A Critical Assessment of the JULES Land Surface Model Hydrology For

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Sciences Discussions Earth System 4 Hydrology and , of the model with ff ), Amazonia hosts a significant , and J. L. Guyot 3 2005 , ´ a (SENAMHI), Lima, Peru 12524 12523 , W. Lavado 1 ´ eveloppement, Lima, Peru ecting the performance in smaller, upper sub-basins. Latrubesse et al. ff a e Hidrolog ( ı ´ 2 , C. Onof 1,2 humid tropical mountain basin of the Peruvian Andes–Amazon at 12 km , W. Buytaert 2 1 ciencies and high correlations between the simulated and observed streamflows, but ffi This discussion paper is/has been under review for the journal Hydrology and Earth System Sciences (HESS). Please refer to the corresponding final paper in HESS if available. Department of Civil and EnvironmentalGrantham Engineering, Institute Imperial for College Climate London, Change, London,Servicio Imperial UK Nacional College de London, Meteorolog London, UK IRD – Institut de recherche pour le d of approximately 6 million km rates of the region. Weical conclude systems that to strategies be toand (1) improve regional the addressing floodplain representation errors in of the in trop- subsurface the representation. forcing (2) incorporating local wetland 1 Introduction The humid tropics hosts extremely biodiverseing ecosystems, climate which and are land subject to use chang- patterns and potentially changing water cycles. With an area those observed in similar environmentse elsewhere. We find reasonablyhigh positive model root-mean-square errors a We attribute this tobaseflow. errors We in also the found water a balance tendency and to JULES-LSM’s inability underrepresent to the model high evapotranspiration investigates how well such modelsvant represent spatial and the temporal major scales hydrological –in fluxes an poorly important at understood, question the data-scarce for rele- reliable environments.360 model The 000 applications km JULES-LSM is implemented ingrid a resolution, forced with dailyare satellite evaluated using and conventional climate discharge-based reanalysiscomparing evaluation data. methods, the The and magnitude simulations by and further evapotranspiration, internal throughfall, and variability surface of and the subsurface basin runo surface fluxes such as Global land surface models (LSMs)(JULES) such are as the originally Joint developedmodels. UK They to Land are Environment provide increasingly Simulator used surface forthe boundary hydrological impacts simulation, of conditions for land-use instance for changes to and climate simulate other perturbations on the water cycle. This study part of the world’s remaining rainforest and is an important supplier of atmospheric Abstract Received: 14 October 2012 – Accepted: 20Correspondence October to: 2012 Z. – Zulkafli Published: (z.zulkafl[email protected]) 5 November 2012 Published by Copernicus Publications on behalf of the European Geosciences Union. Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/9/12523/2012/ 9, 12523–12561, 2012 doi:10.5194/hessd-9-12523-2012 © Author(s) 2012. CC Attribution 3.0 License. 1 2 3 4 A critical assessment of thesurface JULES model land hydrology for humid tropical environments Z. Zulkafli 5 15 20 10 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , , ) ff ). ), ers ff ¨ uger 2011 Giertz Chap- 2005 2011 , ; ), which , , 2006 2006 , , Bekoe ; D’Almeida et al. ; Blyth et al. Guimberteau et al. Alkama et al. ; 2003 ; , 1989 , Giertz et al. Bormann and Diekkr 2006 processes reported in the ; ; , 2003 ff , 2002 Ebel and Loague 2002 ). In this context, an LSM o , , ), while the upper Andes–Amazon , interflows in the organic layer, and ). Another disadvantage of concep- ff Legesse et al. 2003 ). Although the PTF for tropical soils ¨ , osmarty et al. ; 2012 2006 ¨ or , , V 1984 ¨ uger 1999 , Arora and Boer , 12526 12525 Campling et al. Campling et al. ; ; Decharme and Douville ). Moreover, lateral runo Aalto et al. ). Many hydrological studies have continued to focus 1978 2002 , , Cosby et al. 2005 , 1984 , Guimberteau et al. Giertz and Diekkr Darko Dunne ; ) representing various environments (e.g. 2011 2001 , , Vertessy and Elsenbeer Yeh and Eltahir ). Models for tropical basins in the literature tend to be conceptual models Salati and Vose ) such as saturation excess surface runo Paiva et al. 2006 ). LSMs, also referred to in the literature as land surface schemes (LSS) and ), but depending strongly on local calibration. This can be complicated by the low ; , 2010 , Secondly, paramater uncertainty is certainly an issue. In comparison with the tem- Distributed physics-based modelling has been attempted in smaller catchments with However, despite their sophistication, LSMs are newcomers to hydrological mod- Hydrological models are a common approach to understanding and predicting the Baldocchi et al. data of temperate soils (e.g. pell an advantage in thatetation its worldwide data application are means available.example, global However, the datasets while soil of these parameters soil exist,functions and are most veg- derived (PTF), are from which not physical are soil field a maps based. using product For pedotransfer of regression analyses of soil water retention natural pipes are seldom modelled explicitly due to the LSMperate vertical regions, structure. the tropicsproducing mechanisms has ( lagged in soil characterization with respect to runo time and fully-distributed mode, as it simulates the exchanges of energy, water and humid tropical literature ( some success ( More recently, landdrological surface modelling models at (LSMs) globaland have scale the (e.g. been continental scale used Amazon2012 ( for physics-based hy- land surface parameterizations (LSP),elling were community originally to developed by provideweather the the forecasting climate and land-atmospheric mod- global boundary climate condition simulations. in An operational LSM operates in continuous complicates scenario analysis. ate climates that may beare more often problematic over criticized the for humidity tropics. an for For absence basins instance, LSMs of withsoil a a column shallow groundwater ( water model table and as therefore it unsuitabil- assumes free gravity drainage from the elling and