Surface Hydrological Processes Research Articles

Chemodiversity of dissolved organic matter exports from subtropical humid catchment driven by hydrological connectivity

The quantity and quality of dissolved organic matter (DOM) exported from source areas are closely related to hydrological linkage between source areas and streams, that is hydrological connectivity. However, understanding of how hydrological connectivity regulates the export of catchment DOM components remains inadequate. In this study, high-frequency monitoring of groundwater and runoff from subtropical humid catchment was conducted for 20 months, and hydrological connectivity was quantitatively characterized by considering both surface and subsurface hydrological processes. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was utilized to investigate the DOM molecular composition. Results showed that over half of the areas in the catchment could not persistently establish hydrological connectivity with the stream during the rainfall. The average proportion of lignin was the highest in DOM components, followed by tannin and proteins. Additionally, both modified aromaticity index and double bond equivalence reached maximums at peak discharge, reflecting terrestrial materials could increase DOM aromaticity and unsaturated degree. Partial least square-structural equation modeling revealed significantly direct effects of rainfall, antecedent conditions, and hydrological connectivity on dissolved organic carbon (DOC) export. Furthermore, nonlinear relationships were observed between hydrological connectivity and DOC, tannin, and condensed aromatics. Specifically, the instantaneous DOC flux increased dramatically when the hydrological connectivity strength exceeded 0.14; tannin and condensed aromatics exhibited a rapid increase with rising connectivity strength, but remained stable at connectivity strength above 0.25. However, hydrological connectivity showed no significant correlation with unstable components (such as lipids, protein, amino sugars, and carbohydrates). These results provide new insights into hydrological controls on the quantity and quality of DOM export and contribute to developing appropriate catchment management strategies for carbon storage.

Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events.

Tidal Calibration of the Gladwin Tensor Strain Monitor (GTSM) Array in Taiwan

AbstractTo ensure the accuracy and reliability of crustal strain measures, sensors require a thorough calibration. In Taiwan, the complicated dynamics of surface and subsurface hydrological processes under semi-tropical climate conditions conjugated with the rough surface topography could have impacted strainmeter deployment, pushing the installation conditions astray from the optimal ones. Here, we analyze the complex response of 11 Gladwin Strain Monitor (GTSM) strainmeter type deployed in north and central Taiwan and we propose a novel calibration methodology which relies on waveform modeling of Earth and ocean tidal strain-related deformations. The approach is completely data-driven, starting from a simple calibration framework and progressively adding complexity in the model depending on the quality of the data. However, we show that a simple quasi-isotropic model (three calibration factors) is generally suitable to resolve the orientation and calibration of 8 instruments out of 11. We also highlight the difficulty of clearly defining the behavior of instruments that are highly affected by hydrological forcing.

Responses of deep soil moisture to direct rainfall and groundwater in the red soil critical zone: A four-stage pattern

Deep soil moisture (DSM) plays a critical role in vadose zone hydrological processes. However, the recharge processes and their underlying mechanisms remain unclear due to the scarcity of comprehensive deep hydrological data, especially in regions characterized by active interactions between surface and subsurface hydrological processes. To address this knowledge gap, we performed an event-based study using a four-year hydrological monitoring dataset, encompassing rainfall, profile soil moisture, and groundwater along a hillslope at the Red Soil Critical Zone Observatory (CZO) in China. A total of 226 rainfall events were classified into four distinct types (I-IV) using a k-means clustering algorithm. The changes in DSM and groundwater table (GWT) were dependent on the rainfall type. The GWT (0.57–0.97) played a greater role in contributing to the DSM than the direct rainfall (0.48–0.64) based on a structural equation model, which was more pronounced from upper slope to foot slope. DSM recharge pattern can be described by a four-stage conceptual model, as indicated by the relationship between DSM and GWT: a slow and linear increase (stage 1), a small plateau (stage 2), a rapid linear rise (stage 3), and finally a great plateau (stage 4). DSM at the small and great plateau were closely related with the field capacity and saturation, respectively. Rainfall type, vadose zone features, and soil physical properties jointly shaped DSM recharge patterns. These findings can offer valuable insights into understanding the deep hydrological processes of the red soil region as well as regions with similar hydroclimatic conditions.

