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Water is an essential resource for human society. Numerous approaches have been developed in order to assess the availability of water for our societies. Information on the availability of water is readily available from various databases, which, however, often come in spatial aggregation which is too coarse for detailed analysis or local use cases. In this research, we propose a Process-Aware Interpolation (PAI) technique based on previous research in advanced areal interpolation. In areal interpolation, the value of a variable from a source zone is reallocated among intersecting target zones. In PAI, ancillary information on process which causes the values being interpolated is used to improve the quality of interpolation. We test the PAI methodology in a surrogate modelling context by downscaling runoff outputs from the Community Water Model (CWatM) at 30 arc-minute resolution and compare the downscaled output to CWatM model runs at 5 arc-minute resolution. We develop two surrogate models – simplified models emulating a more complex one - based on machine learning (Random Forest Regression) and classical statistical methods (Ordinary Least Squares Regression). The surrogate models are used within the PAI framework as the ancillary information guiding the interpolation. The quality of the interpolation is assessed against a full run of CWatM at 5 arc-minute resolution, and compared to the surrogate models outside of the PAI framework as well as two simpler PAI benchmarks – a constant ancillary variable (rainfall) and an expert-knowledge based model. We find that the developed surrogate models perform significantly better when used within the PAI framework than outside. Further, PAI with the simpler benchmarks can produce comparable quality interpolation to the PAI with surrogate models. The quality of the interpolation is, however, highly dependent on the quality of the source data.

Droughts are causing severe damages to water abundant tropical countries worldwide. Their resilience to water shortages tends to be low, often due to a lack of water infrastructure. Moreover, drought characteristics and risk in tropical catchments are poorly understood, which makes it difficult to select adequate adaptation measures. Thus, reliable methodologies to evaluate spatially distributed drought risk in data scarce tropical catchments are urgently needed. We combined drought hazard and vulnerability related information to assess drought risk in four rural tropical study regions, the Muriaé, subcatchment of the Paraíba do Sul in Southeast Brazil, the Tempisque-Bebedero in North Costa Rica, the upper part of the Magdalena basin, Colombia and the Srepok, a Mekong tributary shared by Cambodia and Vietnam. Drought hazard was defined based on three variables, daily river discharge and precipitation and vegetation condition. Conditions below defined thresholds were transformed into a cumulative drought index. To assess vulnerability, we reclassified and weighted globally and regionally available gridded socioeconomic data to represent the potential of a drought to cause damages in selected socioeconomic sectors of rural tropical regions. Besides illustrating the relative severity of each indicator value, we developed drought risk maps combining hazard and vulnerability severity for each grid cell. While for the Muriaé our results clearly identified the downstream area as being exposed to severe drought risk, the Tempisque showed highest risk along the major streams and related irrigation systems. Risk hotspots in the Upper Magdalena were found in the central valley and the dryer Southeast and in the Srepok in the agricultural areas of Vietnam and downstream in Cambodia. Plausibility of results was confirmed by local scientists and stakeholders, who evaluated the results for each indicator and risk hotspot. The presented risk assessment methodology for data scarce and rural tropical areas offers a holistic, science based and innovative solution to provide relevant drought related information. Being applied to individual catchments, the findings described in this article will enable the selection of data sets, indices and their classification - depending on basin size, spatial resolution and seasonality. At its current stage, the outcomes of this study provide relevant information for regional planners and water managers dealing with the control of future drought disasters in tropical regions.

