Knowledge of
spatio-temporal rainfall patterns is required as input for distributed
hydrologic models used for tasks such as flood runoff estimation and
modelling. Normally, these patterns are ...generated from point observations on
the ground using spatial interpolation methods. However, such methods fail in
reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic,
small-scale rainstorms which are not well recorded by existing monitoring
networks. Consequently, uncertainties arise in distributed rainfall–runoff
modelling if poorly identified spatio-temporal rainfall patterns are used,
since the amount of rainfall received by a catchment as well as the dynamics
of the runoff generation of flood waves is underestimated. To address this
problem we propose an inverse hydrologic modelling approach for stochastic
reconstruction of spatio-temporal rainfall patterns. The methodology combines
the stochastic random field simulator Random Mixing and a distributed
rainfall–runoff model in a Monte Carlo framework. The simulated
spatio-temporal rainfall patterns are conditioned on point rainfall data from
ground-based monitoring networks and the observed hydrograph at the catchment
outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several
candidates for spatio-temporal rainfall patterns are feasible and allow for an
analysis of their uncertainty. The methodology is tested on a synthetic
rainfall–runoff event on sub-daily time steps and spatial resolution of
1 km2 for a catchment partly covered by rainfall. A set of plausible
spatio-temporal rainfall patterns can be obtained by applying this inverse
approach. Furthermore, results of a real-world study for a flash flood event
in a mountainous arid region are presented. They underline that knowledge
about the spatio-temporal rainfall pattern is crucial for flash flood
modelling even in small catchments and arid and semiarid environments.
(a) JJAS rainfall climatology (mm month−1) for the years 1951–2005 using CRU (1951–2005) data set. The rectangular box (5°–35°N and 70°–100°E) represents the Indo‐Pakistan summer monsoon region. (b) ...JJAS rainfall trends (mm month−1 year−1, shades) using CRU data set. Five percent significant values are shown by dots. (c) JJAS climatology of VIMMT (kg m−1 s−1 month−1) for the period 1951–2005. (d) Same as (c) except for trends (kg m−1 s−1 year−1, shades) of VIMMT. Five percent significant values are shown by dots and are omitted where the slopes of moisture transport range between 0.2 and −0.2 (kg m−1 s−1 year). In (d), the contour interval is 0.1 except the initial (±0.05, shaded as white colour).
ABSTRACT
Understanding the effects of climate change and global warming on the South Asian summer monsoon rainfall trend is critically important for millions of inhabitants of this region. This study investigates seasonal (June–September) rainfall trend over the Indo‐Pakistan subcontinent by using 36 climate model outputs from the World Climate Research Programme's Coupled Model Inter‐comparison Project Phase 5. The historical (1951–2005) and future (2006–2100) simulations under two representative concentration pathways (RCPs), RCP4.5 and RCP8.5, are analysed for this purpose. Model reproducibility is evaluated based on spatial correlation of seasonal rainfall and vertically integrated meridional moisture transport between simulated and observed fields. It is found that the majority of models shows reasonable skill in capturing the observed pattern of rainfall climatology and trend over the subcontinent. Our results showed that the models are more skilful in simulating seasonal mean moisture transport than trend over the Arabian Sea and Bay of Bengal. Of the 36 models analysed, only two models HadGEM2‐AO and CNRM‐CM5 closely approximate both the climatology and trend based on statistical performance metrics. Our results suggest that the strengthening of northwards moisture transport over the Arabian Sea and Bay of Bengal is a likely reason for the increasing rainfall trend over Indo‐Pakistan subcontinent in a warmer climate. The RCP8.5 indicates marked increase in both rainfall and moisture transport trends compared to RCP4.5 forcing scenario.
The influence of early spring sea ice at Barents Sea on midsummer rainfall in Northeast China (NEC) is identified based on observational analyses and atmospheric modeling experiments in this study. ...Increased sea ice area (SIA) in the Barents Sea is ensued by positive rainfall anomalies at north of NEC and by negative anomalies at south, and vice verse. Specifically, due to a good seasonal persistence from spring to summer, the preceding sea ice anomalies exert an impact on midsummer surface air temperature anomalies and vertical stability over Barents Sea via the modulation on turbulent heat flux. The anomalous circulation is further triggered over Europe and the Mediterranean Sea through meridional vertical cells, with a barotropic structure. Accordingly, an effective Rossby wave source is excited over the eastern Mediterranean by the advection of vorticity by divergence wind, and causes an eastward propagation of Silk Road Pattern to East Asia. In addition, another SIA-related wave-like train can diffuse directly southeastward from Arctic to NEC in a polar path. Observations and numerical simulations indicate that, in response to increased sea ice at Barents Sea, an anomalous cyclone emerges over NEC, along with easterly or southeasterly over north of NEC and with northwesterly over south, leading to moisture convergence anomalies at north and divergence anomalies at south. Jointly, ascending (descending) motion anomalies favors a wet (dry) summer over north (south) of NEC.