Bidirectional dependency between vegetation and terrestrial water storage in China

Alterations in water availability strongly impact vegetation growth, and vegetation could affect terrestrial water storage (TWS) by altering surface hydrological processes. Understanding the relationships between vegetation change and terrestrial water conditions in ecosystems is crucial. In the research, the partial correlation was implemented to identify time-lag effects and revealed a non-linear interaction between TWS and the normalized difference vegetation index (NDVI) using the Granger causality approach. The results showed that most vegetation in China underwent significant greening from January 2003 to December 2020, and TWS showed a spatial distribution pattern of increasing in the south and decreasing in the north. TWS and NDVI both had significant mutual lag responses. The proportion of the area with a significant correlation between TWS and NDVI increased from 42 % to 63 % after considering the time-lag effect. NDVI was generally influenced by TWS, with 55 % of TWS in the vegetation cover of China having a causal effect on NDVI; conversely, two different vegetation greening methods regulated TWS in vegetated areas in China, and > 59 % of TWS changes were dependent on NDVI. The effect of vegetation dynamics on surface hydrological processes may increase with increasing aridity. Determining the existence of Granger causality between NDVI and TWS has significant implications in terms of comprehending the relationships between terrestrial vegetation and water within the context of a changing climate and for the sustainable use of carbon–water cycle ecosystem services.

Simulation of Solute Transport under Changing Gradient of Water Table

Solute transport in aquifers is usually controlled by dynamic changes in groundwater tables. The influence mechanism of surface hydrological processes, especially precipitation, on solute transport under different water table conditions has not been studied much. Prior to the direct application of the existing solute transport mechanisms and models to the solute transport process, the following questions need to be addressed: (1) Do surface hydrological conditions (e.g., precipitation) cause changes in the gradient of the water table within a specific region? (2) If the gradient of the water table does change, how does the change influence solute transport and distribution in the aquifer? The objective of this study is to investigate how precipitation impacts the gradient of water table and to analyze how the solute transport is influenced by the change in the gradient of water table. To achieve this, we employed an analytical solute transport model for the time-varying gradient of a water table through continuous multipoint monitoring of the groundwater table data. Further, the model was applied to indoor experiments to simulate solute transport with and without precipitation. Finally, the mechanism of solute transport in the aquifer under the changing gradient of the water table was analyzed using mathematical and physical models, and the modeling results were compared with those from a numerical model to analyze the modeling reliability. The results showed that precipitation changed the previously stable gradient of the water table, which further altered the gradient of the water table and the transport path of the tracer. The tracer transport direction was primarily controlled by the flow direction in the sandy aquifer trough without precipitation. However, the route of the tracer transport was consistent with the direction of the change in the water table gradient due to precipitation. The small discrepancy between the results from the analytical and numerical models indicates that the model employed in this study is reliable.

A Transformer-Based Framework for Parameter Learning of a Land Surface Hydrological Process Model

The effective representation of land surface hydrological models strongly relies on spatially varying parameters that require calibration. Well-calibrated physical models can effectively propagate observed information to unobserved variables, but traditional calibration methods often result in nonunique solutions. In this paper, we propose a hydrological parameter calibration training framework consisting of a transformer-based parameter learning model (ParaFormer) and a surrogate model based on LSTM. On the one hand, ParaFormer utilizes self-attention mechanisms to learn a global mapping from observed data to the parameters to be calibrated, which captures spatial correlations. On the other hand, the surrogate model takes the calibrated parameters as inputs and simulates the observable variables, such as soil moisture, overcoming the challenges of directly combining complex hydrological models with a deep learning (DL) platform in a hybrid training scheme. Using the variable infiltration capacity model as the reference, we test the performance of ParaFormer on datasets of different resolutions. The results demonstrate that, in predicting soil moisture and transferring calibrated parameters in the task of evapotranspiration prediction, ParaFormer learns more effective and robust parameter mapping patterns compared to traditional and state-of-the-art DL-based parameter calibration methods.

