Hydrological Data Research Articles

Defining and characterizing the phenomenon of river-bottom tearing scour (RBTS): A case study of the Middle Yellow River

River-bottom tearing scour (RBTS) in natural rivers refers to the incipient motion and transport processes of clay blocks formed by fine cohesive sediment after deposition and consolidation in riverbeds. The RBTS phenomenon can lead to significant channel erosion and changes in river planview morphology, and has, thus, attracted the attention of hydrologists and engineers. In the study, a new determination measure (K) for the occurrence of a RBTS event is derived based on the theoretical expression for the critical incipient velocity of the clay block, K=α2(CL+2λ22), which is a combination of the coefficients of the velocity (i.e., α is a local velocity coefficient), lift force (i.e., CL is the lift coefficient), and clay block size (i.e., λ2 is the ratio of block thickness to length). Furthermore, to explain river behavior during a RBTS event, the Fugu, Xiaobeiganliu, and Weihe river reaches in the Middle Yellow River (MYR) were selected as study areas. Analysis of hydrological data from 1950 to 2023 in the examined reaches implies that a single discharge or sediment threshold cannot predict the occurrence of RBTS. The cross-sectional erosion and deposition volume (CEDV) and the migration intensity of the channel thalweg (MI) also were calculated at the section and reach scales for the examined reaches during the RBTS events. It was observed that RBTS typically causes significant channel erosion originating from the most upstream portion of the study reach with CEDV values ranging from 61 to 6034 m2, while MI values during the RBTS events were close to the multi-year average for the study reach. Finally, a flume experiment simulating RBTS was done to discuss and verify the threshold value of K. Field survey and experimental results indicated that RBTS occurs if K > 0.5, thus, providing theoretical support for the prediction and prevention of RBTS.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Flood risk decomposed: Optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco

Urban flood risk mapping has become crucial for effective mitigation and urban planning. This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning and Multi-Criteria Decision Analysis (MCDA) techniques to overcome data scarcity challenges. We employed the Random Forest (RF) model with nine flood conditioning factors for flood hazard and the Analytical Hierarchy Process (AHP) for vulnerability assessment. To enhance RF model performance, we compared three hyperparameter tuning techniques: Bayesian Optimization (RF-BO), Genetic Algorithm (RF-GA), and Grid Search (RF-GS). RF-BO demonstrated superior accuracy in flood hazard modelling. Flood vulnerability was assessed using AHP, incorporating social and physical factors. The final flood risk map was produced by combining the RF-BO hazard model with the AHP vulnerability assessment. Results indicate that flood hazard in Zaio is significantly affected by factors such as topography and distance to rivers. Moreover, areas with high population density closer to rivers, especially in the south-western residential area, were found to be more exposed to flood risk. The findings highlight the utility of ML models, MCDA, and hyperparameter optimization in urban flood risk mapping, enabling the identification of high-risk urban areas that should be prioritized for flood protection efforts. This approach proves especially valuable in ungauged regions with limited hydrological data.

Water Environmental Risks Encountered during Urbanization in Valley Areas and the Potential Mitigation Effects of Utilizing Reclaimed Water

Since urbanization poses risks to regional environments, assessing and mitigating these risks is a challenge inherent to the urbanization process. The present study employs an adapted methodology based on the water environmental capacity (WEC) to assess the risks associated with the water environment in an urbanizing area located in a valley of the Chongqing municipality in Southwestern China, with a particular emphasis on its representative regional river. By utilizing hydrological data and planned parameters specific to this region, this study estimates the spatial and temporal distribution of the WEC and pollution loads in the river under various urbanization scenarios. It also explores the potential of the utilization of reclaimed water to mitigate these water environmental risks. The results indicate that the extent of urbanization exerts significant influences on the water environment and, therefore, requires meticulous planning. Areas experiencing higher levels of urbanization in the middle reaches of the river are associated with greater environmental risks compared to those observed in the lower reaches, while minimal risk is observed in the headwater. Due to the pollution loads brought about by precipitation, there is a higher level of environmental risk during the wet season (May–October) compared to the dry season. Utilizing reclaimed water can effectively reduce the pollution loads discharged into the river and is the most efficient means of mitigating environmental risks. To effectively manage environmental risks during urbanization in this valley area, further studies should prioritize examining the correlation between the scale of urban expansion and the river’s environmental carrying capacity. Additionally, it is imperative to establish realistic targets for the river water’s quality and discharge standards for the effluent from wastewater treatment plants. Furthermore, it is crucial to determine an appropriate level of utilization of reclaimed water and delve into the mechanisms behind the method used to assess the water environmental capacity. Despite these challenges, analyzing the spatial and temporal distribution of risk during urbanization and evaluating the mitigative effect of utilizing reclaimed water based on adapted assessments of the water environmental capacity hold reference value for the formulation of environmental management strategies and environmental planning. There is significant potential for this approach to have various applications.