Soil and nutrient loss play a vital role in eutrophication of water bodies. Several simulated rainfall experiments have been conducted to investigate the effects of a single controlling factor on ...soil and nutrient loss. However, the role of precipitation and vegetation coverage in quantifying soil and nutrient loss is still unclear. We monitored runoff, soil loss, and soil nutrient loss under natural rainfall conditions from 2004 to 2015 for 50–100 m2 runoff plots around Beijing. Results showed that soil erosion was significantly reduced when vegetation coverage reached 20% and 60%. At levels below 30%, nutrient loss did not differ among different vegetation cover levels. Minimum soil N and P losses were observed at cover levels above 60%. Irrespective of the management measure, soil nutrient losses were higher at high‐intensity rainfall (Imax30>15 mm/h) events compared to low‐intensity events (p < 0.05). We applied structural equation modelling (SEM) to systematically analyze the relative effects of rainfall characteristics and environmental factors on runoff, soil loss, and soil nutrient loss. At high‐intensity rainfall events, neither vegetation cover nor antecedent soil moisture content (ASMC) affected runoff and soil loss. After log‐transformation, soil nutrient loss was significantly linearly correlated with runoff and soil loss (p < 0.01). In addition, we identified the direct and indirect relationships among the influencing factors of soil nutrient loss on runoff plots and constructed a structural diagram of these relationships. The factors positively impacting soil nutrient loss were runoff (44%–48%), maximum rainfall intensity over a 30‐min period (18%–29%), rainfall depth (20%–27%), and soil loss (10%–14%). Studying the effects of rainfall and vegetation coverage factors on runoff, soil loss, and nutrient loss can improve our understanding of the underlying mechanism of slope non‐point source pollution.
We identified the direct and indirect relationships among the influencing factors of soil nutrient loss on runoff plots and constructed a structural diagram of these relationships.
Using potentially best available rainfall data sets for the entire country of Japan (spatial scales of 1‐ and 20‐km), we analyze the 1–24 hr and city‐scale (1–400 km2) extreme rainfalls for both ...current (2006–2020) and future periods (2081–2109) at 1.5 K global warming scenario, complementing previous work that focuses on either coarse spatial and temporal scales or other warming scenarios (e.g., RCP and 2 K warming scenarios). A peak‐over‐threshold (POT)‐based approach is applied to compute areal reduction factor (ARF) for subsequently establishing intensity‐duration‐area‐frequency (IDAF) curves. Our results reveal that ARF values generally decrease with increasing area size and increase with longer duration and are affected by multiple underlying physical phenomena. Moreover, we find a greater increase in the rainfall intensities for shorter durations and higher return periods, ranging from 9.4% (1‐hr) to 6.2% (24‐hr), averaged for all return periods and 8.3% (25‐year) to 7.3% (2‐year), averaged for all rainfall durations. Spatially, extreme rainfall intensities are projected to increase by 8.9% in northern Japan (albeit with a less intense rainfall intensity in the current period), which is greater than the rest of the country (6.8%), underscoring the need to focus more on infrastructure designs in northern Japan. The projected IDAF curves further display an increase in the frequency of extreme events at city‐scale, for example, 25‐year extreme rainfall events in 2006–2020 would likely be 5‐10‐year events in 2081–2109. Our results with the state‐of‐the‐art data and implementable approach can be utilized for policymaking to reduce the warming‐induced risks in Japan and beyond.
Key Points
For the first time, we analyze the city‐scale and sub‐daily extreme rainfall for the entire country of Japan by using high‐quality data sets
Areal reduction factor values generally decrease with increasing area size and increase with longer duration and are affected by multiple physical phenomena
Under 1.5 K warming, extreme rainfall will increase more for shorter durations and higher return periods, especially in northern Japan
This study compares two nonparametric rainfall data merging methods—the mean bias correction and double-kernel smoothing—with two geostatistical methods—kriging with external drift and Bayesian ...combination—for optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical Andean watershed in Peru. The analysis is conducted using 11 years of daily time series from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (also TRMM 3B42) and 173 rain gauges from the national weather station network. The results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. It is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. The mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. Given the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. Based on these results, a systematic approach to the selection of a satellite–rain gauge data merging technique is proposed that is based on data characteristics. Finally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to TMPA and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.
Abstract
Karst aquifers have complex structures and high heterogeneity, making it difficult to describe their hydrogeological characteristics. An essential tool for comprehending karst hydrogeology ...and identifying the hydraulic characteristics of the karst system is a quantitative analysis of the recession process of the karst spring hydrograph. The recession process of the spring hydrograph following a rainfall event can be utilized to assess the internal structure of the karst system as it relates to the karst aquifer's response to rainfall events. In this study, a laboratory physical experiment model is built based on typical karst hydrogeological circumstances, and a corresponding combined discrete‐continuum (CDC) numerical model is established, coupling conduit and matrix. The primary goal of this paper is to investigate how the recession process of the spring hydrograph responds to various rainfall events. Different rainfall intensities with the same rainfall duration and various rainfall intensities with the same rain amount are examples of rainfall patterns. The simulation results showed that the pattern of rainfall events affects the recession process of the spring hydrograph, and the recession coefficient of the corresponding recession stage, the proportion of water volume released by conduit and matrix with the different rainfall patterns are calculated and analysed, and the impact of rainfall events on the recession coefficient of spring hydrograph and the spring water composition has been explored. The research results can provide a reference and basis for the management of water resources in karst areas.
