Rainfall Uncertainty Research Articles

Assessing the Impact of Radar-Rainfall Uncertainty on Streamflow Simulation

Abstract Hydrological models and quantitative precipitation estimation (QPE) are critical elements of flood forecasting systems. Both are subject to considerable uncertainties. Quantifying their relative contribution to the forecasted streamflow and flood uncertainty has remained challenging. Past work documented in the literature focused on one of these elements separately from the other. With this in mind, we present a systematic approach to assess the impact of QPE uncertainty in streamflow forecasting. Our approach explores the operational Iowa Flood Center (IFC) hydrological model performance after altering two radar-based QPE products. We ran the Hillslope Link Model (HLM) for Iowa between 2015 and 2020, altering the Multi-Radar/Multi-Sensor (MRMS) system and the specific attenuation-based (IFCA) IFC radar-derived product with a multiplicative error term. We assessed the forecasting system performance at 112 USGS streamflow gauges using the altered QPE products. Our results suggest that addressing rainfall uncertainty has the potential for much-improved flood forecasting spatially and seasonally. We identified spatial patterns linking prediction improvements to the radar’s location and the magnitude of rainfall. Also, we observed seasonal trends suggesting underestimations during the cold season (October–April). The patterns for different radar products are generally similar but also show some differences, implying that the QPE algorithm plays a role. This study’s results are a step toward separating modeling and QPE uncertainties. Future work involving larger areas and different hydrological and error models is essential to improve our understanding of the impact of QPE uncertainty. Significance Statement This study investigates the impact of radar rainfall on flood forecasting uncertainty. Previous research focused on rainfall–runoff models, ignoring the errors in rainfall estimation. We used a systematic approach to adjust two radar-rainfall products, forcing a simple hydrological model. Results show the potential improvement in streamflow prediction by correcting basinwide bias in rainfall. The optimal correction varies with basin size, location, season, and rainfall amount.

Uncertainty in Evapotranspiration Inputs Impacts Hydrological Modeling

ABSTRACT This work addresses the role of accurate input data in hydrological model simulations and explores the often-overlooked errors associated with evapotranspiration (ET). While existing literature primarily focuses on uncertainties in rainfall, this study underscores the necessity of considering errors in ET, as evidenced by some studies suggesting their substantial impact on hydrological model responses. A comprehensive exploration of uncertainty quantification resulting from errors in ET in hydrological model simulations is presented, highlighting the imperative to scrutinize this facet amidst diverse uncertainties. There are two approaches for addressing uncertainty in potential evapotranspiration (PET) inputs as discussed: directly considering uncertainty in PET data series or accounting for uncertainty in the parameters used for PET estimation. Furthermore, details are provided about the existing error models for PET measurements, revealing a limited number of studies that specifically account for ET-related uncertainties. Researchers commonly address ET errors by considering both systematic and random errors; some studies suggest that systematic errors in PET have a more substantial impact compared to random errors on hydrological model responses. In summary, the objective of this paper is to offer an in-depth exploration of uncertainty associated with PET inputs and their influence on hydrological modeling.

Assessing Alterations of Rainfall Variability Under Climate Change in Zengwen Reservoir Watershed, Southern Taiwan

This study aims to detect changes in rainfall variability caused by climate change for various scenarios in the CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble. Projected changes in rainfall unevenness in terms of different timescale indices using three categories, namely WD50 (number of wettest days for half annual rainfall), SI (seasonality index), and DWR (ratio of dry-season to wet-season rainfall) are analyzed in Zengwen Reservoir watershed, southern Taiwan over near future (2021–2040) and midterm future (2041–2060) relative to the baseline period (1995–2014) under SSP2-4.5 and SSP5-8.5 scenarios. The projected rainfall for both baseline and future periods is derived from 25 GCMs (global climate models). The results indicate that noticeably deteriorated rainfall unevenness is projected in the Zengwen Reservoir watershed over future periods, which include decreased WD50, increased SI, and decreased DWR. Though there were noticeable differences in the rainfall projections by the different GCMs, the overall consensus reveals that uncertainties in future rainfall should not be ignored. In addition, WD50 has the greatest deviated relative change in mean, which implies that the short-timescale rainfall unevenness index is easily affected by climate change in the study area. Distributional changes in rainfall unevenness determined by simultaneously considering alterations in relative changes in mean and standard deviation indicated that there was no single dominant category. However, the top two categories, with summed frequencies exceeding 0.5, characterize different properties of rainfall unevenness indices. The top two categories of WD50 and SI commonly have decreased mean and increased mean, respectively, but nearly equal frequencies of the top two categories in DWR exhibit opposite variations. The proposed rainfall unevenness change detection approach provides a better understanding of the impacts of climate change on rainfall unevenness, which is useful for preparing adaptive mitigation measures for coping with disasters induced by climate change.

