Explicit Solution to Green‐Ampt Equation for Nonuniform Soils

Chapter 2 - Use of hydrological models in erosion and sediment studies: a review

An important part of regional planning of water resources and quality is efficient design of monitoring systems and proper use of hydrologic models (Beven 1993). In the design of monitoring systems as well as validation of numerical models, based on, for example, the equation of continuity such as hydrologic routing models and mass balance nutrient models, it is essential to estimate the uncertainties of the model-predictions. This paper presents an implementation of a first-order analysis for estimating the error-propagation when introducing mass balance models as to predict nutrient-concentrations. The uncertainty assessment, developed from a first order theory, is implemented in the analysis and modelling of Hjarbaek fjord in Denmark. The project includes hydrological modelling of input of water and nutrients to the fjord from tributaries, and a hydrodynamic estimation of water levels and velocities in the fjord. A two-system water quality box-model is used for estimation of concentrations in water and sediment phases. The system uncertainties are analysed, starting with input data uncertainties and the error propagation to the final concentration estimates, in order to optimise the future monitoring programme, and to control the model results.

Hydrologic model parameterization using dynamic Landsat-based vegetative estimates within a semiarid grassland

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

Efficient water management plans are required to ensure food security in areas prone to drought and soil degradation. Hydrological modeling can help to achieve this goal by evaluating water availability for different uses, including food production. Fully distributed hydrological models using digital maps of soil properties available for the soil profile are useful for mapping soil-water balance responses, namely water content and water stress. The accuracy of these model responses depends on the quality of the input data, namely, soil hydraulic properties (SHP). Nowadays, we have free access to worldwide digital 1D SHP grids created using global soil datasets, so detailed studies are now needed to evaluate their use in hydrological modeling. We compared the use of a global and a local SHP dataset to simulate soil-water balance at the regional scale in a small irrigation district located in southern Portugal. We explicitly characterized where differences in model responses occur and related those to the variability of soil physical and hydraulic properties, and land use. Our results showed that the choice of the SHP dataset matters. The statistical and spatial variability of simulated water content and water stress for different land uses depended on the dataset used for the numerical simulation. With the global dataset, water content was higher and water stress lower, and both had less statistical and spatial variability compared to the local dataset results. A practical consequence of these findings is misadjusted irrigation schedules, which can be detrimental to crop production and soil quality. A water management plan based on the global results will determine less water requirements, which may not be realistic for the Mediterranean conditions that characterize the study area.

Domain-partition power series in vibration analysis of variable-cross-section rods

Forest operations, extreme flooding events, and considerations for hydrologic modeling in the Appalachians—A review

An analytical method for solving gravity-induced stresses in slope

Regions of complex topography and remote wilderness terrain have spatially varying patterns of temperature and streamflow, but due to inherent difficulties of access, are often very poorly sampled. Here we present a data set of distributed stream stage, streamflow, stream temperature, barometric pressure, and air temperature from the Tuolumne River Watershed in Yosemite National Park, Sierra Nevada, California, USA, for water years 2002–2015, as well as a quality‐controlled hourly meteorological forcing time series for use in hydrologic modeling. We also provide snow data and daily inflow to the Hetch Hetchy Reservoir for 1970–2015. This paper describes data collected using low‐visibility and low‐impact installations for wilderness locations and can be used alone or as a critical supplement to ancillary data sets collected by cooperating agencies, referenced herein. This data set provides a unique opportunity to understand spatial patterns and scaling of hydroclimatic processes in complex terrain and can be used to evaluate downscaling techniques or distributed modeling. The paper also provides an example methodology and lessons learned in conducting hydroclimatic monitoring in remote wilderness.