The occurrence of landslides is not uncommon in the Western Ghats of Maharashtra, India. However, during the last week of July 2021, an unprecedented number of landslides due to heavy rainfall were ...reported in this region. To determine the cause of the large-scale landsliding, we mapped an event-based landslide inventory using high-resolution satellite imagery and identified 5012 landslides. We analysed rainfall data for the 2005–2021 period to identify the anomaly in rainfall quantity and its distribution which triggered such large number of landslides, particularly in 2021, even though heavy annual monsoonal rainfall is observed every year in this region. It is observed that the quantity of rainfall in 2021 is much lesser than that seen in most of the previous years. Analysis of antecedent rainfall for time-stamped landslides, such as Jui (2005), Malin (2014) and Taliye (2021), shows that two-day antecedent rainfall is the primary trigger of landslides in the region. An in-depth comparison of rainfall variability for all the years which recorded more rainfall than 2021 shows that the amount of two-day consecutive rainfall is significantly higher in 2021, although the cumulative seasonal rainfall is less than in preceding years. Further, this anomalously high rainfall was concentrated around the 600 m – 900 m elevation range. The probable consequence of such spatiotemporally localised heavy rainfall over higher elevations was rapid soil saturation triggering a large number of landslides in 2021. In times when the effect of climate change is visible in the form of weather extremes, the present study may aid in preparedness for response to such disasters.
This study presents a comprehensive evaluation of multi-satellite precipitation estimates against ground rain gauge data over three different climatic regions of India. In this study, performance ...evaluation of Integrated Multi-satellitE Retrievals for GPM (IMERG) a next-generation rainfall mission for observing global precipitation characteristics has been carried out. The IMERG was also inter-compared with the TRMM Multi-satellite Precipitation Analysis (TMPA) product for using contingency table and statistical methods. The dependences of the two satellite rainfall products were examined, with special focus on the reliance of product performance at different rainfall intensities over three different rainfall regime area of India (Upper Mahanadi Basin, districts of Himachal Pradesh (NW Himalaya) and regions of Rajasthan (Thar Desert)). The analysis was carried out on daily and monthly scales. Results indicated that both the satellite precipitation products (SPPs) IMERG and TMPA precipitation products overestimate the daily precipitation. Both the SPPs show good correlation at daily and monthly precipitation estimations, and the performance of SPPs is better in Thar desert area, but poor in the mountainous region. The results also revealed that IMERG precipitation shows better detection capability of daily rainfall compared to TMPA precipitation estimates for most of the rainfall events. In general, IMERG and TMPA overestimated rainfall depths for all rainfall events. This study suggests that there is a need for emendation in precipitation estimation algorithm and validation against rain gauge precipitation to capture the rain events more accurately in the study area.
Abstract
Tropical cyclone precipitation (TCP), accounting for some of the most extreme rainfall events, can lead to severe flooding and landslides, which often occur together as compound natural ...hazards during a tropical cyclone landfall. The impact due to TCP is largely associated with its intensity and spatial extent as the storm approaches landfall. Yet it is not entirely clear how TCP intensity and spatial extent vary from one tropical cyclone to another. In this study, we employ an advanced geostatistical framework to determine the TCP intensity and spatial extent along cyclone tracks for different cyclone categories, defined using the wind speed and tropical cyclone lifetime maximum intensity (LMI) at each track point (“point intensity-LMI”). The results show that when a tropical cyclone with an LMI of a supertyphoon makes landfall and has weakened to tropical storm strength it usually produces the most intense rainfall and covers the largest spatial extent. The total TCP amount estimated using the varying spatial extent helps to determine more accurately the amount of seasonal rainfall that is from tropical cyclones in China. We also determined the rainfall trend from 1951 to 2019 for TCP and found that when compared with the inland stations the historical TCP rainfall trend in those stations near the coastline of China is significantly increasing.
Significance Statement
Heavy rainfall caused by tropical cyclones has caused huge direct or indirect economic losses in the coastal areas of China. This impact is particularly significant when the rainfall intensity is high and the area of heavy rainfall is extensive. Here we investigate the rainfall intensity and spatial extent by classifying and comparing the different types of tropical cyclones impacting China with varying intensities. To do this, we group the tropical cyclone tracks of the western North Pacific Ocean during the last seven decades according to the strength of wind speed across the cyclone tracks. We found that the largest areas and heaviest intensities of rainfall occur when a supertyphoon has weakened to a tropical storm at landfall. When considering all tropical cyclones and their rainfall contribution to rainfall over land stations, we found that tropical cyclone rainfall has become heavier in most coastal areas of China.
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