Hydrological Processes

Manoj J., Ashish; Villinger, Franziska; Wienhöfer, Jan; Loritz, Ralf; Zehe, Erwin

doi: 10.1002/hyp.70541pmid: N/A

Understanding and preparing for extreme events in a warming climate remains challenging, particularly for modelling flash floods in small‐ to mesoscale catchments. While top‐down modelling approaches that describe fluxes at the system scale are often effective for riverine floods driven by saturation‐excess runoff, bottom‐up approaches are better suited to capturing intensity‐controlled runoff generation and associated preferential flow processes. Based on the gradient‐conserving simplification of representative hillslopes, a meso‐catchment scale spatially distributed, process‐based model was applied to simulate a severe summer flood event that occurred in 1994 in southwest Germany. Our approach provides a balance between the complexity required to represent coupled flow processes at the hillslope scale and the practical constraints of scaling these to the mesoscale. Following evaluation against available observations, the model is used to reconstruct flood magnitudes in poorly gauged but severely affected headwater regions in the catchment. The results highlight the influence of spatial variability in gradients and land use on runoff generation in these areas. To further explore these findings, we conducted additional simulations across a range of precipitation return periods to examine the sensitivity of flood response under different scenarios. The results suggest that uncertainties are more pronounced at smaller spatial scales, likely due to data limitations. Finally, simplified nature‐based solution (NbS) scenarios were implemented at the hillslope and headwater scales to explore their potential influence on downstream flood response. This study contributes to improved understanding of overland flow responses over mesoscale catchments, a critical scale for flood management, particularly under increasing convective extremes as a result of anthropogenic climate change.

Dasari, Indhu; Vema, Vamsi Krishna; Jajolla, Bharathsagar

doi: 10.1002/hyp.70565pmid: N/A

This study aims to understand the effect of antecedent soil moisture (ASM) conditions on flood characteristics across nine Indian basins using the Soil Moisture Accounting (SMA) method within the Hydrologic Engineering Center—Hydrologic Modelling System (HEC‐HMS) model. Hypothetical rainfall events representing different scenarios and return periods were generated to assess the combined impact of rainfall, catchment properties, and ASM on flood prediction. The results reveal that while rainfall predominantly influences flood characteristics, ASM conditions also contribute significantly in certain basins. For instance, in the Netravati basin, high rainfall diminishes the impact of ASM on floods, whereas in the Nagavali basin, ASM significantly affects flood characteristics under high‐intensity rainfall due to steep slopes and high infiltration capacity. The Upper Tungabhadra basin shows minimal ASM impact due to its high infiltration and elongated shape, while the Upper Tapi basin experiences significant ASM effects due to lower infiltration capacity. The Upper Mahanadi and Brahmani basins exhibit variable flood responses to ASM, particularly under intense rainfall conditions. In larger basins like the Upper Narmada, Wardha, and Wainganga, the vast area and lower infiltration losses render ASM effects negligible. These findings highlight the necessity of integrating local morphometric, land use, and ASM into flood modelling to improve flood prediction and management.

Lopes, Valéria Cardoso; Pinheiro, Everton Alves Rodrigues; Viola, Marcelo Ribeiro; Jong van Lier, Quirijn

doi: 10.1002/hyp.70568pmid: N/A

Accurate meteorological forcing is essential for simulating hydrological processes in the vadose zone, particularly in regions with strong climatic seasonality like the Brazilian Cerrado. This study evaluates the performance of the ERA5‐Land reanalysis against a high‐resolution observational product (BR‐DWGD) and quantifies how differences between these datasets propagate through a process‐based soil‐water model. We characterized daily meteorological variables—precipitation, solar radiation, air temperature, relative humidity, and wind speed—from both datasets over a 63‐year period (1961–2023) in the southwest of the MATOPIBA agricultural frontier. Bias correction using Quantile Delta Mapping significantly reduced systematic errors in ERA5‐Land for most variables, though daily precipitation exhibited persistent discrepancies in temporal variability. Trend analysis revealed consistent warming and a decline in early wet‐season rainfall across both datasets. When used to drive the SWAP hydrological model, these meteorological differences notably altered simulated water‐balance partitioning: compared to BR‐DWGD, ERA5‐Land produced lower annual transpiration (−110 mm) and soil evaporation (−20.5 mm), and higher deep drainage (+160 mm). These shifts are attributed primarily to ERA5‐Land's tendency to underestimate rainfall frequency while overestimating intensity, leading to altered soil moisture dynamics and stress timing. Our findings highlight that dataset selection directly influences the physical representation of water fluxes in vadose‐zone models, underscoring the need for careful meteorological input assessment in hydrological studies of data‐scarce, agriculturally sensitive regions.