while( there have been point-scalenone has validation thoroughly exercises assessed their inis performance flux the in tropical issue tower upland with sites basins.processes. model First, Such a there structure simplifying that assumption stems mayvironments not where from hold there universalization in may topographically of be complexresponse. locally-observed multiple en- Additionally, there interacting are factors established controlling weaknesses the in hydrological the LSMs even for temper- the ground and vegetationconsistency between canopy. various A modelled processes particularnutrient such cycles, as strength irrigation, photosynthesis, and of carbon crop and growth. LSMsto This provides is study an opportunity the the for impact hydrologists biophysical processes. of change on hydrology in interaction with the other land surface carbon between the land surface and the atmosphere by accounting for processes in tual models is the unidentifiability of their parameters ( availability and high degreeespecially of true uncertainty for the introduced Amazon by whereactive the erosion the river in streamflow morphology the data. continuously uplands change (see This due is to headwaters is a serious concern,basin, not but only also because because of of its its influence link on to the local downstream ecosystemimpact services. of change, but modellersderstanding of and tropical data environments availability face foret issues parameterization with al. and process validation un- ofrequiring models few ( inputs (e.g. 2004 on the continental and2006 lower Amazon (e.g. system receives far lesssuch attention as despite deforestation, being oil subject exploitation,tial to mining, impact increasing and of a hydropower human changing production. impacts climate The and land poten- use on the hydrological regime of the Amazon moisture ( 5 5 25 20 25 20 15 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 2 van ). 2002 , 1988 , ) approach. ), while a brief Kahn 1948 , 2011 ( ) or the alternative is a vegetation parameter Penman 1964 erence approximation to the ( max ff ective parameters that are not Hodnett and Tomasella C ff Best et al. ; 2000 , , where max C / C 12528 12527 Brooks and Corey and infiltration into the soil moisture pool based on the Tomasella et al. ; ff ). This does not necessarily mean that internally all fluxes 1998 ) palm forests of high economic importance ( , 2012 , usion equation, with infiltration and gravity drainage as the upper ff Beven et al. Mauritia flexuosa ´ on River) Basin in Peru which hosts half of the Pacaya Samiria National Re- ˜ n In JULES, the local throughfall rate is proportional to the local precipitation rate by In the subsurface, an instantaneous redistribution of moisture is assumed and wa- The LSM requires time series of meteorological data i.e. incoming short wave and Our approach moves beyond the traditional model evaluation using strictly observa- In spite of the limitations, LSMs are increasingly used to simulate hydrological fluxes The number of parameters required per LSM pixel certainly introduces issues of data Tomasella and Hodnett and lower boundaries respectively,tion and characteristics follow root the uptake model of as a sink. The soil water reten- the fraction of occupied canopy storage, and is a linear functionis of partitioned the into leaf-area-index surface (LAI). runo Hortonian On the infiltration ground excess surface, mechanism, throughfall account enhanced for macroporosity by in a the vegetation-specific soil. factor to ter is exchanged between theDarcy–Richards soil di layers using a finite di tion, sensible heat, latentevaporation heat, estimation canopy is heat, based on andCanopy evaporation the ground is Penman–Monteith surface assumed ( to heat.and occur The at bare potential the soil potential rate, evaporationstate while respectively. are plant restricted transpiration by canopy resistance and the soil moisture sisting of vegetated andbalance, non-vegetated resistance surfaces to with heat distinct andsynthesis, parameters respiration momentum for and transfer, radiation growth, canopy etc. interception, Land-atmosphericfor plant heat each and photo- grid moisture are exchanges calculated byexchanged area-weighted with averaging a of shared the soil tile fluxes, column. and these are long wave radiation, temperature, specific humidity,These wind are speed, used and surface in pressure. a full energy balance equation that includes components of radia- 2.1 The JULES land surface model A full description ofdescription the model is JULES provided can be for found completeness. in Land surfaces are modelled as tiles con- 2 Materials and methods tional data to decidea whether “fit a for purpose” modelpossible simulation is ( system acceptable. that Instead, “shadows”are our the correct aim natural at is system the toof as pixel observations. evaluate scale closely Rather, and as we at intendthe to each reality present time in a step, terms simulation which that ofof is most the the closely main infeasible basin’s resembles statistical given hydrological properties fluxes), the undermost (especially lack the robust the assumption in mean that representing and such the variation a impact system of will perturbations be at the a basin level. (Mara serve, the largest floodable forestlogical reserve in system the provides Peruvian important Amazonia,freshwater where ecosystem turtles the that services hydro- are for vulnerable toaguaje unique extinction, ( species as well of as approximately fish 10 and 000 km in land-surface modelling, which isspatial mainly scales. However, concerned it becomes with problematic representingheterogeneity for fluxes hydrological and modelling, at where large non-linear local responsesespecially in to mountainous perturbations environment where needexpected over a to a high small be degree scale. simulated, of heterogeneity and may be such as discharge atmodelling basin humid and tropical global scales. upland This basins. paper The studies case the study implications is for the upper Amazon River are better alternatives, they stillyounger lack volcanic representativeness soils. for the upper Amazon Basins’ collection and scaling. Consequently, the majoritywell constrained are and e applied homogeneously in space. This may not be a serious issue ( 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), (3) (1) (2) that Clark ff ). 