SyUGDppnItz\/Ib6VQAn0JVdqxilb9hubh0LWdheN2qjUnC0EtueNBM+swFF3zyDkw8+VaadJUXaN86vUxnUTJGoyrpwMRvgNKF27Dw9sPRntTG6kGIG5AhpCFiijG43e7LP5Vg5KdGroZuyuVuDOzyeS+MbhooZIdY0srtstUoXb3yMUA1gVBtv5c9yCqDQu8\/b34C11Bswp3m897fm9nKvOBmQ+ZFNMdp1hHramdankUa8ksjTeI7OVMlZgFtgVdCp2lhWwqtQbsnjWHipzC62VcEhdRbkZenZZEEuDarlTXrgPs9r0FurZeoFIfec9C8A2tf+rwXRw+HjAnWfg8cSD38WsyqqS5m4CiZx63nTawVJ\/zvTSiopvdBszjGONq3FsoipGT\/I7Gq9Cf\/hc6jLkLiZdXHqNI\/jZG3EpleSRAtSwbx0CgFr5K2\/o8m53lTNioVW+Ufz5Jqq61aULBoY9txHpu\/eHh\/H5vTV+6a571pvKajvIC9y7DUNN7XKrxJKnj\/+cqjkYryaKiM5Y6VG7q+RVGBSmvCSt17EpohbeXFQ6e1myf8j7ZelPPWc3MWdTP9YFM\/bmx720sTawcgHupb0yTkecNqTsFPIKSVxaeDoNWBqHgsCRuos+WVSSLFIsFQxuiDmZLet43F+fm1CRH2TOP99avxhgWXjTAVjahopAlvhFxIOQRpgJ3qNOdh4+KuIRcNzH1hx709+WT1cYYaOQsjJqIb3sjSGQxF5gIwK8GeXtE6o7\/uZtDg9mt\/Q8Oan6H+en9CvZp99xjMCo\/MPXJF2\/5VryqvmrzSTRq+igZX2Op4cD6UJ\/j8wZk+r5wNGUcbSsijffRfHe3z5eP6HYz+7u\/PjY3AobEntsBReTx9U0Pc8OD0dC3\/xWnCxSoH9BZeG+imSQ7mP2hCWn6WTZzAKyDtDWxbNfPEafgKPWpc8Ls0CRuRKBrTYsriBr8FzM9tnE5YHAOSxiSqBjpaSSOGMC15g4nGLM0BEwtYzOXuf15NOk7zKuSiBdYGq3GJYnqhho9bYCLBNbKzcFnxi\/xu4XcFKp6X3\/AIrp6\/C5ZXE4hep6znTel\/ytx7a0qLKLiCDVaes2va9q97Exc\/6FN2rU\/ZO6q+DK6H5Cu134ELy0cdacFJ\/Nprue12R6uVd+DFOHZfTe7a140O33I2O3zLV9qQX3tuexdZQ7mwvK\/77ovtvsUTuCQnUWj5uCLrR1dNDg\/HSMvmovm8ShWQ9BWm6nk19\/13g86w4i00rKb6AAwG752M2KmnFk9w9eav9eWFPV27aTF3A9auLKsuj3JDWI0kFXK7yo1pLZQHMnScC25lKTo6Bd7eZYQvl1G7rrn6eC8O8LYoGjihciNJJ7n6VpuUrm4f9M5vIPVc6JNXnS0mkMSBuCeA3sOzcbys0lnzKhduP9lfkQObi4fQS6hfdSVKGjcRnzXsGg4A7QCt7OhR1Z09Rerbr7qzWZ6G00DITjt+A6lOiE8KbTbYmEEouDTHvKNpHDdwjsrRye0hL9AViHwVVzive5g2TOQtA9YSPcHQnAnaLMnSVsbz1hLsJPIPR5OhSVqIxh1neaEKKWd71p9YXzHNRyzgsj2VQ9+g1jEQaVK\/IWzYQZxOw0aaN8YscyxP4tUjtqT2cJqXPsZ+ijjTNONj1JmkLkdwbHAJCrnLpPrVk9TaCkr0g6RETzUlCtv7KULUoN9i0XxeErukAZXHMUzR73H69uWpI7v02OXx0yD++mch+7+AjBLCmoCxbe2HeYSX+1NNLVcGoLlWldu19tvuzn9S1TZUtfefVLVr7Xfc7n\/TrT1rv9v4+\/b1b\/9DbXChmbV1rP0ZF8KY++ovQI3nTfvlY+TZYdLYek0aDND\/6QSkrCUfxmtIry1AK6AOJIZFflE0ymB1DTtOxwrYaQICjxY21mAX6Y3GjJYIvips5mP8NMP59lOqDAsI\/gzedcAIi5s6rXVPCDX\/rgM2RCmAaXyRaNuL0oQ5UcwT5dBqfuUl7pUMQJG4GFmCoG\/kZMkurK+IcyB2STEzviteS18yPEZpUUQx5roUk9i7gbhICyMg8dMNo1DM8hIpfQFKzfLyfuksBb7liOqprpjf8sd8t\/bVs2dBKL1CQC89dAxS66vWAKl6WgFm2WGrAsKW7LV1jV3Roz5Fh1QLqbfAKTi7TgIm7JcuRjyDaHmKm2o0ohiauo2P8lXyzIgxXqPHa26VR6iEn2LbPs5cNWnj51VMYqVGyow4bAz51pbhbPEgV1KhkC2pvzw8MOMqKpBxYMarDwweE8LMsyLt9cQDY8T8pyLRCDBQVPIfElPYZKuFAVme3kh\/k1omCyXlKQOxXbkTYwe0RZWyZewsSgSQL2XhVRbJrhtId5KlIlIkM1jHL09fNn4\/fHXa2Gy8+Xj6+vDUM1jTpGHJffa2KvHy9OzNSeP312+OGvbRm7ND0oA6HKtSZpROhkrtafQ1pEqzwRTwX\/vgoPHqW2PbbUM5dVh0A\/LWJ40DYEUmUTAtGm+OTknjJmKNjxlPWkdc8HS5Dj0ec59ZOoLS2oCKOQ8j0YojkJXU+GphPyjXXSmcGYKdn1v9QK1++TFL01iFJsr6z8K1lchu1St5XnMbOlnrbiyOLMefLIl86hVbRFYL+UzRDqZKKQZsAnwt8gBkGtU2wTOvv4JMHAQewL+XXDtfXXtFI+eShikFNdn0Io4KIDXUzThhWcOepGE0injouJYf1mIZTJzFImS1HkZqXyrM0fHheyRiTusZDemG0DJksQQt4KYlHJj\/SgLwUK0wr46+GQpE+jePMQ7biCnRI6OxCwDVT5QCG5BfF1MhACpCIB1Lvlh1sArAvEJg4A2WPcY1b6Ckjt9mLAl5KDney2K6MlsgY1Wb8JonYStLM6shp8j6gEkecjNjLZ1BlD7Gy7C\/ih8AXZJo9gNuSfyAW\/LcW1vFCvZ97Baz+3vpVqtWC7Bw1lRTyZf0CCwDw5NTLMn7Z4kzV+QUvN4KDMceJQnP\/zg7+kD5Am4SX4Bmx4pJww9lZqbsfoVYIRl2l1BUZznwS83ebwqOGwwRtBHyCj+tCZloOU4ppRZlCJBnhnZMUHkhflR+LUGUKcGW9i6iZu6ivIyAF641PcJqLFCFQBciJYLT58KDeAZ9DGpQ6utiVY9JopPlve0\/fdXihgcpTBnzDVyBlbOj3LTF6y+AdAo27yqyTVwDNkrp4SFWUXktiFCsrT0i2RzUXtuOKR\/\/k1XS1Z8YiDCjJD4rgzqhI0CEiqsUf9nayyCMbixwGInn6JhNuN7osMGZsb9Xnbc0U+FyG3kK4KOIo5DnVkOwC0Q2gM6VsAf5stT6KLWmgflTEwCrXMhZmoBJQKddS2K3kNReSXSnmVQE2X9Az63Me8OMrM99XrtQmNYmZ2t2QzliUIZ6JJNSTYkfy3ODSleVX0g7A4U0pVhuGFDU\/Ogb7LRso9t2NrvgUCavabWUlWhw4Ne3bY0lZ2R1MG\/yU4o+a+twpPJUpZEHw5IISbLWiXxkgwcZTPmrNUGo55hOyCfalfwicVkYvrnhifgQFYInPLetLI3APihMZ4lV6Yplzhza9kV+N8cxfZLZDlgG0dbtzFXlDkNnIQOcT5z5InHPz0MpqwIXR2kGAm82ZlLZbOH4j\/QAjGvrPUBi+Pw8lH1ZPF58mlmmqpvsiSI9Vpa74ncPBw2oI4bIodR6mefp7AMfCSWdq394DWWdeVStYMtt7zglyVDPfQKcjZX1\/JnVa2muruWPFGL6ziPb7az4+gZiRs4jW33T0CtzM+BSJOK11KfEJTDa0vj74\/AtfAjfYoSogFGaMO5pdGwSJVGFp6R5SK1pVodyr3M2U\/ANeQ5O83njMhpfImiUiBZmUWn7f0+7O6+6FXLyv\/UCd8KT\/YhRCF3rhkySvTj4v+gFemcB\/lqtE68gFTm2FdTXvbH2jzVHWTX6HHQkTObSA8cewJn59+ukhBhV7xROQRUmUeuylj4gX04SIsixWzUAldcYAfDtlq6VUO7SgA4G9Siulhy\/NSQDS7u1toh1pB8hvQhyjtHdTuXDcAhU0ANYEMSAWNYggYS6mSuA8yik44ZC9JcTbD2Bhg\/CiVMdwAdAUMnhWLEq9GoeWo5WNTgteQ4CXNJM1oVrzVUc2Glm9cfNjjeWVuam0+DTZa\/3UnXhdHA0dH\/48g8V7g9fuD\/olb8cXhR8Y5U42Vf3RysXMsCoVvbV8GjduHBz65Gd0\/aSWDeoCXTBaHdwPRy0hygoxLLO\/JxeLxbn1D5vdpxfywLqMw0G51BgieDG1M7wp7qo9rju5LsH810PvPtMB959wAuoI93vHiDdb0CY9g5us+qaf6MEbCgqlcE4SzdYbQ8TMCinlJtW3zoojUTHNaWqehkzd3f7YVAyiEtakeCV7+BnP+k8OF9WVIvp59L678MzSnXw79iNbjX7HBz+qgAINdfVhpG8G93qJY2a9DfsEyQOItD2G6jHYbMJPMIF6s4m+zttrb\/VJiE4nAjgJzPiuOpWJrTjT3q0nAl\/gpRik7LBBDsf9tr96T5NNtx25faHThbSgFx9erFXeS2faOtfA8\/KMOgXhgYyfUypeGdGStjqOEOXb44duaoGhvowc1BlRkm00mDoAZogN4zaGx103ayD3JVjP\/rXHqKPtIfoo6H7Ac6gM8dn6ckfDM71uS838LlmGp+VIObsCfLH0EJ\/jOgwtNANwqNuTUDf+6JJd56KFCCjivr8ITojG0Hmgs9N5oGodNf9s9qtxJ3FekX9SQRXTtt6TJl\/wm4rxX\/HIGUY0tgrY4a9NymYHKiFP7id79P30s0H6Ki3csdYlwTqkuf0bC39wGw7L+mH+CH9ECv6YeM9jPlnKYjcmcMPUhBnKyiI\/CEFETvzxVlJQSxg9n6WWsilt3RJLSTokuTR4kvUwlmNWjj7KWohf2AG8t5733rCEAQ4KOvoi3wVfcHsuGVg\/PlaqiJ\/SFUwO26uKqtoCWa3V3yUpASz36\/41rCc+Vdm14mMfAWRgcDxLzrX3CkCIXwuRCKd3VZ7\/WOlftEArANcB5RBzQ0rvDRZ4fkOtNhNHSLzRp0As+4vN9VsZwzRqy3k\/3Klod9f7oVIINuFSB69xcvLe21WRH4tAk4e7xD1jVkhGltttJEsC1YQ8TYxmFhjbYj1F5q519joP29jhA50wGlMxmzpAwYmQdviZcwGc+EWxPv7yxXUcBgwTsih6Hd3222vs902LXJhrec4UdAvszrEGXLbceYAWWHUFyy4aiSch0WDAeXAQyXLSBO\/gcuYoqgUdeUvlXCzAaIhtzSfXeCKaYvxn18eFbr56dVhU2MZq9X4lMOAKiNjOVnvHWfx1FTBIt0mcMHdjFGj61UKjLtGu9HZbXR2FWmk+KWyn\/u9jInLRkito53Glrtz8yL+rbXt7vywGnAbUgu0o3giQIUstzbx1rsZ7z8nn\/L\/tZ1tF1u67EJrf0BbRy+MhBdPNV0d2NeV\/kQlRAZDSEu5qXxgM\/r4nayJJ5+7NXJ01S0dBXCj4WMWszu8qlfLe+FruSv4zx1t\/r8ca67EY6szAMkbsii+a6A3zhy7bMIAH2QgBc\/FKw76Dhj2quJFIyH8JKnKQGD9kMpEMK12OjUsnv\/QIfGqMw2wQZieD0wXXh\/LhfxIhZ+umfEQJlyUBkXKPM4Tjl+3Cq2M2CsH7pW5tYVQ2io9sCMqzFudISmDIzNXIuxJpWGu7aXzKnHAIE5wou1P4E0e1VwGUMbPwF7mZhBlJsPXJip8bbJCWbYf65i2cY8myzFtl+IHXqDmrNVMljRnE6mZqjQHVC\/76i\/kUFoK+bLTg6hZ1vmZ27FTqw0opocFPnM7r+VrWqATOUlzbqmIGnbejCGqpemfJG7lTq9CQcv0qbPhdroEfZGY3WIX6RT0En9J7Gm9Ww96gnqtD7tfeRBItN8Nw5BMe4fWjrJRb6f\/0kVM+qUwRfcJLZNVZlxMQ9+5DGqktoT0XRwr38XRsu\/ivPSyXfkujjTnVLuMi0iAfnw5PGbUNnzHhc6v0+bU+XWqXRPL5EC7I1ZxamKSkwnJZCQa7T3q2diNbrWv+BpZd0U7ft66ghg4hrGJ0e0r8GJd1d26Mv0YTzbPazFwss1zFQPnun\/tRrdVjOZrP2\/RKx\/to6S7niWX12N65K+YM9+YG7+am4VkrpQUzY8l\/8phqzZzzsYPxyFf1+cPWrUpxfzaaBRhDEzg4OvGj+a7oQF+jgyjzHPwHHl\/P19I3SSl\/TNQGmSl8qFFeOVpsfJN\/ofN3Wv41Ou0+10PVbcnf9++2toswJlcWZvkVaI2ttX8hQ248uZIuPbzaFQtU2Tl8rnMBs101jfzUikcqxoIV24iV\/YbP+kssv8dqTf8+jddg1n5CYQXr1+BhLvZ6soz6dp8MqlVcZpGcWOSRgXQgKSxBeDgRSOYQEXF6ooKslWNFn+hxofBXi8NUXDNiWjpBsXNwSxOoAJWVhX8R1R30XHd14UzP9bCbsRK1bdj4xYXfpml8g\/F6LGbjkYFF+jQ7f4eIjcnZeIfqOh6f9\/ptpUQHk0ZOntNtn\/R2uvDW6uz12KeSidGaHDDO+xBK2ntETB6aCWbEEX82GReGCUA8ma3VvldpBmNMcnADNbNH2HK0aRAR5Oi5mgSXF2h4xQ5jgeqvyLKWpfKCpmhFfJqg2XIV8h83A0M7fV\/wJkJ+NTi6GirtEGu+94sHT6gC868ubojeTpDxRjw7t1\/bso30mSs0GapOvx8JS8qylpXUqFH1rAmzwpdHNZr97WPSc+6bllNVtfJwVY\/2WDGC67BDSOr1W3cyLnC\/DWzH2Xcofa8EQ7495rL7odqF9MSrk+B3p6ilq\/Ea5Ri5\/QpTWi8utiSsEE5zUlWLwmu+T8y5IFeW5IvKYvJNWa1NZbY00Vis0E8JBjEQNlJSxSoZDXn8ugmj+yJRxf7E7ZQSq1+bl3QdFqis30sfmUazSSVLSwHZWe5LaL1q9hPwPdQuZQfRN032Wrq6vjUti6FyLzNzdls5s623DQfb3bb7TZQdRaRTBDuzJdpZmkZCZ72aqIC9vNMZIaSgqV6cwLBEZ1VUSWv86X7mDC61QWQ+aJLctrpkph+ABYu9FvNdpGx\/Moic0UNe0gNY+BqQCsJKNV4VjQZW8oxBndHoIL0CJV3nBq2BgUHSUHl2StW+Dp3R+41mVIdQoWEmIQWCySg+p72H23pNVKFeR3DICXdiLwUsN\/+rcHA8VDDDhlodjqkMYvEpWFOVJp3ITbRsMNUCB46Srkro4fJKEoicUcmtFU+j\/FgybhEU3+shG7RYDws1Z+MABsZgS+ObwTWmMgkxAeD1WWCpQKB1KePirfQB25nFf2A+oOTVtbr8NYLZz6hWbOjvAeXwOl1RQTkzdfCZE9PWx1nw2atHGKFkauVZRK4Te3XAiJ8QFPOhp20trrSi3c1grjG4v+A6r6g7m\/uuBZMvEXmtx0vJ7ddj7VyctfxruzAIXdd\/LtQUfTOQfH1WpuLr5rvh9BK4cf22HFF+ja65aHdkZTSFZYwk9GcvEdBOtqk9nnf+mB51pHlNN\/BjI734ct1k9rX5hf4dL5qqMAtWhqqHP089M4XUk\/9+icLjgIsdg3FfnJSZ3JOj+zQgWnFv3cdbxsmFZZvofwafUVAIF0h1TvLLqAKL4fA8a3theN\/rUmD0aHaSPpnh+Ab9X59dbRh7xPV43pDAwRztVgSXKa55aGpFHikEWtbnbY6D9rNFer07WeG9WLh+N9q1VsTdovhhCZksoI0WGrsmwHNKh68VuE+MXHfX8DM4WQV8nuyRikvi5IWcsMsp9bFS2b\/opzXGWjlL0M3UA53jxL7F6kq\/qgR1SqDW2hz3QUO39QFDpZXT2JVkP\/GyL8Kq3ps7BdpeGc5pga0Uod8rBBAflCQTeqrCoqE5sJd5\/YvjuOf1CgNw0BXCxDy5VDBxpxKLeRYmPrHxoIAwwzuXo60MAhUn\/dEvt8Tl40iSIE1Kyf6aeWhPJ21LkRS4cARumrTtuyXzOaOnFqtLPX3prjc74kQ\/QwwRP8hLVRpdrKa8k7ca5JUlPfDUmtpX\/WJdDW5az0snK0uCHTtw8zF6sxAu1aZ\/94UOSJ1JvloxNQB3jpEyfBhPSXTFNmnHzX7FBbtNKHHov8hsUeVxtGxcOY6PAZNEvs0kc9Eu49dHcjDcXwZOoPmWGbCxeoSE44RMByy\/hPAjJUOq2dFC82YUUcpTaz+seir0JjoARLxYjUFVp2PynI7JaNln4WVw86ab1ypHYFjq0+GegQCQZtWS8NqH+N6UPX9pZBugSZcTqaaFnwgnBgtLWChlvwqSp61MCSe4CERvfjeeu7OghRiyfL8uBY\/zYyOs8GEewuE9Uf\/I2XCDdndE87G2bKz8Rb7SXfjT3tHZ+AdvcV+1tO+tOv5r92EnzA1WO0U\/CNNHvS9CpxQdb4FDuy70PloZBfCmRfCZJEwyQMx7IM1JC0qe6ZCrAHgxSzKeIst3XfWS826MLy0aomIctD4RJUXy1W+0lVeCVvuCDPMUK7lNX3tuLz\/7KUbpJOM5bx+pJ62lKBWN9QWEuCWw0WBEzglRC\/sVPg\/OBX9rW7be4m\/nV1UQBP9Z7Jn5enFN3Cc+Jv0G95\/VsX0LsQTSseFeCgAxMmx4NPjqkV6XR7TK5J5xjmYh\/2k\/cWBnNBGGN3InEtmF+usLp7qhKHZ9K9VcwMwFWstaQ0DrGq8kg632F2DszyOlJJye+vF\/4WSMrb2eGMxeuaQTb00FHv9WQ2hKYSOcvDEUcAVAgbyz+odJSAPLa0QQPuIrdA+Sh5qH+Vov7DKaIH9tBpSogyKUQ1JgsPZer0r4d4aliptI2iR+2KHoDhTKmXlSikr15YciA083rMlDSdW03BiP6XhlCgTjAf6Sni4l\/oOKXULiGSFrlKtpBoopFRWDxrqJg\/UiqRfYTj+T0vFEz39ybICd\/JAgXtgbO+hqced15BpI8rXOkPzYJJZGOhLRzZd0tZRoFur7LRivwKSBkfD0PeLAEmDS0k5Va5uMW2DJ7QstAbvIRaDSgDZflkDinxlDLYyYtdDRESGwQWp+6A93IBojlw+xNRGaQOEpIQQiJIlJqMtR4lt563E2eyquLpmSrOMtJsYUXdzI2dcKwdvmlbtbuxu\/Qbeufb2ZAYGOZbNujDaM0xXdTFCNJD5ugBLSyF3ujKMzIco4a8ZiKp2yfZwXSRKVDttdnpl5BHTj\/VU8S\/hb7Oz7OyAbu+Zfg6SzW0Q7nAZGQLEPsic2IBwEejMaiRsBjPOccZzMqWvmR2TSEq10RmkwIhhy0EKwTMkmUKppYCUxgczbOHy+IcPzGxXxdV8zewpxNbEOvGhM1wdnguCABEIAUS2nw73tSa2dnd1uKw1YbUPklJXqV2ngmtLh+SwaNK3OQyF4zhAEaR0m1FmhTAKR5wV05x7jQLvK2l5DxAXtX1YI0+ngvuNN0XAMg7STbhQwCfw66gQENMXuTWCiF6nXbJrriYNG5M33J2tFy92fqt\/TiaRQxpjQJMaQZQHYExzg14Cqtg3LeQSgmsRI3G\/27cK5fzUL3ucNlgYkgddRNjwD7As\/zCVuyY8mVoOEdxIu+Kg\/UTemZ5EwCIMPEIZmsyJUIjpO+bM39VdDQj\/HTNgmGELgRTeP\/Qf0DwHeyXhr0Ral42u09g0uZYMJF5jIAFCCybTdQMQad1\/yYMrjiYqmGUF615nIZhhnc12KsMz\/bPS3fc\/YDUykRvIcuYIhzGdGuklDl9XavlZlD0BsUuJsp\/ksnJ1FVjOWg0u7Dr55\/5eCqMcPxGyaJQUGQ+E1bcO5VO59Znc+CiCAAngXYMF6KsVEeAVZ6A+TjieXq13fY1ie6hA2njZALSnYcMZwwTlhMEh8vVVI0eHUbZEbBsS6ZR+cko03fHB2d\/YTJIuupcsGAyFtJMl8++HstIygE4jNKjZ8ElPUYnJkJOkMltBKjPtAgMdLaHyoNSXtbxY+lySIMdvXCIgYOBj1\/IuEjuWktDkf+eVxlrSafUu8v1eAU5JkS93CTpbioWKiT\/HS40MzphoQNgyEHma8tH4KeGo3FArBaRShQ0A6rbiMf9hc7INrNJ\/x6VWImj\/J5i1\/1c2r0Ga3QE7dArs0IM0A+fHU5MZavn\/ismc3N+vX+njVB9J3D3aa2ydjexDsF5DHFDdmoMh4auj0MGw0pCjU6VuCC5eEANlE54z2\/HmENIKo21hWCsZE23mXUAgtIWvwsZaE2o1uVO+BhQWj2GprlPFryGbY4I3GFHfmVj73UC6mTtzqspjajUfHuOkEhBKTxO1UATm16uFoz2EcdEfVN44ht5g6Gh3TpZkQ34sBYq6+ZBazY8aobD+v+R2NvEglo2KUGDGC3hleKiT1kEqjGecBtgm3F+XMugUMixLN2qqWrBNbNd19OQFClx95SRy13GLLI4Etu2O0hwcda8IwlC5xrKobC+BkPIQ4Fh7m0ycIQ15kIb8z5NDgN9pwjEQssyQNDsOkqqlD7JypN+qcEOxoZ6pw0LEuotEtvw7cO4Q4eVLliKG5sqzmqeUaf\/Z98F0WDcS0ffqd24\/DIeDfVpU7EyJR5yu8OZ3OmBDH7YNrZyDJe4VeLGT2sOgXj1lduL4f8LvYsEFjcU+dL+c1LKrUMwIHgo8aDiWyPpfSMZ0aMzRH9ze\/D+t\/qDdejFs\/rLpAnMRsjj9kkYVbgiUKE6Ytg5bNiQDld7lAhAmSrihGRYFiwu3FrZN61MLN4CwQcZa4SE6nk7+\/9quhrlNZMv+FZmdrYW4RSTHyb5BJipPYjvOsxNXbG820dNLtaAlESNgoCXLkfnvW\/d2NzQIZE9NbVUqRtBA05\/349xzJyy1yoTckOloMdwtxQA\/y0KScSklPBzuhUUGF9V1aEV+Vngu+KsLaQgw1cNU9qXruq+052H2QoCSmCkoKikoKunoQGlPkKsPFjW8CGsaFsiciY5EEOVVf80VRqn9o+dgZYSvnpesNMEweN6nFZUvP9CjFb9RRcD5pUFseCtRHssY7HCyNx8NSNy2CRGGiFgJVEM31F6gdppT23chu90Q\/nPUgBEFX2FBz4Uj5KAbHeKZuQtH+VNi7Zytx6Mq2wB+tpJzh1HLR8zZGlsJ3qLaqBSJv1HFU5Pu1ihEW+g6hUChpVWkIULDdsSXQAGhydQVgqJnrqIdnKRpiXvRJM3073OSSgpDcHVLUjrlY8BTSEynziD\/HB0rL9+PH9H6MnyHZHeZtQEbq8agWbsODrPSwFpj7gz5vlHPPITv1MgJBV9hjZ4QjFs6RSFB3QCwzx3FeFchOFywdMYi76FzSSM6Q09D55qlq8BjnSCCvMH49s5B7+BN4b55OifQ0wmBrJaBvkXAKVsEB7YmmOqpeP4S9V+mcf8ZTdvvbVoX9Fqp1zQexw4SIO5Ss8X3opZc+2Q6mqdsOkb6JynbYDHtc1GuKWW7f2X7IN11QFhPg4WpKMikXLVBHta0LN+5CDwWeQwyHgKDlPjlbLcpWaYhnoYaAbayhCVqHvmvyrO2hxZW1e3H0vtpbc7KbIedehpEsfTk4NL6wiBdCzCeIwU8EL8jDJ3vG4i1Bw06SYOIgwZ78KbsLv2dJm9IT3iGQWNFeYybrtAgQXQmPLmD1VoKsqwMCfuLvHEFs6zPYEIMOmydxClEcFLglgfiFO12CNfUv3sr9vaM6QZl\/vhYaTzTctYSKFxRsyMX1kEkBkMnB8ZnCUZosIUOU32nnVhO5bd5PKTSW4KWQmdiPT5OaoE7\/f6LwCK+fmr5om8fvraIVynXexEo\/GSbk9Kj0YoCOjSRtGHpIJG1dcN978XBvv\/CVKD0\/b54HNDJzJgYhWtuguYEHKEwmc9Bx0dfew9ONVlEF3rGcogWUi\/shtu3oFV0YU9j8B6\/6QEwdgm+7k5Go6wL2NapIW+6gbqYxYeicvz4aJwGLPRhIdFTgd1xs4q6KNemkAaLjseCEIb5+aebk7Mvxzcn7w3ikXDf218W1dmuh1pNBEKygPIjUFKrYzSajdXToHlnqFokNo\/\/CONJOS0uxZS9rLHdtHUlNSzyw739ciEvCIDz7ZcL89Ia3OEC4f4Y