Towards Adaptive Water Management—Optimizing River Water Diversion at the Basin Scale under Future Environmental Conditions
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Pas River Basin
2.2. Problem Perception and Problem Formulation Phase for the Pas River Basin
2.2.1. Problem Perception: Objectives and Optimization Goals
2.2.2. Problem Formulation
Scale and Scenario Setting
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- The LU and LC scenarios were developed using the process-based model framework FORE-SCE Model (Forecasting scenario for land change modeling) [47,48]. The FORE-SCE Model simulated current land use and cover by processing elevation, slope, and orientation and modeled fire recurrence. Furthermore, it models the influence of socioeconomic drivers obtained from interviews with local stakeholders and experts in agricultural and urban development policy fields. The input LU and LC maps were derived from historical remote sensing data (Landsat/Sentinel-2 imageries) for the 1990s, 2000s, and 2018 at a spatial resolution of 10 m.
- -
- For climate projections, historical data (from 1950 to 2018) and future data (from 2041 to 2070) on temperature and precipitation were used. See the procedure described in [49].
- -
- The final accumulated river surface runoff data (i.e., the resulting flow in the river) were produced by applying the distributed hydrological model SPHY (Spatial Processes in Hydrology; [50]) at a spatial resolution of 100 m and at the daily time step. Historical precipitation and temperature data for the period 1950 to 2018 were retrieved from the E-OBS v20e database [51] and resampled to produce a spatial resolution of ~1 km. [49] performed a statistical downscaling of precipitation and temperature with Ordinary Least Squares with yearly daily means using latitude, elevation, and Euclidean distance to the coastline as explanatory variables. For future scenarios, climatic datasets from a five-member ensemble of GCM-RCM chain simulations were retrieved for the development of climate change projections for the Pas catchment [49]. Further details of the procedure to develop climatic historical and future series can be found in [49]. Details of the model parameterization are provided in Table S1 in the Supplementary Materials. As shown in Table S2 in the Supplementary Materials, the results of the SPHY simulation (which are used by the optimization model) are characterized by a decline in precipitation and an increase in temperature and water demand due to land use changes. This, in turn, leads to a rise in actual evapotranspiration, causing a decrease in average instream flow in the Pas River basin, with a mean flow reduction rate of 25% between the basin outlets in the 1980–2012 and the 2041–2070 periods.
Timeframe of Source Data | Considered Period for Modeling | Scenario Name | Description | |
---|---|---|---|---|
Historical | 1980–2012 |
| Present day (PR) | This scenario represents present-day land cover and present-day climate. It is used as a comparison to the historical conditions. |
Future | 2041–2070 |
| BAU future (CC_BAU) | This scenario assumes river discharge is affected by Business as Usual (BAU) future land cover and future climate (RCP 8.5; [49]. It considers the evolution of present-day land use and land cover conditions. In particular, forest patches (monoculture planted forest) development is implemented but not prioritized with the presence of shrubs and rushes. In the upper basin, there is a significant rural abandonment with forest recovery from pastureland, whereas the lower basin is characterized by urban area expansion and agricultural intensification. |
Nature-based solutions prioritization (CC_BGIN) | This scenario assumes an investment in nature-based solutions and an RCP 8.5 climate change intensity conditions [49]. Concerning the “future conditions” scenario, we have a modification of the rules for land use-land cover evolution (e.g., no fires and forest transitions are favored in places where it can have the highest impact on regulatory ES). This results in a prevalence of hill-side forests (e.g., oak, beech, chestnut, birch species) and riparian forests (e.g., willows, ash, alders). |
Environmental Indicators—Definition of Relevant Ecosystem Services for the Pas River
Solution Approach to the Optimization Problem
3. Results
3.1. Performance of the Optimization Objectives
3.2. Spatial and Temporal Distribution of Water Available for Diversion in the Pas River Basin
3.3. Comparison of Results within the Different Scenarios
4. Discussion
4.1. Spatial and Temporal Scale Considerations of Water Available for Diversion
4.2. Supporting Ecosystem Services Objectives across Scenarios
4.3. Optimization Set-Up and Scenarios for Water Diversion Management at Different Scales
4.4. Considerations on Optimization Indicators for Ecological Endpoints
5. Recommendations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Supporting Ecosystem Service | Indicator Description |
---|---|
Provision of habitat conditions for fish | Hydrological regimes linked with the maintenance of habitat conditions that support main life stages (i.e., migration, spawning, hatching, recruitment), especially during dry periods, and ensuring the occurrence of peak flows (e.g., for migration). |
Life-supporting conditions for macroinvertebrates | Flow magnitude and variability conditions. Based on the occurrence of high flow events that promote the highest taxa occurrence probability (itself based on the Intermediate Disturbance Hypothesis; [52]. |
Primary productivity | Hydrological conditions of minimum flow during dry periods fostering the maintenance of primary producers (i.e., establishment success and their ability to develop cover). |
User | Issue | Description |
---|---|---|
Policy and Decision-makers in the frame of water management | Spatial domain | 1. Considering river segment-specific hydrological conditions when developing a diversion management plan can help identify areas of more stable discharge conditions for consumptive use. |
Temporal domain | 2. Management planning should account for changes in diversion conditions throughout the year by retailoring objectives to seasonal scales. | |
Future environmental conditions | 3. When planning diversion management, seasonal shifts due to climate and LULC change must be predicted, incorporated, and aligned with future management objectives. | |
Ecosystem services | 4. Management planning should consider appropriate ES supply indicators and conditions based on the location of the river segment and the conservation objectives. | |
Mixed | Forest indicators | 5. The effects of forest cover prioritization on available river water for diversion would be more evident if forest maturity rather than forest extent is prioritized. |
Optimization modelers for water management/water management analysts | Importance of input data quality for optimization assessments | 6. Incorporate predictions of ecological adaptation to environmental changes for specific water management horizon. |
Selection of the most appropriate scale | 7. Basin-scale modeling supports management scenario testing, while reach-scale modeling is more appropriate for constraint testing. | |
Output type | 8. As large scales require extensive input data, setting clear objectives can help to process the volume of output data and clear communication of results. | |
ES indicators | 9. Prioritize the hydrological requirements of some species downstream of the river network while focusing on others upstream, for example, by applying weights. |
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Derepasko, D.; Witing, F.; Peñas, F.J.; Barquín, J.; Volk, M. Towards Adaptive Water Management—Optimizing River Water Diversion at the Basin Scale under Future Environmental Conditions. Water 2023, 15, 3289. https://doi.org/10.3390/w15183289
Derepasko D, Witing F, Peñas FJ, Barquín J, Volk M. Towards Adaptive Water Management—Optimizing River Water Diversion at the Basin Scale under Future Environmental Conditions. Water. 2023; 15(18):3289. https://doi.org/10.3390/w15183289
Chicago/Turabian StyleDerepasko, Diana, Felix Witing, Francisco J. Peñas, José Barquín, and Martin Volk. 2023. "Towards Adaptive Water Management—Optimizing River Water Diversion at the Basin Scale under Future Environmental Conditions" Water 15, no. 18: 3289. https://doi.org/10.3390/w15183289