Table 2.
Overview of input hydroclimatic variables used to understand droughts risk and numerical models used to simulate the efficiency and performance of NBS against drought risk.
| Purpose | Type of NBS (place) | Models and tools to simulate NBS efficiency | Input hydroclimatic parameters | References |
|---|---|---|---|---|
| Hydrological and economic modelling to estimate costs and benefits of ecological restoration for increasing annual streamflow | Re-vegetation of hillslopes and degraded land, removal of invasive plant species. | ACRU | Terrain topography, daily rainfall, temperature, soil descriptors, land use/land cover. Restoration costs (e.g., project duration, extent of target area, degradation level, type of water yield prioritised). Benefits based on water gains and average water value. |
Mander et al. (2017) |
| Observation to alleviate hydrological drought as part of an integrated water resource management plan. | Increasing the water table in the main waterways and increasing the beds of the small waterways. | SIMGRO distributed process-based model to simulate groundwater and streamflow time series | Terrain topography, soil type, geological strata, land use and hydrological variables. | Querner and van Lanen (2001) |
| To simulate plant transpiration and photosynthesis and thus estimate the vulnerability of coastal cottonwoods in south western Canada to sustained meteorological drought and variation in river flow | Trees: types, density, trunk size, volume of branches and leaves, height, and rooting depth (south western Canada) | ParFlow-TREES | Meteorological variables (CO2 concentration, atmospheric pressure, photosynthetically active radiation, temperature, wind speed, precipitation, vapor pressure deficit). | Tai et al. (2018) |
| Hydrological modelling to estimate the impact of global warming which could change dry spell length and the effect of drought risk on main water supply sectors. | Area specific drought reduction strategies and incorporation of droughts in current area readiness exercises. | Finnish Environment Institute's Watershed Simulation and Forecasting System (WSFS) hydrological model | Rainfall, wind speed, RH, air pressure and cloudiness, daily temperature. | Veijalainen et al. (2019) |
| To investigate the potential of wetlands and salt marshes to reduce drought risks in the Bojiang Haizi River basin, Erdos Larus Relictus Nature Reserve plateau. | Wetlands, salt marsh and retention ponds (Global) | SWAT | Land use, topography, soils, wetland field data, precipitation, temperature, solar radiation, wind speed, RH, potential evapotranspiration. | Li et al. (2019a) |
| SWEMs is an important tool to forecast the effect of meteorological variables - precipitation, atmospheric CO2 concentrations and temperature on soil erosion and agricultural drought and used to assess the effects of forest, cropland and vegetation on soil erosion and drought risk. | Forest, cropland and vegetation (Global) | Soil and Water Integrated Model (SWIM) | Temperature observed soil erosion, precipitation (rainfall, rainstorms, and freeze-thaw cycles) and atmospheric CO2 concentrations. | Guo et al. (2019) |
| To evaluate the efficiency of plants with deep roots to seasonal drought risk or to mimic changes in rooting depth with time. | Drought tolerant, crops, root depth (Global) | HYDRUS 2D/3D | Plant root water uptake in the horizontal and vertical directions, soil hydraulic functions and root distribution with depth. | Ghazouani et al. (2019) |
| To investigate vegetation and hydrological responses to global warming in a forested mountainous watershed dynamic vegetation model (LPJ) coupled with a 3D hydrogeological model (MODFLOW) to estimate the effect of global warming on a small forested temperate watershed. | Forests, vegetation, herbaceous surroundings (Strengbach, Vosges, France). | Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ), MODFLOW | Mean meteorological data (precipitation, amount of wet days, cloud cover, air temperature), vegetation and soil. | Beaulieu et al. (2016) |