•Reasons for use of distributed process-based hydrological models are reviewed.•Avenues for developments of process-based hydrological models are presented.•Hydrology will depend on appropriate use ...of process-based models.
Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
Determining the flow regime of non‐perennial rivers is critical in hydrology. In this study, we developed a new approach using CubeSat imagery to detect streamflow presence using differences in ...surface reflectance for areas within and outside of a river reach. We calibrated the approach with streamflow records in the Hassayampa River of Arizona over 3 years (2019–2021), finding good agreement in the annual fractions of flowing days at stream gages (R2 = 0.82, p < 0.0001). Subsequently, annual fractions of flowing days were derived at 90 m intervals along the Hassayampa River, finding that 12% of reaches were classified as intermittent, with the remaining as ephemeral. Using a Hovmöller diagram, streamflow presence was visualized in unprecedented spatiotemporal detail, allowing estimates of daily fraction of flowing channel and annual fractions of flowing days. This new tool opens avenues for detecting streamflow and studying hydrological and biogeochemical processes dependent on water presence in drylands.
Plain Language Summary
When and where streamflow occurs is critical for understanding Earth processes, as well as for policy and regulatory purposes. It is well‐known that most dryland rivers are non‐perennial and have high streamflow variability in space and time. However, there is a lack of observations to capture the dynamics of these river systems. Here, we present a new approach to determine the presence of streamflow using commercial small satellites which provide almost daily imagery at 3‐m cell sizes. Using this method, we compute streamflow presence over the Hassayampa River of Arizona. Our results highlight that the flowing fraction of the river varies from season to season and that the fraction of flowing days in the river varies greatly along its total length. This work suggests that the imaging capacity of small satellites can improve the detection of streamflow in dryland rivers as compared to current ground methods.
Key Points
CubeSat satellite imagery can detect streamflow presence in dryland rivers at high spatiotemporal resolution
Robust differences in near infrared band signals between a river reach and the surrounding areas allow detection of flowing water
A Hovmöller diagram of streamflow presence provides a rapid visualization of spatiotemporal variations along the river length
The application of physics‐based distributed hydrologic models (DHMs) at hyperresolutions (~100 m) is expected to support several water‐related applications but is still prevented by critical data, ...model validation, and computational challenges. In this study, we address some of these challenges by applying the TIN‐based Real‐time Integrated Basin Simulator DHM at a nominal resolution of ~88 m in the Río Sonora basin, a regional watershed of ~21,000 km2 in northwest Mexico. First, we generate reliable high‐resolution (1‐km) hydrometeorological forcings by bias correcting reanalysis products with ground observations and applying downscaling routines that use terrain information at high resolution, which is available globally. Second, we develop a strategy to obtain high‐resolution (250‐m) grids of soil parameters by integrating a coarse‐resolution soil map based on the Food and Agriculture Organization classification with recently released high‐resolution global data sets. Third, we apply the model over a decadal period (2004–2013) and use a set of complementary tools, including Taylor diagrams, connectivity analysis, and empirical orthogonal function analysis, to assess its ability to simulate spatial patterns of land surface temperature through comparison with daily remotely sensed products. We find that (i) the hyperresolution‐simulated patterns capture the spatial variability of land surface temperature quite well and (ii) vegetation properties are the major physical factors controlling the discrepancies between simulated and remotely sensed products. The strategies presented here are based on global data sets and robust statistical techniques that can be utilized in different settings with other DHMs, and thus, they provide valuable support for the scientific community focused on hyperresolution hydrologic modeling.