Aguiar‐Campos, Natalia; Ishida, Yoko F.; Edwards, Will; Laurance, Susan G. W.

doi: 10.1002/hyp.70569pmid: N/A

Tropical forest transpiration strongly influences the global hydrological cycle and is often estimated through sap flow measurements. Due to their high diversity and complex canopies, estimates often rely on mean sap flow measured from cohorts of similar trees, although within‐cohort variability remains poorly investigated in tropical forests. We aimed to quantify sap flow variability in a cohort of 10 similarly sized conspecific trees in an Australian tropical rainforest. Over three campaigns (dry, wet, and dry seasons), we measured sap flow simultaneously on north and south stem aspects and at two sapwood depths using heat‐ratio sensors, and estimated crown exposure for each tree. Between the first and second campaigns, a tropical cyclone increased mean crown exposure of the cohort two‐fold. We found that sap flow readings varied up to 14‐fold between trees under the same environmental conditions, decreasing to nine‐fold when within‐tree variation was accounted for. Regardless of aspect or depth, sap flow varied by an average of 52.5% within trees across the study period. Although sapwood depth was a poor predictor of sap flow, averaging across the radial profile decreased within‐tree variation to 34%. Despite similar stem dimensions, crown exposure largely explained variability among and within trees. During the dry seasons, north‐aspect sap flow remained stable, whereas south‐aspect sap flow increased by 44% following cyclone‐induced increases in exposure. In contrast, variation within and across trees was substantially lower during the wet season, presumably due to more uniform soil water availability. These results yield two recommendations to reduce uncertainty in tropical forest transpiration estimates: (i) sap flow should be measured at more than one point (at different aspects and/or depths) to account for the 52.5% difference in readings per tree; and (ii) crown exposure should be incorporated into stratification approaches to reduce within‐cohort sap flow variability.

Alsumaiei, Abdullah A.

doi: 10.1002/hyp.70551pmid: N/A

Accurate and physically consistent soil moisture forecasting is essential for irrigation water supply, yet purely data‐driven models may generalise poorly and exhibit nonphysical behaviour under changing forcing and management conditions. This study develops a water balance regularised neural network for daily multi‐depth soil moisture forecasting and evaluates its performance across three agricultural monitoring stations in Kuwait (Rabyah, Sulibyah and Wafra) and three depths (10, 20 and 50 cm). The model predicts one‐day‐ahead soil moisture increments using rainfall, pan evaporation, and lagged soil moisture, while a soft water balance regularisation constrains storage changes through learnable balance and drainage coefficients. Performance is benchmarked against an unconstrained neural network using RMSE, MAE, R2, and Kling–Gupta efficiency metric, and further assessed through time series agreement, residual diagnostics, split‐based generalisation, paired comparisons, and bootstrap uncertainty analysis. The constrained model outperforms the unconstrained network in 6 of 9 station–depth cases for RMSE and 5 of 9 cases for MAE, with the most consistent gains observed at 50 cm depth, reflecting storage‐dominated dynamics. Bootstrap analysis indicates positive mean improvements of 4.73% in RMSE and 5.68% in MAE, although confidence intervals include zero, indicating heterogeneous benefits across regimes. A conceptual partial root zone drying application demonstrates how physically interpretable soil moisture forecasts can support threshold‐based irrigation control. Overall, the results indicate that water balance regularisation enhances the stability and interpretability of neural network based soil moisture forecasts for irrigation water supply.

Ye, Peng; Shi, Zheming; He, Guanru; Qi, Zhiyu

doi: 10.1002/hyp.70549pmid: N/A

Utilizing groundwater‐level responses to periodic Earth and atmospheric tides provides a cost‐effective method of estimating hydraulic parameters. Yet its broader adoption has been hindered by insufficient analysis of model applicability and inherent non‐uniqueness of solutions during parameter inversion. In this study, three passive tidal response models were employed to estimate hydraulic parameters, using Well ZK1in Qinhuangdao, Hebei Province, China, as a case study. Three aquifer pumping tests yield aquifer hydraulic conductivity Ka$$ {K}_a $$ = 1.72×10−7$$ 1.72\times {10}^{-7} $$ to 4.12×10−7m/s$$ 4.12\times {10}^{-7}\ \mathrm{m}/\mathrm{s} $$. While Earth‐tide response model yields Ka$$ {K}_a $$ = 4.16×10−8−8.24×10−8m/s$$ 4.16\times {10}^{-8}-8.24\times {10}^{-8}\ \mathrm{m}/\mathrm{s} $$ that are an order‐of‐magnitude smaller than those from the pumping test, and barometric‐response model yields Ka$$ {K}_a $$ = 1×10−8−4.43×10−6m/s$$ 1\times {10}^{-8}-4.43\times {10}^{-6}\ \mathrm{m}/\mathrm{s} $$ that overlap pumping test results. Aquitard's vertical hydraulic conductivity yields Kl=$$ {K}_l= $$ 1.15×10−8−4.51×10−8m/s$$ 1.15\times {10}^{-8}-4.51\times {10}^{-8}\ \mathrm{m}/\mathrm{s} $$ from the pumping test, with fitting results from the Earth‐tide and barometric‐response models being Kl,=$$ {K}_l,= $$ 4.34×10−8−9.37×10−6m/s$$ 4.34\times {10}^{-8}-9.37\times {10}^{-6}\ \mathrm{m}/\mathrm{s} $$ and Kl=$$ {K}_l= $$ 2.25×10−10−7.9×10−7m/s$$ 2.25\times {10}^{-10}-7.9\times {10}^{-7}\ \mathrm{m}/\mathrm{s} $$, respectively. Specifying Sϵ$$ {\mathrm{S}}_{\epsilon } $$ can constrain the inverted parameter range of the barometric‐response method. However, non‐physical solutions (Kl>Ka$$ {K}_l>{K}_a $$) may arise for leaky models, so the reliability of inverted results should be verified against site‐specific hydrogeological conditions during application.

