the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Nov 2024
GRILSS: Opening the Gateway to Global Reservoir Sedimentation Data Curation
Abstract. Reservoir sedimentation poses a significant challenge to freshwater management, leading to declining storage capacity and inefficient reservoir operations for various purposes. However, trustworthy and independently verifiable information on declining storage capacity or sedimentation rates around the world is sparse and suffers from inconsistent metadata and curation to allow global-scale archiving and analyses. The Global Reservoir Inventory of Lost Storage by Sedimentation (GRILSS) dataset addresses this challenge by providing organized, well-curated and open-source data on sedimentation rates and capacity loss for 1,015 reservoirs in 75 major river basins across 54 countries. This publicly accessible dataset captures the complexities of reservoir sedimentation, influenced by regional factors such as climate, topography, and land use. By curating the information from numerous sources with disparate formats in a homogenized data structure, GRILSS serves as an invaluable resource for water managers, policymakers, and researchers for improved sediment management strategies. The open-source nature of GRILLS promotes collaboration and contributions from the global community to grow the dataset. By providing essential reference data on sedimentation to understand the global challenge of reservoir sedimentation, this GRILLS dataset represents a gateway for the global community to share sedimentation and storage loss data for sustainable operation of world’s reservoirs for future generations.
- Preprint
(2296 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 03 Jan 2025)
-
RC1: 'Comment on essd-2024-470', Anonymous Referee #1, 18 Dec 2024
reply
Q1. Some sentences in L25 lack references. For example, “However, these dams and their reservoirs pose significant environmental challenges, including the destruction of ecosystems, loss of biodiversity, disruption of aquatic life, greenhouse gas emissions, and barriers to fish migration.” Please revisit and be specific, for instance, reservoirs pose significant environmental challenges (suggest 10.1007/s00382-024-07319-7), greenhouse gas emissions (suggest 10.3389/fenvs.2023.1304845), loss of biodiversity (10.3850/978-90-833476-1-5_iahr40wc-p1339-cd), etc.
Q2. Please be specific with this term “to implement sediment management techniques,” e.g., of what?
Q3. L45 should be strengthened with more references. Please revisit and improve.
Q4. Please provide reference(s) for this claim “While indirect methods are less expensive, they rely on the availability of in- situ data, such as soil moisture and soil type for numerical models.”
Q5. The authors acknowledge the highly variable nature of the source data, with sedimentation reported in different units (MCM, MT, percentage loss) and over different time scales. While they attempt to standardize this with conversions and assumptions (e.g., assuming a 1-year period when duration is unspecified, using estimated bulk density), these introduce significant uncertainties. The accuracy of calculated sedimentation volumes hinges heavily on the availability and reliability of bulk sediment density data, which the manuscript admits is often lacking. This inherent inconsistency in the underlying data makes comparisons between reservoirs challenging and potentially misleading. The assumption of a one-year sedimentation period when not specified is arbitrary and likely inaccurate in many cases, further contributing to the uncertainty.
Q6. While the dataset includes a large number of reservoirs, the manuscript acknowledges underrepresentation of smaller reservoirs (<0.1 MCM). This biases the overall analysis of capacity loss rates, as smaller reservoirs tend to have higher rates. The geographical distribution of data also appears uneven. While the goal is global coverage, the methodology hints at potential over-representation of data from regions where more readily accessible English-language publications exist. This bias could skew global assessments of sedimentation patterns and influence conclusions about regional variations. The heavy reliance on GDAT for georeferencing also introduces potential biases if GDAT itself has systematic errors or uneven global representation in which I found the GDAT often lacks comprehensive dam records, particularly for small dams and reservoirs and many regions, especially in developing countries, have poor or outdated records, leading to incomplete datasets.
Q7. The manuscript identifies several factors affecting sedimentation, including topography, climate, land use, and reservoir shape. However, the analysis primarily focuses on simple bivariate relationships (e.g., capacity loss vs. catchment area, capacity loss vs. dam height). This neglects the complex interplay of these factors and their potential non-linear interactions. For example, the relationship between catchment area and sedimentation is likely mediated by land use, soil type, and precipitation patterns. Without accounting for these complexities, the analysis lacks the depth needed to draw robust conclusions about the drivers of sedimentation. The use of readily available digital elevation models (DEMs) for catchment delineation, especially at coarser resolutions (90m), might not accurately capture the true catchment boundaries, particularly in complex terrains or flat areas, influencing the calculation of catchment areas and related analyses.
Citation: https://doi.org/10.5194/essd-2024-470-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 100 | 17 | 5 | 122 | 7 | 6 |
- HTML: 100
- PDF: 17
- XML: 5
- Total: 122
- BibTeX: 7
- EndNote: 6
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1