Learning for improvement

Understanding the effects of climate change on surface-subsurface hydrology is critical for improving water resources management in a basin. In such cases, the use of hydrological models to quantify and assess water resources is a common practice. With the increasing population and human interventions, the land use changes drastically. The land cover plays a vital role in hydrology as it defines the properties of land surface in the models. So far, majority-of the studies accessing the future climate change consequences on hydrology take into account only the meteorological variables under different climatic projections, neglecting the future land use changes assuming it as static. However, that is not the case, because majority of the earth's surface has altered as a result of human activities, and these changes are represented in models via land use maps.The study presented herein, aims to assess the surface-subsurface response of the catchment under combined effect of meteorological variables and land use future projection. The analysis is performed on the Aa of Weerijs catchment which is a meso-scale transboundary watershed between Belgium and the Netherlands. The future projections of the meteorological variable were obtained from the Royal Netherlands Meteorological Institute (KNMI-14) website for the Netherlands and the same trends were implemented for the Belgium part of the catchment. For the land use, the European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover (LC) maps of the study area for the year 1992 to 2021 were downloaded and linearly projected for the year 2050. The developed projected map was also compared with projected land use map of year 2050 by LUISA (Land Use-based Integrated Sustainability Assessment) modelling platform.To investigate the hydrological regime of the area, the fully distributed physically based hydrological model coupled with a hydrodynamic model using MIKE-SHE and MIKE-11 modeling tools was developed. The base model was set up for the year 2009 to 2016. In addition to discharge, the groundwater heads are used to evaluate the model performance.After setting up the base model, firstly, we analyzed the surface and subsurface response of the catchment considering that the land use in the area is the same as it was in 1992. Secondly, we analyzed the catchment response for the year 2050 by considering the meteorological variables as well as land use future projection.The study provides unique estimates of future climate change and associated hydrological implications. The findings of the study will be valuable to plan and suggest significant modifications in the current strategies for water management in the area. Moreover, it can contribute to the efficient integration of spatial planning with water management.

Future directions for physically based, distributed‐parameter models intended for use as hydrologic components of sediment and nutrient transport models are discussed. The attraction of these models is their potential to provide information about the flow characteristics at points within catchments, but current representations in process‐based models are often too crude to enable accurate, a priori application to predictive problems. The difficulties relate to both the perception of model capabilities and the fundamental assumptions and algorithms used in the models. In addition, the scale of measurement for many parameters is often not compatible with their use in hydrologic models. The most appropriate uses of process‐based, distributed‐parameter models are to assist in the analysis of data, to test hypotheses in conjunction with field studies, to improve our understanding of processes and their interactions and to identify areas of poor understanding in our process descriptions. The misperception that model complexity is positively correlated with confidence in the results is exacerbated by the lack of full and frank discussion of a model's capability /limitations and reticence to publish poor results. This may ultimately diminish the opportunity to advance understanding of natural processes because the managers of research resources are given the impression that the answers are already known and are being provided by models. Model development is often not carried out in conjunction with field programs designed to test complex models, so the link with reality is lost.

