Abstract
Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study ...the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), have been shown to be good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the Excess Infiltration and Excess Storage Model (EIESM), artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.
Rainfall is time concentric in nature. The spatial and temporal distribution of rainfall is changing over the Earth with recent anthropogenic warming. The study explores various characteristics of ...annual and seasonal concentration of rainfall across India using the precipitation concentration index (PCI) and its trends during the period of 1986–2015, based on a high‐resolution gauge‐based rainfall data (0.25 × 0.25°), obtained from the India Meteorological Department (IMD). An intercomparison is made with 11 other gridded rainfall datasets to infer whether these datasets can reasonably reproduce the spatiotemporal distribution of PCI and its trends in various homogenous rainfall regions of India or not. These datasets are categorized into gauge‐based (APHRODITE, GPCC, CPC), satellite‐derived (CHIRPS, PERSIANN‐CDR), and reanalysis (JRA‐55, MERRA‐2, NCEP‐2, PGF, ERA‐Interim, ERA‐5). On annual scale, about 8.52, 24.1, and 67.38% of area of India are under moderate, irregular, and strongly irregular rainfall distribution, respectively. Spatial variation of PCI in India is influenced by geographical factors such as latitude, longitude, and elevation. Significant increasing and decreasing trends in annual PCI have been observed in the northeast, eastern, and western coasts of India, respectively. Gridded data intercomparison suggests that the gauge‐based APHRODITE, GPCC, and satellite‐derived CHIRPS datasets better perform in capturing the temporal and spatial variation of PCI across India when compared to the IMD gridded dataset, whereas the ERA‐5 performs better among the reanalysis datasets. However, the rainfall datasets exhibited marked differences with the IMD while estimating the annual and seasonal trend and its magnitude across various regions of India. The JRA‐55 overestimated areas of positive trend and its magnitude on annual and seasonal scale. These findings have practical implications for hydroclimatic studies.
Rainfall is seasonally concentrated in India and exhibits moderate to highly irregular distribution. Rainfall is highly concentrated in arid and semi‐arid regions of NW India. Gauge‐based APHRODITE and GPCC data effectively capture the spatial pattern of annual and seasonal PCI in India compared to IMD gridded dataset while CHIRPS better performed than the PERSIANN‐CDR among satellite rainfall datasets and ERA‐5 and MERRA‐2 datasets among reanalysis datasets. Gridded rainfall products exhibited higher bias in observational data‐sparse regions. In the figure, annual and seasonal PCI over India (a) and its coefficient of variation (b) during the period of 1986–2015.
Increased temperature rates have the potential to change the rainfall regime in a given region, as well as to intensify its extreme events, which may lead to significant and negative socioeconomic ...and environmental impacts on urban populations. However, knowledge about the extent of changes in rainfall rates in Rio de Janeiro City (RJC) remains incipient; thus, it is necessary applying indices climate change to help better understanding this phenomenon. The aim of the current study is to investigate changes in rainfall distribution and increase in the number of extreme rainfall events in RJC. Daily rainfall data deriving from fifteen weather stations distributed in RJC were analyzed in the RclimDex software and Mann–Kendall test. The analysis has shown increased rainfall rates from the beginning of the series to approximately the first ten years of study. Total rainfall rate has decreased after this period. Rainfall intensity in almost all seasons has decreased after 2005; this outcome has indicated reduced annual rainfall rate and number of wet days. However, there was prevalence of positive trends in daily rainfall rates (Rx1day) and in total rainfall of five consecutive days (Rx5day). The increased number of extreme rainfall events in RJC can cause sudden inundations, floods, runoffs and river overflows with potential to cause landslides and human death due to irregular occupation of hills and slopes.
Numerous studies have evaluated the reliability and hydrologic utility of various rainfall data sets through hydrological modeling. However, the calibration of hydrological models compensates for ...errors in rainfall inputs. The drivers, conditions, and factors affecting the calibration of hydrological models given the accuracy of rainfall inputs are not well understood. Here, we explore hydrological model adaptability to rainfall inputs of varied quality and its potential mechanisms. Twenty‐eight rainfall products from multiple sources are collected for a headwater catchment in the Southern United States. These rainfall data sets include measurements from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting model simulations. Such rainfall data sets with varied errors are used to independently calibrate a widely used conceptual Xin'anjiang (XAJ) hydrological model. Results suggest that the hydrological model can often adapt well to two scenarios of inaccurate rainfall inputs producing high‐performance streamflow simulations. This adaptive ability is controlled by an adaptable threshold of the overall bias of the rainfall inputs. Moreover, hydrological model adaptability to rainfall inputs is further influenced by how event‐based rainfall bias shapes the overall rainfall bias, especially from those of heavy rainstorms. The hydrological model can adapt to those rainfall inputs that contain important information content for model calibration. Notably, the adaptability to rainfall inputs of the XAJ model is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage, yielding satisfactory effective rainfall. The study quantitatively sheds new light on hydrological model adaptability to rainfall input quality.