Dampak Pemanasan Global Terhadap Kesejahteraan Ternak Dan Produktifitas Di Kawasan Perdesaan

Global warming is increasingly becoming a significant threat to livestock welfare and productivity in rural areas, which rely heavily on the livestock sector for food security and income. This study aims to analyze the impacts of climate change on livestock productivity and evaluate adaptation strategies implemented by farmers in rural areas. The methods used include qualitative and quantitative approaches through surveys, in-depth interviews, and direct observation of farmers in climate change vulnerable areas. Data were analyzed using descriptive analysis and inferential statistics. The results showed that increased temperature and rainfall uncertainty had a negative impact on livestock health, feed availability and productivity. Adaptation strategies implemented by farmers, such as livestock species diversification and more efficient feed management, have helped mitigate some of the negative impacts, but limited access to technology and resources is a significant constraint in improving adaptive capacity. In addition, extension support and climate information are still limited, especially in remote areas. The discussion underscores the need for a holistic approach involving technical, social and policy aspects in supporting farmers' adaptation to climate change. Greater support from the government and relevant institutions is needed to improve farmers' access to information, technology and financing. In conclusion, the impacts of global warming on the livestock sector in rural areas are significant, and effective adaptation requires cross-sectoral support to ensure the sustainability of livestock productivity.

Fully analytical method for probabilistic stability analysis of the rainfall-induced landslide considering variability of saturated hydraulic conductivity and rainfall uncertainty

Fully analytical method for probabilistic stability analysis of the rainfall-induced landslide considering variability of saturated hydraulic conductivity and rainfall uncertainty

Irrigation of ‘Prata-Anã’ Banana with Partial Root-Zone Drying in a Semi-Arid Environment

Considering the uncertainty of rainfall and prolonged droughts in semiarid regions, optimizing water management through techniques like partial root-zone drying (PRD) is crucial for sustainable banana production. This study aimed to evaluate the ‘Prata-Anã Gorutuba’ banana under irrigation by PRD. The experimental design was randomized blocks with five irrigation strategies (PRD7 50%–50% ETc and 7-day frequency of alternation of the irrigated side—FA, PRD14 50%–50% ETc and 14-day FA, PRD21 50%–50% ETc and 21-day FA, FX 50%–50% ETc and fixed irrigation, and irrigation with 100% ETc on both sides of the plant—FULL) with five replicates. Soil water content, physiological, vegetative, yield characteristics, and water productivity were assessed over two production cycles. PRD on the dry side lowered soil water content below optimal levels for banana cultivation, increased transpiration, and decreased photosynthesis and instantaneous water use efficiency with rising temperatures, while photosynthesis increased with stomatal conductance. PRD reduced plant vigor and delayed flowering in the first cycle. Compared to full and fixed irrigation, PRD conserves water while maintaining crop yields. Water productivity was higher under PRD, with PRD14 (50% ETc and 14-day alternation) offering the best water use efficiency while maintaining yield, making it suitable for ‘Prata-Anã Gorutuba’ banana cultivation. The study recommends PRD for sustainable banana farming in regions with limited water resources, contributing to sustainable agricultural practices and better water management.

Long-Term Seasonal and Annual Rainfall Trend Analysis of Giridih District, Jharkhand, India

District Giridih is primarily rain dependent on its 95 per cent net cultivable land under summer-monsoon crops. It is classified as a mono-cropping area as it lacks irrigation facilities, essential for winter crops. In the last few years, the district experienced water stress due to the shortage of monsoon rainfall. We study long term seasonal and annual rainfall trend analyses for the Giridih district of Jharkhand in India using linear regression modelling. The last 100 years of data were taken for the study and were divided into two-time phases of 50 years each to analyse the changing pattern of rainfall trends. We found a decreasing trend in monsoon rainfall from 1 mm y-1 to 3 mm y-1 between the initial (1921-1971) and final (1972-2021) phase of the study period. Similarly, the return period of droughts (rainfall deficit years) has also reduced from 11 years to 5 years. The study has shown that the rainfall pattern has changed, with the winter months showing a decrease and the pre-monsoon months an increase in their relative contributions to the annual rainfalls. The study may help in the formulation of water resource conservation-oriented decision-making to cope-up with the rainfall uncertainties and meet the future water demand.