Существующие в настоящее время гидрологические модели обеспечены лишь крайней ограниченными методическими средствами их калибровки, верификации и тестирования. Крайне актуальной представляется разработка на единой методической основе системы оценок качества при решении ряда типовых задач гидрологии – параметризации и верификации гидрологических моделей, имитационного и сценарного моделирования гидрологического режима, прогнозирования, инженерных расчетов. Без развития системы оценок качества дальнейшее развитие и эффективное практическое использование гидрологических моделей невозможны. В работе показана логика и, отчасти, история развития некоторых важнейших идей и подходов, применяемых в оценивании качества моделирования и прогнозирования, представлены направления их развития. Выполнена необходимая систематизация терминов рассматриваемой предметной области, начиная с базовых понятий «предсказание» и «прогноз», с рассмотрением дополнительных дефиниций: «непрерывные – агрегированные» прогнозы; «краткосрочные – долгосрочные» прогнозы; «методический», «стандартный», «идеальный», «инерционный», «режимный» прогнозы; и др. Рассмотрены этапы развития подхода «оценки умений» (skill scores) в разработке мер оценки качества гидрологического моделирования и прогнозирования, в основном на примере появления и модификации базовой для гидрологии меры – коэффициента эффективности Нэша-Сатклиффа. Особое внимание уделено двум разложениям меры NSE, показывающим её сводимость к базовым статистическим параметрам выборок измеренных и прогнозных значений переменной: средним значениям, среднеквадратическим отклонениям и коэффициенту корреляции. Предложено обоснование модификации оценки NSE путем применения её к предварительно ранжированным выборкам значений переменной. Сформулирован подход к оцениванию качества моделирования и прогнозирования на основе последовательно статистического подхода, с использованием статистических критериев сходства маргинальных распределений фактических и прогностических значений переменной. Намечен ряд задач перспективных исследований в области развития методов оценки качества гидрологических моделей и прогнозов. The currently existing hydrological models are provided with extremely limited methodological tools for their calibration, verification, and testing. It seems extremely relevant to develop, on a unified methodological basis, a system of quality assessment for a number of typical hydrological tasks – parameterization and verification of hydrological models, simulation and scenario modeling of the hydrological regime, forecasting, engineering calculations, etc. Without solving this problem, further development and effective practical use of hydrological models are impossible. The paper demonstrates the logic and, to some extent, the history of the development of some of the most important ideas and approaches used in assessing the quality of modeling and forecasting, as well as to outline some prospects for their development. The necessary systematization of the terms of the subject area under consideration has been carried out, starting with the basic concepts of “prediction” and “forecast”, with consideration of additional definitions: “continuous – aggregated” forecasts; “short-term – long-term” forecasts; “methodological”, “standard”, “ideal”, “inertial”, “regime” forecasts; and etc. The stages of development of the “skill scores” approach in the development of measures for assessing the quality of hydrological modeling and forecasting are considered, mainly using the example of the emergence and modification of a basic measure for hydrology - the Nash-Sutcliffe efficiency coefficient. Special attention is paid to two decompositions of the NSE measure, demonstrating its reducibility to the basic statistical parameters of the samples of measured and predicted values of the variable – average values, standard deviations and correlation coefficient. A rationale for modifying the NSE estimate by applying it to pre-ranked samples of variable values is proposed. An approach to assessing the quality of modeling and forecasting is formulated based on a consistently statistical approach, using statistical criteria for the similarity of marginal distributions of actual and forecast values of a variable. A number of tasks for future research are outlined in the field of developing methods for assessing the quality of hydrological models and forecasts.

Climate change significantly alters the low-flow regimes of river basins worldwide and presents significant challenges to water-scarce regions, especially in dry regions. This current study investigates the impact of climate change on projected low-flow variations in the Maduru Oya River Basin in Sri Lanka, focusing on the reach to the Padiyathalawa stream-gauge station. The study utilizes a lumped hydrological modeling framework, which used the HEC-HMS rainfall-runoff model to simulate streamflow behavior considering anticipated climate scenarios. Projections for future precipitation were obtained from the CNRM-CM6-1 Global Climate Model (GCM), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), and subsequently downscaled through the Long Ashton Research Station Weather Generator (LARS-WG) according to two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The precipitation data, downscaled to the local scale, were integrated into the HEC-HMS model to forecast future river discharge and investigate possible changes in low-flow characteristics. The 7Q10 low-flow index, which is defined as the minimum average flow in a continuous seven-day period with a recurrence interval of ten years was used for estimating and comparing low-flow characteristics. The model parameters were calibrated and validated using historical data from 1997 to 2019. Three objective functions namely: Nash-Sutcliffe Efficiency (NSE), Mean Relative Absolute Error (MRAE), and Percent Error in Peak Flow (PEPF) were used for optimizing model parameters. Future precipitation was projected for short-term (2021-2040), medium-term (2041-2060), and long-term (2061-2080) durations. The projected precipitation data was subsequently input into the developed HEC-HMS model to obtain future streamflow projections for the specified periods. The results of the climate change scenario analysis showed that precipitation may vary due to climate change within the range of -16 % to -5 % for the 2021-2040 period, - 4 % to 1 % for the 2041-2060 period, and 1 % to 21 % for the 2061-2080 period. The results indicated a likely increase in low-flow values across both SSP scenarios. The flow-duration analysis showed that the Q90 flow, representing the flow level that exceeds 90% of the time, is expected to increase, reflecting an upward change in streamflow for low-flow conditions. These findings are important for water resource managers working in the area to plan for and adapt to the impacts of altered low-flow regimes that can impact water supply, agriculture, and overall ecosystem health. Further studies should consider incorporating the use of hydrological models coupled with diverse climate scenarios to better capture the uncertainties related to climate predictions and land-use changes. These would provide a better understanding of the impacts of climate change on river basin hydrology in dry regions like the Maduru Oya River Basin.

Bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations