<p>We present a new runoff map for Norway for the reference period 1991-2020. A new framework combing precipitation runoff-modelling with geostatistical interpolation was used. The precipitation-runoff model WASMOD was used to simulate runoff on a 1x1 km grid covering all Norway and nearby catchments in Sweden and Finland. The parameters of WASMOD were conditioned on land-use and climate classes and calibrated globally using data from 215 streamflow stations. Figure 1a) shows the runoff estimates from WASMOD that are biased when compared to observations (Figure 2a) .</p><p>To correct the biases in the gridded simulations we used runoff observations from 732 locations of which 198 had data covering the whole period. A geostatistical approach was used to estimate the 30 year mean annual runoff from short records (1-29 years) for 482 catchments. For 45 catchments with substantial glacier coverage or regulation capacity, a manual extension of mean annual runoff was performed. Another 7 stations with between 25 and 30 years of data were included.</p><p>The biases from WASMOD were corrected by simple linear regression using the raw runoff map as a covariate and the runoff observations as the dependent variable. The regression coefficients were modelled as spatial fields using a geostatistical Bayesian approach where SPDE and INLA ensured fast Bayesian inference. The mean annual runoff after the correction is shown in Figure 1b), and in Figure 1c) the difference between the two previous maps is shown. Figure 2b shows a scatter plot of corrected and observed runoff. The scatter is now much smaller.</p><p><img src="data:image/png;base64, iVBORw0KGgoAAAANSUhEUgAABFcAAAJBCAYAAACDAVpBAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAAGXRFWHRTb2Z0d2FyZQB3d3cuaW5rc2NhcGUub3Jnm+48GgAAIABJREFUeJzsvX2cXVlZ5/vb3SI0OKbSCljpoioFjYCMQqppDVGTLl56xDdUxHFoY/TqqLnekdLKiFG4LV6kvFgZSr0adUYhxgalRy8jitCCRQKEaJMqHGTkpbFSRXVKGiEn4qVBpPf9Y52nznNWrbX3Wvvl7H1O/b6fz/mcc/Zee+9VldSz1vqt5wVoD7cBSANeDwF4djNdJIQQQgghhBBCCBl+LgB4ZtOdIIQQQgghhBBCCAGA65ruACGEEEIIIYQQQsgwQ3GFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhBBCCCGEEEJKQHGFEEIIIYQQQgghpAQUVwghhESzfDVNfcd95wghZJjx2TfaPUJ2cAOAvQAe0XRHSDxZc7xB92XYoLhCCCEkCg6uhBBCCMlgH4C/A3AvjNBChgSfgNxEX4YRiiuEEEIKIwMud24JieZ3ALw+oN2XA/gLAH8GLlIaQ9s32j1CcvkogOcDeDKAX264L6QCaOvCoLhCCKmT5wF4L4CXNN2R3cjCSpourPQGQ/25DLN7kwToX2DY52XRwcGYEC9PAvBVAe3+EcDLATwHwH+ptUcjQN12T8iye1U9k5Ah568A/BqAHwPwlIb7MnJ0riymnSuLKQBcTtP0clqt3cmaxy1fNc+r+pmjAMUVQkid/DmA8wBeCeBZDfeFlCBmAKWgQkjlvAvAq2AWKc9suC9DR1Gxw2fLLq6FtyVkhJkE8FMAfhXAS2E21K632twJ4DPd86RixvadSABgbOtUqfto+2ULyT7WOuZF+gn65bWMCwDmAbyn6Y4QQoL4YgCr3fenAPjXZrsz+shC4uRMkuhFxcmZsAHTR9HFQ9ZAvXw1TUMHckJGjHcB+DcAngYT+vN1AP6le/xBR/sbAHwQwIcBPHdAfRwaBmH3Lq4BB6d771n47Jrcj3aPDDk/COA0zFryIwAeB+BLAbwZwLdYbV8L4AUwdu5zg+viaJJurqc4N4XkDiTiuQL0hJYi2OJK3nxveqz3eX9CW6YZxl8GxRVC2sE+APsB7AFwFWbS79OwnwOTM+CHAPzuIDq3W8naoZVFRuzkvoioIouPPFf6rLaEjDjvgllsvAEmdPKLu8e3AHw7TEilzS8AeBmAaQCX6+/icDAou+fyWLHx2TJbpLH7R8gQcSvMOuwcgH8PE7p4HYDbAXwHjIed5ptgPJmfC+Btg+vm6JFurvfs0rkpAMC12cU+YUU8jUNFD9nkCp3rZQkry1fTVJ8X1jq7Z47HsCBCSBEWAdwP4N0wuxTvAfCJ7nGX8Xw7gPtgxBVSkipc3Ac1yIUM1nk7wISMME8C8EIAPwngGwH8BIAxAH8I4GGO9r/fff+mgfSuRbTN7h2c7rdd+nOW3RNhxb6ekCHiPwF4CGZO94/dYw8BeAt2CisA8Nfd9yfV37Xhp0gek86Vxb78J7HeJEXF5JC+7rbQIYorhJAifBBmMXAQZgd1BsAfwXiVfZejfQrgLhi39xsH1MeRJHSB4dsNzRM7XOeLLEhCFg32wJ2VNE1/Z24DMkLcB+BrAPwGjCfLrwH4RQCPB3Cbo/2HAVwB8LUD6l8rKGv38gi1e9quhSw8bOxrLq65bZp9jHaPtIyvBbCGcO+5T8GEOj66rg6NCnlixbXr7kYyMZUkE1N99qkzPr99fZaw4rq/b47nmsfZXin2s1xeK9Nj5uWb47kqsQ0zFFcIIUX4bwCWYDLBX4bJqfJjMCLKYc81yzCJzmYG0L+hJW8RcXImSfQCwm4v5+2cA3LN7N4kycsF4EKuy7red53ruF5k+BYp0h/XrscoDMCEwCR6/Lx17K3d96/0XHMfgMfU1qOGsKv8aGzRJMTu2RS1e/a1LoFFjsl7rCBdZ4gmITXwKACfjLzmX8BS8rlCaZ7Hydi+E0nnymKa3oUU6IUEyXW+6/Mq+xSd45XB93sYdjH5i5ruACFkaDkA4PkwpUQfC5M7AAAe6Wl/X/f9sTX3a+hZWEnT0B3YGLfyvMSy+nPe4OqLz9XHqxygXWVPq7o3IS3jE933cc/5T2PEPAB1Mlpfm1ivlJD2IXbPFliWr6ZpbDjP8tU01SKyJMV1PTtrF1fsK+0faZhPAJiIaH89gC9BL4SIZBAqsMjn0Oti8M3x9idJEpvTJZQmQtfrgOIKIaQIrwbwYgAfg/FekZwrv5RxzT933/fW27XRxl6EuAYgaVM0SWyeCDM91p+czBZTQneIfQuUPNFGt5keY6Z6MnJ8Wff9057zj4I/efhIYts9l3ASItBkkWcrbTsXKiJLO1tQkWN2O+Hi2s7KR9KGAgtpmHfAhIbf1v2suREmDEjzJBiBZavmfg09tnDhCvMRYcWEAp2o9Pm+qkHa3mR5x8Q+Q1hYMQK02MRhFloYFkQIieUggDmY0nqPB/A9AH4GwCkAX8i47lHd9121KChKXniQnHe1OzmTJKGVerKO+5AwHV9egJgktnZJ09g+7rZEaWRX8PXd97/2nJ8G8MCA+jJQyto9YKetqMru6evK2D2XyCJcXOu9Dk73C+VMfktaxBLMXO4PANwB4GaYPCynALze0f6bu+/nB9K7ISDPXmQJFZ3xeXTG5/tEjqywn9gEuTpHinzWzyiScFfIS5xr28ZhE1YAiiuEkHie2n2/C9liis3ju+8fr7Y7o4tvoXH6vHlNLvXOu9rqSX/MQiIkB0HoffKeW3bBIDsrRX5OQlrAlwL4CvX9FgA/C+BvYcqc2kwBmASwUn/XmiHP7mW1FVF5YaXfHoTYhVDboQWQ0HuJ3dTXuWyfnbvF1452jzTMBoDnwIi8vw/gIzBezD8Et4ByFMambQyqg8OA7+92bOsUxrZObXuoAP0CifZqyRM7XOfycq/IZ52cNvY+LlzJbkMSg2s7V0bYGRSx4srDAfxHAH8Kk7H+UzDxc/d1j/0n9HanCSGjyUe7798J4+YJADcBOANjI3wchinV9776ujb8yO7rwWm/e/vxw/3vQt6ur0sYiRVdbHf0IhUzqsCVn0DgYoMMEVMANgH8L5gFyr0w5exfBGMvbe7onv/zQXVwEGhbl2f3JpfSVIssdiJcl7AR6sXnQts9O2+Kr33WMZ8XihZdfAKM/Cy0e6QFXIKpdPaVAJ4L4GkwQvEvWu2e2223NNDetRRti3ybVVL5x+Wdot9tQkOkXe18Qs3Y1qnM6119EXHIdW0o9hzP5UFT+OY1E5Nz5UaYah9PhFElz8GIK2n33E0wf1D/O4Bnw5QKJISMHucA/BmAH4eZ6H8a5u//bQD+NeO6OwC8F71kjcRDSFLHhZXeAiO0TGlVyGDnElayRA+NHQ4UguR6Cbm/3ddhdC0lI8+ruu+fAvA8mIXJ78BUY3MlfnwYgB8AcAFGiBkpQuxenreeUEcIjSSl9YX1+EQPG5/ty/NWkT6E9lX6FNKekBJ8BH579AgA/0/3/JmB9ajlZP1daq+Usa1T2+LF2NapbdEl67oQ8so1C1ockefb/auSkBBxm7qS65YhpiOLMDvVt8EksXTxaJgF1r0AfrhUz/xcADAPk0CTENIM1wM4BuAZMIlq3wrg7TAuoR/BTrfQbwDwThjx9fTgujn86CoTWljRbU6f7/di8bmRu+5tH7OT1WZd41okuBYYoYJLDFpoCYULDTLkvAQmafizAfxlw32pFbuUvD6mvVZs7z2XPXLd32eLQtvbniWDsntFYIUh0hAJgNfAeOEdBnCx2e60F/vvUwsqQmd8PlNg8XmjhLbV12SJJrpSkXzPu9bV59j5myukyKYtAktMWNAzYdRHn7ACmB3pV3XbEkJGly8A+F0YseSnYYQVwOy62sLK9QB+FSbW9ncH1cFRQQ+4rmoYtrAC7MwHELqjCoQLK4BbxKlzIaEH1yKJbJte5BBSgq8B8DKYBJIjLawA2V4sOizSzsFi2z35HPK3HyM+6NCeGPvaFBRWSAM8FcA/APg+UFjxcjk1SWNdoS6SuBbI9lypSlSQ5+uwJEFEFC2s2IQKK0CYWCLtQtu2JVQoRlz5FIDHBbR7PID/r1h3CCEjyMsAPB1GiPlcw30ZemyvFVlg2IsMTczEv+pJeJX3Y2Ugsku5EcBvAHg/gB9tuC8DR3KqaMHFJawIIrDoHCR5Qkte+fms/mnvwpD7DZq2Cz9kZPlbmEqSb2i6I23GFka0QGDnLfHlMckTFfQ1MeFAfZ4zGaJKUbJEkxhRpW3EiCtnAPwEgDsBPBnAI9W5LwawHyZc52UAfq+i/hFChptvhskSvwCTp2VXo4URqWZR9j7ATs8VG53QMW8QzkoUJvcJXTjY7dq04CBkiPgUTGjlMwH8U8N9ica2e0VzROnrXB57gvYoCQ3NyUoGG2L3tBefy+41bfsosBBSL64S8DEewy60B4n2YrHJSzBr50rxVf7ZnyRJjBeMDglyfQf6k9u6hCERUezXMBNr7H8MJmntjd3vD8KEB3xJ9/tnYOKBXwGT6LYOmHOFEDLU6EWClA4Nvc6XTNGFXmTIMRlAs/Kt2NcIZTxgdAK1Jif6TS9yCNmt2KJKVuhP1nV5uOye5DTIsz20e4SQKiiSVFpXxnEh4oQWWVyeL/uTJMnbSPN5zOQlqd2zfAIAkNzRryF0riymIaFDdv+rpg15V2KqBQHAbwI4C7N78lQAXwqTT+EagA/C5FpgSBAhhHiwFwoxA69ejIQsOFz3loHHlRC2TNiNneBx+aqJI5Z7rnV6g34TOQq4uCCkOaS6mXibhAordtu8ZN7CoLzmXHZvdq9Z2Kx1enZPclnR7hEyupT9+3YltRXySiJnHbOv94kbWcKKiCqAEVbSu/qdKJJuoluX94rrOVUILPo+bRBVhFhxBTDiydu6r4d1j32+sh4RQsgIIbkC7JwBwM4Fhm7rOq8zyut2LuooRerDXmAIIqzoXRjf5KNI9Z88uLAgpBlcds8nrOTZPX0P2+7lhUTWia+ksiyKtN2bHhus5wptHyGDxxZQXbmedBvfecCIBVUkaA0tl+xrp0UVwRZWNKG5WeznSTWkPHSCXyPk7Oxf08TkXAGAvTA5Vy4B+CyAf+m+HgSwCuCVAB5bZQcJIWRYkUWAS1jJu8bFxbX+nAW6YkUsMUJG3gAf0w+fu6uvP0Vib4c9XpeQUaCo3XPZQG33su4ZE2aZR4jdayMUVggZDJJXRYSREAE1a4NJqgZV3U9BhBJbxLicpkEeJ4IdEgRkCy4hxIhAoW2bIsYA3wTg3QAeAZOYchMmyVoKk4NlHCZ55XUAjgC4r9Ke9mDOFULI0OBaKPgWB66qE7JYsM/5JvbSJmsBEluWVAb7op4lInaEXq/bx3q0yLVtchElZLcxubTTu8Rnk3yCsss7z2X3tF2s0u4V9TSxxd0i9ivW1tLuEVI/Lo88l0eKfLevz6pUFiuqZP2t6/wpLs+Ta7OLfclnJZzH5XXiuj6La7OL29ddm13ccb2cL0OMENQEMZ4rPweTpf7JAH4IxoPlVwD8KoCfhykP+CQAfw/gFyrtJSGEDAnaWwVwT/ZjEjSGJq8V8rxIii4YygorRdpTWCFkeJhcSlMRVY4fzg7ZCbVDIZ4vYvOq8CSpInSnjK0scq1cR7tHSP3I/M1VHUg+1+09FiKsbHNkvf9lIYJKoVLLjvvtWT6xLajECjOjQoy48nQA/xVAlun/DIBfB3CgTKcIIWSYmVwKd4cPweXRkkXWwF4kZKbqXCh1PbOJfhJCep4qG3O93Cinz5tz8q5F5ZDFR2zZ5ioWNFXcw2Vj8+yuPh9rx2j3CBksOvwwz2aUCRUqQ9tDZ0aZGHHlHwB8dUC7GQCfLNYdQggZHuzJv1TEAMxiQ87LMcGVqFYnq3W1Dd2VzatIEZtrpcyg71tQSGyxfd72WslqG/IcQkj1aA8V+W6f08cEV6JaX16WIuJ0lq2KtWN1LHZ8dsy2e/Yr9N6EkGrRczzbVtn5VrLImuPF2prSiW6PrG97lMR6q+wI6Tk3ldnWbu8KExpFYsSV0zDhQL8F4NkAnghgDKYc834A3wDgFEza3t+stJeEENJifCKK/m6f86FFFFtQCXF99w3gci6sF9VgCzl6sSBlSu3jvvuEJLydHqNrPCF1szGXJMcP7xRZ6sC2d1kefG2xezY+EbmM3bPvQwipDy2wZM3BXDYo1MMllMtpmvrmOVp02S51fG4qUwTJQwskecKI3Va3l3NlBZa251sB4kox/wWAFwBYBPAjnjZXAPxHAL9fsl+EEDI0yG6GT2DxJXR0CSF2mT67TYyLaZNVI2Lc4GPu5UtyS2GFkMEiIUAisEysrgAANg/M7Gh3Un33laXXts1l9/JCI6uye3V46xVp57N7FJQJGSx6jpfXVs/bfAlss2xViFeKtNF/+77rtsUMJbAYoSNc4AgRWLR44mrnElpiGQZhBYirFiRcB5PU9ikwVYISAFcBfBjA+wE8VFnv3LBaECGklWhx5fjhndUu8gZoHcerB+WLa8WSNVZR+aIoMeKJTBDKuLpygUFIM7i8V3QyW7F7Oi+LbQPtyhtAz0uljO2j3SOEVIXYrYPTxj7pOZ5r88x1D1t0KeN5FiKu1Jl7RQQTn6CiqxJJCJJUEYplWIQVIM5zRXgIwP+C8VK5EaYU81VkJ7olhJBdix50J5d2erdkVdUoir0jUrQSRSxFJgr2LoxrV0ZKBVbTS0JIXbg89WwBRnv05REjLjflrUe7R8hoYyfkjhVVqiRGVN0OD+pSpdjiE1WEPcsn0LmymO5ZPoGkr/QzTKloRznoyjrXILHiyiRMSebnA3isde7jAN4C4JUwXiyEELJr0G7s2u3dl5hxYnVlh/u87NS6dm9jsBcYpZKfDQB7ouCaOIzKoEvIKKFtnXw+6WkrYUMTxy6lm47zPq+VUBto271Beq3Y4Toh0O4RMnxkze1ibU5V+ZLynqs31qaV2DK2dapPfCkqvPjyqIj4ktzRHynjsmujZOtifpAnArgI4BMA/juAj8F4rDwEYC+AxwH4dpjktrcBeF+F/dQwLIgQMpTITod4rojAond6y7rF68HanqzXtdioK6EiXd4JaTe+aj92m9Pne+IKgD67B/TbONvuFfVcqVtc0cm5i9hAXfWDto6Q4ce2ObbHcFUhQfsTfz6+mLLurucXEVh0qE9MslpbdBkVYjxXfhbAB2EqBX3W0+YXALwRwJ0AvrNc1wghZPTQIUH2AsMmZte2CWTiUHRxkQcXHoS0m5iSyeKpp0UWuYcrYW0RcVkvOOrOu6KrnRVBexS6vAtp9wgZbVxzp5D5lM+mNVU5rGiC2vQupKMosMSIK08CcBf8wgoA/CuA34MRWQghhFj4xBTba0XIWljonVMbHccfM0mPyc1SdnERii+siYsPQoaPb3txLxxSEkK6ROSyiWzrzr9Cu0cIcSEeKlleK0JsLjxp78qrF0uVG2NFyyund8Erfg+r8HJdRNsNAN8Y0O45ALaKdYcQQkaT5atu9/mD03G7vzZa4MgaJEN2b+tIels0hlc8Ynz94QKDkPYjYUPHDxth2VVFSFNEUBFc9m/Q1YLqhnaPkHYRU5657LMGUZTAToDbFMMqrABxniuvBnAOwJsA/CGM2PJJAF+AqRq0D8ALAHwPTMJbQggZSULyDGg395MzSXJxrX83tcxAGyOCxEzGYwfukB2PmIHa93x7d8XOVeCqurHW6U1omqogQsgoEZpfRT7biW7t84LOraK/x2DbokH8zVcdDin383kEZtk9hhURMlhctixrfhcjsPQloHV4KO+GOY14tCR3INGft88dWUcyMdXK30Nsp74ewP8N4JDn2ksw+Vb+rGS/smBCW0JI4+Qt2u2BVy8Y9CAbM+C6YnNd54Ylka29OAlJvuZbSGikje9+FF0ICcMWVPIEFt8u7sHp4nYvCxEjxDboc3V6rdQdEhQbcmmXc3bZPto9QsLwlVm2z/vQYd5V2TqNncjWTq4ds1FWZVLbKtBiius7AODIeq/9xFSSbq73zp+bMufPTTXm/RITFgQA7wbwDQC+rPv+zQC+FcBhAI8G8AzUK6wQQkgrkF3WrMWE67MwuzdJYie5MmDqJLJ54UDDivxM9s9WpKx0kyVaCRlFQlzhBVtYrnJxX1cy7aax7XqsF8r0mN/u0f4REk6MrdOIrXP9vdVhswYRMjQIbCHFJaxoj5V0cz3t82BRwktTFB3gHg5TmvnfdL9/GsBHATxYRadyoOcKIWSgyG6faxdXt5NFhGvxoHcxfOd86N3ZkB1N8dyQCXlVk+m6JgRZP1fRHVyg/+f2TXK4i0uIG7F3Lm+VyaU0PX7YVD/TpeRd9/Cdq8pzRbBDZdrqsVcH2u5pAdr3e6DdI6SHz0NPysgfP9xLvg34bZ3P08VXfrmoIKK90LTnSpH7+uxZiPeKLsEs32vFIazkXdJE6FBMzhUAeBqAVwD4dwAeZp37PIB3APg/AVws3TNCCGkJeiKa5xafF3NrHwtZAPjCf2LIqiwUc30dhAgoITvUWlTKco/X0E2eEDdZLvG6pLxuay8+QhYbw0IbRRUgO1TSFx5Ju0dID7FTk0v99kvs3Onz/bbNzi0VKqzYFC0ikFV1KMZOVWnT6hJWJCzIhPhMbXuqhAgrTREjrswAeBeA9wJ4MYBNAJ8CkMIktB0H8N0wAstzAbyzyo4SQkhT5OUd0AuNrEmr7Ukh7zFJzvIGQ9ltsJO7lnEZrXNRoScXrp8vRFABzM8pCwwtsMj9h3ExR0iTyELj9Pne5425JDl9HphYXcGbVoEJAKcx47SR8tkOC5L3OqpnyN99Vs6lWNooqrjsnhwvEjpJyG4lL7+KIB4stseyfHcl8wb6bV1VtsQ1x9T3rqPyY5NsJ7JVgkqowLIjbGgAxORcOQHgPIBZAKdhqga9GyZM508B/FcA3wTgjwCcLNCXBMDegNf1Be5NCNkl5E3YYyb0y1fT1NdeBlJ7B1dysWShB8YyE2E9eIqA0hmf355wVzHJHsTCwpdfJRTXz6mPcYeWjDp5eQFi8wb42h86dindmMv+e/JV+7GTMMb0x8ZXdjnUYy30GW0UVoQ8uycCTJt/BkJiybJlCytpGmPrXPO4idUVHDp2KR0fn0snVlcgr6oZ2zqFsa1Thf4+ZW6a5xVTN9dmF7Fn+USt4UDpXUjTzfVUCyn297ZVDYoRV/YD+BOY0ss+UgBvBPCEAn05DJO3Je81A+CxBe5PCNlF+AYd10LbNxhfXMtPXHv8MLYHXxmgswZ31/PLJnnUHh+uRcUoJ74NgV4rZLfgsz2+HAHikZKHa4HxA+8H1u45u2375NkX13oCS1YeqqqT21bJKNhKerCQUSbU1uUJLjJv07Zw7Z6z2+/yChVYXLZW7Fxd3iS++2bZsapsnM650hRtCxGKGdTOwFQE+lYAD2Xc7w8A7IHxYqkDJrQlhDhDbOT4xbV8F09B5w/w7cjqgVnvcGzM9WJvddIz6UdWpnhf4tU8bBEldIAsMqi3fYFhl1zO62/W76CtizxCND7X81DXdkEvJPLsnoQByYIDAKZvP7r9efPAzPY9FlbS1Oe5AlTrvbLdl4CE2EXuOSrQ7pFhxGXTfPYv5D5Z14g9FDv38nFj335k5ZZkfHzOhPTcfnTb1uXlX5HS84A7ma1OFtsZn6/M0y6P0OpqoaWYJaFt3flWtisASZnlIvcaoHdLzIOeCiNsfAjAfwfwMQD/COPJshfA4wA8H8AhAM9BfTlXKK4QQgD4K/C4hA27jcsdNM/d3RZZpEpG1mJCPz9LEMq/g6HMpF8vQEIG82FZYMQITa6fmwsMMmy4EijqYz6xRYvJE6srfcKID3vhAQAPe8mrAfRsoI32WKnC7oVCccUN7R4ZRnyJYu1KZj57aF+XJ8hMLqWpLbC89qvNuc0DMwD8Ng9w55ey7ZwtsHTG5wGU92qpymaFCivCICoEASgsrDQRMhQTFvQBAAcBXAbwUgCvA3APgLfDiC0LAP4ZwDeAyWwJITWSNymXCb0O63Fh50vJe54eOG1hJc/t3VeKT9rFhu5IjhU770oWdsKzUaFIJST5t+ECgwwLrsSJGllk6PN5+QeyXN3lWr2YmL79aJ/Ximux4tu1tanyb89lz6pywR+FxJDy+6HdI22mSL6UKu6vPfk2D8xg+vajuHPrLO7cOtvXzhZWTs4kSZ5gM4i/t6aElYFwbsq8IkkmppKmcrEUfej1MJ4qe2EEmn+EqR6UlY+lKui5QsguRVfisXdDbS8WexDN2tXQIosO7SnSlxh8A25WpYuQcsUu10+Xd0fWomGUxBdB/7xcXJBhweWRAvS7pLvO6+NCVo6VPA8Wuf/p8/0hkZqD0z1BOzQ8SKgyTAjYad9iKmiMov0D+sNRCWkb2hNFjmnb5rNzup2+l+85IXO8yaU03ZhLEm0zRVyx+wi47V3WHK+q0KAqbVURcWUgnivitZIlsiivlqYT3MZ4rmj2AXgigJsBPL77vq+qThFCSBYibOgdgTKL5