2001 ( ectively ff 2009 , ). On average, ). Precipitation 1 ). The flood wave to the outlet along C 2009 d , Kvist and Nebel ´ aramos) on the Andes. In Garreaud et al. ). The average yearly tem- , as measured at San Regis 1 − s 3 C, and the austral winter (JJF) is ). The climate of the region has ◦ 2009b ( ) is the sum of all contributing local Garreaud et al. ). Precipitation is also orographically ; 1 sim ). The Andean ranges, with elevations 2009a Q are the two parameters of the model that , ff 2001 , 2008 will vary by its distance 14 km) resolution. Each pixel is assigned a set , i 12530 12529 ∼ ´ on River is 14 900 m ˜ n )) 2 Espinoza-Villar et al. i ´ t on (Peruvian Amazon) River Basin upstream of the conflu- ˜ − n Kvist and Nebel t ( based on a time-moving surface partial contributing area. In ective hydrometeorological controls separating their west and 2 i ff Espinoza-Villar et al. ff Q ) and ´ on River Basin is about 23–27 ˜ + n S) ( latitude-longitude ( ) ), a grid-based implementation of TOPMODEL calculates the local 1 ◦ ◦ i t ) formulation. In the alternate soil hydrology model of JULES ( 2009 to the outlet from pixel ( − t t ( Bookhagen and Strecker 1 i 2008 routing 1980 , Q W, 4.4 ( ( ).The tropical wet climate supports expansive lowland, montane, and flood- generated by JULES consists of local surface and subsurface runo ff ◦ 1 ff n = i X i i 1 2 2009 = d d , C C t = = The basin is underlain by silty Gleysols in the river floodplains, and young unstruc- The lag time For the study basin, we evaluated both the basic JULES (JULES-BASE) and the sim, 2 1 i i FAO ( able forests as well as wet grassland (locally known as p exceeding 4000 m are e east and connecting regions at lower and higher latitudes ( tured Cambisols in the Andean foothills and Leptosols and Regosols further upland reduces the temperature ( characteristics vary across latitudes as influencedena by operating synoptic at meteorological varying phenom- timecontrolled, scales with (see the Table wettest bandof found the at an Andes altitude ( of 1.3 ma.s.l. on the eastern face perature in the Mara warmer than austral summer (DJF), during which cloud formation and rainfall e station (74 Garreaud et al. 2.3 Data 2.3.1 Study area The study area is theence Mara with the Ucayali Riverthe that yearly discharge forms in the the proper Mara Amazon River (Fig. tively. Finally, the simulated flowhydrographs at in the the outlet basin, ( lagged in time. Q been discussed extensively by various works, see for example where the subscripts 1 and 2 represent surface and subsurface components respec- t are optimized through a Monte-Carlo simulation. the stream network: t and Gedney The runo need to be routed for aOur meaningful model assessment against is streamflow measurement simplebetween data. the delay pixel function, and with thevelocity outlet the for divided the delay by surface the for and flood subsurface each wave runo velocity pixel ( being the distance Genuchten saturation excess runo this configuration, the model appliestivity an exponential with decay depth, to assumes the a soilto null hydraulic generate conduc- flux lateral lower flows boundary, for and the applies subsurface. an anisotropic factor JULES-TOPMODEL parameterisations in distributed mode.into The pixels study of basin 0.125 is divided of soil parameters, the distributionlogical of variables the from land global cover datasets. types, Theinitialise and model the time-series goes internal of through states. meteoro- a warming up period to 2.2 Runo 5 5 25 20 15 10 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , ) 2011 ) were Nesbitt , ). These 2006 ( Kalnay et al. 1998 ( ) remote sensing Best et al. resolution) of eld et al. 2007 ◦ ffi , erence vegetation indices ). A gridded map of mean ff She a, Peru (SENAMHI) and the ı ´ ). ce version 7.7 Central Ancillary 2008 ffi ). The HWSD sources the ISRIC- , man et al. ff 2007a , latitude-longitude (111 km) grids were 2009 Tomasella and Hodnett Hu ◦ , a e Hidrolog ı ´ ) was used. FAO 12532 12531 Lehner et al. a, Ecuador (INAMHI) monitoring networks (for ı ´ 2000 Josse et al. ( ). The global soil textural map provides information resolution, ◦ latitude-longitude (14 km) grids using the lapse rate in- 2009 ◦ , FAO a e Hidrolog ı ´ ) at 1 km resolution ( Loveland et al. ). This is a 90 m resolution field-verified mapping based on satel- ) (the data will be henceforth referred to as TRMM). The simulation 2009 , 2007b 2009 , ( FAO ) climate reanalysis product merged with ground data (the data will be hence- The land cover was parameterized based on multiple land cover maps. The primary A new set of land surface parameters governing the soil hydraulics were created The model default vegetation parameters (see Tables 5 and 6 in Josse et al. Nacional de Meteorolog precipitation. 2.3.4 Streamflow data Daily streamflow data for fourChazuta, hydrological stations were at obtained Sanchemical Regis, through Borja, control HYBAM Santiago, and of (geodynamical,from HYdrological erosion the and alteration Servicio Biogeo- and Nacional material de transport Meteorolog in the AMazon Basin) the TRMM 3B42 (versionproduct 6, was bias-corrected 0.25 withand TRMM Anders 2A25 climatologyof (0.1 JULES was performedand 2008 over to the coincidence with entire the basin periods of for available a data from period NCEP of and the 11 TRMM yr between 1998 The global land surfaceused model driving and data included developedtemperature, meteorological by pressure, variables specific such humidity,first as wind, generation long NCEP and (US and precipitation. National1996 short-wave Center The radiation, of dataset Environmental Predictions isforth the referred to asfurther NCEP). disaggregated The to dataset 0.125 atterpolation 1.0 method described inmethod the was same used paper. to The disaggregate precipitation. nearest An neighbour alternate interpolation precipitation dataset from digital ecological systems database ( 2.3.3 Meteorological data (NDVI). Additionally, canopy heights of broadtree leaves were mapped from the local 90 m resolution hydrographic dataset ( grassland above the tree