Hydrological Practice Research Articles

The future of flood hydrology in the UK

Abstract A ‘roadmap’ for the future of UK flood hydrology over the next 25 years has been published, based on a wide-ranging and inclusive co-creation process involving more than 270 individuals and 50 organisations from different sectors and disciplines. This paper highlights key features of the roadmap and its development as a community-owned initiative. The roadmap's relationship with hydrological research and practice is discussed, as is its context within the wider flood risk management innovation landscape, including funding. While the paper has a focus on UK flood hydrology, reflecting the scope of the roadmap, it is also considered in the context of advances in hydrology internationally.

Responses of soil enzyme activities and bacterial community structure to different hydrological regimes during peatland restoration in the Changbai Mountain, northeast China.

Appropriate hydrological management is critical for peatland restoration. An important prerequisite for peatland restoration is a recovery of soil biological processes. However, little is known about the effects of different hydrological management practices on soil biological processes during peatland restoration. In this study, the variations in soil properties, enzyme activities, and bacterial communities across different peatlands, namely natural peatland (NP), peatland restored under high water level (HR), peatland restored under alternating high-low water level (HLR), peatland restored under low water level (LR), and degraded peatland (DP), in the Changbai Mountains were investigated. Results showed that soil organic carbon, soil water content, and total nitrogen in NP were significantly higher than those in restored and degraded peatlands, and these soil properties in restored peatlands increased with the water level. The activities of soil hydrolases including β-1, 4-glucosidase, β-1, 4-n-acetylglucosidase, and acid phosphatase in NP were higher than in restored and degraded peatlands, while the activity of polyphenol oxidase in NP was the lowest. In restored peatlands, all measured enzyme activities decreased with the decline in water level. Both bacterial diversity and richness in NP were the lowest, while the highest diversity and richness were observed in HR. Redundancy analysis indicated that soil organic carbon, water level, soil water content, total nitrogen, and pH were the most important factors that affected the soil enzyme activities and bacterial community. Our findings give insight into the effects of different hydrological regimes on soil biological processes during peatland restoration. Maintaining a high water level early in the restoration process is more beneficial to restoring the ecological functions of peatlands than other hydrological regimes.

Different Vegetation Information Inputs Significantly Affect the Evapotranspiration Simulations of the PT-JPL Model

Evapotranspiration (ET) is an essential part of the global water cycle, and accurate quantification of ET is of great significance for hydrological research and practice. The Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model is a commonly used remotely sensed (RS) ET model. The original PT-JPL model includes multiple vegetation variables but only requires the Normalized Difference Vegetation Index (NDVI) as the vegetation input. Other vegetation inputs (e.g., Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) are estimated by the NDVI-based empirical methods. Here we investigate whether introducing more RS vegetation variables beyond NDVI can improve the PT-JPL model’s performance. We combine the vegetation variables derived from RS and empirical methods into four vegetation input schemes for the PT-JPL model. The model performance under four schemes is evaluated at the site scale with the eddy covariance (EC)-based ET measurements and at the basin scale with the water balance-based ET estimates. The results show that the vegetation variables derived by RS and empirical methods are quite different. The ecophysiological constraints of the PT-JPL model constructed by the former are more reasonable in spatial distribution than those constructed by the latter. However, as vegetation input of the PT-JPL model, the scheme derived from empirical methods performs best among the four schemes. In other words, introducing more remotely sensed vegetation variables beyond NDVI into the PT-JPL model degrades the model performance to varying degrees. One possible reason for this is the unrealistic ET partitioning. It is necessary to re-parameterize the biophysical constraints of the PT-JPL model to ensure that the model obtains reasonable internal process simulations, that is, “getting the right results for right reasons.”

Ideological and Political Design and Practice of Hydrology and Water Resources Curriculum based on Blending Learning

Curriculum ideological and political is a breakthrough and new starting point for the establishment of morality in colleges and universities, and is an effective way to train socialist builders and successors. We mainly take the geographical science professional course "Hydrology and Water Resources" as an example, starting with the curriculum ideological and political design concept, design ideas, teaching implementation and effects, and discussing the strategy of developing curriculum ideological and political under the online and offline mixed teaching mode, and It analyzes the methods in detail and methods of integrating political identity, professional ethics, professional literacy, scientific exploration spirit, dialectical thinking, scientific and cultural literacy, environmental protection awareness and other ideological and political elements before, during and after class, and implements the goal of training geography teachers to achieve the purpose of educating people for the party and educating talents for the country. Through a questionnaire survey of the 2019 students of geographic science class, the result shows that the ideological and political design and practice of hydrology and water resources courses based on the online and offline hybrid teaching of Chaoxing Xuetong have been highly praised by the students.