Plain Language Summary
Accurate rainfall is an ideal input for the hydrological model to simulate streamflow reliably. However, some inaccurate rainfall data sets can be compensated by hydrological model calibration to generate good streamflow. It is still unclear what kind of rainfall inputs the model calibration can and cannot adapt to. Here, we explore a large number of rainfall data sets from different sources and their corresponding streamflow simulation performance after independent model calibrations. It is found that the hydrological model can adapt well to two scenarios of inaccurate rainfall data. The adaptive ability of the hydrological model calibration to rainfall inputs is not only affected by the accuracy over the entire period but also by the accuracy of individual rainfall events, especially those of the most severe rainstorms. Therefore, a good hydrological simulation does not always mean its rainfall input data are reliable. This study enhances a quantitative understanding of how the model calibration adapts to the errors in rainfall input data. The findings are expected to provide quantitative guidance for bias correction and data fusion of rainfall products.
Key Points
Two scenarios of inaccurate rainfall inputs and a threshold of overall rainfall bias are identified for hydrological model adaptability
Hydrological model adaptability is further influenced by how event‐based bias of individual rainstorms shapes the overall rainfall bias
Hydrological model adaptability is mainly controlled by a bias reduction through adjustment of evapotranspiration and soil moisture storage
The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981–2020 using computational and spatial analysis tools. Daily gridded ...rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann–Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991–2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40 years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters.
Subseasonal to seasonal (S2S) tropical rainfall predictability is assessed both from an analysis of the spatial scales of observed rainfall variability data, as well as from an S2S model reforecast ...skill. Observed spatial scales are quantified from gridded observed daily rainfall data, in terms of the size (area) of daily contiguous wet grid‐points (referred to as ‘wet patches’), as well as from the spatial autocorrelations of 7–91‐day running averages of rainfall. Model S2S reforecast skill is measured using the anomaly correlation coefficient between observed and simulated weekly and monthly rainfall from an 11‐member ensemble of European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecasts (1998–2017). Both measures of S2S predictability are found to be systematically lower over land than sea, usually peaking at the start or end of the rainy season and decreasing during the core. Small spatial scales and low skill over equatorial/northern tropical Africa and western Amazonia coincide with small daily rainfall patch size and strong synoptic‐scale (≤7 days) variability there. Over most of South and SE Asia, daily wet patches are larger and strongly modulated by intraseasonal oscillations, boosting S2S rainfall predictability, while this is offset by large daily mean rainfall intensities that increase the noise. In consequence, S2S rainfall skill here generally remains low. Several land areas (as around Maritime Continent from the Philippines to Northern Australia, Eastern and Southern Africa, Eastern South America) exhibit larger spatial scales and skill, especially where the relative amplitude of SST‐forced interannual variations is strong. Most of the Maritime Continent illustrates such behaviour, but even here, the time‐averaged spatial scales and skill drop during the core of the rainy season.
Analysis of scales and skill of tropical rainfall, from daily to interannual time scales, using empirical estimates of ‘signal’ and ‘noise’ and numerical ensemble of S2S forecasts—larger scales and skill over ocean than over landmasses and for the start and ending stages of the rainy season than during its core
Variable combination of signal and noise explains the spatial modulation of scales and skill
Ordering of external variance and skill computed on running 31‐day periods belonging respectively to the start, core, and end of the rainy season across the landmasses. The start, core, and end stages of the rainy season are defined from the smoothed climatological daily mean. The core is the period around the maximum receiving 50% of the total amount. The start and end stages are defined as the increasing and decreasing slopes before and after the core. The colours in (a) show the six possible orderings—shown on the panel below the map—amongst the three stages. The lower panel (b,c) shows the percentage of area belonging to each ordering for the (a) external variance and (b) skill of ECMWF from the 8 to 38 day amount. This figure shows that skill and external variance usually peak either at the start and the end of the local‐scale rainy season rather than in its core.
Understanding changes in rainfall at a continental scale can inform adaptation and resilience in all sectors that are sensitive to rainfall. This study examined spatiotemporal changes in annual and ...seasonal rainfall, extreme and non‐extreme rainfall, and their probability and intensity over the period from 1960 to 2019 across Australia for six rainfall zones (RZs) using high‐quality daily rainfall from 7,593 climate stations. The results revealed statistically significant changes in long‐term rainfall, with an increasing trend in Northern and Central Australia, and a decreasing trend across Southern Australia where the main agricultural areas are located. The cross‐wavelet power spectrum analysis denoted strong coherent resonance cycles in spring–summer or autumn–winter seasons with varying phase differences across six RZs. More specifically, summer rainfall occurred more regularly in the summer‐dominant and arid RZs, whereas winter rainfall became more unreliable in the winter‐ and winter‐dominant RZs. The changes in regional rainfall were characterized by changes in probability and/or intensity of extreme and non‐extreme rainfall revealed through the Spearman Rank correlation. The upward trend in summer‐dominant and arid RZs appears to be a result of increased extreme rainfall intensity and probability, and non‐extreme rainfall probability, especially in summer. The decreased rainfall in the rest of the RZs can be attributed to decreased extreme rainfall probability and non‐extreme rainfall intensity. These results can help decision makers in the agricultural and water resources management sectors identify risks and opportunities, and to devise strategic plans to mitigate droughts through land use planning and construction of infrastructure.
We examined spatiotemporal changes in annual and seasonal rainfall, extreme and non‐extreme rainfall and their probability and intensity over the period from 1960 to 2019 across Australia. The results revealed statistically significant changes in long‐term rainfall, with an increasing trend observed in Northern and Central Australia, and a decreasing trend detected across Southern Australia where the main agricultural areas are located. The changes in regional rainfall were characterized by increased extreme rainfall intensity and probability, and non‐extreme rainfall probability in the north, and decreased extreme rainfall probability and non‐extreme rainfall intensity in the south.
Rainfall exerts a controlling influence on the availability and quality of vegetation and surface water for herbivores in African terrestrial ecosystems. We analyse temporal trends and variation in ...rainfall in the Maasai Mara ecosystem of East Africa and infer their implications for animal population and biodiversity dynamics. The data originated from 15 rain gauges in the Mara region (1965-2015) and one station in Narok Town (1913-2015), in Kenya's Narok County. This is the first comprehensive and most detailed analysis of changes in rainfall in the region of its kind. Our results do not support the current predictions of the International Panel of Climate Change (IPCC) of very likely increases of rainfall over parts of Eastern Africa. The dry season rainfall component increased during 1935-2015 but annual rainfall decreased during 1962-2015 in Narok Town. Monthly rainfall was more stable and higher in the Mara than in Narok Town, likely because the Mara lies closer to the high-precipitation areas along the shores of Lake Victoria. Predominantly deterministic and persistent inter-annual cycles and extremely stable seasonal rainfall oscillations characterize rainfall in the Mara and Narok regions. The frequency of severe droughts increased and floods intensified in the Mara but droughts became less frequent and less severe in Narok Town. The timings of extreme droughts and floods coincided with significant periodicity in rainfall oscillations, implicating strong influences of global atmospheric and oceanic circulation patterns on regional rainfall variability. These changing rainfall patterns have implications for animal population dynamics. The increase in dry season rainfall during 1935-2015 possibly counterbalanced the impacts of resource scarcity generated by the declining annual rainfall during 1965-2015 in Narok Town. However, the increasing rainfall extremes in the Mara can be expected to create conditions conducive to outbreaks of infectious animal diseases and reduced vegetation quality for herbivores, particularly when droughts and floods persist over multiple years. The more extreme wet season rainfall may also alter herbivore space use, including migration patterns.
Accurate and in-depth rainfall studies are crucial for understanding and assessing precipitation events’ patterns, intensities, and impacts, enabling effective planning and management of water ...resources, agriculture, and disaster preparedness. Despite many rainfall studies in Bangladesh at the national and regional scales, study on the spatiotemporal rainfall variability is still rare at the local scale. The current study aims to apply Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) techniques to assess the long-term annual and seasonal rainfall trends and variability over the southeast region of Bangladesh. Monthly rainfall data from ten Bangladesh Meteorological Department climate stations between 1981 and 2022 was used for the analysis on annual and four seasonal scales. The precipitation concentration index results showed significant variations in annual rainfall across the study area, whereas seasonal PCIs were consistent with moderate rainfall. According to standardized rainfall anomaly findings, each station experienced at least one severe to extremely severe drought episode during the 42-year study period. Homogeneity tests revealed significant breakpoints in some rainfall datasets, while 78% were declared homogeneous. MK, MMK, and ITA techniques revealed similar increasing and decreasing trend patterns throughout the study area. Annual rainfall showed an upward trend in the coastal part and a downward trend in the northern part of the study area, with monsoon rainfall exhibiting a similar trend pattern. The ITA technique outperformed the MK and MMK techniques in detecting trends, identifying significant increasing and decreasing trends in 76% (38 out of 50) of the observations, while the MK and MMK techniques detected trends in only 8% and 44% of the total observations, respectively. The outcome of the current study is expected to be helpful for the sustainable planning and management of water resources in the southeast region of Bangladesh.
This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981–2014. The models and the ensemble mean were assessed based on the ability to reproduce the ...annual climatology, seasonal rainfall distribution and trend. Statistical metrics used include mean bias error, normalized root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models' performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown inter‐annually. Some models could not capture the rainfall patterns around local‐scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL‐ESM4, CanESM5, CESM2‐WACCM, MRI‐ESM2‐0, NorESM2‐LM, UKESM1‐0‐LL, and CNRM‐CM6‐1. The models CNRM‐CM6‐1, and CNRM‐ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. Caution should be taken when employing the models in seasonal climate change studies as their performance varies from one season to another. The model spread in CMIP6 over the study area also calls for further investigation on the attributions and possible implementation of robust approaches of machine learning to minimize the biases.
Evaluation of the general climate models in CMIP6 over Uganda.
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