Direct evidence and mechanism for biocrusts-induced improvements in pore structure of dryland soil and the hydrological implications

As a crucial living skin inhabiting the soil-atmosphere interface, biocrusts play a vital role in various soil properties and processes, especially surface soil pore structure and soil pore-related hydrological processes, such as infiltration and evaporation. However, it remains unclear how biocrusts affect pore structure in the micrometric point of view. In this study, X-ray computed tomography and image analysis were employed to quantify the differences in soil pore structure between bare soil and three types of biocrusts (cyanobacterial, cyanobacterial-moss mixed, and moss crusts) at 0–50 mm depth. Three-dimensional images were segmented and analyzed to assess morphological and geometrical properties, such as porosity, pore volume, degree of anisotropy, Euler number, pore shape, pore connectivity, and pore tortuosity. Our results showed that the porosity of three types of biocrusts was, on average, 100% higher than bare soil, and the porosity ranked in order of moss crusts > mixed crusts > cyanobacterial crusts > bare soil. In comparison to bare soil, pore surface area density, mean pore volume, and node density of biocrusts were increased by 45%, 422%, and 52% on average, respectively. Biocrusts had larger fractal dimensions but lower degrees of anisotropy, mean tortuosity, and Euler number, which indicates the pore structure of biocrust became more complex and stable with higher connectivity compared to bare soil. Additionally, elongated pores had the largest contribution to porosity, and these pore proportions were significantly higher in biocrusts than bare soil. More importantly, the pore network model and network analysis revealed that biocrusts have a higher connected porosity (17% vs. 4%), connected porosity/isolated porosity (11.7 vs. 0.6), average coordination number (8 vs. 5), and number of channels (14,480 vs. 807), but a lower average channel length (2.7 vs. 3.8 mm), which indicates that the physical and topological structures of pore network in biocrusts were reconfigured and exhibited a better pore network connectivity. Moreover, the biocrusts increased soil water holding capacity by 87% on average, but decreased saturated hydraulic conductivity by 55% as compared to bare soil. From the reshaped pore structure, improved soil water holding capacity, and decreased infiltrability of the biocrusts in comparison to bare soil, we conclude that biocrusts play a vital role in surface soil water balance, which subsequently affect surface hydrological processes (e.g., runoff generation and evaporation) in drylands.

Normalized Temperature Drought Index (NTDI) for Soil Moisture Monitoring Using MODIS and Landsat-8 Data

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales.

An improved non-point source pollution model for catchment-scale hydrological processes and phosphorus loads

Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (pKS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively.

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.

Decadal expansion and contraction of permafrost in the Three-River Source Region, Qinghai–Tibet Plateau (1901–2020)

Permafrost and its spatiotemporal variation considerably influence the surface and sub-surface hydrological processes, biogeochemical cycles, fauna and flora growth and cold region engineering projects in the Three-River Source Region (TRSR), Qinghai–Tibet Plateau. However, the dynamics of permafrost over a relatively long term duration (e.g. >100 years) in the TRSR is not well quantified. Thus, the spatial and temporal variations of the temperature at the top of the perennially frozen/unfrozen ground (TTOP), active layer thickness (ALT) in permafrost regions and the maximum depth of frost penetration (MDFP) in the seasonally frozen ground of the TRSR during 1901–2020 were simulated using the TTOP model and Stefan equation driven by the widely used reanalysis Climatic Research Unit 4.05 dataset. Results revealed that the permafrost in the TRSR over the past 120 years did not degrade monotonically but experienced considerable fluctuations in area with the decadal oscillations of climate warming and cooling: shrinking from 263.9 × 103 km2 in the 1900s to 233.3 × 103 km2 in the 1930s, expanding from 232.3 × 103 km2 in the 1940s to 260.9 × 103 km2 in the 1970s and shrinking again from 254.1 × 103 km2 in the 1980s to 228.9 × 103 km2 in the 2010s. The regional average TTOP increased from −1.34 ± 2.74 °C in the 1910s to −0.48 ± 2.69 °C in the 2010s, demonstrating the most noticeable change for the extremely stable permafrost (TTOP < −5 °C) from 8% to 1%. The regional average ALT increased from 2.68 ± 0.52 m to 2.87 ± 0.46 m, with the area proportion of ALT > 3.0 m by 12% from 1901 to 2020. Notably, minor changes were observed for the regional average MDFP, probably due to the increase in the area proportion of MDFP < 3.0 m (caused by climate warming) and MDFP > 3.5 m (owing to the transformation of permafrost to seasonally frozen ground) by 7.39% and 4.77%, respectively. These findings can facilitate an in-depth understanding of permafrost dynamics and thus provide a scientific reference for eco-environment protection and sustainable development under climate change in the TRSR.