3NngHAPzhWu2qcR+vKBBhErxFFoPWq67oEkXoACPj\/WWM9nQ6OK07xr7bN9YS0utnUBe96K6oPJXfKd3UC0pzwzu7JSBxR8s5WWcsrYCwIelbBXfaR\/2w8rJIXtl5UQYl17CG9HkZ5yoqV+uVFnn6tOZAB5D0hqxGWS2rrS+j\/QpX11\/WFvNoJN+iiUK5CECYdUCkRvRVTCjPEZaimQS09QfNpyz79OAMxgbEo2ys0yxftLaAokKH0r54ocWElNukZy1bOBgC1cMx1oENN9BKqnjF0Yj4R7NgLkKnMnAWnU9D6YcAeBUXbvBaCdIrhGEvGO+em9hyX0896\/lwUH\/wCDG9zhe4HnIOfsFBNiiH\/H0Jyx+BVh3thZvAC9rEC8zJYBhuRPwIEPcswzW16+dG0SZgpUJGM9fwhdI4y78fmeQEjIBioH4tIR6TEVUw7YHIfnCOAvxZFDk1CDGKWQrkeRdcO7MIEBcPWFwzzSkHFcmuHJjEOPV+w5naQqTD2uPhmd4Pbh4jfG4atcpI3SBm\/3tkc\/fHt1NBPgQvW9ojoMzQKHO3x756pqChPp+mXlMGX\/06XCWtlHFagrGGpWLYKrsOyA1n6whiMLabJ0yY0JZhX1BRdprRT9H79mCRoVxGmWPi5j6SvJg+KwOkvfBNt5wr9mmG66BInfLXiIm76ClvmA0Q9G\/dsdNrS5IcV8VSeYsZQMR2UATkFiWkTeH7DU+LDxaW398flvDq0GFgObI5vBOmABC2GltjuGuBoEF+xltcg0vw0G4s1mgGNRtSz4TjQETBS3GtBMG0R3MBLFayyVaNIz+0JZmkStXSgOgWQU0F29Vw6nHXUOwPepmGcWdu3P47SbPLagfaI36gTZTP8CFXb4rI1vjw3aE3WA\/kI4HyRXKrhdVQB4H2mR6EXdbdULfOhHHwWHn4LCJiENya6BfrSP81DWmDXm2K0RJo2+\/1tk7+gedV6v+weV\/d\/4Rvu6+7rzuvL583ekfrt7M+4er7hudqKNGKEEhbpit+XUwAbnMyi1T889+ZBrRg4EzwlAmInADIHpqHgDm8kHpWNeccob8MsTAEDExkiNrk7+PzDaLdcqmhVHpPwwL0A6MGBcwijEvVpAJWcOLk0DM8CL1FiYwAfEEla3qeeGHxi9g1uYD8o2z0qusDvPqXTBxN7BEV0\/D8Nl8VKc36IeGlfYDtzYfeCUSQgzs+JmTRqaGbxyZin2+JCLJ8\/z+WegsZTsXcKY9171XkofH0\/Cf7EH9BBqNf7KHGiMLB3QWWurQ+SZZpPDHKY4CrX2YIH8anip72tetq4H+gHNoSWYWvuNa2YVe9lKWLfzi1bIevBWP3mnvhxsKn3b1hqF8nhgb1WtSMBiiI15WgrNhOVKqYFlwzVswEH4KACswNDJ9IOQVxBpvYtstYzR4aX9txwSKR414Af0TQGWiTnzrYvD2WAud1BDx3T6hbp\/9XsMcBbQhw0pAR9FYxZXPH5KYm4D26qb2mgC8q5vaD4K4\/chN7XQYHtEC2euGA+amdpBXM8tcc8iPOhNId3LM3B5hTPw4jwT8\/f7ZOM6t4S35JI\/r0vQ1H\/ESzTl2saElZkQGq8JYMK9lvGtp\/z8ogqH5CAQ\/iIDBw\/54PGDM3fiO1jqIzorA\/o+ArAikQZI5J3ZGPGeEl\/fhsvXygCBmax+LvDwY5wPZJkp+m3F3kzhYUQJehZPCVXoC7s18AG2Hq+99A3aVN2NXc2tX69aAqlw5PSprgmi6YGrW2tTa1BsZ07yyQuNraGe61c7YuBTbORWH0M7Csq61c4SjENo5wmGIjWgyZvuPj9KQErgndjY4sTOXMTsbJBAuwJiNEDP8CyC0YGqeYFxOYG3AE4L+aBebeoAeaRcbezC3M3hcXsrWM66IRZGIYcbtBJ7so89D\/OyPoY8KC9QxIxpHzn5x7AuH8DF7e2iJvmwyG6R0VtJVKU\/TDLg9o+4SfOY4NGbIe7fvoxOdQGl89qcaR4X6DpnkWnTZDQhY4DiKl8Bxq\/e65zI+\/BO0IkcQB0M47FdY45nrDdrqPBcUzB7m7Kox3EJjw3Peghv6Jzc\/UIiNLbUXYVlLKSpbyF5hDX7ykmQIGts6cn\/vDc8o\/iDQ3iBrg7yhLamftsexz0LGWac2XAdysJbh62qkFsg+ayOXqso4GB6zI\/dQ7hdNkRQpNXHSQC3FgRx6FeyUtfkRaV9bbEoVQBQAwoRZm6GqAvgo0Zhq9biXtp62MaTrh6ruenWp7GqsqOpuit30m\/ub67p02O07tIw3K\/OftowjMa2bG+EnN88ozmYi1krx2vz5n5Pf74a3f4p2LnkewC3DJ4uFjFaWxtqIQjYmjtNBNNiEH4lxDSq\/xOE3Pf5+zuDtrUsutqsS1cTQ2tOENWAl5tbG51WwToXI8jyyBueR22xe8ysya04EYWGrrDLYeiHepm8MCTMLjifMlvwP0uW2z0JOv1lEG99kk9AsC1YF5qKxgfxJWIftVzd9Fzb9vePqjCuqAkTywZRD69mvnQP9\/foi8UWTj7ZcoiGvOEOplbMa93lASnSJFi0bjHF11s4s8UyucqCUII8JtxBAb5oRULK\/7cEk6zmRtc\/3Q279Z8jHgwl3U7kTao9Mx8VuEBYSQogAqMwp9h4QQH7HwIjGYSD2SwCN4434IJycH4BomARWDpSWPefwoGf9Tc3juCl7jtQ0CHXf9F7Ctq2EcT2cRH0m7s5dmtfk9koASaXsfnNZpL\/FgkqmaikoWXYrRbffv68rLK5hbRJm9u03ZPLygLx7eVAr3dVL\/5ClX7aX7+nlJTev+sjbot9vZb\/f2lm9epH+gE+GtfkSmf1amUQvcyXKdOuFhNl3OOFvy4CC39wJ394RdHVKMsMiXmnCFWppIFbIQdy+yic0QsPK+xqjcKMi1TQ0J+EybV+5RV1kNd5XtbHTSEfD1EKEeHuIkEzuRXRVX\/uVcZZoPwPtGNKraT8lvIRUEexa8Th5qAQjMX3jYC1ZDBSAx9r8Ss2KvbjtDgFZR5WZ7US1P+dhUGl42Puo9WGiCHgG4tQPIvALlFafZ9VXpEVTqbvasjmwRKVyoE3Ihv+qd0Qb1KiqSVcVaYppRJ9BLpvvbDRIUWgptby13bDUYErdKaSDSwUoLxVor6ebLVBv0KLY2l4VFDFs2l4UohB9DOtBqM\/yY3saPpihcGTYmZfGYXge8fh\/AnaPKk\/ljLmZhLF35xgeEx6mCZvTVRCnzjkfGnTJY0DXLOKYz40ib21ee6VaG4NibQya9sT+gZX\/v1brGaO00COfSPIH1VxWsvxZG8bdPcab4KBqEQZsq3iD78LxAIIR3D34H6U5BUH1EYLqNwIEnzF29MnktX6KKEWSv8ZY7mk4U4S1ovxQfCFehs1AXKxCWIsX9Z0EoAC128DuLW5Ljtx+gYFNgAI91wSUResnUU86dRdC5wF7Lu4xVZPwXnHYTCJd2niLgkO5MjhS69eGFGBlqMfNhVWlE85VPQBh0CqAYIl4yXdKHVhI7hTPljTwJrljb9qiiMR10gaQkLdjHRMWGUMDd1iI8sDfihL3ufeTy91rO9rhxU1ygVdpXPWl\/c7tDe6OLtXSjnS6l6O78e6+nG0hOkHrFrDyvNpqyyjBTiumME48BTpOwKvkozBWvW26hDSIwhKvoea1lAlt7STuxA7L2NKPpexnnttwRbjVpR\/u8RFnnOu6sSxU+OsayhbX7tnkLuCnO55W1vi+TJz6TSQFfrrT8Av0TmpCGraF6hX77xbqdtB2i\/QQ8qFxAeaAzlTDHzgVNIKVN+tZESytV\/DfH9w8wf\/nZUcPjwEXGPzSwrPFb+hb8kZPurCSSRee3bkwHVtlbFG6RBOpA9lhkgwFrHnTML6H8LT7apqH9uEgPcB7Tw0Ka1M+hK31AlsD6qRy2RJkkqpMxYDIdZRR23KEH0+Kdnp+K4hVWrRFXsSfpuzPJcsq9Y9lxb\/ULymZXRPPwJ4vzXDiw2JhKAfoIljxC\/eKRAdw3LCldxU3\/vgvqecqoYt0ej17PJX2FmHcitv5Fk4j\/ITzPVg+zrfKqSedP4Ox4a+oYyrkGMERmral6UvM2qCOB3nWim7frkU5qAW6xCD3VBsnDVY97O7m24op\/B2msMeEs7h5uQB9lBz2dlYuYutl5vA5WzCDfJdhCzeAJVdNE1fahGnffxMNpRf+cilkys+TjKWrslMgUVz9mvmdWnYsfpg3EdlQNV4yBz6GFL9PMU7BGYlhJOo4FgQV8r0LGLqXzA9kNNkfkbt1xTTMJGVTlmZdQK6n3cyDJ0H8b3pnCeHrj+3krdbmj6aMrqovvlNLcPv\/KpPJyFd\/wUVXfa5ohOq5bfxM4Zg4sbPurZ1ZQFT6ZlicnnJT2K1QhLLEZRnMVe4KyICEK+avyNrgDS7eMECjk4tSF\/q0bu1sMNeuz7Xrc3E9\/6XswbXNJi+7717vjHPQazLm6Z0tvr7pSoVsLaNIA2IHWVk0miGfFhXjOCebNI75JU1nQeQYB70ekGpVapIxjnevaFiDgBbjX0rGYlPJaGMiGV4jhhachtcNmdWvBaHhfWRSmThKsVPH6h3yL0UlVxyX+Nwu3JfGE+Yf88fHnnVU6JZv2GHJ0BKRV2\/Ya6ueFhFQ7EKrGaoDG8IOBvKhwgGyxFgk9JXiWWtTtg1QfGxCcX4LYRZMT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\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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nucyob4k.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"dag\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G-P19 \u00b7 TOPIC \u00b7 uncertainty-and-validation<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Systems Science and Modelling<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Systems Science and Modelling<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Uncertainty and validation<\/h1><p class=\"nx-tagline\">A model result is useful only when its uncertainty is stated and its fitness for a purpose has been tested. Verification checks that the code solves the equations intended; validation checks that the model represents the real system well enough for the decision at hand. This page sets out how uncertainty is quantified and models are verified and validated, and what evidence serves the decision makers who rely on model results.