line. 2.3.2 Land surface data The watershed boundary for the study basin is delineated based on HydroSHEDS the southwest, shielding by the Eastern Andean range generates a drier climate and source is the Digital Ecological( Systems Map of the Amazon Basin of Peru and Bolivia Database ( World Soil Information Soil and Terrain (SOTER),to which have in highest South reliability America ( isfor considered the topsoil (top35 cm) 30 cm) and bottom and 2 subsoil (total that depth of was 265 assigned cm) layers to in the JULES. top 2 (total depth of lite imagery. In areasClassification outside Map of of this map’s coverage, the 1 km IGBP-DIS Land Cover and standard deviation of thefrom topographic this indices dataset. for TOPMODEL was also generated using the pedotransfer functionsfunctions developed were by derived using a databasethe from the best Brazilian Amazon available and pedotransfer aresilt considered function and for clay tropical fraction in soil. the The soil, functions which are take available values through of the Harmonized World Soil are applied uniformly inspatial distribution space were with obtained from theProgram the and exception UK are of Met based canopy O on height satellite and derived LAI, normalized whose di 5 5 20 15 10 15 10 20 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 1 ff − Coe ; ), while 2012 ) are taken , 2007 4 , ciency (NSE), Daren-Harmel ffi ). However, a sea- 2011 , ). Missing days from the ) observed in Paute Basin e model e 2 ff ) observed a negative bias in in the NCEP reanalysis data, 2011 Guimberteau et al. 1 ( − 2010 ( Rollenbeck and Anhuf ; and Table ´ on Basin is the austral summer. ˜ n 1 Bruijnzeel et al. ) were calculated adapting the method in Ward et al. . 2006b B , . culties with water balance closure in similar 12534 12533 U Clarke et al. . ffi U and ). . ratio occur during the austral winter. This is incompati- B . ff Buytaert et al. U . 2007 100) (4) 100) (5) ; ( L / / ) are evaluated by calculating the statistics of the spatial variability ratio, i.e. the total observed streamflow to the total precipitation 2002 ff PER PER , ff − + ) to account for streamflow data uncertainty. The entire time series – within the entire basin. In the absence of a dense network of local (1 (1 ) which in the case of the Mara t t 2 (see a comprehensive study by ). Ratios typical for humid tropical environments are in the range of 0.6– 1 3 obs, obs, 2007 − , 1981 Q Q , = = t t . . ). Indeed, large errors have been reported in the literature with the TRMM and B B Campling et al. . . U U Previous studies that highlighted di The internal model fluxes (i.e. evapotranspiration, canopy throughfall, surface runo . . Zadroga L ( as this can be1990 significant mmyr in montane forestssonality can analysis be of significant, ranging theoverestimations between water of 22– balance the (results runo ble not with shown) cloud water highlights input, that which the would occur largest during periods of persistent cloud cover 3.1 Uncertainties in the observations Potential errors in the observationsanalysing of the precipitation runo andinput streamflow (Table were assessed by 0.7 ( the values found ingests the severe study errors basins inor either exceed unaccounted the 0.80, sources streamflow with of water. measurements values For or up the precipitation latter, to cloud products, 1.76. water input This may sug- be responsible, magnitudes have attributed thesegauge to data errors for the in upper precipitation basin (i.e. because upstream of of Brazil) the ( scarcity of mean and spread of the distributions. 3 Results and discussion the maximum annual daily rainfall of up to 80 mmday simulation for the basin will produce similar natural variability, assessed in terms of the 2002 NCEP precipitation over the Amazon Basin. both datasets underestimating precipitationon during average. In the the dry Brazilian season Amazonia, by 50 mmmonth observations, the distribution of observations from the literature (Table available from the modelling period of 1998–2008 were used. and subsurface runo over each major biomes – lowland forestforest and (between flood forest 1200 (below and 1200 ma.s.l.),shown 3500 ma.s.l.), montane in and Fig. upland (above 3500 ma.s.l.) systems as as substitute for observations from a “real” system. The assumption is that the best calculated deviations wereand modified Smith using approach 1 described by ( Regis. 2.4 Model evaluation Streamflow simulations were assessed withthe the root Nash mean Sutcli square error (RMSE), the relative bias and the Pearson correlation. The U For this study, the probable errorviation ranges of (PER) the were estimated errorscalibration using between campaigns. the standard the The de- water gaugedtions, i.e. balance and Paute, closure field-measured Nueva Loja, is dischargesincluded Nuevo assessed for during Rocafuerte, comparative and and analysis. the The San 4 last SebastianBasin additional three in from stations Ecuador, sta- INAMHI which are are is located tributary in to the the Napo Peruvian River Amazon further downstream of San time series were excludedper from uncertainty the bounds analysis, ( Daren-Harmel and and Smith the 95th percentile lower and up- station locations and synthesis, refer to Fig. 5 5 15 10 20 25 25 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . e ff 3.1 s over 3 ciently modelled, despite ffi ) is a prominent feature and is ). Large, lowland basins such as 3 1992 ), who found comparable, unrealisti- , ). JULES’ poor estimation of this base- 2011 ( 2011 , 12536 12535 ) show that with the exception of San Regis, the 5 Rasanen et al. ), but in the case of NCEP, the reanalysis model has Ward et al. ratios were found at the Northern Andean Basins (Paute, 2007 ff , Buytaert and Beven man et al. ratio values over the Paute Basin in Ecuador and the Baker Basin ff ff ). The ratios calculated with the TRMM product are higher and increasing ratio, and the opposite is true with the NCEP simulations. The correla- Hu 3 ff routing scheme. illustrates several spatial and temporal trends in the model performance in- ff 4 ratios. This may highlight that the underestimation of precipitation by global ratios with NCEP precipitation are generally lower prior to 2004 but deteriorate ratio ( ff ff