A digital close range photogrammetric observation system for measuring soil surface morphology during ongoing rainfall

Soil erosion is a hydro-geomorphic process occurring over the earth’ surface. The observation of soil surface morphology during ongoing rainfall at fine spatial and temporal scales are critical for the study of soil erosion. A digital close range photogrammetric observation system based on wireless networking technology was explored and established in this study. Through wireless networking, multi-cameras were concurrently controlled, solving the problems of synchronous acquisition of underlying digital image, big data transmission and storage. Image sensors collected data in parallel based on network command, and noise on the images such as raindrops was eliminated based on the K-means clustering algorithm by serialization technology during data acquisition. After parallel preprocessing, high density 3D point clouds and digital elevation models (DEMs) were reconstructed. The evolution of soil surface topography was dynamically monitored by instantaneous image acquisition at different time intervals during ongoing rainfall. The results showed that the measurement precision of the established system could reach a sub-millimeter level with the minimum single measurement error being 0.0069 mm. The maximum error between check point coordinates generated by photogrammetry and those generated by a Total station was 5.7 mm on the horizontal axes and 9.6 mm on the vertical axis. The temporal and spatial resolutions of the observation system were 1 min and 2 mm, respectively. Compared with a runoff sediment collection method, the average relative error of the system for estimating soil loss was 5.63%, and the accuracy of single observation was up to 99.58%. The observation methods explored in our study provided a reliable way to monitor soil erosion processes, which was also helpful to analyze the role and influence mechanism of soil erosion in surface hydrological process.

Spatiotemporal features of the soil moisture across Northwest China using remote sensing data, reanalysis data, and global hydrological model

Soil moisture is an important factor affecting the change of land surface hydrological processes and the distribution of material and energy exchanges between the land and atmosphere and vegetation’s temporal and spatial distributions, especially in arid and semi-arid regions. This paper focuses on soil moisture features across Northwest China, the core region of the Silk Road Economic Belt. Six soil moisture datasets from the period 1981–2020 were employed, which included ERA5 (the European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis 5), ESA-CCI (European Space Agency Climate Change Initiative), GLDAS (Global Land Data Assimilation System), MERRA-2 (The Modern-Era Retrospective Analysis for Research and Applications, Version 2), RSSSM (A Remote Sensing-based global 10-day resolution Surface Soil Moisture dataset), and SSM-Feng (Regional multimodal fusion of surface soil moisture data in China). The temporal and spatial variation of the linear trend and abrupt change characteristics at seasonal and annual scale were explored. The results are as follows: 1) ESA-CCI, GLDAS, and MERRA-2 showed a slow increase in annual soil moisture tendency at a rate of less than 0.001 m3/m3/year, while ERA5 and SSM-Feng showed a significant decreasing linear trend at a rate of 1.31 × 10−4 m3/m3/year and 1.01 × 10−4 m3/m3/year (p &amp;lt; 0.05), respectively. 2) In autumn and winter, only GLDAS and MERRA-2 showed significant increasing trends. In the growing season (i.e., from April to October), the soil moisture of ESA-CCI, GLDAS, and MERRA-2 significantly increased at the rates of 3.29 × 10−4 m3/m3/year, 3.30 × 10−4 m3/m3/year, and 6.64 × 10−4 m3/m3/year (p &amp;lt; 0.05), respectively. 3) ERA5 and ESA-CCI have frequent abrupt changes in 1984, 1987, and 2006 for ERA5, 2010–2012 and 2019–2020 for ESA-CCI. 4) In terms of spatial variations, most datasets show that soil moisture has increased across most regions. The ERA5, ESA-CCI, GLDAS, MERRA-2, and SSM-Feng datasets show decreased soil moisture in the Tarim Basin. The conclusions of this study deepen the understanding of temporal and spatial variation in soil moisture in arid areas of Northwest China. Through these conclusions, a certain theoretical basis can be provided for the complex water cycle process in the arid region.