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/systems-modelling\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/systems-modelling\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-canvas\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Systems<\/strong> \u00b7 around this subject<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-systems-coupled\" data-cx=\"systems.coupled\" data-variant=\"section\" aria-label=\"Coupled model runner: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h2 class=\"nx-fk-title\">Coupled model runner<\/h2><p class=\"nx-fk-sub\">Uncertainty and validation \u00b7 Coupled model A \u00b7 Scenario set 3<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Ensemble running<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Systems Science and Modelling<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"map\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Model coupling graph<\/h4><p class=\"nx-fk-panel-sub\">nodes: models \u00b7 edges: exchanged variables<\/p><\/div><\/div><div class=\"nx-cx-map\"><svg class=\"nx-cx-svg\" viewBox=\"0 0 480 250\" role=\"img\" aria-label=\"Model coupling graph: schematic on placeholder geography. Illustrative.\" focusable=\"false\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Coupled components<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-systems-coupled-t1\"><table class=\"nx-fk-table\" data-fk-table=\"cx-systems-coupled-t1\" aria-label=\"Coupled components\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Component<\/button><\/th><th scope=\"col\" data-col=\"c1\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Time step<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Exchanges<\/button><\/th><th scope=\"col\" data-col=\"c3\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Validated<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-systems-coupled:r1-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Hydrology<\/td><td>1 day<\/td><td>runoff<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Power system<\/td><td>1 h<\/td><td>demand, outages<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Economy<\/td><td>1 quarter<\/td><td>output, prices<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Partial<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Population<\/td><td>1 year<\/td><td>exposure<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Ensemble output<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Ensemble output\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-systems-coupled-c2\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Median|acc|l||0:46.88930871663615;1:48.382727899961175;2:53.15111459260807;3:49.6319303933531;4:54.22778686471283;5:54.58065072633326;6:57.642870298959316;7:54.74668015427888;8:59.36784723931923;9:59.70844189180061;10:62.170529891736805;11:59.92279673963785;12:62.038072685990485;13:64.62024663686752;14:63.606801979802555;15:64.33690411550924;16:66.65044042086228;17:65.92819625772536;18:66.9514666439034;19:72.06773663787172;20:69.37130523333326;21:71.4712570881471;22:74.88129506940022;23:71.65441935108974~Member spread, upper|s3|l||0:52.88930871663615;1:56.58272789996118;2:63.551114592608066;3:62.2319303933531;4:69.02778686471284;5:71.58065072633326;6:76.84287029895931;7:76.14668015427888;8:82.96784723931924;9:85.50844189180062;10:90.1705298917368;11:90.12279673963786;12:94.43807268599049;13:99;14:99;15:99;16:99;17:99;18:99;19:99;20:99;21:99;22:99;23:99~Member spread, lower|s3|l||0:40.88930871663615;1:40.382727899961175;2:43.15111459260807;3:37.6319303933531;4:40.22778686471283;5:38.58065072633326;6:39.642870298959316;7:34.74668015427888;8:37.36784723931923;9:35.70844189180061;10:36.170529891736805;11:31.922796739637853;12:32.038072685990485;13:32.62024663686752;14:29.606801979802555;15:28.336904115509242;16:28.65044042086228;17:25.92819625772536;18:24.9514666439034;19:28.067736637871718;20:23.37130523333326;21:23.471257088147098;22:24.881295069400224;23:19.654419351089743\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Coupled model runner: the screen's structure, terms and states for Uncertainty and validation.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><p class=\"nx-caption\"><strong>Fig.<\/strong><span>Coupled model runner. Design wireframe with placeholder names and illustrative values, not a record or a live system.<\/span><\/p><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Hazards and exposure in North America<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Uncertainty and validation: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" data-variant=\"inset\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Model validation is the assessment of how accurately a model represents the real system for a specific purpose, after verification has shown the code is correct. With uncertainty quantification it states how far results can be trusted, using data not used in calibration and reporting every major source of uncertainty.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Hazards and exposure in North America.<\/h2><p class=\"nx-lede\">For uncertainty and validation, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and land cover with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-cmap-field\" data-nx-map=\"ctx-field\" data-cmap=\"north-america:risk\" data-ml-src=\"risk\" data-ml-plan=\"p-732cc3f7\" data-ml-relief=\"1.3\"><div class=\"nx-wm\" data-nx-water=\"ctx-risk\" data-wm-domain=\"ctx-risk\" data-wm-tiles=\"on\" data-wm-flat=\"on\" data-wm-focus=\"-170,7,-52,72\" data-ws-live=\"off\" data-claim=\"INTEGRATED\" data-wm-panel=\"closed\" data-wm-mode=\"2d\"><div class=\"nx-wm-stage\" tabindex=\"0\" role=\"application\" aria-roledescription=\"interactive map\" aria-label=\"Hazards and exposure, North America: satellite and model layers from NASA on a 3D terrain of ASTER GDEM elevation. Arrow keys move; with Shift they turn and tilt in 3D; plus and minus zoom, 0 resets; I inspects, question mark lists the keys.\" aria-describedby=\"nx-wm-help\"><canvas class=\"nx-wm-canvas\" aria-hidden=\"true\"><\/canvas><p class=\"nx-wm-noscript\">The interactive map needs JavaScript; the layer list and sources are below.