ff ciencies. On the other hand, the Santiago Basin and to a lesser extent the Borja Basin, show The observed hydrographs (Fig. Several noteworthy trends can also be observed in the interannual variation in the The most unrealistic runo ´ aramos and jalcas, ffi a much less seasonallybaseflow variable component. response, The in baseflow which isAndean a likely wetland to flashy and be regime lakes sustained overlays that byp a form an larger a extensive system major of partflow of may these be attributed upper to mountain thestores basins incomplete provided (i.e. representation by of these lateralfull local fluxes and basin topographic the scale, depressions. natural where This theobserved limitation model at prevails fails at San to the Regis. replicatethe The the Ucayali-Maranon extremely role depression regulated of ( flow the regime floodplain at this scale cannot be ignored, as Here, the shapes ofseveral the missed peaks rising and limbs under/overshootingbe and of expected the recession at time the are to fine su peak. temporalthe scale These runo of errors the are model to and the additional uncertainty from river can be extremelythe flashy with course discharge of decreasing severalwhere by days. more there The than is model 5000 m dominant seems orographic capable control of reproducing on this the response basin hydrology, i.e. in Chazuta. capable of attenuating a large volume of the flows. in mid-2003 ( between 1998 and 2004 but showruno a sharp decreasing trendafter after 2004. 2004, In contrast, yielding the valuespoint for above both 1. datasets is Thethis not is significance clear linked – of to in changes the the in case year the estimation of 2004 algorithm TRMM for as data, one it a of may the turning be contributing possible satellites that Figure been held static throughout the timeof series. a Nevertheless, there drier is climate the during generalat tendency the Borja, wet not season shown) (based andstarted on the the fact in that trend 2003–2004. calibration of Therefore, campaigns maximum despitethe for annual possibility the the flows of streamflow strong stations a case high for streamflow precipitation data uncertainty, uncertainty3.2 cannot be discounted. Simulation of streamflow dicators that are in line with the observed trends in the water balance in Sect. ing the majority ofis the accurate. fluctuations in This the pointcreasing hydrograph, is trend provided after further that the evidenced the yeare by 2004 water and balance the simultaneously RMSE, results which in positive takes Nash a Sutcli general de- diverges between the simulationssimulations, with the bias TRMM starts and toin decrease NCEP. Moreover, in the with 2004, runo which the coincidestion TRMM with between the improvement modelled andentire observed modelling time period; series this suggests are that relatively the stable model throughout is the reasonably capable of captur- cally high runo in Chilean/Argentinean Andes, evenuncertainty with will interpolated limit rain our gauge abilitythese data. to upper The basins. identify high and data reproduce the hydrological responses of runo The relative bias is increasingly negative from the largest to the smallest basin and precipitation products isgions. most This problematic is at compatible small with scale and over mountainous re- Borja, Chazuta, San Regis,runo and Nuevo Rocafuerte have lower and more reasonable found poor correlation to the gauged time series. Santiago, Nueva Loja, and San Sebastian, Table and an extension of the dry season when compared to gauged data. Both studies also 5 5 25 15 20 15 25 10 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ). 2006 cient for , ) clearly 7 ffi Guimberteau d’Orgeval et al. ) also underes- were unreason- ). Both these pro- ff 2011 ( 1999 , Finch and Haria ; 2012 , Blyth et al. ) (with JULES) and Cox et al. ) to assess its robustness in repre- ective rainfall to the routing (as ev- 8 ff with JULES-TOPMODEL), resulting in 2011 ( ff 12537 12538 ´ on River Basin. A more optimal model may be the Bakopoulou et al. ˜ Blyth et al. n ) implemented a 2-D routing scheme based on the kinematic 2010 ( ) (with ORCHIDEE-LSM) in Amazonia. is a comparison between the modelled and observed flow duration curves, 6 2012 Dadson et al. ( ), who model the surface area and volume of swamps and floodplain in order A similar bias was observed by This reconfirms the need to simulate better the movement of lateral fluxes within the For similar reasons, JULES-TOPMODEL is underperforming when compared to Figure 2008 within the literature ranges. likely requiring an instantaneous redistribution of soil moisture over the entire grid in senting the overall hydrological balanceeral of negative a bias humid in tropical the basin.flood simulation We forest of identified ET evapotranspiration a (ET). is gen- The evengiven simulated lower the mean than for higher that water forunderestimation availability the of in lowland canopy floodplains. forest, interception, One which but possibilityitive is the for counter-intuitive evidence skews for this in this low is the bias weak. is distribution, Despite an pos- the simulated canopy throughfall (TF) largely resides timestep by converting the floodedand fraction into consequently an plant open roots, wateras surface. however, do The open subsurface not water gain tiles accessmates in is to JULES solely this due do to available not the moisture, the infiltrate. increase flood in Therefore, forest the the system open improvement of waterfloodplain to ET, the which implementation ET Mara may with not esti- be the su ( ORCHIDEE land surface model by to calculate the waterthis retention period. time, However, their allowing model reinfiltration splits into each the coarse subsurface LSM during grid into smaller subbasins, no observed improvement to the ET. basin. wave assumption that continuously estimates floodter extents level based of on each the grid simulated wa- cell. The land cover tiles in JULES are updated in the subsequent This assumption prevents drawing upter of table soil dips moisture below during the dryis maximum periods an soil when attempt depth. the to The wa- address JULES-TOPMODELthe this implementation model limitation is with persistently an saturated underlyingident