Viticulture in Argentina under extreme weather scenarios: Actual challenges, future perspectives

Viticulture in Argentina is an important socioeconomic sector, reflected in a significant wine market and tourism. However, climate change and related extreme events are serious concerns. The main issues are heatwaves, hailstorms, and heavy rainfall, resulting in damage to vineyards. While climate change impacts have already been discussed for regions such as the Mediterranean, the literature lacks an up-to-date overview of Argentine viticulture and potential mitigation solutions. In a country culturally and economically connected to the world of wine, it is strategic to bridge this gap to be prepared for a climatically adverse future. This perspective paper presents an overview of Argentine viticulture and its relationship to climate change. We focus on the Mendoza region, one of the most productive areas and home to cultural landscapes where internationally recognized wines are produced. Climate change is already occurring, a fact we observed by analyzing data from the past decades. We discussed how heatwaves in the lowlands drive farmers to move to the Andes slopes looking for more favorable conditions. But new threats arise, such as extreme rainfall. Due to surface hydrological processes, they can cause land degradation and compromise vineyards. We investigate these phenomena in detail, highlighting how they represent a growing challenge that must be addressed for the sustainable development of future viticulture in the area. Therefore, we propose mitigation strategies for more resilient production, drawing inspiration from the Sustainable Development Goals and suggesting a framework that can be extended to broader contexts worldwide.

Review: Groundwater research in the Ethiopian Rift Valley Lakes region

Despite its proximity to many research institutions in the country and prevailing environmental and water security challenges, water resources research in the Central Rift Valley of Ethiopia has few decades of history. Research undertaken so far, mainly focus on the lakes' environment and anthropogenic activities in their proximities. Worsening deterioration of the water resources and environmental conditions; and the need to address overlooked but determinant natural and anthropogenic processes spurred a critical review of what has been done so far. This work provides an overview of the history of water research in the central rift valley and tries to reveal research gaps related to surface water-groundwater interaction, water quality, and changing trends in the hydro ecosystem and possible causes. Apart from this, articles dealing with the geological and structural setup of the central rift valley were systematically reviewed to show their control over the hydrologic system. The review work has revealed that although the current state of the central rift valley is a product of anthropogenic and geogenic processes; which are happening within the sub-basin and its adjacent basins that need to be addressed at a higher thematic, spatial, and temporal scopes, there is gap in reviewed research, to address the issue at this level. The forefront environmental challenges and the need for quick fixes, lack of data, and funding are found to be some of the reasons to limit the scope of research activities, mainly to shallow groundwater zones and surface hydrological processes around the lakes. This approach has hindered seeing the bigger picture and resulted in ineffective environmental and natural resources restoration measures and policy decisions.

Influence of the 2021 Brood X cicada emergence on near surface hydrology in forested and urban landscapes

AbstractThe 2021 emergence of the 17‐year Brood X cicadas (Magicicada septendecim, M. cassinii, and M. septendecula) saw billions of cicadas emerge from the soil throughout the midwestern and eastern United States. The emergence left connected burrows visible at the surface, which are hypothesized to affect near surface hydrologic processes. To investigate these processes, we used single‐ring, dual head infiltrometers to measure field saturated hydraulic conductivity (Kfs, n = 70) across patterns of emergence and land use in south‐central Indiana, USA. Our experimental design included locations with and without cicada burrows in forested (undisturbed) and urbanized (disturbed) areas. Across undisturbed sites, we found a significant 80.8% increase in Kfs between soils with (median = 14.1 cm/h; n = 20) and without (median = 7.8 cm/h; n = 20) cicada burrows. At disturbed sites, we found no significant difference in Kfs between sites with (median = 4.2 cm/h; n = 18) and without (median = 4.4 cm/h; n = 12) cicada burrows. We found a significant correlation between the number of burrows present at the surface and Kfs rates for undisturbed sites ( = 0.42; p = 0.008), while no correlation was found for the disturbed sites ( = −0.09; p = 0.62). Our measurements suggest that the effect of burrows on Kfs is minimized in urbanized areas, potentially due to compaction and other impacts from human disturbance that mitigate the presence of macropores left by cicadas. In contrast, surface‐connected macroporosity from Brood X cicada burrows in undisturbed areas act as a conduit for precipitation into the soil profile and bypass flow into deeper horizons and the shallow groundwater table, with implications for runoff dynamics, soil and groundwater recharge and quality, and nutrient cycling.