<\/p><div class=\"nx-wm-presets\" role=\"group\" aria-label=\"Quick views\"><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"true||||\" aria-pressed=\"false\">True colour<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"ntl||||\" aria-pressed=\"false\">Night lights<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"cover||||\" aria-pressed=\"false\">Land cover<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"flood||||\" aria-pressed=\"false\">Floods this week<\/button><\/div><div class=\"nx-wm-panel\" id=\"nx-wm-panel\" role=\"region\" aria-label=\"Map layers\"><div class=\"nx-wm-panel-head\"><p class=\"nx-wm-panel-title\">Layers <span class=\"nx-wm-count\">3<\/span><\/p><button type=\"button\" class=\"nx-wm-icon nx-wm-panel-close\" data-wm-act=\"panel\" aria-label=\"Close the layer list\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-panel-body\"><div class=\"nx-wm-group\"><p class=\"nx-wm-group-title\">Hazards and exposure, for this page<\/p><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"true\" aria-pressed=\"true\" data-wm-z=\"20\" data-wm-lag=\"1\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite image\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">True colour<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS NOAA-20 corrected reflectance \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"true\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ntl\" aria-pressed=\"true\" data-wm-z=\"21\" data-wm-lag=\"0\" data-wm-op=\"0.9\" data-wm-short=\"Night lights\" data-wm-prov=\"NASA LAADS DAAC\" data-wm-kind=\"Satellite radiance, corrected for moonlight and atmosphere\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Night-time lights, daily<\/span><span class=\"nx-wm-layer-src\">NASA LAADS DAAC VIIRS Black Marble (VNP46A2) \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ntl\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"cover\" aria-pressed=\"true\" data-wm-z=\"22\" data-wm-lag=\"0\" data-wm-time=\"latest\" data-wm-op=\"0.7\" data-wm-short=\"Land cover\" data-wm-prov=\"NASA LP DAAC\" data-wm-kind=\"Satellite classification; latest year GIBS publishes\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Land cover type (IGBP), yearly<\/span><span class=\"nx-wm-layer-src\">NASA LP DAAC MODIS Terra and Aqua land cover (MCD12Q1) \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"cover\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"flood\" aria-pressed=\"false\" data-wm-z=\"23\" data-wm-lag=\"0\" data-wm-short=\"Flood water\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite detection; clouds leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Flood water, 3-day composite<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS Global Flood Product \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"flood\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"people\" aria-pressed=\"false\" data-wm-z=\"24\" data-wm-lag=\"0\" data-wm-op=\"0.8\" data-wm-short=\"Population density\" data-wm-prov=\"NASA SEDAC (CIESIN, Columbia University)\" data-wm-kind=\"Census-based estimate, static (2020)\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Population density, 2020<\/span><span class=\"nx-wm-layer-src\">NASA SEDAC (CIESIN, Columbia University) Gridded Population of the World v4.11 \u00b7 <span data-wm-date=\"people\">static<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"people\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"imerg\" aria-pressed=\"false\" data-wm-z=\"25\" data-wm-lag=\"0\" data-wm-short=\"Precipitation\" data-wm-prov=\"NASA GES DISC\" data-wm-kind=\"Satellite estimate, near real time\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Precipitation rate, daily mean<\/span><span class=\"nx-wm-layer-src\">NASA GES DISC GPM IMERG \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"imerg\" hidden><\/div><\/div><\/div><\/div><div class=\"nx-wm-rail\" role=\"toolbar\" aria-orientation=\"vertical\" aria-label=\"Map controls\"><button type=\"button\" class=\"nx-wm-icon nx-wm-open\" data-wm-act=\"panel\" aria-controls=\"nx-wm-panel\" aria-expanded=\"false\" aria-label=\"Layers\" title=\"Layers\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 2.5l-6.5 3.5 6.5 3.5 6.5-3.5zm-6.5 6.5 6.5 3.5 6.5-3.5m-13 2.8 6.5 3.5 6.5-3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" 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stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-menu\" id=\"nx-wm-menu\" role=\"menu\" aria-label=\"Expert tools\" hidden><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"inspect\">Inspect a point<kbd>I<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"measure\">Measure distance<kbd>M<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"compare\">Compare two dates<kbd>C<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"share\">Copy a link to this view<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"png\">Export as PNG with credits<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"xt\" data-wm-xt=\"6oPh6EhWj+gDMzpLLT8H\/wVfxJakWdyAB8snze2Nado=\">More expert tools<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"keys\">Keyboard shortcuts<kbd>?<\/kbd><\/button><\/div><div class=\"nx-wm-keys\" role=\"dialog\" aria-label=\"Keyboard shortcuts\" hidden><p class=\"nx-wm-keys-title\">Keyboard, with the map focused<\/p><dl class=\"nx-wm-keys-list\"><\/dl><button type=\"button\" class=\"nx-wm-icon nx-wm-keys-close\" data-wm-tool=\"keys\" aria-label=\"Close the shortcuts\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><p class=\"nx-wm-hint\" hidden>Hold Ctrl or \u2318 and scroll to zoom<\/p><p class=\"nx-wm-toolbar\" role=\"status\" hidden><\/p><div class=\"nx-wm-tip\" role=\"tooltip\" hidden><\/div><div class=\"nx-wm-pin\" hidden><div class=\"nx-wm-pin-head\"><p class=\"nx-wm-pin-title\"><\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"unpin\" aria-label=\"Close the card\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-pin-chart\"><\/div><div class=\"nx-wm-pin-body\"><\/div><\/div><div class=\"nx-wm-dock\"><div class=\"nx-wm-time\" role=\"group\" aria-label=\"Imagery date\"><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"prev\" aria-label=\"One day earlier\">\u2039<\/button><p class=\"nx-wm-time-label\">Latest available<\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"next\" aria-label=\"One day later\">\u203a<\/button><span class=\"nx-wm-track\" role=\"slider\" tabindex=\"0\" aria-label=\"Imagery date, up to 30 days back\"><\/span><button type=\"button\" class=\"nx-wm-latest\" data-wm-step=\"latest\" aria-pressed=\"true\">Latest<\/button><\/div><div class=\"nx-wm-chips\" role=\"group\" aria-label=\"Legends\"><\/div><p class=\"nx-wm-attrib\">NASA GIBS \/ EOSDIS \u00b7 ASTER GDEM (METI, NASA) \u00b7 Blue Marble (NASA) \u00b7 \u00a9 OpenStreetMap contributors (ODbL) \u00b7 Natural Earth<\/p><div class=\"nx-wm-legends\" aria-live=\"polite\" hidden><\/div><\/div><\/div><div class=\"nx-container nx-wm-below\"><p class=\"nx-wm-status\" role=\"status\">Loading the latest NASA imagery.