A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies

AbstractUnderstanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.

Reconstruction and verification of mechanism and triggering conditions of the Hermitage landslide at the Vaches Noires cliffs (Normandy)

The Pays d'Auge region is known for its diversity of instability including large and deep-seated landslides. Among them, the “L'Hermitage landslide” is one of the largest historical phenomena in the Vaches Noires area of Normandy, France. As it was triggered during a period when few observations were performed, there is still considerable uncertainty about the conditions under which it was triggered. While it remains an epiphenomenon in the Vaches Noires area, the fact is that the area could be prone to other large landslides. It therefore seems essential and necessary to study these old landslides in order to better understand the evolution of this type of cliff. This article presents a detailed study of this major landslide. The main objective is to reconstruct the evolution of the landslide and verify the conditions that led to its triggering. To this end, a conceptual model of functioning, including a trigger scenario, was proposed using a multidisciplinary approach integrating historical, geomorphological, geological and hydrological data. This scenario was then evaluated and verified by finite-difference numerical modeling (FDM) with FLAC2D®. This approach enabled to propose probable trigger conditions and to identify more precisely the different contributing factors, causes and mechanisms influencing its development.

A Comparative Study on 2015 and 2023 Chennai Flooding: A Multifactorial Perspective

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management.

Influence of Hydrological and Climatic Changes on Tree Growth in Narew National Park, NE Poland, over the Past 50 Years

Over the last 50 years, groundwater levels have been decreasing and air temperatures have been increasing in Poland. Maintaining this trend may make it impossible to maintain hydrogenic habitats in good condition. Reactions to ongoing climate change recorded in tree rings may be a good indicator describing the degree of this threat. The aim of this study was to determine the influence of climatic and hydrological changes on the growth of scotch pine (Pinus sylvestris L.) and black alder (Alnus glutinosa (L.) Gaertn) in the Narew National Park over past 50 years. The research was based on tree increment cores extracted from 42 pine trees and 57 alder trees that were randomly selected and ranged in age from 25 to 88 years, as well as climatic and hydrological data. Standardised data (5-year index) were analysed by species, generation (Y—21–40 years, M—41–60, O—61–90), and decade using correlation analysis. The results of the study show that pine responded more strongly to changes in air temperature and changes in precipitation totals, while alder growth was more strongly related to fluctuations in the water level of the Narew River and changes in precipitation totals. Our research showed that differences in response to environmental factors also occur between tree generations. The decrease in the water level of the Narew River that occurred over the last 50 years did not adversely affect the condition of the trees. Analysis of DBH growth rates showed that the younger generations (Y, M) grow faster than the older generation (O). This is positive news for managers of commercial and protected forests, but accelerated tree growth may be associated with specific consequences, such as increased wind damage as a result of reduced wood density or reduced sensitivity of trees to climate and hydrological changes.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China.

