DOI: 10.48364/isimip.769035
title: ISIMIP3a reservoirs & dams input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>In order to offer a consistent and common source of reservoirs and associated dams for climate impact modelers, we joined the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011a, 2011b), product of the Global Water System Project, with a set of dams provided by Dr. Jida Wang, from the Kansas State University (KSU). In total, the database includes 7330 dams, constructed/under construction from 286 to 2020, and a total global cumulative storage capacity of approximately 7000.5 km³. The dams from KSU (11) were constructed or showing some impoundment in Google Earth/Landsat imagery from 2016 to 2020, adding thus some value on the future projections of ISIMIP.
description: The data is based on Lehner et al. (2011a), Lehner et al. (2011b), and Jida Wang et al. (KSU/Kansas State University, personal communication, data starting in 2016). Because the data from KSU is yet unpublished, modeling teams using it are asked to offer co-authorship to the team at KSU on any resulting publications. Please contact info@isimip.org in case of questions.<br>The original GRanDv1.3 dam locations were mapped to the global 30-min drainage direction map (DDM30, Döll, P. and Lehner, B., 2002), by applying the following algorithm: Firstly, the locations have been rounded to the closest 0.5° grid cell centre. Then, the area of the upstream catchment draining into the GRanD reservoirs (previous version of GRanDv1.3) in the DDM30 map have been calculated and compared against the ones reported in GRanD. All dams with an upstream area bigger than 10000 km² in GRanD and more than 50% deviation form the GRanD upstream area (CATCH_SKM_GRanD &gt; 10000 and ABS(CATCH_SKM_DDM30 - CATCH_SKM_GRanD) &gt; 0.5*CATCH_SKM_GRanD) have been shifted to the 8 possible neighboring cell centers. If this resulted in an improvement, the dam was moved to the grid cell center resulting in the smallest deviation in the upstream area (i.e. MIN(ABS(CATCH_SKM_DDM30 – CATCH_SKM_GRanD))).<br>A visual validation and manual relocation were applied for GRanDv1.3 dams with a maximum storage capacity greater than 0.5 km³ (1108 dams), and additionally validated with the data in Müller et al. (2016, https://arcg.is/2cn93Km). Our manual relocation followed three criteria: (1) Locations at main rivers were corrected to tributaries if wrong. (2) The order of convergence of tributaries into a mainstream with a dam was considered. In the case that the tributary flows into the main river after the dam, the dam location was moved one cell upstream if possible to preserve the routing order and water storage, and therefore the timing of the river flow. (3) Dams located in tributaries without representation in the DDM30 routing network, were still located on DDM30 to the most suitable position.<br>The dams from KSU were mapped manually to the DDM30 routing network.