eOHe6+iwkrM82Uw1KWDO1cW086VxdwSolmeKnnJy/IG4d2e+JaQNuKySb6FhAjFvuTaLpf2EFFFPzdPWAkJkxS0DQy/qhih9nJU7R+FFdJ2xK6JR4i2cy4xucj9bWHEZctEWPHdJ9RrRuaHurqPLawA2K4aFGt7RtVW9SGCSgHvlaaIFVeeDeASgA2YkKA3dF9v6x77WwDfVmUHCSEEMINZnpBoN2KrAAAgAElEQVQSI3DoQdw+FoIWVarwguiMz2/H3mZN+n0eKaPguk4Iyca14NDHXG7r9vfjh3cKKqFeK3pxY/dFhJWQcvRAeWHFZSft0EdtM237aXvzjaqwLOGjrJpG2kyIYBEzRwttm5fkdmPOCMoxIrTG93dXdwnjQdGGakE2TVcPivmPchjAXwL4f2HyrXwMwCdhyi/fCGACwHcBOAqT2PZPK+1pD4YFEbIL0e6iEtNfdhfDvr/+ru8p+Vt0LgFN0Ulrkeo2OuwntmLQboYJHckwM7mUpuJZZ1eqKGv/ZBFhe+7lVdkQu5clpmR5sejwHftzjFjsuo8P1/1H3X7av0vaPdJGfKFA+ntV97fvmWfrXOFKebhsn53MVosrIlKEhgjVYbeK5lxphUjkSHbbVHhQjOfKiwH8MYAXwggs7wWwBpPgdgXAnwD4AQC/CeAnq+wkIYQAvQFQCyuaMjtz9m6w716+0soxlB0URzUxbZ24kgg30xNCwhFvEVtYkbCf2Al/Fln3KvKMmPAgoYg9sxN7h7TX3iq7Cdo9MixIfiedE6+MrQvNyVKVPQ314CsChRUHVthQk3lXYsSVrwBwLqDdORgvFkIIqRRfMrOFFRPLKt/tONfY5+icKoJeKBS9L+BPOOtr6woDosdKPAybIsOKbfekDLzLDsp76AJBhBu5X57A4rJ9PhElr1qbkBfeUyV2mBAhpH1MLvVKxst3nVg2r3JaCFnJcV3HqxBdsuYhWqTIClGs2m6NbZ1qZ5WgWFqU0Dbm4b8F4KkweVc+52nzMABvAvBZAN9RrmteGBZEyC4mb4DTE31fKI+E+fhc3UNDf3S7vES0NjEDZEziWuJHfo/cvSXDhq/KjytBreDbqc1ye3fdJy9kUp/TYkqM50pIKeXdXOmsDLR7ZFiwPfNsXNV6fPYs9pyvAlGRkCCNbQenx/q9RHyhQULdIYxlhZXWeK5YYUHD4rnySzCVgT4EYAnATwH4fgDfB+AnACwA+BsAhwD8fKW9JITsWmI9RHSCWZewsrBihJWs6+1n+vpQZSLG0PZcOJSHiR1J2wmdyOvwoKzk3C4Xe41r8eDzgHH9/YR6qfjw5Zey29ADrTi0e6TthORWyfOuy7OdWYKL63gV+V5ciIAigopLWAHa72nXmoS256Z6r4b5ooi2awCeAeCnAXwLTPllzTqAtwB4FYC/r6R3hJBdj4gjevDTCwR757bI7oL2ZHGFF/mQCgx68PNN/vW50LAg+3pCyO5AT+jFa2VidWX7/OaBme3Pp8/3Et76sF3sdTlln7Diu9fFtZ22UXZnywosdtikTlarv/tsLe3kTuzfISFtJGTudvp8sTmez/su5F5V5WDR2AJKnkBRl12rKhzo2uxiezxY0HxYUGwp5isA5gA8EcAjADwGJhfLowDsB/BjoLBCCKkBe2dWyNqNdXmtuNrJgqDszt7+JEl8g6A9sc0qoSw7tNyprRb5t6F7PBkmRDTRgkoMtt3ziTBFdmiz/paK5Fxx2cUswdr1mbih3SNtxi4p77JTeaXmQ54Rciz02qLoPCcitHTG53eILnUyEnlWWkqsuCJcD5N35RMAOjAlmH8GxqOFxpsQUgvioikDrwy0Og734HSxKhWh17gWC0Vzovg8WJpaKIzKYBsjZBEyDGzMmYn95oGZHSLLxpyxiYK9CLDFZ1+uAXlfvmrK3Ydg56gS+6tfeeT9XUpyR5+tbLvbfBug7SPDgrZP2q5leeaF4kqEG+OZEiuw+OyfFlHGtk4VFlVEpNEv0jyx4socgI/DJKz9HQBjAN4N4CyAOwH8KYB7YDxZCCGkNOJNogdA3+AL9HKuZO3S2YsNX9Jb+zpJkOvbjb2cpun+xNynTMJafT0XDMXRnj8iZHH3lgwDrkWACCwuZNc3L0lj3gJlYcWd0DsLXxLwGLIEFBJGloBCu0faisvW2XM8vYHms3V5hJZizupn2Rws2r7ZAksMWUKKS3DRbesSYdqSeyW5o3knjxhx5QUAfhnA3QBeCuB5AP4CJjzoyQBuAPBMAP8WwEuq7SYhZDfiEjjsssuCPeD5QnxcIUSuJLY+RGCxWeuYsKDYJLdZ+VcGudAos3vSNnQ+Bv07FOGLkGFB5wbQZZN95CV01PbPt0hw2UJf6CUQZz9d2HaOIksxxO7ZIgvtHmkroUm0gXLiSJlqP1XdJ8umxcy92uqd0nTOleQOJG0QVoC4hLbfB+DXYKoEAcBfAViGKc38oe6xiwBeCeC7q+ogIWT3opPZ6kWGeI6ECiqSsFawFyeucqIuASXLa0XuU8T92iUEDJpREVY002NcWJDhxC41ak/otRdLVqWLrGdk5aDSIT0X17IFHVahaQ8yhtDukWFAz+u0nYspl5x1PPR8KDH3yQqJ1AJJZ3x+x3dXu7LUJcrsWT7RuNdKW0QVIUZc+TKYakDCR7vvH7PafazblhBCCuMaXPXApgcumdxr4cM1sIXs2vquzTtXRUWGIlWEymInVRsluMAgw4aUTN6YSxKxV5NL5hjQL3KE7vBKlaHNAzOZoUU+tMhSJJ9VHk3YvVGFv0syDMSIFEWFkbpKKIeg7WTe36MtemSJINo7pGlBQ2iyUlDbRBUhJizo/QB+GCbPynUwHixfgElmq/keAH9XSe8IIbuO2IHUJay4vmthpUg5v7wBsmzCwCZc4dvqXloU/W/AHANkmBB7lBV+I9ieLFIxzXWtPjaxuhJs91z2tA5hhVTL7N4koahM2kzZvCWu+8Ucr5s67OSe5RM7BIymw3Ca7EObQoBcxHiuLAB4L4AtmEpBXwTgRwH8FoCvghFUng3gWQBmq+0mIWS3IIPuyZkkmVxKU71rq5HJvy9MRwY4EV/0jq8+V9Ui3C4LGlI+1C432pYcK8Oaf0X//nRyYULajti9jTlj9w4du5RuetqKd0sWsrA4fhg4jRlnWI/2SLGPu47VBT0tqoN2j7QdPcfL8r6rQhypWshxEWsbYze1+gSMI+vm/dzU9rkmPVia8lpJ70IKtNdzJUZc2QTwNQDuAHAjgD8E8Lfdcz8P4D8A+J8AngsgYO+FEELcuHZxs8KETp/PFk/K5gTQlXt8oomII6HCStaxQZAlnhQRVoZVkCGkDehwIDkmQgtg7NvJmSRBd7EgxzV2Oea8xYnOraLFlCwvFXqwtBuWXCbDgMs22bn1QmxYaLuqCbWBRed3TkHlvgeK3axG2uA900ZiSzE/AODVAF6GnrDyOwAeB+CLATwDwF9W1jtCyK5FCytZyRRtYcWHLCJcsbBVh5DosqJtq3xRVyhQ24QVLjLIMCGLismlNJUcKVpAsT367OuzcrH4EngX2dGtUljJE6NDcJUaJYS0k0ElnrWpynvFnkPGUtpOicAiggsocLSRGHHl+2FElYfV1BdCCAGwcyB0ucBnDcJ6F1YWEi4BRbtPh+ZU8Xmf2MftBUObvFYIIe1A27GNuSQRYQXoJaLVuIQVYKeN9FXbODjdbwttu1h28QCE2zUpG1zUDsYkgtwtcEwhbSZU5IjxRvHZutj75BFrF8v8LWaG+jgEFtIuYsSVwwB+AcDbAdAplBBSCwsrJs+KLDTsBYaIJa4Ejvq7nTPADg3Si4rlqyZO3TcY5gklrp3XkIFVFhekepjQlgwD2mNlcilNL5y5xfn/NmSBkCVCy8LAtntiF6sUVULDIst69XXG5/s85trmPdcEzLdC2szCSppKKI9PaAm1dT5BWbBDyIt6rxSxjbWInDc/pv99l9PWfCtAXM4VwORUuR4mJOiVAH4FwD9X3SlCyOhjJ5O186xMLqXOhI5yje3qLtdJmJDs0PrKNwuXU3Pe9nC5nKap7KrKYkHvsroEltgBtYldRi5ACGkO1yRfe