Nonparametric extrapolation of extreme quantiles: a comparison study

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.

A spatio-temporal analysis of rice production in Tonle Sap floodplains in response to changing hydrology and climate

Rice is one of the most important agricultural commodities throughout the Mekong River Basin including the Tonle Sap Lake floodplains in Cambodia. Recent increases in hydropower dams along the Mekong River have likely altered the surface water hydrology impacting the arable areas and soil qualities for rice production in the Tonle Sap lowland. Along with the hydrological impacts, the region’s rice farming is facing a rapidly changing climate. It is critical to understand how the hydrological changes associated with dam development impact the region’s rice production in a changing climate. The aims of this study were to assess the impacts of recent increases in hydropower dams on the timing and areas of rice cropping in the Tonle Sap floodplains and to evaluate the effects of changing hydrology, rising temperature, and adaptive farming practices on rice productivity using a process-based rice crop model: ORYZA (v3). The effect of dams on arable areas for rice was identified by a remote-sensing method based on the PhenoRice algorithm for the period of 2001–2019 in two rice-growing provinces: Kampong Thom and Battambang. The PhenoRice method identified an increase in rice growing areas as well as shifts in both timing and location of rice cropping towards the sources of irrigation during the dry season since 2010. The ORYZA model simulated a substantial yield reduction with an increase of 2 °C in air temperature in the region. The model predicted that the rice productivity in the region is sensitive to soil organic carbon content which is expected to change with surface water hydrology. The model also predicted that region’s rice yield can increase by optimizing the timing and amount of N fertilization. The findings from this study highlight how hydrology, climate, and agronomic practices can interact to impact rice production in the Lower Mekong Region and provide insights for effective water management and agronomic practices to attain food security in the region in a changing climate.

The Abuse of Popular Performance Metrics in Hydrologic Modeling

AbstractThe goal of this commentary is to critically evaluate the use of popular performance metrics in hydrologic modeling. We focus on the Nash‐Sutcliffe Efficiency (NSE) and the Kling‐Gupta Efficiency (KGE) metrics, which are both widely used in hydrologic research and practice around the world. Our specific objectives are: (a) to provide tools that quantify the sampling uncertainty in popular performance metrics; (b) to quantify sampling uncertainty in popular performance metrics across a large sample of catchments; and (c) to prescribe the further research that is, needed to improve the estimation, interpretation, and use of popular performance metrics in hydrologic modeling. Our large‐sample analysis demonstrates that there is substantial sampling uncertainty in the NSE and KGE estimators. This occurs because the probability distribution of squared errors between model simulations and observations has heavy tails, meaning that performance metrics can be heavily influenced by just a few data points. Our results highlight obvious (yet ignored) abuses of performance metrics that contaminate the conclusions of many hydrologic modeling studies: It is essential to quantify the sampling uncertainty in performance metrics when justifying the use of a model for a specific purpose and when comparing the performance of competing models.

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Behind every robust result is a robust method: Perspectives from a case study and publication process in hydrological modelling

Transpiration of Anatolian black pine and sessile oak forest stands in a sub-humid region of Turkey

Transpiration is a key component of the watershed water budget. Therefore, determining the transpiration of forest stands with different characteristics have been of interest for watershed hydrology and forest management practices in a wide range of environments. The objectives of this study were to compare transpiration of Anatolian black pine (Pinus nigra Arn. subsp. pallasiana (Lamb.) Holmboe) and sessile oak (Quercus petraea (Matt.) Liebl.) tree stands and to model transpiration based on the measured climatic factors. Stand transpirations were calculated from sap flow measurements made by the trunk heat balance method. We conducted an exploratory factor analysis (PCR) to detect affecting meteorological factors of stand transpiration, and we developed linear regression equations to predict transpiration of pine and oak stands. Mean daily and yearly canopy transpiration (Ec) were 1.05 mm day-1 and 378.3 mm year-1 for the pine stand and 3.52 mm day-1 and 801.7 mm year-1 for the oak stand. There was a highly positive correlation between daily stand transpiration and wind speed, global radiation, air vapour pressure deficit and air temperature, but a negative correlation with relative humidity for both stands. Soil water potential had little effect on stand transpiration. The model equations accounted for 81% of the variations in transpiration for the pine stand and 85% for the oak stand. Therefore, the transpiration of forest stands should be considered for effective vegetation management practices, as model equations to estimate the transpiration of pine and oak stands in the region.