Effect of Phosphorus and Sulphur Levels on Yield Attributes, Yield and Quality of Groundnut (Arachis hypogaea L.)

Background: Groundnut is one of the best known oilseed crops and belongs to the family Leguminosae. However, the productivity of groundnut in India is less as compared to average productivity of world due to uncertainty in monsoon rainfall and incidences of diseases, insect pests and weeds. The main cause of low groundnut production is an unbalanced and insufficient usage of nutrients. Because groundnut is a legume-oilseed crop, it has a high phosphorus, calcium and sulphur demand. Methods: A field experiment consisting of four phosphorus levels (Control, 40, 50, 60 kg P2O5 ha-1) and four sulphur levels (Control, 25, 50, 75 kg S ha-1) was laid out in split plot design to analyze their effect on pod yield, yield components, physiological and quality parameters of groundnut. Result: The results indicated the application of 60 kg P2O5 ha-1 and 75 kg S ha-1 significantly improved the pod yield and yield components except number of kernels per pod and harvest index, physiological parameters except leaf area index and all the quality parameters. However, the difference between 50 and 60 kg P2O5 ha-1 and 50 and 75 kg S ha-1 was found non-significant for most of the studied parameters.

A stochastic rainfall model for reliability analysis of rainfall-induced landslides

ABSTRACT Rainfall is an important trigger for a large number of landslides. Physics-based quantitative risk assessment of rainfall-induced landslides requires modelling of rainfall uncertainty in addition to soil parameters. Explicit modelling of rainfall uncertainty should consider the uncertainties in rainfall intensity, duration, as well as the occurrence of rainfall events. Towards this aspect, this study proposes a stochastic rainfall model, where the joint distribution of rainfall duration and intensity is constructed using copula theory and the occurrence of rainfall events is described using Poisson process. Based on the proposed stochastic rainfall model, a computationally efficient reliability method with a machine learning-based surrogate model is developed for assessing the failure probability of a slope subjected to rainfall infiltration within a given time period. An illustrative example with real rainfall data from Singapore is utilised to demonstrate the proposed approach. The results suggest that the slope failure probability is less sensitive to the uncertainty in rainfall occurrence, but highly sensitive to the adopted inter-event time definition for characterisation of the rainfall event. Overall, this study provides a useful stochastic rainfall model and some practical guidelines for quantitative risk assessment of rainfall-induced landslides.

Analysing spatio-temporal drought characteristics and copula-based return period in Indian Gangetic Basin (1901-2021).

Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in theIndo-Gangetic plainregion. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts.

Enhancing Groundnut Productivity in Anantapur District, Andhra Pradesh: Identifying Constraints and Developing Adaptation Strategies

India is a world leader in groundnut farming. A purposive and random sampling technique was used for the study and conducted in KVK operated mandals of Anantapur and Satya Sai district. Ten mandals of KVK operational area were selected purposively where Cluster Front-Line Demonstrations were conducted and from each mandal 2 villages were selected purposively by KVK. Eight farmers from each village were selected randomly, thus making a total of 160 respondents for this study. An ex-post facto research design was used and the data were collected by interview method and enquired about the constraints faced by them for low groundnut productivity and queried suggestions to overcome the constraints faced by them. From the study, it was revealed that major constraints of the farmers faced were uncertainty in rainfall during crop growth period (96.25) followed by lack of irrigation facility (92.5 %), shortage of labour during critical stages (88.75%), low production due to pest and disease infestation (87.5%) and non-availability of appropriate market price (86.25) as reported by the farmers. Among the suggestions offered by farmers it was the supply of inputs at subsidized rate (76.25 %) ranked first followed by remunerative price should be made available to the groundnut farmers during drought years (75.0%) ranked second and irrigation facilities should be made available (72.5%) ranked third.