<\/p><p id=\"nx-wm-help\" class=\"nx-wm-help\">Drag to move. Ctrl or \u2318 and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.<\/p><p class=\"nx-caption\"><span>For uncertainty and validation, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and land cover with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.<\/span><span data-honesty=\"true\">Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.<\/span><\/p><details class=\"nx-wm-credits\"><summary><span class=\"nx-wm-credits-line\">Data: NASA GIBS \u00b7 ASTER GDEM \u00b7 Blue Marble \u00b7 \u00a9 OpenStreetMap contributors \u00b7 Natural Earth \u00b7 Copernicus Sentinel-2 L2A \u00b7 NASA HLS S30<\/span><span class=\"nx-wm-credits-more\">Sources and licences<\/span><\/summary><p class=\"nx-wm-credit\"><ul class=\"nx-wm-credit-list\"><li data-wm-credit=\"nasa.gibs.field-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.gibs.water-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.lance.viirs-water\">VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.laads.black-marble\">VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.modis-viirs.land\">MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"sedac.gpw-v4\">Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"noopener\" target=\"_blank\">CC BY 4.0<\/a><\/li><li data-wm-credit=\"nasa.gpm.imerg\">GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa-meti.aster-gdem-v3\">ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"naturalearth.all-scales\">Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. <a class=\"nx-link\" href=\"https:\/\/www.naturalearthdata.com\/about\/terms-of-use\/\" rel=\"noopener\" target=\"_blank\">Public domain<\/a><\/li><li data-wm-credit=\"nasa.gibs.reference-osm\">\u00a9 OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.openstreetmap.org\/copyright\" rel=\"noopener\" target=\"_blank\">ODbL<\/a><\/li><li data-wm-credit=\"nasa.earth-observatory.blue-marble\">Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li>Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, \u00a9 OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.<\/li><li>Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, \u00a9 MapLibre contributors.<\/li><li>Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer<\/li><li>Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).<\/li><li>Optional layers (35 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.<\/li><\/ul><p class=\"nx-wm-credit\"><strong>Layers and dates<\/strong><\/p><ul class=\"nx-wm-credit-list\"><li>True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/li><li>Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/li><li>Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/li><li>Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/li><li>Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 \u00b7 <span data-wm-date=\"people\">static<\/span><\/li><li>Precipitation rate, daily mean: GPM IMERG, NASA GES DISC \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/li><\/ul><\/p><\/details><\/div><\/div><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/uncertainty-and-validation\/#map\" data-nxf-title=\"Systems Science and Modelling live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"project\" data-variant=\"stack\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Uncertainty and validation: how the risk develops.<\/h2><p class=\"nx-lede\">Quantifying uncertainty, validation against observations and the limits of calibration. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3.85 8.62a4 4 0 0 1 4.78-4.77 4 4 0 0 1 6.74 0 4 4 0 0 1 4.78 4.78 4 4 0 0 1 0 6.74 4 4 0 0 1-4.77 4.78 4 4 0 0 1-6.75 0 4 4 0 0 1-4.78-4.77 4 4 0 0 1 0-6.76Z\"\/><path d=\"m9 12 2 2 4-4\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Verification precedes validation<\/h3><p class=\"nx-p nx-mt-1\">Before asking whether a model matches reality, modellers check that the code is correct, through tests, convergence studies and comparison with known solutions.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Validation is purpose-specific<\/h3><p class=\"nx-p nx-mt-1\">A model valid for one question may be invalid for another. Validation is judged against the accuracy a specific decision needs.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Uncertainty has several sources<\/h3><p class=\"nx-p nx-mt-1\">Input data, parameters, model structure, numerical error and natural variability each contribute, and each needs a different treatment.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M2 12s3-7 10-7 10 7 10 7-3 7-10 7-10-7-10-7Z\"\/><circle cx=\"12\" cy=\"12\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Calibration can hide error<\/h3><p class=\"nx-p nx-mt-1\">Tuning parameters to match past data can make a model fit for the wrong reasons, so validation needs data not used in calibration.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Model credibility<\/td><td data-label=\"Definition\">The degree of trust that a model is fit for a specific use, based on evidence.<\/td><\/tr><tr><td data-label=\"Term\">Calibration<\/td><td data-label=\"Definition\">Adjusting model parameters so that model outputs match observed data as closely as possible.<\/td><\/tr><tr><td data-label=\"Term\">Model discrepancy<\/td><td data-label=\"Definition\">The systematic difference between a model and reality that remains after calibration.