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

EVALUATING REMOTE SENSING-BASED DROUGHT INDICES: STRENGTHS, LIMITATIONS, AND APPLICABILITY ACROSS SUB-SAHARAN AFRICA'S AGRO-ECOLOGICAL ZONES: A REVIEW

This study reviews the application and effectiveness of various remote sensing (RS) indices for drought monitoring in Sub-Saharan Africa (SSA). Given the region’s diverse climatic zones and frequent drought occurrences, accurate and timely assessment tools are crucial. The study examines indices from different spectral regions, including optical, thermal infrared, and microwave bands, focusing on their spatial and temporal resolutions, data availability, strengths, and limitations. Optical indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are effective in semi-arid and sub-humid zones where vegetation density varies. Thermal infrared indices, including the Temperature Condition Index (TCI), the Vegetation Health Index (VHI), and the Temperature Vegetation Dryness Index (TVDI), provide insights into thermal anomalies and vegetation health, with TCI particularly suited for semi-arid zones and TVDI useful in both semi-arid and sub-humid zones. Microwave indices, such as the Normalized Backscatter Moisture Index (NBMI), Vegetation Optical Depth (VOD), and the Microwave Polarization Difference Index (MPDI), excel in capturing soil moisture and vegetation water content, proving useful in humid forest and semi-arid zones. The integration of these indices with other meteorological and hydrological data enhances drought monitoring and management strategies. Recommendations are made for the optimal use of these indices across different SSA agroecological zones.

A Biogeochemical Comparison of three Representative Lakes of Costa Rica

ABSTRACT Lakes are widely distributed across Costa Rica, from coasts to the highest elevation regions, and located in the main terrestrial biomes. However, updated biogeochemical information about the main types of lakes is still lacking. We present comparative biogeochemistry (water chemistry, stable isotopes, and picoplankton) for a coastal lake (Lake Madre de Dios, LMD), a volcanic lake (Lake Barva, LB), and a glacial lake (Lake Ditkevi, LD). Sampling was conducted between February-November 2022 including dry and rainy seasonal conditions. Hydrological and chemical conditions were evaluated using water and carbon stable isotopes, dissolved organic matter (DOM), major ions, and microbiota analysis. Isotopic data (δ18O, δ13CDIC) confirmed lower evaporative losses for the maar and tarn lakes and productivity response to precipitation inputs. Excitation/Emission matrices confirmed the prevalence of fulvic and humic acids in the coastal and glacial lakes, mainly aromatic proteins and soluble microbial by-products in the volcanic lake. Picophytoplankton (PPP, ∼0.2 to 10 μm) was mainly represented by phycocyanin-rich picocyanobacteria (PC-Pcy) events in the three lakes, but maar and tarn lakes had more representation of phycoerythrin (PE-Pcy) events. We confirmed fluctuations in PPP cell abundance in the lakes lower than in temperate lakes. Like in other eutrophic lakes, Pcy also dominated over picoeukaryotes (Peuk) algae abundance. This work seeks to promote adopting an ecosystem approach to study the biogeochemical functioning of tropical lakes using the combination of chemical, hydrological, and biological data and to provide baseline information for future studies (e.g., climate change and pollution impacts) on tropical lakes of Costa Rica.

Study of the Impact of Landforms on the Groundwater Level Based on the Integration of Airborne Laser Scanning and Hydrological Data

Study of the Impact of Landforms on the Groundwater Level Based on the Integration of Airborne Laser Scanning and Hydrological Data

Human Activities Impacts on Runoff and Ecological Flow in the Huangshui River of the Yellow River Basin, China

This study analyzed 61 years of hydrological data from the Minhe and Xiangtang Hydrological Stations (1956–2016) to examine hydrological changes and ecological flow assurance rates in the Huangshui River Basin, China. Using the Mann–Kendall trend test, IHA/RVA method, and ecological flow calculation methods, the study revealed the following results: (1) After 1994, increased human activity in the Datong River led to a measured runoff decrease compared to natural runoff. Although human activities in the Huangshui River’s main stream were present before 1972, after 1972, these activities intensified, resulting in a more pronounced decrease in the measured runoff. (2) Ecological flow analysis indicated that the main stream of the Huangshui River and the Datong River have ecological flow assurance rates of 100% for all but a few months, where the rates are 98%. The water volume is sufficiently abundant to meet ecological water demands.

Identification of the Runoff Evolutions and Driving Forces during the Dry Season in the Xijiang River Basin

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 108 m3) and maximum (54.67 × 108 m3) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.

Hydraulic Risk Assessment on Historic Masonry Bridges Using Hydraulic Open-Source Software and Geomatics Techniques: A Case Study of the “Hannibal Bridge”, Italy

This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch “Hannibal Bridge” (called “Ponte di Annibale” in Italy) using the Hydraulic Engineering Center’s River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning’s coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge’s materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge’s structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation.

