Recent Changes in Average Recurrence Interval Precipitation Extremes in the Middle-Atlantic United States

Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e. the 2-year storm) to 1% (i.e. the 100-year storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the Middle-Atlantic region. At about a quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations the rainfall extremes based on the 1950-1999 partial duration series show a significant (p >0.90) change in the 100-yr ARI relative to the 1950-2019 period. Collectively the results indicate that given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-year intervals.

On some aspects of southwest monsoon rainfall over Tamilnadu

The rainfall during southwest monsoon season over Tamilnadu is quite significant from the point of view of water storage in major reservoirs as northeast monsoon rainfall, which is about half of the annual rainfall, is not stable enough due to its large interannual variability. The southwest monsoon rainfall, on the other hand, is more stable. The north-south oriented trough over Tamilnadu and adjoining Bay togetherwith upper air cyclonic circulation/trough in lower tropospheric levels account for three fourths of significant rainfall occurrence during southwest monsoon season. Rainfall during southwest monsoon and northeast monsoon seasons was found to be independent with a small negative correlation of -0.18. This shows that the southwest monsoon rainfall may not be of much use to predict the pattern of northeast monoon rainfall over Tamilnadu.

Bias Correction Method of High-Resolution Satellite-Based Precipitation Product for Peninsular Malaysia

Satellite-based precipitation (SBP) is emerging as a reliable source for high-resolution rainfall estimates over the globe. However, uncertainty in SBP is still significant, limiting their use without evaluation and often without bias correction. The bias correction of SBP remained a challenge for atmospheric scientists. In this study, the performance of six SBPs, namely, SM2RAIN-ASCAT, IMERG, GsMap, CHIRPS, PERSIANN-CDS and PERSIANN-CSS in replicating observed daily rainfall at 364 stations over Peninsular Malaysia was evaluated. The bias of the most suitable SBP was corrected using a novel machine learning (ML)-based bias-correction method. The proposed bias-correction method consists of an ML classifier to correct the bias in estimating rainfall occurrence and an ML regression model to correct the amount of rainfall during rainfall events. The performance of different widely used ML algorithms for classification and regression were evaluated to select the suitable algorithms. IMERG showed better performance, showing a higher correlation coefficient (R2) of 0.57 and Kling-Gupta Efficiency (KGE) of 0.5 compared to the other products. The performance of random forest (RF) was better than the k-nearest neighbourhood (KNN) for both classification and regression. RF classified the rainfall events with a skill score of 0.38 and estimated the rainfall amount of a rainfall event with the modified Index of Agreement (md) of 0.56. Comparison of IMERG and bias-corrected IMERG (BIMERG) revealed an average reduction in RMSE by 55% in simulating observed rainfall. The proposed bias correction method performed much better when compared with the conventional bias correction methods such as linear scaling and quantile regression. The BIMERG could reliably replicate the spatial distribution of heavy rainfall events, indicating its potential for hydro-climatic studies like flood and drought monitoring in the study area.

Spatio-temporal variability of climatic parameters across different altitudes of North- Western Himalaya

Climate change impact varies across different altitudinal ranges and demands local specific management strategies for water resource and farming system management. The present study analyses spacio-temporal climate parameters across different altitudes of Himachal Pradesh a hilly state of India. Analysis shows that annually, minimum temperature has significantly decreased by -0.09°C at altitude I (350 - 400 m) while maximum temperature has significantly increased by 0.05°C at altitudes I and II (1400-1500 m) and decreased significantly by -0.08°C at altitude III (2000- 2100 m). Higher regions Altitude – IV (2900-3000 m) received lowest rainfall (746.1 mm) with 30.2 % variation. Seasonal rainfall variability was higher in post monsoon (102 - 174%) and least in monsoon (21 - 57%). Annual rainfall at altitude I is strongly irregular (PCI 20.1 to 22.3), followed by altitude – IV (PCI 15-25); altitude – II irregular (PCI 15-20) and altitude – III moderate to irregular (PCI 12 -19) rainfall. Seasonal Index values for four altitudes fall between 0.91-0.96 revealed that rainfall is irregular and markedly seasonal with longer drier season. Higher wavelet powers in altitude - I and II after 2005 suggests frequency of extreme rainfall occurrence had increased.