Historical Ecology of the Mekong River Version 1 (29-06-2020) If used in a publication, please cite as: Kallio, M., Walker Vadillo, V., 2020, Historical Ecology of the Mekong in a set of 5 diachronic maps. Helsinki, Finland. DOI:10.5281/zenodo.3827019 The set of maps presented here are an attempt to illustrate the complex dynamics that sit at the heart of the Mekong River Basin, and the long-term interactions that humans have had with this watery world. On the one hand, it aims to capture the changing landscape that follows the regional monsoonal climate, which affects both navigation and water-related resources. It also aims to bring forth the long-term interactions between people and their environment, presenting a diachronic view of late prehistoric, protohistoric, historic, and contemporary settlement sites following geographically-oriented landscape approaches postulated by the Maritime Cultural Landscape (Westerdahl, 1992) and Historical Ecology (Crumley, 1994). The data was originally compiled by Walker Vadillo for her study on river navigation during the Angkor era (see Walker Vadillo, 2016), a work that was made possible by the generosity of the Centre for Khmer Studies (CKS) through their Senior Fellowship program, the ENITAS grant from Chulalongkorn University, and various grants received from the University of Oxford (Oxford Centre for Maritime Archaeology, St. Cross College, and the School of Archaeology). The maps deposited here were funded by the Helsinki Collegium for Advanced Studies through the Core Research Fellowship program. The first version of the map, in the form of a topological map, can be found in Walker Vadillo’s doctoral thesis (2016). A monograph based on the thesis is currently in preparation and will be published in the British Archaeological Review (BAR) series Cultural Studies in Maritime and Underwater Archaeology. Further articles in regards to navigation and fish exploitation are currently in preparation, details of publications will be uploaded here as they become available. Call for contributors: The archaeological sites presented in this version of the map are not exhaustive and represent only major sites. The authors hope that the maps will be further expanded and corrected through cooperation with other experts as archaeological work continues to be conducted and new sites discovered. The authors would like to extend an open invitation to experts to make corrections and/or share relevant data in order to improve the map and further our understanding of the historical ecology of the Mekong River Basin. If interested, please get in touch with the authors about ways in which you can contribute. The work is distributed underCreative Commons Attribution 4.0 International Licence. See the licence terms at https://creativecommons.org/licenses/by/4.0/.The repository contains high-quality TIFF image files for each of the maps, as well as lower resolution PDF versions. References: Crumley, C. (1994) Historical Ecology: cultural knowledge and changing landscapes. Santa Fe: School for Advanced Research Press Walker Vadillo, V. (2016) The fluvial cultural landscape of Angkor: an integrated approach. Doctoral thesis, Oxford School of Archaeology Available at: https://ora.ox.ac.uk/objects/uuid:20b045c4-3e2e-4f61-99b2-5fcd904e3cdb Westerdahl, C. (1992) The Maritime Cultural Landscape, International Journal of Nautical Archaeology, 21(1), pp. 5-14 Data sources and references Albrecht, G., Haidle, M.N., Sivleng, C., Hong, H.L., Sophady, H., Than, H., Someaphyvath, M, Kada, S., Sophal, S., Chanthourn, T., Laychour, V., (2000) Circular Earthwork Krek 52/62: Recent Research on the Prehistory of Cambodia, Asian Perspectives 39 (1-2), pp. 20-46 Bringkoff, T. (2015) City population: Cambodia. At https://www.citypopulation.de/en/cambodia/ Bruguier, B. (2000) Les ponts en pierre du Cambodge ancien: Aménagement ou contrôle du territoire? Bulletin de l'École française d'Extrême-Orient 87 (2), pp. 529-559. Garnier, F. (1885) Voyage d’exploration en Indo-Chine. Paris: Hachette Great Britain Naval Intelligence Division (1943) Indo-China geographical handbook. Cambridge: Cambridge University Press Halls, A.S., I. Conlan, W. Wisesjindawat, K. Phouthavongs, S. Viravong, S. Chan & V.A.Vu (2013) Atlas of deep pools in the Lower Mekong River and some of its tributaries. MRC Technical Paper No. 31. With accompanying compact disk (CD), Mekong River Commission, Phnom Penh, Cambodia Harmand, D. (1876) Voyage au Cambodge, Bulletin de la Societe de Geographie, pp. 337-368 Hendrickson, M. (2007) Arteries of Empire: An operational study of transport and communication in Angkorian Southeast Asia (9th to 15th centuries CE), PhD thesis, University of Sydney. https://ses.library.usyd.edu.au/handle/2123/6135 Hendrickson, M. (2010) Historic Routes to Angkor: Development of Khmer road system (ninth to thirteenth centuries AD) in mainland Southeast Asia. Antiquity 84 (324), pp. 480-496. Jonquière, E. Lunet de la (1901) Atlas archéologique de l'Indo-Chine monuments du Champa et du Cambodge. Paris : E. Leroux Jonquière, E. Lunet de la (1911) Inventaire descriptifs des monuments du Cambodge (Tome 1). Paris: E. Leroux Kettner, A.J., Brakenridge, B.R. (2020) Dartmouth Flood Observatory. http://floodobservatory.colorado.edu/. Accessed 2020/05/01. Kummu, M. (2009) Water management in Angkor: Human impacts on hydrology and sediment transportation. Journal of Environmental Management 90 (3) pp. 1413-1421. Lorrillard, M. (2014) Pre-Angkorian Communities in the Middle Mekong Valley (Laos and Adjacent areas), in Revire, N. and Murphy, S. (eds) Before Siam: essays in Art and Archaeology. Bangkok: River Books & the Siam Society, pp. 187-215 Madrolle, C. (1930) Traveller’s handbook to French Indochina and Siam. Paris, London: Librairie Hachette Office local du tourisme au Cambodge (1928). Carte routière de la Cochinchine et du Cambodge. Phnom-Penh : Société d'éditions Khmer. O’Reilly, D., Shewan, L., Domett, K., and Sopheap, A. (2020) Revisiting Prei Khmeng: The excavation of an Iron Age Settlement and Cemetery in Cambodia. Asian Perspectives 59 (1): 33-60 Pavie, A. (1901) Mission Pavie, Indo-Chine, 1879-1895. Paris: Ernest Leroux Sam, N., Lieng, S., Thor, S. (n.d.) Improving Inland Capture Fishery Statistics in Cambodia. http://www.fao.org/3/ad070e/ad070e05.htm. Accessed 2020/05/01. Sar, V. (2010) The Current Conditions of Waterway Systems and Transport-Related Infrastructure in Cambodia, unpublished presentation for “Seminar on the Promotion of the Mekong River Cruise Tourism”, Hotel Pacific, Tokyo, 10/Sep/2010. At https://www.jttri.or.jp/koku/koku_semina/pdf/100910_seminer_Vutha.pdf Seng, S. (2010) The Mekong River System (basin) in Cambodia, Cambodia Inland Waterway Navigation. Cambodian Ministry of Public Works and Transport. At http://www.mpwt.gov.kh Service géographique de l'Indochine (1898). Khong – Indochine. Hanoi: Service géographique de l'Indochine. Yamazaki D., Ikeshima D., Sosa J., Bates P.D., Allen G.H., Pavelsky T.M. (2019) MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research 55, pp. 5053-5073.