Zhi, Xiuying; Niu, Jianzhi; Huang, Jiale; Yang, Tao; Zhang, Linus; Berndtsson, Ronny

doi: 10.1002/hyp.70495pmid: N/A

Understanding the synergistic impacts of climate change and human activities on runoff and sediment dynamics is crucial for sustainable watershed management in the Yellow River Basin. This study examined hydrological variations in the Helong section of the Chinese Loess Plateau from 1980 to 2020. The Mann–Kendall and Pettitt tests were used to detect trends and abrupt changes in precipitation, runoff, and sediment load, while the double mass curve method quantified the relative contributions of climate change and human activities. Pearson correlation analysis was applied to explore the relationships between land‐use change and water–sediment variations. Results show that mean annual precipitation (317.5 mm) exhibited a slight increasing trend, whereas runoff and sediment load averaged 2.99 billion m3 and 279 million tons, respectively, both displaying significant decreasing trends. Abrupt changes occurred in runoff in 1998 and 2004 and in sediment load in 1998 and 2002. Land‐use patterns shifted markedly, characterised by decreasing cultivated land and increasing forestland and grassland. Attribution analysis indicates that human activities dominated runoff reduction, contributing 84.7% during 1999–2004 and 128.2% during 2005–2020, while contributing 71.8% and 113.4% to sediment reduction during 1999–2002 and 2003–2020, respectively. In contrast, climate change played a relatively minor role and partially offset the reduction in later years. These findings reveal that large‐scale ecological restoration has fundamentally altered the precipitation–runoff–sediment relationship, shifting the basin from a climate‐driven to a human‐regulated hydrological regime.

Ren, Jie; Sui, Jiaheng; Wang, Jie; Zhang, Hongbo; Zhuang, Ting

doi: 10.1002/hyp.70552pmid: N/A

Weir‐induced hyporheic exchange plays a critical role in regulating nutrient cycling, contaminant transport and thermal regimes within riverine ecosystems, with significant implications for watershed management. To elucidate the underlying mechanisms, this study integrates controlled laboratory flume experiments with a coupled surface–subsurface numerical model to quantitatively assess alterations in the groundwater temperature field following weir installation and to identify the dominant factors governing exchange dynamics. Results indicate that near‐bed velocity variations exhibit a positive correlation with surface water velocity (v) and weir height (h), and a negative correlation with weir width (d) and water depth (hw). Specifically, elevating the weir height from 0.1 to 0.2 m amplified the maximum cross‐weir pressure gradient by 102%, with the value rising from 52.9 to 106.9 Pa. Concurrently, the maximum streamwise Darcy velocity exhibited an order‐of‐magnitude increase from 1.58 × 10−5 to 2.99 × 10−4 m/s. Furthermore, the spatial extent of the hyporheic thermal front expands significantly with increases in v and h. Numerical simulations reveal that a distinct low‐pressure zone develops immediately downstream of the weir structure, which drives the formation of upward flow fields upstream and downward flow fields downstream. These findings establish a quantitative, pressure‐driven framework linking weir geometry directly to hyporheic exchange intensity—a linkage previously unexplored in systematic flume experiments. The results provide a mechanistic basis for optimising weir designs to enhance biogeochemical processing and thermal regulation in engineered river corridors.

Hashimoto, Kotaro; Nanko, Kazuki; Tagawa, Yoshiyuki; Levia, Delphis F.

doi: 10.1002/hyp.70534pmid: N/A

Beven, Keith; Van Stan, John T.

doi: 10.1002/hyp.70556pmid: N/A

This paper provides a historically‐grounded review and research agenda on a generally neglected issue in hillslope hydrology and hydrological modelling: the role of stemflow in initiating preferential flow in soils. While stemflow typically represents a small fraction of incident rainfall, it can concentrate water fluxes by up to ~20‐fold at tree bases, creating localised infiltration intensities that exceed those from throughfall. Some new historical context for throughfall and stemflow studies from the 19th Century is presented, including a summary of stemflow research in a wide range of vegetation types and environments. The evidence for preferential flows resulting from stemflows as a ‘double‐funnelling’ effect is reviewed, emphasising tracer‐based studies used to follow flow pathways. Although stemflow‐driven preferential flows have been shown to occur commonly and, in some cases, to rapidly transport water to zones of saturation and consequent downslope flows, such processes have not been included in hydrological models to our knowledge. Thus, their significance at hillslopes and catchment scales remains an open question. The paper concludes with a needs analysis that identifies key observational and modelling challenges required to quantify stemflow‐preferential flow impacts at larger scales.