Key Points
Long‐term (10 years) hyperresolution (88 m) hydrologic simulations are performed in a regional watershed (21,000 km2)
Global and local data sets are integrated to generate high‐resolution hydrometeorological forcings and soil properties
Simulated and remotely sensed spatial patterns of land surface temperature are compared to validate the model and diagnose its deficiencies
Flash flooding affects a growing number of people and causes billions of dollars in losses each year with the impact often falling disproportionally on underdeveloped regions. To inform post-flood ...mitigation efforts, it is crucial to determine flash flooding extents, especially for extreme events. Unfortunately, flood hazard mapping has often been limited by a lack of observations with both high spatial and temporal resolution. The CubeSat constellation operated by Planet Labs can fill this key gap in Earth observations by providing 3 m near-daily multispectral imagery at the global scale. In this study, we demonstrate the imaging capabilities of CubeSats for mapping flash flood hazards in arid regions. We selected a severe storm on 13–14 August 2021 that swept through the town of Gila Bend, Arizona, causing severe flood damages, two deaths, and the Declaration of a State of Emergency. We found the spatial extent of flooding can be mapped from CubeSat imagery through comparisons of the near-infrared surface reflectance prior to and after the flash flood event (ΔNIR). The unprecedented spatiotemporal resolution of CubeSat imagery allowed the detection of ponded (ΔNIR ≤ −0.05) and flood-affected (ΔNIR ≥ +0.02) areas that compared remarkably well with the 100-year flood event extent obtained by an independent hydraulic modeling study. Our findings demonstrate that CubeSat imagery provides valuable spatial details on flood hazards and can support post-flood activities such as damage assessments and emergency relief.
The effects of forest treatments on watershed hydrology have often been studied in isolation from climate change. Consequently, under a warming climate, it is unclear how forest thinning will impact ...snowpacks, evapotranspiration, and streamflow availability. In this study, we used a distributed hydrologic model to provide insight into the effects of warming and forest treatment on the hydrologic response of the Beaver Creek watershed (∼1,100 km2) of central Arizona. Prior to the numerical experiments, confidence in the hydrologic model performance was established by comparisons to long‐term observations (2003–2018) of snow water equivalent and streamflow using station observations and through spatially distributed estimates. Results indicated that warming during the 21st century could increase mean annual streamflow by 1.5% for warming levels up to +1°C, followed by a −29% decrease for continued warming up to +6°C, due to the varying effects of warming on snow sublimation, soil evaporation, and plant transpiration. On average, forest thinning increased streamflow by +12% (or 7 mm/yr) through lower plant transpiration by −19% (or −18 mm/yr), while also increasing the change in soil water storage by +42% (or 11 mm/yr). Forest thinning delayed the detrimental effects of warming on streamflow until +4°C, as compared to +2°C without forest treatment. Furthermore, model results suggested that forest cover reductions laterally displace water availability and evapotranspiration to downstream sites. These model‐derived mechanisms provide insights on the potential for water resilience toward warming effects afforded through treatment projects in southwestern US ponderosa pine forests.
Plain Language Summary
The effects of forest thinning on watershed hydrology have often been studied in isolation from warming. Thus, it is unclear if forest thinning conducted under different warming levels will have hydrologic impacts on snow and streamflow conditions. Here, we used a hydrologic model to study these interactions within the Beaver Creek watershed in Arizona. First, we built confidence in the hydrologic model through comparisons to snow and streamflow observations. Then, we conducted a range of different warming scenarios with and without forest thinning. We found that warming could increase streamflow up to +1°C, but then led to larger decreases in streamflow up to +6°C. In contrast, forest thinning increased streamflow and delayed the negative effects of warming up to +4°C. The application of the hydrologic model with the warming and forest thinning scenarios provides new insights on how warming effects could be reduced through management projects in ponderosa pine forests.
Key Points
Distributed simulations in mountainous watershed show good match with warm and cold season hydrologic data over 16‐year period
Independent and combined scenarios showed that forest thinning reduces the streamflow impact of warming up to a 4°C increase
Forest treatment effects propagated downstream by displacing evapotranspiration from forested uplands to near channel regions
Feedback mechanisms between abiotic and biotic processes in dryland ecosystems lead to a strong sensitivity to interannual variations in climate. Under a future regime of higher temperatures but ...potentially increased rainfall variability, drylands are anticipated to experience changes in wind and water transport that will alter plant community composition and feedback on landscape connectivity. Here, we present a conceptual framework for understanding the coupling of vegetation productivity, aeolian transport, and hydrologic connectivity under anticipated changes in future climate, which suggests that a more extreme climatic regime will lead to more connected landscapes with attendant losses in soil, nutrient, and water resources. When enhanced connectivity triggers state changes, irreversible changes in ecosystem functioning can occur, with implications for the future of global drylands.