Examining optimum prediction time of rainfall dynamics based on chaotic perspective at different temporal scales: a case study in Bojonegoro, Indonesia

As the driving force of the hydrological system, rain has severe impact when dealing with petroleum mining activities, especially in protecting assets and safety. Rainfall has high variability, both spatial and temporal (chaotic data). Due to this reason, ones can only create long-range prediction using the stochastic method. Here we use the Lyapunov exponent to analyze the nonlinear pattern of rainfall dynamics. This method is useful for identifying chaotic deportment in rainfall data. This study uses rainfall data for six years obtained from one of the largest petroleum mining sites in Bojonegoro, Indonesia. Rainfall dynamics have been analyzed on three different time scales, namely daily data, 5-day, and 10-day. The time delay (τ) was obtained by using the Average Mutual Information (AMI) method for the three-rainfall series (3, 2, 3, respectively). The observed rainfall data in Bojonegoro show signs of chaos as the finite correlation dimensions (m) attain values about 4 for all time scales. The maximum Lyapunov exponent λmax for each of three-rainfall series in Bojonegoro is 0.111, 0.057, 0.062, respectively. These values were analyzed to find the optimum prediction time of rainfall occurrence to perform better forecasting. The result shows that the optimum range of prediction time for daily, 5-day, and 10-day have 9, 18, and 16 times longer than their temporal scale.

Integrated Strategies for the Management of Wadi Flash Floods in the Middle East and North Africa (MENA) Arid Zones: The ISFF Project

Sustainable management of wadi flash flood (WFF) risks is desperately needed to secure development in wadi systems. Due to rapid flow generation with sudden high flood peaks, spatiotemporal variability of rainfall occurrence, and poorly sited rapid development, most Middle East and North Africa (MENA) region have no comprehensive proper protection from WFFs. In arid regions, single mitigation measures, including storage dams, recharge dams, artificial lakes and embankments, are implemented, although soft mitigation measures are not dominant, such as early warning systems. The single management strategy under climate change impacts is not adequate to reduce flash flood risks; an integrated strategy is required. The objective of the international symposium on flash floods (ISFF) project has been to develop scientific understanding of WFFs in wadi systems; monitor, model, and mitigate; issue warnings; and plan urban development by discussing and networking the strategies in the MENA region. To achieve this goal, the project defines priorities for future research challenges and potential projects for WFFs. This chapter provides a state-of-the-art scientific basis in terms of integrated flash flood management. Further, priorities are defined for the main research gaps, and the emerging research methodologies can contribute to guide the management of WFFs in such regions.

Climate-Induced Perspective Variations in Irrigation Schedules and Design Water Requirements for the Rice–Wheat System

Conceptualizing the implications of climate change for crop evapotranspiration (ETc) and subsequent net irrigation water requirement (NIWR) is critical to sustaining Pakistan’s agriculture and food security. In this article, future ETc, NIWR, and design water requirements (DWR) were projected for the rice–wheat system of Punjab, Pakistan. Consistently increasing temperatures signify an impending hotter transition in the future thermal regime, accompanied by a substantial increase in monsoon rainfall. Future climate warming accelerated ETc and NIWR, which were compensated by 2–5 and 1–2 additional irrigations during the rice and wheat seasons, respectively. Future rice and wheat required 13–18 and 2–5 irrigations per season, respectively. Effective rainfall increments did not compensate for the warming-driven higher ETc and NIWR because of uneven and erratic rainfall distribution. Rainfall occurrence and the duration of peak irrigation demand were mismatched, resulting in surplus rainwater availability during the future rice season. The results suggest that DWR for 5- and 10-year return period droughts during the baseline period (965 and 1000 mm, respectively) should be revised to accommodate the additional 100–200 mm of irrigation water per season; otherwise, the study area will face an acute water shortage in the future.

An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

Storm-runoff processes in a mainly waterlogged low mountain range catchment in the national park Hunsrück-Hochwald, SW-Germany

<p>This study investigates potential effects of wetland restoration on storm flow dynamics in a mainly waterlogged low mountain range catchment located in SW-Germany. Here, wetland drainage networks are being sealed, aiming to achieve rising soil water tables and reestablished peat vegetation. With the help of hydrograph separation, multiple linear regression (MLR) and covariance analysis (ANCOVA), runoff-governing storm properties and sealing influences were analyzed. Results show, that not only natural storm parameters (precipitation sum, rainfall intensity, antecedent precipitation and temperature) exert influence on storm-runoff, but sealings also led to significantly altered processes: On the one hand, storm-runoff coefficients increased in sealed catchments, resulting most likely from more saturated soils, providing a smaller infiltration capacity. This is a desired effect of rewetting but coincidently a downside regarding storm flood prevention. On the other hand, lag times, meaning the timespan between rainfall occurrence and the hydrograph starting to rise, were noticeably prolonged. This effect can be potentially beneficial when it comes to storm flood prevention. Overall, statistical models including sealings showed more satisfactory results describing stormflow variance compared to models without sealings. Therefore, sealings do exert – statistically proven – an effect on storm runoff. The heterogeneity of the results, representing a dense gauge network spread over an investigation area of roughly 7.5 km² shows, that a high-resolution sampling, both spatially and temporally, is vital. That is since runoff processes in waterlogged low mountain range catchments are still poorly understood.</p>