Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032

Effect of Solar Farms on Soil Erosion in Hilly Environments: A Modeling Study From the Perspective of Hydrological Connectivity

“…Scenario 1 (S1) adopted the local climatic variables and landscape of the Hongsibu site to assess soil erosion following the installation of the solar farm. Because USFs are widely distributed across the climate gradient (150–800 mm year −1 ) of the Chinese Loess Plateau (van Hateren et al., 2023), Scenarios S2 and S3 were designed to represent the cases where USFs were built at other sites with similar thick loess soil on the plateau (Zhu et al., 2018), but where annual precipitation is two‐fold and three‐fold that of the Hongsibu site, respectively. Additional numerical scenarios (S4–S6) with increased rainfall variability (or decreased rainfall frequency, but where the amounts of annual precipitation were set to be the same as scenarios S1–S3, Figures S3 and S4 in Supporting Information ), were investigated, to highlight the impacts of varied rainfall patterns on hydrological behaviors in USFs in the context of climate change (Quijano‐Baron et al., 2022).…”

A recipe for why and how to set up and sustain an experimental catchment

“…Analogously, at the same time, Burt and McDonnell (2015) promoted a renaissance of field hydrology both at the individual and community level, stating that “hydrology is an outdoor science—where our data come from and our ideas are ultimately tested” (Burt & McDonnell, 2015). More recently, Van Stan et al (2023) invited researchers to spend more time under the rain arguing that “The combination of human experiences in the storm […] with technological tools arguably produce the best odds for scientific advancement.” We are now in the digital and virtual era and hydrology is experiencing an unprecedented availability of big data based on remote sensing, machine learning, and a plethora of more or less complex models to investigate surface and subsurface processes at different spatial and temporal scales (Adamala, 2017; Chen & Wang, 2018; van Hateren et al, 2023; Vereecken et al, 2022). Thus, despite the recommendations reported above, the following questions can arise: As computer power is becoming less expensive, field work more risky, and field data collection increasingly limited by financial and logistical constraints (Burt & McDonnell, 2015; Seibert et al, 2024), does it make sense to keep collecting experimental data, in some cases at high spatial and temporal resolution, that we can hardly analyse, at least during a typical PhD or postdoc time‐span?…”