The LTAR Integrated Common Experiment at Upper Mississippi River Basin-Platteville.

Alternative agronomic practices are needed to address the various climatic, agronomic, edaphic, and water quality related challenges faced by the dairy farmers of the Driftless Area (DA) in the Upper Mississippi River Basin (UMRB). These practices should be innovative in nature, inclusive of regional stakeholders, and sustainable to meet the future food and climate related challenges of Wisconsin agriculture. Here, we outline our Integrated (grazing and cropland) Long-Term Agroecosystem Research Common Experiment at the University of Wisconsin-Platteville Pioneer Farm (UW-P PF) in the UMRB and describe our collaboration in this USDA network. In this field-scale experiment, we are comparing the conventional dairy production system common to this region (i.e., corn-on-corn [Zea mays L.] for 4 years, followed by alfalfa [Medicago sativa L.] for 3 years, with no cover crops) with two alternative dairy production systems-(1) soil health management with no-till, cover crops, and application of a novel manure-based nutrient-rich stable product, and (2) management intensive grazing-and rotational grazing on pastures established with diverse forage-legume mix. Meteorological, edaphic, hydrologic, and agronomic data are collected and analyzed at regular frequencies. Going forward, the experiment will continue as a form of stakeholder-driven adaptive research and receive evaluation on a regular basis to determine whether any changes are required to address the "real-world" challenges faced by the farmers in the Midwest.

What controls hydrology? An assessment across the contiguous United States through an interpretable machine learning approach

Machine learning (ML) is increasingly perceived as a futuristic, superior data-driven approach to scientific discovery. It has already demonstrated remarkable performance in forecasting and prediction, yet its role in enhancing our understanding of hydrological processes remains underexplored. Traditional hydrological interpretations have relied heavily on model-dependent interpretation methods, focusing on the predictive accuracy of ML model predictions. Since hydrological models are built on a collection of assumptions and simplifications, model-dependent approaches might suffer from limited model realism, adequacy, accuracy, and equifinality issues. To address this gap, this study provides an ML approach that works in a model-independent context, working directly on hydroclimatic data collected through monitoring systems.We apply our model-independent interpretation approach to a carefully designed set of hydrologic data collected across the contiguous United States to address the following questions: (1) What are the primary controls of runoff-generation mechanisms, and how can such controls be attributed to catchment properties? (2) How and under what circumstances can the history of climate variables, such as precipitation, be a surrogate for present-time state variables, such as soil moisture and snowpack? We show that the ML approach aids in distinguishing catchments characterized by strong overland flow, interflow, or baseflow components and those primarily driven by rainfall, snowmelt, or a mix thereof. We further show that typical surrogate variables used in hydrology may come short in representing the dynamics of catchments that exhibit a complex interplay of rain and snow.

Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach

Accurately characterizing the spatial variability of tension water storage capacity (TWC) within a catchment is challenging due to limited in-situ hydrologic data availability. Conventional conceptual rainfall-runoff models typically rely on an empirically specified TWC distribution. However, this empirical distribution lacks a physical foundation and fails to effectively redistribute critical hydrologic components, such as local capacity and contributing area, to real-world contexts. To overcome this limitation, the topographic wetness index (TWI) and its generalized form (GTWI) are introduced to bridge local topographic information with hydrologic components. Four TWC distribution curves are contrived based on the empirical parabolic distribution, empirical linear distribution, TWI, and GTWI, respectively. The effects of these alternate TWC distributions on streamflow are investigated within the framework of the HYdrologic MODel (HYMOD) across 460 Australian catchments. The results illustrate that the GTWI-based HYMOD (GTHYMOD) outperforms other models in terms of daily streamflow, with high Kling-Gupta Efficiency (KGE) attained in 74.8% of the study catchments during the validation period. The eastern coast of Australian catchments presents a superior streamflow performance compared to that in the western coast. GTHYMOD demonstrates its superiority in characterizing spatial variability, an aspect HYMOD lacked. This study has the potential to refine the empirical TWC distribution from a physical perspective and advance our comprehension of underlying hydrologic behaviors.