Characterization of prairie pothole inundation using AnnAGNPS under varying management and drainage scenarios

Farmed prairie potholes are small, isolated depressions frequently classified as semi-permanent wetlands that make up a significant portion of land area in the Des Moines Lobe (DML) of the larger Prairie Pothole Region (PPR). Historically, these depressions have been subjected to significant drainage to improve their agricultural capacity. However, many assessments of the economic return of continuing to farm these depressions suggest that continued attempts to produce conventional row crops is not profitable and has other ecological consequences beyond crop drownout. This study expands the existing discussion of land use and drainage alternatives in a watershed modeling context. This study utilized the Annualized Agricultural Non-Point Source (AnnAGNPS) model to individually simulate the long-term hydrology of 6 prairie potholes using a matrix of land use and drainage modifications. Results suggest the presence of artificial drainage is the dominant factor in prairie pothole hydrology, while retirement and no-till practices can provide moderate reductions in flood inundation. Conservation tillage induces minimal change on flood metrics. Results show that average annual maximum inundated surface area is reduced by at most 50% across all simulations and the median annual days flooded could be reduced by 25 days, though this is less consistent when isolating high-precipitation years. Regardless of drainage status, in all scenarios there are, on average, more than two inundations events per year lasting 2–4 days. Longer events occur approximately once per year on average. Area inundation frequency curves suggest up to a 20% reduction in maximum pothole area inundated annually can be achieved at the 2-year return frequency. The availability of this data helps characterize the hydrology of farmed potholes more generally over a wide range of conditions, providing a reference for the prioritization of potholes for conservation or alternative management.

Comparison of seven simple loss models for runoff prediction at the plot, hillslope and catchment scale in the semiarid southwestern U.S.

Infiltration excess overland flow is the dominant mechanism for runoff generation in many dryland watersheds. Event-based rainfall-runoff models therefore partition precipitation into two components: loss and excess precipitation. The latter is then transformed into a runoff hydrograph. Numerous loss models have been developed over the past century ranging from simple empirical to sophisticated physically based methods. Complex models can lead to equifinality and associated uncertainty at larger spatial scales with varying soil and cover conditions. Simple models are therefore widely used in hydrologic practice. In the absence of measured data in many arid and semiarid regions, model parameters are often estimated based on laboratory or field infiltrometer tests. Given the documented importance of spatial scale on the runoff response in dryland catchments, it is not clear how models parameterized at the point or soil column scale will perform at the hillslope or catchment scale under real-world conditions. In this study, we compared the performance of seven simple loss models with three or less parameters: the Philip, Smith-Parlange, Horton, Kostiakov, curve number (CN), initial and constant (IC) and the linear and constant (LC) models. The latter is a modification of the IC model introduced in this study. We estimated parameters at the plot scale (2.8 m2) using rainfall simulation and then tested model performance at the hillslope (1.5–3.7 ha) and catchment scale (2.4–2.8 km2) based on measured rainfall-runoff data at two sites in New Mexico and Arizona, U.S. Results show that rainfall simulation can be used successfully to parameterize loss models at the hillslope scale. At the catchment scale, most models showed positive bias, suggesting that other losses (such as channel or transmission losses) play an important role in determining the catchment runoff response. Rainfall intensity and temporal distribution were found to be crucial for accurate runoff prediction. Models that are sensitive to rainfall intensity during the entire simulation (Philip, Smith-Parlange, Horton, Kostiakov, LC) therefore performed better than those with an initial abstraction term (CN, IC). During intermittent rain, the best results were achieved by methods expressing infiltration capacity as a function of cumulative infiltration (LC, Smith-Parlange).