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"project\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Verification tests passed<\/td><td data-label=\"Unit\">percent of test suite<\/td><td data-label=\"Cadence\">per release<\/td><td data-label=\"Source type\">Model test records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Validation error against independent data<\/td><td data-label=\"Unit\">error metric<\/td><td data-label=\"Cadence\">per validation<\/td><td data-label=\"Source type\">Validation studies<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Prediction interval coverage<\/td><td data-label=\"Unit\">percent of observations inside stated intervals<\/td><td data-label=\"Cadence\">per evaluation<\/td><td data-label=\"Source type\">Out-of-sample evaluation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Sensitivity indices<\/td><td data-label=\"Unit\">share of output variance by input<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Global sensitivity analysis<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Models with documented credibility assessment<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per assessment<\/td><td data-label=\"Source type\">Model documentation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Independent reviews completed<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per model<\/td><td data-label=\"Source type\">Review records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/uncertainty-and-validation\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Uncertainty and validation as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g-p19-topics-uncertainty-and-validation-f-t nx-dag-g-p19-topics-uncertainty-and-validation-f-d\" data-nx-z=\"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\"><title id=\"nx-dag-g-p19-topics-uncertainty-and-validation-f-t\">Uncertainty and validation: from horizon scanning to wind-tunnelled policy<\/title><desc id=\"nx-dag-g-p19-topics-uncertainty-and-validation-f-d\">Signals from horizon scanning are clustered into drivers; the most uncertain and impactful define scenario axes; narratives are written; policies are wind-tunnelled against each scenario and signposts monitored. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"project\" data-variant=\"stack\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">ASME V&amp;V 10, V&amp;V 20 and V&amp;V 40<\/td><td data-label=\"What it gives\">Verification, validation and credibility standards<\/td><td data-label=\"Licence class\">Licensed standards, purchase required<\/td><\/tr><tr><td data-label=\"Source\">NASA-STD-7009<\/td><td data-label=\"What it gives\">Credibility assessment of models and simulations<\/td><td data-label=\"Licence class\">Public domain, US government<\/td><\/tr><tr><td data-label=\"Source\">IPCC uncertainty guidance note<\/td><td data-label=\"What it gives\">Calibrated uncertainty language<\/td><td data-label=\"Licence class\">Public, IPCC terms<\/td><\/tr><tr><td data-label=\"Source\">SALib and other open sensitivity analysis libraries<\/td><td data-label=\"What it gives\">Implementations of global sensitivity methods<\/td><td data-label=\"Licence class\">Open source, MIT licence<\/td><\/tr><tr><td data-label=\"Source\">Published validation studies in each field<\/td><td data-label=\"What it gives\">Evidence on model performance<\/td><td data-label=\"Licence class\">Licence per publication<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/uncertainty-and-validation\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Systems Science and Modelling prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Model owners<\/td><td data-label=\"The question\">Whether a model is fit for a stated purpose<\/td><td data-label=\"Evidence this page serves\">Credibility assessments; validation reports<\/td><\/tr><tr><td data-label=\"Who decides\">Decision makers<\/td><td data-label=\"The question\">How much weight to give model results<\/td><td data-label=\"Evidence this page serves\">Uncertainty statements<\/td><\/tr><tr><td data-label=\"Who decides\">Reviewers and auditors<\/td><td data-label=\"The question\">Whether verification and validation were adequate<\/td><td data-label=\"Evidence this page serves\">Protocols and review records<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Verification and validation protocols for network models<\/li><li>Uncertainty statements in calibrated language<\/li><li>Credibility assessments for models used in decisions<\/li><li>Validation reports against independent data<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" data-variant=\"inset\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">What experts ask about uncertainty and validation.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Which sources of uncertainty matter most for the decision at hand?<\/li><li>How does uncertainty in inputs and parameters propagate to outputs?<\/li><li>What observations can test the model, and are they independent of calibration data?<\/li><li>How far beyond the calibration conditions can the model be trusted?<\/li><li>How should uncertainty be communicated to people who must act on it?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms-and-drivers\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms and drivers<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Kinds of uncertainty and why calibration is not validation.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Modellers distinguish aleatory uncertainty, the inherent variability of a system, from epistemic uncertainty, which comes from incomplete knowledge and can in principle be reduced. Structural uncertainty, about whether the model's equations are right, is often the largest and hardest to quantify. Deep uncertainty arises when experts cannot agree even on probability distributions.<\/p><p class=\"nx-p\">Calibration adjusts parameters so that a model matches observations. A well-calibrated model may still be wrong: many parameter combinations can fit the same data, a problem called equifinality, and a model tuned to past conditions may fail under new ones. Validation therefore requires tests against data not used in calibration, and ideally under different conditions.