Model predictive control and rainfall Uncertainties: Performance and risk analysis for drainage systems

Model predictive control and rainfall Uncertainties: Performance and risk analysis for drainage systems

Assessment of the impact of rainfall uncertainties on the groundwater recharge estimations of the Tikur-Wuha watershed, rift valley lakes basin, Ethiopia

Spatial recharge estimation uncertainty is directly proportional to uncertainty in input precipitation data Thus, the main objective of this study was to investigate the recharge uncertainty by using improved spatial rainfall observations. The physically based fully distributed hydrological model WetSpa was used to simulate 20,000 possible combinations of parameters for two model setup. The M1 model setup was developed based on the rainfall measurements obtained from rain gauge stations scattered in and around the Tikur-Wuha watershed in Ethiopia, while M2 model setup was developed using bias-corrected satellite rainfall estimates (SREs) based on Climate Hazards Group InfraRed Precipitation (CHIRP) merged with relevant ground station records. The required parameter combinations were generated using Monte Carlo simulation stratified by applying Latin Hypercube Sampling (LHS). One hundred best performing parameter combinations were selected for each model to generate spatial recharge statistics and assess the resulting uncertainty in the recharge estimates. The results revealed that enhanced spatial recharge estimates can be produced through improved CHIRP-based SREs. The long-term mean annual recharge (218.29mm) in the Tikur-Wuha watershed was estimated. Model parameter calibration performed using discharge measurements obtained from the Wosha rain gauge station located in the subcatchment area of the Tikur-Wuha watershed had a Nash-Sutcliffe efficiency of 0.56. Seventy percent of the watershed showed a coefficient of variation (Cv)<0.15 for M2, while 90% of the area exhibited a Cv<0.15 for M1. Furthermore, the study findings highlighted the importance of improving evapotranspiration data accuracy to reduce the uncertainty of recharge estimates. However, the uncontrolled irrigation water uses and the total recharge coming from the irrigation fields scattered across the Tikur-Wuha watershed were not considered in the study, which is a limitation of the study. Future studies should consider the contribution made by irrigation water to the total recharge of the watershed.

Combining Hydrological Modeling and Regional Climate Projections to Assess the Climate Change Impact on the Water Resources of Dam Reservoirs

Climate change may significantly impact the availability and quality of water resources in dam reservoirs by potentially altering the hydrological regime of lake tributaries and the corresponding flow–duration curves. Hydrological models driven by climate projections (downscaled to the watershed scale and bias corrected to eliminate systematic errors) are effective tools for assessing this potential impact. To assess the uncertainty in future water resource availability, resulting from the inherent uncertainty in climate model projections, an ensemble of climate models and different climate scenarios can be considered. The reliability and effectiveness of this approach were illustrated by analyzing the potential impact of climate change on the water availability at Brugneto Lake in northern Italy. This analysis was based on climate projections derived from an ensemble of 13 combinations of General Circulation Models and Regional Climate Models under two distinct scenarios (RCP4.5 and RCP8.5). The semi-distributed HEC-HMS model was adopted to simulate the hydrological response of the basin upstream of the lake. The hydrological model parameters were calibrated automatically via the PEST software package using the inflows to the lake, estimated through a reverse level pool routing method, as observed values. Future water availability was predicted for short- (2010–2039), medium- (2040–2069), and long-term (2070–2099) periods. The results indicate that the uncertainty in reservoir inflow is primarily due to the uncertainty in future rainfall. A moderate reduction in water availability is expected for Brugneto Lake by the end of the current century, accompanied by modifications in the flow regime. These changes should be considered when planning future adaptation measures and adjusting reservoir management rules.

Evaluating the response and adaptation of urban stormwater systems to changed rainfall with the CMIP6 projections

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change.

Probabilistic hazard analysis of rainfall-induced landslides at a specific slope considering rainfall uncertainty and soil spatial variability

Probabilistic hazard analysis of rainfall-induced landslides at a specific slope considering rainfall uncertainty and soil spatial variability

A calibration method for SWMM to mitigate the impact of the structure defect without considering runoff on building walls

In this study, we propose a hypothesis that an automatic calibration framework can address modeling uncertainties in the Storm Water Management Model (SWMM) due to structural defects that result in the inability of the model to account for runoff generated on building walls from wind-driven rain. To test this hypothesis, we introduce a rainfall error model into the calibration framework to indirectly consider the effects of inclined wind-driven rain on building walls. We couple the optimization algorithm Differential Evolution Adaptive Metropolis (DREAM) with SWMM using newly developed API functions. To demonstrate the effectiveness of the framework, we conduct a case study in Guangzhou, China and assess the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries. The results show that the framework can improve the average Nash–Sutcliffe index of selected events by more than 5%. It also captures peak flow more accurately. This framework contributes to the theory of SWMM calibration by accounting for structural defects and considering rainfall uncertainty.