Numerical simulation study of urban hydrological effects under low impact development with a physical experimental basis

Global climate change and rapid urbanization led to frequent extreme rainstorms and waterlogging. Low impact development (LID) as an essential measure of urban stormwater control, it is necessary to explore the urban hydrological response under low impact development combined with physical experiment and numerical simulation. This paper takes the Beiyangyuan campus of Tianjin University, where numerous LID practices have been implemented, as the study area. The prototype permeable pavement in the campus was scaled down to a laboratory-scale for infiltration experiments. The results show that the Horton infiltration model could describe the infiltration process of permeable concrete and grass-planting brick. The initial infiltration rate of the permeable concrete is large but decays faster. In contrast, the infiltration process of the grass-planting brick decays slower, and the stable infiltration rate and infiltration capacity are higher than that of the permeable concrete. The mean infiltration rates measured for the permeable concrete and the grass-planting brick were 95 mm/hr and 190 mm/hr, respectively. A SWMM-MIKE21 coupled model was constructed based on the LID experimental results to explore the effects of the removal or density reduction of the LID practices on surface hydrological processes. The results show that permeable pavement performs best in the reduction of runoff volume, inundation area, and inundation depth, while the bio-retention cell takes the second place, and the sunken greenbelt has limited effect. Frequent light rainfall is sensitive to the density of LID practices, and the implementation of even a tiny percentage (4% of the catchment area) of LID practices can be effective in controlling runoff. The removal or density reduction of LID practices has little effect on the inundation region but leads to an increased possibility of flood risk due to increasing inundation depth.

Interactions between shrub encroachment and water infiltration on the hillslope of a typical steppe

AbstractShrub encroachment has been a worldwide phenomenon, but the interactions between the surface hydrological cycle and the shrub‐encroached landscape at different hillslope positions remain poorly investigated. The present study was undertaken to explore the interactions between the water infiltration patterns affected by shrub encroachment. At four slope positions of a hillslope encroached by Caragana microphylla Lam, soil water content and temperature were continuously measured at 10‐min intervals at four or five depths in both the presence (shrub patches) and absence (grass matrix) of shrubs. Then, the infiltration of rainfall and meltwater was estimated based on analyses of the above data. The rainfall infiltration ratios (IRs) of grass matrix were as high as 0.78 ± 0.08, except at the lower site, which had a value of only 0.47. The IRs of shrub patches increased from 0.38 at the top site to 0.77 at the lower site. In snowy years, snow and ice were blown upward by wind and accumulated in the shrub patches and their leeward areas, which resulted in higher water input for the shrub patches than for the grass matrix. Shrub encroachment changed the microtopography and soil properties of the hillslope and further affected the surface hydrological processes. The feedbacks between shrub encroachment and water infiltration varied among sites of the hillslope, which may in turn have affected the development of shrub patches.

Tracking snowmelt during hydrological surface processes using a distributed hydrological model in a mesoscale basin on the Tibetan Plateau

Mountain snowpack is an important water resource in high altitude and latitude regions where terrain is typically complex. However, only limited information has been published on snowmelt pathways in such regions and their de facto contributions to streamflow and soil moisture. To fill this knowledge gap, this study integrated a snowmelt pathway tracking algorithm to the high-resolution physically based Distributed Hydrology Soil Vegetation Model (DHSVM) to track snowmelt movement and to quantify snowmelt contributions during surface hydrologic processes. A simple reservoir operation scheme was also integrated into the model. The modified model was applied to a dammed mesoscale watershed in the northeastern region of the Tibetan Plateau, China, to explore relevant snow and reservoir effects. Results show that both the annual snow contribution to soil moisture (SC-SM) and the snow contribution to streamflow (SC-S) significantly decreased between 1965 and 2019. On a monthly scale, SC-SM amplitudes were highest in the upper soil layer, while peaks in deeper layers lagged behind those in upper layers. Moreover, mean monthly SC-S from all stations revealed bimodal distributions that corresponded to the snowfall season. Finally, reservoir regulation measures only exerted minimal impacts (≤2.0 %) on SC-S. If current climate change rates continue on the same trajectory, mountain snowpack reductions will be the primary cause for decreases in monthly and annual streamflow at the outlet of this basin. To mitigate climate change impacts, better water resource management is needed in this watershed.