<\/p><p class=\"nx-p\">Validation is purpose-specific. A model may be adequate for estimating averages but not extremes, or for one region but not another.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"project\" data-variant=\"stack\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Methods and their limits.<\/h2><\/div><div class=\"nx-section-body\"><ol class=\"nx-rows\"><li><span class=\"nx-eyebrow nx-index\">01<\/span><div><h3 class=\"nx-h4\">Sensitivity analysis<\/h3><p class=\"nx-p nx-mt-1\">Identifying which inputs drive output variation. Global methods are computationally demanding.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">02<\/span><div><h3 class=\"nx-h4\">Probabilistic propagation<\/h3><p class=\"nx-p nx-mt-1\">Monte Carlo and related methods, using <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">probability and statistics<\/a>. Results depend on assumed input distributions.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">03<\/span><div><h3 class=\"nx-h4\">Bayesian calibration<\/h3><p class=\"nx-p nx-mt-1\">Updating parameter beliefs with data and representing model discrepancy. Priors and discrepancy models involve judgement.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">04<\/span><div><h3 class=\"nx-h4\">Out-of-sample validation<\/h3><p class=\"nx-p nx-mt-1\">Testing against independent data, held-out periods or different locations. Suitable data are often scarce for extremes.<\/p><\/div><\/li><\/ol><p class=\"nx-note nx-mt-3\">No validation proves a model correct. It establishes confidence for a purpose, within stated conditions.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Uncertainty budgets \u00b7 Surrogate models for expensive simulations \u00b7 Credibility assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Bayesian calibration \u00b7 Proper scoring of predictions \u00b7 Ensemble statistics<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/systems-engineering\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Systems engineering<\/span><span class=\"nx-mledger-role\">Requirements for decision models \u00b7 Interface control for coupled models<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/systems-engineering\/\" aria-label=\"Systems engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/decision-analysis\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Decision analysis under uncertainty<\/span><span class=\"nx-mledger-role\">Framing the decision before the model \u00b7 Value of information for models<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/decision-analysis\/\" aria-label=\"Decision analysis under uncertainty: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Validation is limited by the quality and range of observations; rare events and future conditions cannot be validated directly. Uncertainty quantification depends on the uncertainties modellers choose to represent. Credibility assessments involve judgement. Systems Science and Modelling certifies no model; owners and users decide.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is the difference between verification and validation?<\/summary><div class=\"nx-answer\">Verification asks whether the model is solved correctly, that the code does what it is meant to. Validation asks whether it is the right model for the purpose, by comparing it with reality.<\/div><\/details><details><summary>Can a model be validated for the future?<\/summary><div class=\"nx-answer\">Not directly. Models can be tested on past conditions and processes; confidence for new conditions rests on whether the model represents the underlying processes.<\/div><\/details><details><summary>What is global sensitivity analysis?<\/summary><div class=\"nx-answer\">A method that varies all uncertain inputs together to find which ones drive most of the variation in model outputs.<\/div><\/details><details><summary>Can uncertainty always be quantified?<\/summary><div class=\"nx-answer\">Not always. Under deep uncertainty, experts may not agree on probabilities, and other approaches such as scenario analysis are used. See <a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/topics\/optimization-under-uncertainty\/\">optimization under uncertainty<\/a>.<\/div><\/details><details><summary>What is equifinality?<\/summary><div class=\"nx-answer\">The situation in which different parameter sets produce equally good fits to observations, so calibration cannot identify the true values.<\/div><\/details><details><summary>Does GCRI validate models for regulators?<\/summary><div class=\"nx-answer\">No. GCRI does not certify. Its method notes describe how validation can be done.<\/div><\/details><details><summary>Why is calibration not enough?<\/summary><div class=\"nx-answer\">Because matching past data does not show that a model will perform under new conditions or for different outputs.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around Systems Science and Modelling.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/model-intercomparison\/\">Model intercomparison <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/coupled-systems-modelling\/\">Coupled systems modelling <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/networks-and-cascades\/\">Networks and cascades <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/agent-based-and-complexity\/\">Agent-based and complexity models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/optimization-under-uncertainty\/\">Optimization under uncertainty <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/digital-twins-method\/\">Digital twins: method and limits <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/\">The Global Centre for Risk and Innovation home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/trust\/\">GCRI governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Systems Science and Modelling<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. 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