6McOnZp+/PmgRkAKt9KBhOrK9vtAXdYpat8vSB2r0h+KrGRMbmmqoQ2jZB24Mqbos+5Pttk2TpbUIn1cNF9C7mmiODss3cxnnV9oT7d5LUAjLeK/t4gDEfyEyuufALA8wC8BMDPAfhJAL8O4PcAfLTarhFCRhEZ1MSzxHbptEUWWVicRG/XQ7u2+wZJXxUhH7JAWL6apq4EtnkLBLpjE0J8aLtmJ6nViWu1QCLJbU8vGbsH9Hu6SDvt3SdJcUMqCmliRRVbdHYdF1yiND32CBlNfKKKTwix29qeLVWF9Zw+H3evqpLWFglX9FbhaUhYaZuQ0mavFSA+oS0AfB7AKwA8BaZi0H8GcB+AvwawCJOb5WlVdZAQMprogda1U7ExZxYdksBRD4q2MAOYhYq8gN7AqO99ca03EIrHCpAd+1/GdZ05AAghQM+NXVcD0rZJ7JbYPRFcfDlWNJsHZvq8XLJc4H0iSqwY7RKdfclpKTwTsjvwVfvRCbl1W93eV0GoaF/0tdqzOUR0ripxd5k5YGiJ5bpLMbdNWBkGYj1XNOsAfhwmye3zAXwHjLDyaABvBPCdpXtHCBkZXEKKrvSTJbQcP9zvyeJD3+/imllI6EXDwene5H9/kiQisLgm/2USLcbcp6rnEPO7ZK4V0jYkh5TvXEjYj12WXn/XIUEi4PhEmSr/PnwhknkVgOi1Ug/Mt0KaJM9DxZdrJa8scxY+O2d7vci8sCphJcQDL1ZY2bN8YlsoCRU06hJWpC9tFFba7rUClBNXhE8BeE33BQDjAB5ewX0JISOCHjztwVC7atqDsmtw1YNjXrytLawIPjFFn4sJCfLRGZ/Pzf5NYaU6+LskbUKLKoeOXUovnLmlT/SQz1UlXgT6PWO0KK1xea9UWWI5Tzzh3ykho4WdT0U8jnWYoqttnq2z53hi07I22bLIu66qxLVFPVZixIw6hBX9/DYKK8NCjLiyDhMSlMdWwb4QQnYJdvJFIF8o0YOiCDJlFiEavRjI23HlwoAQEsLxwyZfinw/dOxSCpUfRWygbffEtrl2W/VOrBzXttS+V9FFSAgh9lBEatpNQnYPeSKyJs/TxT4unn5V5WKpCoaBE6H1rjUOLgCYB/CepjtCCIljcilNJxyLC70A8OViyUqEFjLI6lKkuhKQxpeYkQuE4YBu8aRtyMLCLikP7KwG5NvR1clwxVbq9j5Xd51/anZvkhSpBKRxhfvQJjYP7R5pC648J4IOf/TN8WzPEbviYwhFhZeYcCCbIsKKeIY0HQrUFg8VCUOy3zXDEBIEVBMWRAghXkISMvqQnCm+HQ9XDG/IoOoqrWwLKa72+jsXFYQQHy67t3bPWQDA9O1Hd3ju2bh2bW0vFH2+jJ0NhblSCCE2IorEJsWWa+2iA3bJeJ0nqi5vlSrDIkMoEn5TV36VNuVWkZ/Rft+zfGJohBWAniuEkIrJ2rlwEeK2rgdf2X315VPxDfBZg6cWSvLKitrtSTvg7i1pEtvu2R56tueKFld8NlB2YE/O9LxO8hYw2ubaldPk+qoXEqNmD8e2TqEzPt90N4Kg3SODRts6ly3JslEh5ZjF1tkbX7LRVqXAUkW55SKJa4swqslrQ3+usX0nhsbW0XOFEFIprt1UWVjYu7V2OBCQvTOh3dr1gkMjg+XFtZ0Vhnyu8VmJa23hZdQWElkMyyKDCwzSNLaXycSxS9t2xpVjCui5yZ9Ez1a68qzYdk+Ohe4U2671cl0ZkUUvfHabXWwLtHukCXxCyNjWKazeMA+fF4tPGLE9VYBeVbPLaZrKJpduF7KhlvWcWHw2rkg4UBEho85yy3WXcq7i2cMkrADAdU13gBAymiys7Cw/6so7YF/jO2e7h7pEErvM6PHD/QJOXs4Bn9u7LCB2G8MgrBDSJiaX0jQ2RMfVXtst2+6JHTs4bV55ib3rqg4kCw4KK4TsLhZW0nRhpX9OBgAHHjyF6bGebYpBCzWdK4tp58qi047a9w15Tll716SNG4T40ZawIBfDJqwA9FwhhATiSkCWh5Qi1bu4QkwVCxl09W6tb7CUwb6M66jslOxGQcVmWLxXCKmDInYPAC6cuSU51LV7WSFAWXZKzi2s5Nu9LM8/185uGe8VXa6eEDIaxFRgFC+U5avGs2Rs61RvgT4LTMucIScXi9gf2wNmz0MvBM5NYQ9OYP1FaV+ItssDuWjOF3mmxjXvq9I7LybHSptFD+KH4gohJJciQoUM0pNLabqpjvtEFXuwzMoNYOdy0QO0a4DVoUIhiwlXmJArCe5uoM3CCt3iSZ0UsXsbc0my7b2S46lXJXl9dS0+5NigkzkOExSXyahTdCNKhyhOj89jD0zuDvl7kTmT2Bef8Kvtz7ZIc9MR4KZ7gZsf0/c3KGJO7MaXy87Z8zh5Tt3zu1DRZFDCSpsFnGH0WgEYFkQIySEveZmL5avGZTTmObN7k0S7mFY54S+yS1tkACeEjAYLK2l6+nxYUm59jbZ7mwdm+sotu4Rlu9qZRlzrxW6dPm9smbyEkBLLRRN97yYhWdBiSluFFQrLpElsWwfAKaxoQrxi+v7ebn7MjvPi2eKyTbHzO2BnzpSxrVOF8qjE0GYxg1QDxRVCSDB2fG1eOz34yuLCN8CGLBD0vQRXYrPYRZHgqxpke6tQdGmO/UmSyKvpvpDRJs9m2Ug7W0TJukeMCF2V3QvNh+AKjdyNYksb0HaPto80jfZamd2bJGsdYPWGeax1/MJfqK27NrsI3H8rcG4K1667e/u4CCtATwQpY4/kXlOvSzD1Ov5JtYWxfScSeTXdl6JQXCGEZCKT8FhPEhl87aS2ci9934PT4cKNq282rh1ie7fXhS/W1neODAaZrHFRQdqOeKK47J6vvet4Ec+9kDxWDAEaHmj3SJvRAosc883jpIxyqPfKtdnFvkSuod4kZeyb9igJfV7dnm1NVvIZJCKkDLOgomHOFUKIF12VoijiXg/0BmPXABy6q5HVF51XwLfQ8IUI2WFAru8ARZam4AKDDBMuu+daWLjCLrUQHCJuh9g9ISREUts+hkc2C+0eqZMyif+BnblMxJOl6Bxv29bc4M590hmf3yF8FJ2byb0kB0rdQkYbw4Ga7JP9+x4VYQUAhvEHuQBgHsB7mu4IIaPKwkqa2pPvUM8S3wDqWliIeONKtJiHvsbV3uWlcvq837Ve4xrUJTTIlezWhUwA2hqzP0xwgUEGgct2hYYExdg93da2SUC2/cuzlT67B4TZPmBnCKRLYGF4UP3Q7pG6KGrrsgSSLFunhWMXvmSzefMoLbR0xud32CnfPFHbrywvlZj5W4i3S6yYMaqCzygLKwDDggghHlyJE6tCu5FmlQktgx5IXYuLLOwB2hUaFLpLUndytDrhjjXZjRTJ1xSCXdbZ95wy9s9n92zbF/KMrJBI2gZChpuiuelCCC0J7ztn2xeZR7nyQGVhJwUfNuoUP9roSTMqDKNSRM8VQmrEHhRj86GIcDK7N0n0Z/v+J2eSxDUA653cqgbErEHdFx7k25l1hQr5oPdKMXzlT7mTS+picqk/+TYQ7rUC7BROFlbSVF+fZff0AifUuySEPAElxHPP57niakvqgXaPVIXLa66InQP6bZm2e67vmtiqPvZ8wOU9bM+18gQYl91ybYRV7bkCtMt7pZDAcmQdODfV/w6YzwFk/Tyj4sFCzxVCiJcik3tfvC1ghJeTM0lycNrvXhrqXRKD6+eQ3Qy9AJEBN2vBICUAYxYVdZT3G0TliNhdoirI+j1dTsvFhxNSFXZ1M1eyRrFxeYJyXVQlTrdNWBnVijnD7OVIho/QuZYtqOQlphU757N1MZ55a52dAodr/iVtdBWhLPvkmtvIPTrj89uvGELbh4olyR1IkjvQnqo5R9Z7Qor9XhGdK4sjMcdjQltCyDbLV9PUHvjyvFYkgZnrXJEKQBo7WVpWO6DYYkJfk+WxEosdC1wnstCoWnywfxcxv5+iv0tXwjphFBdUZPD4vEq011zebm7eAsG+Ps9jJe8ZvrxSsTYvr739N9s2zxRtA+qyezZVjguEjBIxXi9lKOoFHJuQuy1exskd/ZElIrA0Jj5UJKLkCUutEZJKMow/BMOCCKkAe4GhhZWQATNLVAl9ftFrgf5FQt4ixG6T1Q4Yjom0T2ioc6ERusioczFCgYWUYXIpTW1XeNcxH7bbeyx5oZB51Gn3NHWLqEXJ+vuvy/a1QVyh3SMxuOyU7YESe33RPriIKSYwtnVqO3xFFucuESRmQysmvDuWPA+0kFAcW1zR1CWwZPYrVFzJCA0K9dgZBYGFYUGEEADxSRR1+4WVNLXd5LOIaRtLSH6VvAUGkzXuJOZ30vRChJA6EY+TWIG4zlDIrCocoXZPE/L3vhvs5G74GQnJIyvMx9c+9hl2hSBN1XlHQkLAdxOVJbfNEGF2UwJdhgURsouxvVfsc/LZ1cZ2c8/yYtFiirSTRLk+ocVeLNgVf3QoUF6ZP2mn37PIS2rbZuryaBnG3wUhsYTavdNL+X9PvnvF5F7x2T0gzO7ZbeugLbbSZfuqsntN/4yX0zSl9wrJwxXymFcOHnCHMdrtsrxYfJ4uPlvnCuXWoorTC2V253P7PFAKhAzVQVZoM2CEojJCg8+zo4xHS2afYkOCIhPcjiL0XCFkFyG7D3qwcw2yRXYdQrxRLq71wolEWPGJMr4SenaeAhmkfXlXYgfQ2b0mWaK8t21CW7RPbfhZJIlcmUS5TGpLYllYSdPJJfM6ftjYELs6kLZ7eSVKT84kiYgdobZSFjnyHHsR4hM/5Dl1lG3WiE3Wr7y/09jE3mUoa/fK2r42iEeEhJC1aQbEhT/mHbOPy/wyy9ZptP3yhfLYQkVnfN45hwidU2ib4LMLZecpeWFJ12YX+16ACQW6NruYGRKUxdi+E6WS33q9g4qIJCWElVFIaktxhRBSCKn8I6+8/Cu2CLKwYnK8hCxODk73u877BBYXo+DWPYjKQINAL1C4WCGDQtuLvCSyeegd2ryqGVnX55UodYkt0nd5D8m5UgbttZFnR+W8r63v+qz7jordI6RJYnKt6HZaGKna1mnEjrkqAwH9x+SzS9ita05RRmAJTZArgkpr843sYi+UIjAsiJBdhO2VonMHhA6c4qGiQ3qySi/P7k0SvQiwd4iPHw6rehGyeMhKkhYyQJatblQGewFRl4fGoCpsZFHG1X4UxDIyOCaX0nRjLkkmHWE8diJbl/AittFVTtlnM10eebbdA/oFY9vzzmcTXX0sK6z47F7o35rrb1iuzcttYD+7Tru0P0mSYfV8o90jIbjKIId4kAix17nsI9DvyRziXbfdZhrADfNOe6GFiqJ/Dy6hNsQu2PYsBgkTsoUT8dCoWlAZ23ciKer9UTZkKY89yycqz5/TRiiuEDLC2AsCPUi6KlTk7TDoikIhbeVdT6D1dRtzSXKye+zYGXNM+nRweufOrBzT5/Rn14IkRFjJE1XqnpT7Bvy6ntcGYUUQgcUltLj+3ZoUwMhooMUWbRNdIowtSMtnn6AhHnlZ97DtrrbF+trQkJ8iJZnrImbxkbXQGXVvFdvuhdo/QjQu0dfXxvc9L+9eSN4Wl6gCmPFa28w8m9YnsljkzeWyBJA8exI6xytaZcgloLTWS8XHuanKSjJnMXS/FwcMCyJkxNFunZNLaSr5BPQEP7Qs3+zeJHHlQglJSrt8dWdFIV1l6Phh906ujSsxrS8/C1Bux0FTl3t6E4uIti1cuIggVWILJL7vG3O9pI2Sl0W3y/JQyQtptPMOxP4M2kNF2zXbTur+xCK5VfLOS5vYPAR5IT9Fzo0K9kLRDsGyf3cUlUkeWaE7rgS1mjwb5QtjPDidHf4j4eMh/ddcXNtp0/LyP5VF5nhV2Df7vsV71TJCwoMYQkRxhZBRxR7wXDuyRe6rJ9syANoTPxFMpCKQXOfaYXUdC92FbWK3tso8AEXucTlN07KeJ8PgGr/W2bm4IyQPsXOuMCDACCq2QJGXwBZwL1zs70VyG7jIyq0i2GJy3bbQJ7RkUWUOhFGze3miv233QhLGk92FLWYsX90ZDqTfQ++j24udse+h/z/q57jGamlbRAB2iSwu9N9T2c2amHlHnuBcZI7XZELXoJAgn3hybipaWJEkvKPgraKhuELIiKIHvNBdiazBWAZueeUNePbgpL1f7LZVVbywkaRnOu6/ygoXw5pssS191pMYJrglVSDeKL48K4JUDJKcLPpa3QZwV1nzYYswPu8XLea4PFE0WWXkfR57dVJW8GzK/rTF7gHxv0MKzMTm5EzPkzhLfHPZLu3tqz1QYjztXAKgrwJkWTsVOkd0iRxl/u6b+LsLTYLbKIFCSnIHEnkNoFetgeIKISOKz1XTXkC4rtHhQ0LW4OaKs9W7KK6dkayBtiqxxVf6V39uatLapol+XWjxyf55fd/XOu7fDT1YSAg+zxVt92yhI8/bRWyh7d3iK2Ov7Z6+Rj9XSkKHYOeUqkJMqeJvqYggOqp2T/9ceXbPbmt/1v82tHvERZbYmxUelGc/lq+maUjot8zx9KvoBkmI52AsZcoo24T+/dnPK2PrynrZFfEEKZLINpmYSpKJqQTnpvpKSLsElTyBZZQ8WIbxh7gAYB7Ae5ruCCFtJi/ru15ISMUMu5qFPif3dFW6kPAfnfDWheta+7x939ByozpmPa+NUPVEP3ZAjH2+vn/ZvpcdvPXz5V6junAiw4Nt97RnCrBTQBF745rcZ3m/yHUnZ6qxe/Z9866rq0JQEWj3aPfIYPEJuy6vOd1GRBJ7viT2wOcBMz1m/p+nm+vmfNdrYf1FKfYn5r7TY8DY1ikAxvvCNRfz2ckinnwhVGnnYkLz5PfbpK0rEl4kAosWQtK7sH2f3eaBUhR6rhAyosgAZIf62IkbXZUr7O+yS6LPubxa9MCZtRvhO543qIZ4tNhhP1WGAWURM2mXCUiT+HZWY64lpK2IvRNhRUKAdBstHLvICivyYedLcZ3LOxZCGe++phYcbYB2j4wC2m655nh5oT1jW6e25yHaCyX3wfc9YF433Wte6NmAtWK/GA0AACAASURBVE5+SEtbqprVTVmvmSpsTawXiM9zRQQVCivhsBQzIbsEXylQuzyzRnZuQ5I92l4vE6srAIA3rQKbB2a8VS7se/sElrxB2S5pae/M1O21IvfLE1mqElaq2DXVoTiA+3cU8hwuOkibsYVhoFcGHjA2aGJ1BZsHZqLuK3ZPhA4trEysruA0ZnLF6yzyxOYiVB1iIvcbVLLYQdg9V9vYc4QMmqwwIf19egzAg+azCCv2NXr+1Oe1cueP9LWbuvm3gZsmt/+W1joAbpiHb7rmE4WHxdbFiMllBJaqPFhEYPF5sYztO5HIuSzxhMJKHBRXCBlR7IFFCyC6ig/Q26EVQQTAjoWGnNPH7TAe3XbtnrOYvv1obj/t3AOu0KBQZCKg8U2cq3S/jEF2dpqqNuQia4HEBQQZJvIqY7i8VcS22QJLlj30oa8B+u2ZDvPJ8uqrY6EhyOKAeTyyF0u0e6TtuKqX+SqWLayYXCoaMxfpeSu4Ku30zZ/OTQFH7wXOvrSvjVzTJ5x45m3ShzKed64Kky7qsHWhAkuRZ9Y5/8oSWUYlz0mboLhCyIjjygdgV/GRnVbNxOoKNtRnFxtzSXLa4zofIqzUgUtgyeNymqZVDWKhHixthAsKMirYu7giqoi3ihaUtRDss3UabfdkQeMSS0SsEcFEiyYugSVEVKnKrb7qhUeo3WubjaHIRIYdl8dKVu6VtQ4w7cmJkuXhe212EXuWbzUCCwCcfSmuXXc3gPkdYon9vcpwoNh7aVvnq2YUQ6wHSyjaU7guKKQMhjaJK9cDCCmQ/fC6O0LIKKETzUqCWBlgbOHF9jY5dOxSumndT3IYHDp2KUU3WeSOHC6HZ7w7s65BPyZhY1biWtl5sUOEXNgu4fagVtcioG2LC0JGEVdJUTvho2btnrMA/KKwz+6J7dr2ToHbwyXU7gmDyE0wPTacdo82lJAwtA3UHh8y/9EigQ4B6lxZTPcsn0B6F1IjqpzANZgEthLavOdZb0Vn/C24uNr/TJetq6oCZBm7KD9r3d57Re87jBtyxE2bBqhvBPDagHb7APwHAG+stTeEjBCuMnr6++RSmsoOLpDtdbJ5YGaHu7xvkeBa4MjAm1VhI09YEbTAEnNOt/GdKzqBr3PndlALIUJGAdvG2MLKoWOXTCLGrt0DwjzuJEQoz+5pgVnEmbxEk8KgxBWbsnbPZ1OrLktK20dID988y3XOhdib2b09YUW4NrsIwJ2sdn/in+OF2roiNFmOPs9rpQpxRbxYaOeGkzZ5rrwTwBMC2l0A8PGa+0LISOEz9q6dXN/iQhYUIW7zQH+1Ihlk9bN06b/Qcss2rkm8LapoT5aQ6+tibOsUOuPzpUOQONgSEkbIJFcLK/q7toO2oByS/NZn9+yFR905VgZNVo4r2j1C6sHnkWcLHL6SxzvymBxZ3/44tm8q0bk6shK1avtWl8jSZMWhkHCgsqFHZSqakXYwjP9wFwDMA3hP0x0hZJiRwc+VzFbCgy6cuSU5dOxSunlgpq+kqbSXBYacC0EGJxFUJFTJJsR7RVfe0bsqWkwJqQjhouzAZu+2irhS1f0JIfHoEEbb5r18/Cju3OoXV8T2lbV7ZRYYLluYFR6ZR9HqOCEMW84VQkYJW9SwvYd1NUc9r9PXXU7TdOp1iRFYzk1FV4rRXnt1CMdNeq0IdSW1JaPBdU13gBBSL8tX01QPrgsrve96obF5YAYXztySAL2FxaFjl1I5ZlNUWJEB5+B0T1iRz65BMzRWVwstevGgyzIPCtcCw+VSSwipB7FzYuMml3qfBbFhYuNEWNHHbIoKK76d5bzqRnmE2DfbHtZFHUkeCSH5+OZ4LmFFmFhdweRS//xw+WqarnVMKJBJVhvfj7I2LY+q8rcQUhdtCgsihFSI7SEy2U3C6Es0K22gdnNlgXH8MCBVNuxSprJgsXcodBs96XZNwKsYLDvj8zu8Q4pQVYnkrB1c7t4SUg9ZiwkXF87ckrhyTm2dWerb2QX6BZUYu3dxrb9fOidLVS7zMQKLiyps0uxe2j1CBoUrYXdWe12hrE8kdlynq8qkm+sp7nvAfLn/VgAmsa38Pfv6EWJ/ixJajtlFFR4leRWD6LWyu6G4QsiI4hIsZEGg3dxFLNkeIHWlnzPmzZczQA+ertwBCytpqhOl6apFLrQniz7mQjxVtJgin+1woSpEl1hcpUm5uCCkfuxJ/aFjl1IoN3iXHQR6HnsiKruEFakQlLdwWFjxJ+y2BaAyrvN5FdEG7bXnEpZp9wipnizb4cr55BNfXLnwABUedOeP9Bp3yzBP3b8BAOhcWUxXb+hdW2cSW5syAksV6LyB+jshDAsiZIixS8tpXIKIjR6YdZvjh92DdszAqUvw2YsM+3uImGK7v3fG572CiRwvIqhUXQ6PyckIqRbt8p5VJUOLI7awotscP2zOy8v3PKDfLvpsZKid9NlZIDtUEugl6rY/a0KFlbVOtSE9+5Mkod0jpDyuEG5BeyKHhOKcnDG2zi4frwm2A/c9gPQd9/ZVFbJpa6Juhi+SuqHnCiFDhJ2sTIsUIS6hgL+Kheu7jZ0cTbxeZDdXY++qyIAmiwWd0DYPWSRoD5S8XVlbWAn1ZqljMcAFBiHFkZBGwG3n7KTcm9Z5l8eKPh9r97bbR5RVFnuX56niS/Cdh1RGs4+FXku7R0jzuKrsiN2SHHhSQh4wtk570dnXZs3xTmInOtxlrQOMzS5iz5EXAvc9AJx9qWkk7wBw9F4ceNDMpVwJdOuiyZCgQdyTDDcUVwgZEuyEZfpcnjumHvTqGABdcbuygNAD/8Hp3uBtiywhuMSQkGpAruuYYJaQ4WJyqT8UUVc787XXnzfmjN1zLSpiEBua5W5vhw2JzQ3ZzS2ycChSNaiKawkh1eCb44kYPKFEFanoKEwu1SdsJBNTCQCk77g37RNWuux56IXA1ikAgxVYCGkrw6i2sRQz2ZWEDFYhg1rRTO6uLPA6cZlr0aAXGHI+qyKQPic7sVn5BATZOYgN6WFeFELaTZZ4AvQWHn27uSq0RzxWYqr7aHzVL/TOcJ64Yts+X+n52BwCdsy/jc922nZvrcPdV0Kaxjd30+WTJem28LCXvHr7s/ZOLvp8+9rOlUUT+nNkHdtJbW2B5egrcO0rz+H0P8wHzUFj0X3S9w6xlcyLQpqAniuEjAAyMc/KP1Al9kDl2q2wd26z3OFdrvA6n4AsEkKEj7xKPVVdQwhpFqlipquc2RV+gLiyyVloQUV7AuZdJ7avaALGkAWBq3qFFqBdQsv+xFwz6KS3hJBiTN9+tK+q2YW57DCfoogtGdubJJ0riylwN8ZuO5Gkm+spXv7b/WFC99+KzpEUB28oX/0sNHdM7HPyqvsQUiUUVwgZAuyBxBYqtMghx+ySoFUo9iKi6HvZVYN82MKKnSMgb9ERKqzo9xjBhB4rhLSLPK8VbeP6Sot28eUdiMUOA5JjYveyQn1c57StE1FZv+vzocKKfo9ZRHAnl5DmyRILxKZtwHjo2VXN5PoqN9O0XZAcdducmwJuuhc4+goAwLWvfC7WOsVyRWlChRXdR9o60kYorhDSYlwDpggrvhAd1wBVZFDRgkzILoEshHQehKxFR2icf5lki1pkse9xOU1T13FCSLPoMB7JkyJChs8LxXW8yGLDlVAyj9Pnd5Z1Boz900K4S1TJouhiIMsVnnaPkHaQJ/7axyeX0vSCx/7F2jr9bC1QuDbi1jrA9Pg8LqfzadK1G50r59I9yyew/qIUa51nBAsr9kZc0VAmaa9tne63rqRJUYUMGpZiJqTFnD7fL1oAfsHC3lmNRbu4Z5V4drXXyWuzdpuzvFNcg/Ps3l5JzzK47qHLhRJC2oUILHYSWxdVhf2EklWxKBSfLZQQyaoWBFxYEDIc5M3fyto5224dnN45x7MFChEnZDNMjnXG57vCSrjHisuOl523uvot3+VV9t6ExELPFUJahp180a7EU1ceFUFKPEtln7z20h97cTGxuoLTmMHxw72FhM+N05WLgIMiIbsHl93rq/BTo92Lid+3d5u13ZtQeV8E8TLUdi9rMVKl3XPdi4IyIc0RkrdpEHM8+RwSWiMCS5U5SwYtiBMySCiuENJSXDuhdSaolQm/DPiuBLmu/ojLvl16WS8wLq7tdOOU5wLFkjwSQkaPQ8cupZvWsbrsnp2jSr6H2L0sT5WJ1ZU++wfsFDq0zSWE7C58NqZuW2cXH/AJJjqkULxV9HlXWHdWVTVCdhMMCyKkRRw6dimdWF3ZIazUvZORt1t6cLr3AnoDr10d6PR5s7CQHdyQ59JDhZDdjdi9Q8cupXYZ5TqfWzRPgQvttWILKy5o9wjZvWjbU1VoTOjz5HuIuOsSX6bH/GXe854rxIZREjJMUFwhpCW43MsH5TppV8OQzwen/QlypU3WpKBosjIgruIFIWQ4OXTs0o5wwkHaPaBnn2wbaLfX5zfmksQlIssxCQcqIuBwt5eQ0UPmWdpTt25RxX62LerIZ1/OOxGBRVARUaWvehCyfw4KLGS3wbAgQlqCnqhfOHNLgjP1P1Nc0+2yzYAZbCU0SMfb+nIH2MnKJOdKyEKpSAlRQshoceHMLSbcZgC2zxYwXN+1+7zd7uRMkoiHitjutXvOQsqkhhBblYgQMrzYCWQHIazYoopta+xwcDscSNqtdYywIqLKnuUTODALrN4wH1VCmYIK2Q1QXCGkBRw6dimNnZiXRZKU6cE2r2ydFlOy8qTIzxK7Az091ovlpds8IaPN+PhcunbPWQDYtn11e63o5Lh6wZG3QPCJLKfPm1AgLY5PrK7g5NwtwT+HtqW0e4SMHk1sHGmBOMsjL0/ctYWWPcsncG12cftckb4xoS0ZZYbxP/cFAPMA3tN0RwipivHxuRQwC4wLZ8In5UUJyU4vOwy+0s/6Wr3QEDh4EkKyELsHAFtbS62ye0C27dPJvDW0e4QQjRYmBiWe2sm6887Ld9+mmXitiLDSGZ8vVXms7gpwhDTJMP7HprhCRg7JOzAIYUVje6/YCwXh+OHeuRCxpco+EkJGk7aIKyKobMwliZ1UV8iye1ItjbaPEGLThLgi+MIbXdgedJfT3jVTr0uAI+sAgGRiinaOkAwYFkRIwzQhrMhAm5UzJZSD0+5kaIQQ4kMnsh20qGyLKhLSM9Htk6uUMtATUWworBBC8hiksGKLKT7bZSOh2ZfTNJU8K9vc9wBw/60V95SQ0YPiCiENs3kgLOlrlehcA64dDZ0/QBYZMjDbHix23hZCCMmjSbtniyoh2OLzyRmTh4DCMiEki6byKJ2cSZLJpTQVe2cLLFkhQPpdVwZK7hjKiAdCBgpLMRPSMIMuOyqDrR5wT5/v74e9aysLC5d3y/LVNM1KbksIITaDFlZsuxcjrGjEBrKyGSGkTcgcz34XtM2VOZsur5yFJLAlhORDcYWQXcTCSpoeP2x2L3QeFRvfwkPvesi1UrKZEELaxsJKT1Cx2Twws/2yyRJfTp/v2TwKy4SQJtEiSowXsYgqUqHR/gz0ktjueeiF5TpJyC6C4gohuwRdik8+i8giOxobc0myMZckOgeCXmSIoCLHxOuFEELaiivXgAgqYvOK3JfCMiGkaewyy678T2Lj9PxvrbNTTNHH1zrA6g3zxmvl3FSdPwIhIwXFFUJ2ESFJF8V1/sKZWxK9o2sLK0Xd6gkhZFDIYsIlorhEFV9y3YnVle0XQFGZEJJNE7no7Dme3jyTPi2smCqROhdMnkjMsCBCwhnGxEQsxUxIIFJqGegN9HpRIFUuJpfSdGPOvPvuZSe51RU1ZGeYFTMIIU0j5Uflu8uu2fbOVyFIztlo20e7RwhpAm3rfGKOlGKWJNxFnjM9ZkKExvadoK0jJAd6rhDSAIPa0XBlqRchZGOu5x66MZckh45dSvXOrH1NVn4CQghpC7bYITu3Ytv094nVlb6wSN89fXaPHiyEEJtBzfGyhF0dAiTCyuzeJJkeAw48eGr7lZfQ9uIa8PpVoDM+X23nCRlRKK4QMmDsXdUm0IuIyaU0PXTs0vZEwLWIcC0gbK8VQgjxkeUVVyc6oe2FM7dshwbp/hw6dindPDDj7aN46unvAu0fIUTThjmefv7CSpqKx0rnymK6naR2+QQA45ESUjHIlZ+FELKTYXTvYlgQIRVhhwqJa7wsJOxFxOnz2HGOIUGEkGFjYSVN7TxSgN9DhXaPEDJsLKykqS67PLZ1CgD6qv9cu+5uACZ5rQvJx0JbR0gYX9R0BwghzWHnJbAXFq48BPb30+e5e0sIGS7E9tl2Lyv3ioZ2jxDSdvQcb/lqmh7wtOuMzwP0TCGkEsqqkGMAvgDg0xX0JRR6rhBSEvFYkURnclyH/4QuMoDsXAWEENIGXHbPDnl0ee3Z53UZ59o6SwghBZEwoNm9/UlsxXtFQoKuzS5ibN+J3ES3rvx9hBA3sTlXvhzAKwF8AMDnAVwF8E8wAssHAJwCcFOVHSSEVIsv0Zq9E+srSWrD3VtCSNtx2b2sXFI+4cTOv0IIIU2ibdvyVZNfJUsM6YzPY/1FKZI7kHTG55EnrOSVaSaE9BOjRD4OxlskAfAmAB+DEVceAnAjgH0AvhXAIwAcAfChSnvag54rhBRED8J2wjP5HFv9gqVICSFtJtbuubz2XMfouUIIaRNaKNECi+29sj9JksupOZaXqJZeK4TEEZNz5ecAfAJGOPknT5sTAP4cwMsBfG+5rhFCqmL5aprauw+TS2mqhRF70ZEnssjC4mTVnSWEkArQdk/smVQE0sKIzr/iu5d4rIR69BFCyKCwvU90OJCII7bYkiWqUFAhpDgxYUFfA+B34RdWAOCzAH4TwNPKdIoQUi1aWDl9vrfQODmTJLKgWFhJU5frPMN+CCHDiLZ7th3TQopt93z5ViisEELahu2VMj0GXE7TdHqsX2TJC/8hhFRDzEThj2BySf9QTrv/AuBWAN9YtFM5MCyIkBJMLqXpxlxPVBF8IorLg2VjziSEZDgQIWQY8Nk9oN/2Se4pV5iQXM9wIEJIW+lcWUw74/MY2zqFzvh8X9iPneA2i4trDPkmpAgxfzTPAnAPgLMA/gAm58onYZLZSs6VFwD4cQB3AHh9pT3tQXGFkAx0RQz9HcjOp6IXGFJN4/R59CVvlB1daXtwOr5/dDclhFRNUbsnSIikXPemX9lp96QdEG/7aPcIIWWxQ30A46UCAFOvS3BtdnG7EhBgqgHZAovmwIOnAGBHm6JJbCnGEBIXFvSXAJ4P4CCAt8BUB/oHmDwsHwKwDJPQ9hjqE1YIIRksrKTpwelqBziXi7xrsSLuqNNj2ffzVSsihJAi2MKKTZHQRm33XNWBYhcftHuEkDqYel2Cqfs3gJvuxZ6HXggcWQduute8DxhfeDkhu4miC7CbATwVwB4YgeYqjMDyIQB1/1HRc4WQQLLKj7qqXwA7XeRdniu6nd7BFWElL/s80FuccKeDEFIlZe0e0O+5Auz02gPivFdsMYZ2jxBSlnRzPcWdP+I+efQVwP23Yv1Fxhza4UGdK4vpnodeiGvX3Q0AWL1hHkD1pZdp68huI6ZakOY+AJ8B8CgYgeafAHwc9QsrhJBA8nYPXIsFjcs7xbcwEdY6Pe+VEIGFgy4hpE4OTpvFguRRybJfgBJhBtA3QggpSufKYooPP9A7cOnj5v2Wx3qvkQ2w9C6keOiFwH0PYM/9J3BtdhEAhRVCqiAmLAgAngDgtQA+BeB+AB+G8VbZgsm/8noAX1Vh/wghFXP8sHltzCVJnru8yx3exh6M1zr5wgoTpRFC6kBCI+UF9N7F7ontE/un7aDLJq7dc3bbFmrR+eJa/GLk5EyS0PYRQsrQubLYv3kmwopw9BVIbrs1EdFEM7Z1yoQN3fcAcPalwJH1vjwtVUE7R3YrMZ4rT4YJxfkYgFcD2IQRWVKYhLbjAL4LwF8BuA3ApSo7SggJQxKeaQFDFhw6CZorY7y9UJBd3onVFfzA+82x16LfeyXUNT4mSz0hhMSgPfXEzrmSP2r7CPg99zYPzPTs3vhRvBY9zz2pKBRK1bvBhJDdy/LVNO2MGZEkue3WBADSpz89BYDkfe9L8D4Av/NWpHchXbcS1Y5tmQS2OPvSyvulE4ITJ18E4KcAvA5mDU1GlBhV8QyAaQDPBfA5T5vrAfwxjODyHeW65oU5VwiJZPlqmsoCI6uE8vLVNNULAZ1zZe2eswCA6duPAjCLj5DKGaySQQipG5ddc4kr+pwIvi7xQ2zf2j1n8fLxo3jtV5vjeblXLq71vuvn5iXdJYSQoqTvuDfdFlrUZ5vLaZqObZ0yiW+7XLvubnTG5/H61WLPpk0L5tsBvAbATQA+23BfSI3EhAU9EcAfwi+sAKYs810AnlKmU4SQ8kjW9oUVs4iws7jbOww+rxJZTIio4oI7s4SQJtFeestXe3YPMLZN7J2cl3N54u/07Udx59bZzGdLeJAIKxfXdt6X4UCEkKroXFlM07tgXpvraXLbrUn6jnvT9B33prj5MUjvQmqHDskcrzM+j/WbJk0i23NTmaWaSaX8IIDfB4WVkSdmoH89jEvT9yA7ce3vApgC8OwS/cqCniuEONA7o6GumXrHVXu22O1cyW0F23uFniqEkEEiXiuxLunaZuVVGBJcFdMA7t4SQurlcpqm+xNjZ9LNdWOv7nugv9HNjwHOTQEArs0ublcAAoydu5z22zktqoRskg25nbsOwAtg1pFfDuD5AB6EcQq4AuAru+cfAvA/AHxQXTsLoNNtdweALwXwJwBWYFJjfC+AxwA4D+AvHc9+LExajVsB/A2AZwJ4JIC3W+0eD2AGJgrkoe6xI93X52BSb7xDtX90t28TAG4AsN7t+6dVm9tgCs+swKT4eCKAd3Z/HlIDMX8kt8L8pzkP4A0w/4D/CPOPfyOMm9N3dl/fCuDPK+1pD4orhDiwFwdaEMnLD5C3yHDdU1cOOn546AddQsiQUibOX
Abstract. Climate change impact assessment related to floods, infrastructure networks and water resources management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on historical observation-based gridded data product, with coarser scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.
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