Hydrological models are commonly forced with variables describing climate. These variables are often obtained from climate models, requiring processing via interpolation or downscaling in order to be useful for the hydrological modelling study. Outputs from climate – as well as hydrological – models are generally expressed as spatially explicit fields, but the results from hydrological models are rarely interpolated or downscaled. There can be many reasons for this. For instance, the existing model output may be of incompatible spatial units (raster, when vector needed, or vice versa), or of a different scale (regional, where local scale is needed). Runoff generation is a local process which is determined by the influx of water, landuse, vegetation and soil characteristics, and there have been many applications of its interpolation to watersheds, river lines or as continuous fields. However, most of the methods employed in runoff interpolation are statistical and do not account for process characteristics in runoff generation in the interpolation step. Here we present a novel spatial interpolation method for the purpose of downscaling coarse resolution runoff products based on areal interpolation. Areal interpolation is a process where a variable from a source zone is reallocated to overlapping target zones. We combine two advanced methods: Dasymetric Mapping (DM), which is simple Area weighted Interpolation (AI) informed by an ancillary variable, and Pycnophylactic Interpolation (PP), which is designed to refine the spatial distribution of a variable within the source zone. Each of these methods preserve mass balance – the volume of runoff from source zone is preserved in the target zones. Our methodology can address the Modifiable Area Unit Problem (MAUP) – a statistical bias caused by the sensitivity of analytical results of spatial data to levels of aggregation (the scale effect), as well as the arbitrary sizes, shapes, and arrangements of zones (the zoning effect). Addressing MAUP enhances usability of existing model results for runoff estimation because the zoning can be modified to fit the needs of a new analysis. The method is also able to take into account the spatial distribution of characteristics which govern runoff generation in the interpolation step. To test the methodology, we downscale a coarse global runoff product, LORA (Linear Optimal Runoff Aggregate), on to 126 Australian catchments with natural flow regimes, and compare how AI, DM, PP and the combined PP-DM fare against streamflow records. A recently developed topographical index, DUNE (Dissipation Per Unit Length), which is able to distinguish topographies with different runoff regimes, is used as the ancillary variable in DM and PP-DM. We assume that runoff is highly correlated with precipitation and we assume it can be interpolated with a smooth function. We also assume that topography can inform us about the actual distribution of runoff generation within a source zone (the spatial unit in a runoff product). We find that the simple AI method is more efficient in replicating the runoff profile in arid catchments where potential evapotranspiration is higher than precipitation. However, as precipitation increases and aridity is reduced, DM and PP-DM prove more efficient in replicating the recorded runoff. Using DUNE as the ancillary variable also results in higher performance in catchments with variable topography and performs worse in less variable terrain. In catchments with a high range of slopes, DM and PP-DM utilizing DUNE are consistently better than AI or PP, which do not utilize DUNE. Additionally, we find that in wet catchments which are located entirely within a single source zone of runoff, the performance is higher using DM, PP, and PP-DM than with the simple AI. In catchments which are covered by multiple source zones there is no clear benefit in using the more advanced areal interpolation methods over AI. Our results show that the method is able capture the spatial variability of runoff generation, but this requirescareful selection of the ancillary variable or a combination of ancillary variables. It is also evident from the results that arid and wet catchments require a different approach in runoff downscaling. Further investigation with a larger sample is needed to fully understand the properties of the downscaling methods.