Urbanization modifies land surface characteristics with consequent impacts on local energy, water, and carbon dioxide (CO2) fluxes. Despite the disproportionate impact of cities on CO2 emissions, few ...studies have directly quantified CO2 conditions for different urban land cover patches, in particular for arid and semiarid regions. Here, we present a comparison of eddy covariance measurements of CO2 fluxes (FC) and CO2 concentrations (CO2) in four distinct urban patches in Phoenix, Arizona: a xeric landscaping, a parking lot, a mesic landscaping, and a suburban neighborhood. Analyses of diurnal, daily, and seasonal variations of FC and CO2 were related to vegetation activity, vehicular traffic counts, and precipitation events to quantify differences among sites in relation to their urban land cover characteristics. We found that the mesic landscaping with irrigated turf grass was primarily controlled by plant photosynthetic activity, while the parking lot in close proximity to roads mainly exhibited the signature of vehicular emissions. The other two sites that had mixtures of irrigated vegetation and urban surfaces displayed an intermediate behavior in terms of CO2 fluxes. Precipitation events only impacted FC in urban patches without outdoor water use, indicating that urban irrigation decouples CO2 fluxes from the effects of infrequent storms in an arid climate. These findings suggest that the proportion of irrigated vegetation and urban surfaces fractions within urban patches could be used to scale up CO2 fluxes to a broader city footprint.
Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain ...and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km2), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971–2000) and a future (2041–2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs.
•Statistical analysis in a basin in Sardinia shows high uncertainty of climate projections of precipitation extremes.•Soil properties and topography control the basin response to extreme storms.•Statistical downscaling of precipitation is useful to improve accuracy of physically-based hydrologic simulations.
Surface soil moisture plays a crucial role on the terrestrial water, energy, and carbon cycles. Characterizing its variability in space and time is critical to increase our capability to forecast ...extreme weather events, manage water resources, and optimize agricultural practices. Global estimates of surface soil moisture are provided by satellite sensors, but at coarse spatial resolutions. Here, we show that the resolution of satellite soil moisture products can be increased to scales representative of ground measurements by reproducing the scale invariance properties of soil moisture derived from hydrologic simulations at hyperresolutions of less than 100 m. Specifically, we find that surface soil moisture is scale invariant over regimes extending from a satellite footprint to 100 m. We use this evidence to calibrate a statistical downscaling algorithm that reproduces the scale invariance properties of soil moisture and test the approach against 1-km aircraft remote sensing products and through comparisons of downscaled satellite products to ground observations. We demonstrate that hyperresolution hydrologic models can close the loop of satellite soil moisture downscaling for local applications such as agricultural irrigation, flood event prediction, and drought and fire management.
Dryland ecosystems are often characterized by patchy vegetation and exposed soil. This structure enhances transport of soil resources and seeds through the landscape (primarily by wind and water, but ...also by animals), thus emphasizing the importance of connectivity - given its relation to the flow of these materials - as a component of dryland ecosystem function. We argue that, as with the fertile-islands conceptual model before it, the concept of connectivity explains many phenomena observed in drylands. Further, it serves as an organizing principle to understand dryland structure and function at scales from individual plants to entire landscapes. The concept of connectivity also helps to organize thinking about interactions among processes occurring at different scales, such as when processes at one scale are overridden by processes at another. In these cases, we suggest that state change occurs when fine-scale processes fail to adjust to new external conditions through resource use or redistribution at the finer scale. The connectivity framework has practical implications for land management, especially with respect to decision making concerning the scale and location of agricultural production or habitat restoration in the world's drylands.
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