Are There Benefits from Thermal Bacteria for Health? The Hydrogenome Role

In recent years, natural thermal mineral waters have been gaining the special attention of the scientific community, namely in the prevention and treatment of some diseases, due to the microbial properties that exist in these habitats. The aim of this work was to characterize the physicochemical composition and the microbial taxonomic communities present in three thermal waters of the Galician region in Spain and two samples of the northern region in Portugal. These collected water samples were analyzed for physicochemical characterization and the respective hydrogenome of the waters using next generation sequencing together with 16S rRNA gene sequencing. The sequencing showed a high diversity of microorganisms in all analyzed waters; however, there is a clear bacterial predominance of Proteobacteria phylum, followed by Firmicutes, Deinococcus-Thermus, Aquificae and Nitrospira. The main physicochemical parameters responsible for the clustering within the Spanish waters were sulfur compounds (SO32− and S2−), CO32− and neutral pH, and in the Portuguese waters were Mg, Ca and Sr, nitrogen compounds (NO3− and NH4+), Na, Rb, conductivity and dry residue. This work will allow for a better understanding of the microbial community’s composition and how these microorganisms interfere in the physicochemical constitution of these waters often associated with medicinal properties. Furthermore, the hydrogenome may be used as an auxiliary tool in the practice of medical hydrology, increasing the likelihood of safe use of these unique water types.

A Review on Rainfall Data Resolution and Its Role in the Hydrological Practice

Data collected by gauges represent a fundamental force in most hydrological studies. On the basis of sensor type and recording system, such records are characterized by different aggregation time, ta. In this review paper, a comprehensive rainfall database of rain gauge networks operative worldwide is used to determine the temporal evolution of ta. As a second step, issues related to the limited and heterogeneous temporal resolution of rainfall data are discussed with regard to avoiding possible errors in the analysis of historical series. Particular attention is focused on quantifying the effects on the estimation of extreme rainfalls that play a crucial role in designing hydraulic structures. To this aim, algebraic relations for improving a correct determination of extreme rainfall are also provided.

Verification of Regional Deterministic Precipitation Analysis Products Using Snow Data Assimilation for Application in Meteorological Network Assessment in Sparsely Gauged Nordic Basins

AbstractSparse precipitation information can result in uncertainties in hydrological modeling practices. Precipitation observation network augmentation is one way to reduce the uncertainty. Meanwhile, in basins with snowpack-dominated hydrology, in the absence of a high-density precipitation observation network, assimilation of in situ and remotely sensed measurements of snowpack state variables can also provide the possibility to reduce flow estimation uncertainty. Similarly, assimilation of existing precipitation observations into gridded numerical precipitation products can alleviate the adverse effects of missing information in poorly instrumented basins. In Canada, the Regional Deterministic Precipitation Analysis (RDPA) data from the Canadian Precipitation Analysis (CaPA) system have been increasingly applied for flow estimation in sparsely gauged Nordic basins. Moreover, CaPA-RDPA data have also been applied to establish observational priorities for augmenting precipitation observation networks. However, the accuracy of the assimilated data should be validated before being applicable in observation network assessment. The assimilation of snowpack state variables has proven to significantly improve streamflow estimates, and therefore, it can provide the benchmark against which the impact of assimilated precipitation data on streamflow simulation can be compared. Therefore, this study introduces a parsimonious framework for performing a proxy validation of the precipitation-assimilated products through the application of snow assimilation in physically based hydrologic models. This framework is demonstrated to assess the observation networks in three boreal basins in Yukon, Canada. The results indicate that in most basins, the gridded analysis products generally enjoyed the level of accuracy required for accurate flow simulation and therefore were applied in the meteorological network assessment in those cases.

A Study of the Dynamics of Soil Erosion Using Rusle2 Modelling and Geospatial Tool in Edda-Afikpo Mesas, South Eastern Nigeria