and unconfined in loses aquifer; the the however, larger e contribution to surface runo et al. We further comparedfluxes to JULES’ the values simulated published in internal the literature variability (Fig. of surface hydrological shows that the partialably areas overpredicted. In contributing the to lowerhigh saturation basin, even for excess 50 % the runo flood gridbox forests is that saturated goes on through3.3 average, seasonal which flooding. is Simulation of internal variability of surface hydrological fluxes However, there is anregion overall and underestimation overestimation of of peakcomponent the flows, in discharge further the in confirming model. a the missing low flow to attenuation mid-flow JULES-BASE. The flashinessexaggerated of and the this is response suspectedthe simulated to mean be by topographic due indices JULES-TOPMODEL to calculatedskewed, errors is over and in the the the coarse parameter model time-series scale. grids Indeed, average were of negatively the grid saturated fractions (Fig. Studies focusing on simulations ofbiases the particularly soil moisture in state the havefree lowest indeed layer gravity shown in negative drainage the assumption soil profile, ( suggesting a weakness of the which provide a better insightThe into slopes the of model the performanceprecipitation over curves as the are the entire driving reasonably flowtion variable. regime. well intensities This simulated, from is particularly the likely observationlution with due than dataset, the to the which better NCEP TRMM also and estimates comes therefore of at may precipita- be a better finer at spatial capturing reso- local convective rainfall. timated primary production, whicha function may of be photosynthesis correlatedcesses rate are as within dependent the on the the model canopy soilit moisture, ( conductance is as likely is is that also the the bare low surface bias evaporation, with and ET is due to the model overestimating soil water stress. 5 5 25 25 20 15 20 10 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ff ff e ff ce) ffi 12538 and surface moisture fluxes in ff 12525 12525 , 2011. 12551 12540 12539 , 2006. , 1998. and consequently streamflow. In the latter case, data errors 12525 ff doi:10.1086/498101 The study is funded by the Ministry of Higher Education, Malaysia, and doi:10.1175/2010JCLI3921.1 ciency. We further assessed whether the model is capable of behaving as a mirror logged and logged forest areasdoi:10.1016/S0022-1694(98)00108-5 of Central Kalimantan, Indonesia, J. Hydrol., 206, 237–244, Meyers, T., Munger, W.,tini, Oechel, R., W., Verma, Pawto U, S., study K. 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Climate, Geol., 114, 85–99, In constructing a robust model for impact analysis of a resilient system such as the A final nonetheless important observation is that in the upland biome, ET is better ffi Asdak, C., Jarvis, P., van Gardingen, P., and Fraser, A.: Rainfall interception loss in un- Bakopoulou, C., Bulygina, N., Butler, A., and McIntyre, N.: Sensitivity analysis andBaldocchi, parameter D. D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bern- Arora, V. K. and Boer, G. J.: An analysis of simulated runo Alkama, R., Decharme, B., Douville, H., and Ribes, A.: Trends in global and basin-scale runo Aalto, R., Dunne, T., and Guyot, J. L.: Geomorphic controls on Andean denudation rates, J. Acknowledgements. grant NE/I004017/1 ofRichard the Ellis and UK Douglasmentation, Natural Clark Grenville Environment (the Lister Center Research (Universityfor of of access Council to Hydrology) Reading) the (NERC). for and Unifiedadvice assistance Model Keir We on ancillary with Bovis pedotransfer thank data, (UK JULES functions. and Meteorological imple- Toby Marthews O (the University of Oxford) for References of these natural features. explore adaptation of the model structure to better represent the hydrological functions dated areas of the Andean wetlands and in the Amazon floodplain. Future research will regime at fine temporal scale. Amazon, it is important tointernal represent states the and hydrological fluxes, perhaps system more holisticallye than in it terms is of to the scoreimage a of near-perfect real Nash Sutcli systems elsewhereWe in have terms identified of the the modeldue basin’s weakness internal to in of errors the hydrological estimation in fluxes. of predicting ET soil and water suspect availability this due to to be misrepresentation of the inun- reliable due to abaseflow large component uncertainty in the inable upper the to Andean driving reproduce Basins. the data Inof well-regulated and the the regime the flood peneplain, as poor forest. it the Nevertheless, simulationlogical neglected model for the of modelling, is a hydrological global the JULES un- functions model is that capable is of not purpose-built producing for reasonable hydro- simulations of the flow the poor estimationin of well ET estimated instead runo compensated makes for up model for and parametermodelling. the errors water and balance created a errors, false resulting impression of good 4 Conclusions Our study has shown that hydrological predictions with the JULES-LSM’s can be un- ing in flooded versusfiner dry scale grids. sections of the grid, althoughsimulated it and may because less of problematic errorsgeneration. in over This the is water balance, a this clear corresponds contrast to poor to runo the simulations in the lowland forests, where the subsequent timestep. 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B.: Representation of water table dynamics in a land surface 5 10 20 25 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | obs ) 2 Q − Normalized mean (mmkm obs Q (all available data) Mean -NCEP P / obs available data Q (1998–2008) precipitation. ) Period of 2 = P -TRMM P / obs Q (1998–2008) 12550 12549 S 93 363 848 1986–2011 16 601 45.6 S 200 114 991 1986–2011 4539 39.5 S 290 23 806 2001–2011 1585 66.6 S 1840 4917 1999–2004 109 22.2 S 180 69 175 1998–2009 3042 44.0 S 189 27 534 2001–2011 2176 79.0 S 290 5329 2000–2011 459 86.1 N 299 4640 2001–2011 593 127.8 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ observed streamflow; = W, 4.5 W, 4.5 W, 3.1 W, 2.6 W, 6.6 W, 0.9 W, 0.3 W, 0.0 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ obs 78.0 78.6 76.1 75.4 77.0 76.8 Q ´ ´ on on ˜ ˜ n n ´ on down- ˜ n ´ ´ on 73.9 on, upstream 77.5 ˜ ˜ n n tributary toupstream of Mara Borja Santiago tributary todownstream of Mara Borja tary to Mara stream of San Regis Napo Napo StationSan RegisBorjaSantiago PER (%) PauteChazuta 6.28Nuevo RocafuerteSan – Sebastian 7.05 0.82 13Nueva Loja – – 1.31 1.36 1.20 0.94 – 0.81 0.71 1.47 1.10 1.14 1.76 0.80 1.00 1.11 1.09 0.39 1.88 Data uncertainty and water balance closure calculated at the hydrological stations. Hydrological stations description probable error range; = StationSan Regis River basin Mara Coordinates Elevation (m) Drainage Area (km Borja Mara Santiago Santiago – northern Paute Paute – tributary to Chazuta Huallaga – southern Nuevo Rocafuerte Napo – northern tribu- San Sebastian Coca – tributary to Nueva Loja Aguarico – tributary to Table 3. PER Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (mm) ff (mm) ff (mm) Runo ff 1083 1046 1039 (mm) Runo ff transpiration (mm) Runo 3563 3334 63903300 4473 transpiration (mm) Runo 5400 6210 12552 12551 Pasoh, MalaysiaLambir Hills, MalaysiaFrench GuyanaBelem, Para BrasilienS Carlos De Rio Negro, VenezuelaS Carlos De Rio Negro, VenezuelaReserva Ducke, BrazilLake Calado, BrazilReserva Ducke, BrazilReserva Ducke, BrazilManaus, Brazil 3664 2740Reserva Ducke, Brazil 1800 3500Reserva Jaru, Brazil 1819 3200 2065 2209 1303 1448 1502 2391 2870 2391 1905 1346 2636 3188 1119Columbia, 2450 3000 Cordillera 3563 CentralEcuadorIdemCosta Rica, MonteverdeCosta Rica, Talamanca 1318 1966GuatemalaIndonesia, SulawesiPeninsular MalaysiaPanama 3150 1759 2128 Papua New Guinea 67 2175 2320 Peru, Central CordilleraTanzania, Usambara MtsAustralia, Se Queensland 2500Australia, N 2340 Queensland, Ub 640 SiteIdem, 2810 Mt Lewis 115Idem, Mt LewisBolivia, Yungas 2320China, 2900 Unnan, Ailao MntIdem, 2300 Xishuangbanna 2080 1679 Colombia, Central 2500 CordilleraCosta 2985 2220 Rica Monteverde 1230 Windward 3800Forest 1350 2394 Leeward ForestCosta 780 Rica, 3680 Monte DeWindward Los Forest Olivos IdemEcuador 1775 Idem 1930 3315 2115 1967 Guatemala, Windward 2610 1485 3970Hawai, Maui, Leeward 1554 Honduras 2030 Indonesia, West 1426 Java 1477 Mexico, Veracruz 1625 Tanzania, Mt 2358 Kilimanjaro 1554 2546 972 1215 2520 2318 2600 1010 1679 2553 1851 2140 2010 3300 1158 3300 2938 2480 2500 3000 3500 1638 2106 889 1819 3498 2640 2034 2275 2475 3325 ) Estado Amazonas, Venezuela 3244 1492 2595 195 1848 2007 ( ) Central Amazonia, Ducke 2500 1310 ) Bolivia, Yungas 2310 1825 Loja, Ecuador, Site 2 Loja, Ecuador, Site 3 Idem, Dry YearThailand, Kog-MaVenezuela, San EusebioVenezuela, La MucuyAustralia, Queensland, BlColombia, Central CordilleraIdem, Central CordilleraIdem, ZipaquiraCosta Rica, MonteverdeIdemGuatemala, WindwardGuatemala, Windward 1575Hawaii, WindwardHonduras 2085 Concave 1960 1700 Slope 5300 3125Honduras Covex SlopeHonduras Ridge TopJamaica, 1455 Pmull ForestMalaysia, Peninsular 4310Peru, Central CordilleraPhillipines, Luzon 1615Puerto Rico, 2500 Palm ForestBolivia, Yungas 2600Hawaii 1500Jamaica, Mmor Forest 2700Puerto Rico, 1252 Windward East 6000Idem Peak Reidge 1856 1372 1513 2850 6201 Elfin, Idem 1688 Leeward 2115Elfin, 2750 WindwardIdem, Sheltered 1273 4500Reunion FranceSpain, 5086 3910 La GomeraMadagascar, PerinetColombia, 1437 Sierra Nevada 2855 2825 5150Ecuador 2288 Kenya, Kericho 1410 IdemKenya, Kimakia 3537 Tanzania, Mbeya 500 6480 Queensland, Gambubal 1935 2081 4800Idem, Upper 6000 Barron 5400Idem, 1354 Mt 2685 2544 St Lewis 4500Hawaii, Volcano Np 1985 3000 2080Indonesia, 3105 Jawa 660Ecuador 3363 Mexico, VeracruzThailand, Kog-Ma 1713 Venezuela, 3863 San Eusebio 2130Venezuela, La Mucuy 1270 1350 1290 Costa Rica, 2050 Monteverde 2305Queensland, Bellenden 1925 KerPuerto Rico, Luquillo 2985 MountainPuerto 375 Rico, Luquillo 2015 MountainLa 6000 Gomera, Canary 3040 5760 2500 Islands 1342 8910 1256 4860 1271 3300 1153 3720 1386 1465 3100 832 1433 1768 1249 1560 3125 1450 900 2140 1459 1225 1010 1155 1270 1426 982 813 905 783 675 920 566 533 ) Loja, Ecuador, Site 1 2011 2011 ( ( 2005 ) 2291 1026 ( 2007 ) West Benin 1157 867 ( ) Bananal Island, Brazil 1692 1332 ) Kalimantan, Indonesia 2199 1918 2002 ( Bruijnzeel et al. Bruijnzeel et al. Rollenbeck and Anhuf 2009 1998 ( ( ´ arez et al. Continued. Summary of fluxes from humid tropical hydrology literature Fleischbein et al. Reference Location Precipitation (mm) Evapo- Throughfall (mm) Surface Subsurface ´ on-Ju ReferenceLowland forests References in References in LocationNegr Asdak et al. Campling et al. Flood Precipitation forests (mm)Borma et al. Montane forests References in Evapo- Throughfall (mm) Surface Subsurface Table 4. Table 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (mm) ff Ucayali river from the 680 1154 1191 (mm) Runo ff its of the basin to form the tion of the Andean ranges where transpiration (mm) Runo 2000 1200 600 850 ´ on River merges with Ucayali River from the ˜ n 24 12554 12553 El Angel, EcuadorCotacachi, EcuadorPiedras Blancas, VenezuelaAndean paramos, fromtoColumbian paramos 800 1150 1500 700 6000 3000 Loja, Ecuador, Site 2 Ningar, EcuadorJima, Cuenca, EcuadorChimborazo, Ecuador - Humid- Dry- PantanalAzoguez, EcuadorCuenca, EcuadorPichincha, Ecuador 900 1000 950 1860 1270 1500 900 600 Loja, Ecuador, Site 3 Ecuador Soroche,Machanagra, Ecuador 800 450 500 DudaJadanMataderoMazarYanuncayTomebambaPaute 1120 1230 750 1100 1160 980 1030 200 600 ) Machangara, Ecuador 1100 2006a ( ) Huagrauma, Machanagra, ) Loja, Ecuador, Site 1 2006b ( Buytaert et al. 2005 ) Burgay 820 ( 2007 ( Continued. The Peruvian Amazon Basin. The Mara Buytaert et al. ReferenceGoller et al. Upper Andes References in Location Precipitation (mm)Celleri Evapo- Throughfall (mm) Surface Subsurface south and Napo RiverAmazon from proper river. the The north basinthe beyond relief climate the (in and physiography red) downstream are shows limits highly the of variable location the of basin the Andean to ranges form where the Fig. 1. Table 4. : The Peruvian Amazon basin. river The merges Marañón with Fig. 1 south and Napo riverAmazon from proper the river. north The basin beyondthe relief climate the (in and downstream red) physiography lim shows are the highly variable loca Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