Best Fitting of Probability Distribution for Monthly and Annual Maximum Rainfall Prediction in Junagadh Region (Gujarat-India)

Rain is a meager and crucial hydrological variable in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. Water resources development of any locality depends on amount of runoff generated and rainfall received. Appropriate probability distributions need to be selected and fitted to the historical time series of rainfall for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. This research attempts to fit eightdifferent theoretical probability distributions to the monthly and annual maximum rainfall for one to five consecutive days to select the best one for the better prediction of maximum rainfall. For determination of goodness of fit Chi-Square and Nash-Sutcliffe Efficiency were carried out by comparing the expected values with the observed values. The results indicated that the Gumbel distribution emerged to be the best fit for the prediction of monthly and annual maximum rainfall of Junagadh Region.

Water requirement of Urban Green Infrastructure under climate change

The increased uncertainty of rainfall and high urban temperatures resulting from climate change present challenges for water management in Urban Green Infrastructure (UGI). UGI is an important component of cities, and it plays a crucial role in addressing various environmental issues (e.g., floods, pollutants, heat islands, etc.). Effective water management of UGI is essential to ensure its environmental and ecological benefits in the face of climate change. However, previous studies have not adequately investigated water management strategies for urban-scale GI under climate change scenarios. This study aims to estimate the current and future water requirement and effective rainfall (rainwater stored in the soil and plant roots available for plant evaporation) to determine the irrigation requirement of UGI during periods of rainfall deficit under current and future climate conditions. The results indicate that the water requirement for UGI will continue to increase under both RCP4.5 and RCP8.5 climate scenarios, with a higher increase projected under RCP8.5. For instance, the average water requirement for UGI is currently 731.29 mm, and it is projected to increase to 756.45 mm (RCP4.5) and 816.47 mm (RCP8.5) in summer during the period of 2081-2100, assuming low managed water stress condition. In addition, the water requirement of UGI in Seoul is the highest in June (approximately 125-137 mm) and the lowest in December or January (about 5-7 mm). While irrigation is unnecessary in July and August due to sufficient rainfall, other months in Seoul require irrigation when rainfall is insufficient. For example, continuous insufficient rainfall from May to June 2100 and April to June 2081 would need >110 mm (RCP4.5) of irrigation requirement even under high managed water stress condition. The findings of this study provide a theoretical foundation for water management strategies in current and future UGI settings.

On constructing limits-of-acceptability in watershed hydrology using decision trees

A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. For instance, in rainfall-runoff models, measurement uncertainty exists due to errors in measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in mathematical representation of hydrological processes. Parametric uncertainty is a consequence of our inability to measure effective model parameters, limited data available to calibrate model parameters, and measurement and structural uncertainties. Measurement and structural uncertainties are inseparable without additional information about measurement uncertainties. The existence of these predominantly epistemic uncertainties makes the model inference difficult. Limits-of-acceptability (LOA) framework has been proposed in the literature for model inference under a rejectionist framework. LOAs can be useful in model inference if they reflect the effect of errors in rainfall and streamflow measurements. In this study, the usefulness of quantile random forest (QRF) algorithm has been explored for constructing LOAs. LOAs obtained by QRF were compared to the uncertainty bounds obtained by rating-curve analysis and the LOAs obtained by runoff ratio method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff ratio method is expected to reflect uncertainty in rainfall and streamflow volume measurements. LOAs obtained by using QRF were found to envelop the uncertainty bounds due to streamflow measurement errors. The variation of width of LOAs was similar for QRF and runoff ratio methods. Further, QRF LOAs were scrutinized in terms of their ability to reflect the effect of rainfall uncertainty, both qualitatively and quantitatively. Results indicate that QRF LOAs reflect the effect of rainfall uncertainty: increase in standard deviation with increase in mean streamflow values and decrease in coefficient of variation with increase in mean streamflow values. A mathematical analysis of the LOAs obtained by the QRF method is presented to provide a theoretical foundation.