Abstract The Revised Universal Soil Loss Equation (RUSLE) was used to study the soil erosion processes in Edda-Afikpo mesas, Lower Cross River watersheds,Nigeria. The mesas occupy an area estimated at 60km2 on a surface relief of about 284m. DEM data, satellite images and basemap of the area were used. Remotely sensed data were ground-truthed through extensive field works. The results show that the process is facilitated by the Trifecta of hill slope hydrology, geology and land use practices. Steep hill Slope of values 78 % at the major hot spots, very fragile, dry and non-plastic sandy soils all aid sediment detachment. Analysis of the index properties which include Liquid Limit(LL) of 25-30,moisture content(w%) of 5.9-7.4,permeability of 1.541x10-3 – 1.636x10-3 cm/s and shear strength of 36-42 KN/m2 predispose the sediments to detachment and erosion. Based on the analysis, the amount of soil loss in the project area is about 1373.79 ton per year. Soil erosivity factor is high at the mesas(5023.83 MJ mm ha−1 h−1 yr−1 - 5069.51 MJ mm ha−1 h−1 yr−1) The sandy layer attain thickness of 50m-60m in places and with high pore pressure development, slope failure are triggered during intense storm events. In terms of vulnerability level in erosion risk, high to very high constitute 4.1% of the watershed which translate to 5.05km2 of the 59km2. The various processes occur simultaneously and are exacerbated by human factors through seasonal bush burning and development along drainage lines. The study reveals that 18.8% of the available land for development is at high to very high risk of erosion. The soil loss model has been validated and the hotspots from the map coincide with the gully sites. The results of this research can therefore be used for conservation and adaptation purposes.

Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Mitigating flood risk using low‐cost sensors and citizen science: A proof‐of‐concept study from western Nepal

AbstractThe generation of hydrological data for accurate flood predictions requires robust and, ideally, dense monitoring systems. This requirement is challenging in locations such as the Himalayas, which are characterised by unpredictable hydroclimatic behaviour with dramatic small‐scale spatial and temporal variability. River level monitoring sensors that are affordable and easy‐to‐operate could support flood risk management activities in the region. We therefore identify potential for a local participatory monitoring network that also serve to overcome existing data gaps, which represent the main bottleneck for establishing an effective community‐based flood early‐warning system. We have applied a citizen science‐based hydrological monitoring approach in which we tested low‐cost river level sensors. Initial results, collected over summer 2017 from two stations on the River Karnali, suggest that our system can successfully be operated by non‐scientists, producing river level data that match those obtained from an adjacent government‐operated high‐tech radar sensor. We discuss potential opportunities to integrate these low‐cost sensors into existing hydrological monitoring practice. Combined with an adaptive, community‐led approach to resilience building, we argue that our low‐cost sensing technology has the potential not only to increase spatial network coverage in data‐scarce regions, but also to empower and educate local stakeholders to build flood resilience.

Monthly river flows in Texas for natural and developed conditions

River flow characteristics and computational methods for converting sequences of monthly flows between natural and regulated conditions are explored based on experience in assessing water availability throughout the state of Texas in the United States. Diverse climate, hydrology, economic development, and water management practices across the state combined with continual population growth and implementation of a statewide water availability modeling system makes Texas an excellent case study of stream flow characteristics and modeling and analysis methods that are relevant worldwide. Stream flow is extremely variable, subject to severe multiple-year droughts, intense floods, seasonality, and continuous fluctuation. The effects of population and economic growth, water resources development and management, and climatic variability on river flows vary with different conditions found across the state. The modeling system provides capabilities for adjusting observed river flows to represent natural conditions, simulating regulated flows representing specified conditions of development, and performing statistical frequency and reliability analyses.

Mapping Flood-Based Farming Systems with Bayesian Networks

Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice.

On the Rainfall Intensity–Duration–Frequency Curves, Partial-Area Effect and the Rational Method: Theory and the Engineering Practice

This research evaluates the partial-area effect and its relationship with the rainfall intensity–duration–frequency (IDF) equations. In the Rational Method, if the critical rainfall duration is shorter than the time of concentration, the partial-area effect occurs. We proved that the partial area could exist for the general ID equation i=a/(b+td)c, only when c>1. For these equations, in the application of the Rational Method, the maximum discharge at basin outlet occurs for rainfall duration (td) equal to b/(c−1). Nevertheless, for that case, the Depth Duration Frequency (DDF) has a maximum at that rainfall duration. These situations are present in engineering practice and will be discussed in this paper. Research was done to look for IDF equations with c>1 in hydrologic engineering practice. It found 640 inconsistent IDF equations (c>1) in four countries (Brazil, Mexico, India, and USA), which means that a fundamental principle for building consistent IDF equations (i.e., c>1), published in the scientific literature since 1998, did not reach the hydrologic engineering practice fully. We provided some analysis regarding this gap between theory and engineering practice.