1998 2000 2002 2004 2006 2008

...... 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Runoff ratio Runoff : Temporal and spatial trends in the runoff ratios calculate Temporal and spatial trends in the runo The major biomes in the Mara 0.6 and 0.7 and TRMM and NCEP precipitation. The expected value for humi Fig. 3 and TRMM and NCEP precipitation. The0.6 expected and value 0.7. for humid tropical regions are between Fig. 3. Fig. 2. an ecosystem mapeach and of altitude the and natural biomes. the evaluation of JULES was performed separately for

: The major biomes in river the basin. Marañón The classific

Daily rainfall (mm) rainfall Daily Daily rainfall (mm) rainfall Daily (mm) rainfall Daily (mm) rainfall Daily

5 0 00 50 100 150 5 0 00 50 100 150 0 50 100 150 0 50 100 150 2008 Date

Chazuta

E & NCEP, or- S-TOPMODEL

10001.0 0.0 −1.0 . . 0.8 0.4 0.0 2500 1000 10050 0 −100 1998 2002 2006 ints for the modelling ce indices. Blue: JULES- rage precipitaiton time series. LES-BASE & TRMM, magenta:

Date

Santiago

. . 0.8 0.4 0.0 2000 1000 1.0 0.0 −1.0 10050 0 −100 1998 2002 2006 2007 28 27 12558 12557

Borja

Date

0040 6000 4000 2000 1.0 0.0 −1.0 . . 0.8 0.4 0.0 10050 0 −100 1998 2002 2006 Date San Regis Chazuta Santiago San Regis Borja

Modelled versus measured streamﬂows at all four gauging points for the modelling 2006

Temporal and spatial trends in the hydrological performance indices. Blue: JULES-BASE

. . 0.8 0.4 0.0 15000 5000 1.0 0.0 −1.0

10050 0 −100 1998 2002 2006

0010015000 10000 5000 0 0060 10000 6000 2000 0 00 40000 20000 0 15000 10000 5000 0

Correlation RMSE NSE

Relative Bias, % Bias, Relative : Modelled versus measured streamflows at all four gauging po Streamflow (m3/s) Streamflow Streamflow (m3/s) Streamflow (m3/s) Streamflow : Temporal and spatial trends in the hydrological performan (m3/s) Streamflow Fig. 5. period 2006–2007. TheBlack: observed overlying flow, gray: barplots 95 %JULES-TOPMODEL are confidence interval, and the blue: TRMM, JULES-BASE and basin-average red:and TRMM, JULES-BASE precipitaiton NCEP. magenta: and time NCEP, orange: series. JULES-TOPMODEL Fig. 4. and TRMM, magenta: JULES-TOPMODEL andJULES-TOPMODEL TRMM, and red: NCEP. JULES-BASE and NCEP, orange: Fig. 5 period 2006-2007. The overlying barplots are the basin-ave Black: Observed flow, gray: 95% confidence interval, blue: JU JULES-TOPMODEL &TRMM, red: JULES-BASE&NCEP & NCEP, orange: JULE BASE & TRMM, magenta: JULES-TOPMODELange: &TRMM, JULES-TOPMODEL red: &NCEP JULES-BAS Fig. 4 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

05 0 5000 500 50 10 05 0 5000 500 50 10

−78 −76 −74 −72

Streamflow (m3/s) Streamflow Streamflow (m3/s) Streamflow 29 30 12560 12559 Borja Exceedance Exceedance San Regis JULESTopmodel_TRMM −78 −76 −74 −72 Observed 95% confidence interval TRMM−JULESbase NCEP−JULESbase TRMM−JULEStopmodel NCEP−JULEStopmodel 0 −2 −4 −6 −8 −10 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

: Mean area-saturated fraction of the gridbox from the JULES

: Comparison of flow duration curves from observed and simula Mean area-saturated fraction of the gridbox from the JULES-TOPMODEL simulations. Comparison of flow duration curves from observed and simulated streamflows.

05 0 5000 500 50 10 e0 e0 e0 e0 1e+05 1e+04 1e+03 1e+02 1e+01

Streamflow (m3/s) Streamflow Streamflow (m3/s) Streamflow Fig. 7 Fig. 6 Fig. 7. Fig. 6. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent ﬀ Subsurface Runoff (mm/yr)

0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000

.0 0.004 0.000 .0 0.004 0.000 0.0030 0.0000 0.0030 0.0000 S-TOPMODEL &TRMM, d with the variability found ished values from different world he surface hydrological ﬂuxes in each Surface Runoff (mm/yr)

0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000

.0 0.004 0.000 0.0030 0.0000 0.0030 0.0000 0.004 0.000 31 12561 Throughfall (mm/yr) Throughfall

0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 5000

.000.0015 0.0000 0.004 0.000 e0 8e−04 0e+00 0.0000 ). Blue: JULES-BASE and TRMM, magenta: JULES-TOPMODEL and 4 Internal basin’s surface ﬂuxes simulated by JULES compared with the variability found Evapotranspiration (mm/yr) Evapotranspiration 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 0 500 1000 1500 2000 2500 Montane forests Andean grassland Flood forests Lowland forests Lowland

: Internal basin’s surface ﬂuxes simulated by JULES compare

.000.0025 0.0000 .0 0.005 0.000 0.0030 0.0000 .0 0.004 0.000 world locations (Table in the literature. Eacheach subfigure biome, is juxtaposed a with kernal a box density plot plotTRMM, red: of of JULES-BASE the the and distribution surface NCEP, orange: of hydrological JULES-TOPMODEL and published fluxes NCEP. values in from di Fig. 8. in the literature. Each subfigure isbiome, a kernal juxtaposed density with plot a of t boxlocations plot (Table 4). of Blue: the JULES-BASE distribution &red: of TRMM, JULES-BASE publ magenta: & JULE NCEP, orange: JULES-TOPMODEL &NCEP) Fig. 8