Abstract: The Tropical Rainfall Measuring Mission (TRMM) and then Global Precipitation Mission (GPM) are the most important and widely used data sources in the forecasting of drought, flood, and water resources management. However, since this sensor’s data is primarily used for tropical regions, it is necessary to evaluate the accuracy for optimal use of the data across varying climatic and physiographic conditions. In this study, the accuracy of the satellite data for a span of 17 years (2000-2017) for three climatic zones has been explored using synoptic ground station data. The climates include a) arid and low rainfall, b) semi-arid and low rainfall, and c) humid and high rainfall. We evaluated satellite data accuracy in drought and wet conditions based on the Standard Precipitation Index (S.P.I.) and different seasons. For available ground control stations, 13 stations were used in the humid, seven stations in a semi-arid climate, and 12 stations in the dry climate. The results show that the monthly precipitation product of GPM (IMERG product) and TRMM (TMPA/3B43 product) overestimate the rainfall. In the arid climate, the precipitation is estimated 43%, in the semi-arid environment 50%, and in the humid weather 11% more than the ground-based data on average. Therefore, to use satellite data in different climates, it is necessary to make corrections to obtain precise results. Based on 32 ground stations, the correction coefficient has a positive relationship with average precipitation and altitude and an inverse relationship with the latitude. Further in-depth investigations showed that the accuracy of satellite data in wet conditions is higher than the accuracy of normal circumstances, and the accuracy of normal conditions is more accurate than drought conditions. Besides, the accuracy of satellite data in wet or dry conditions increases with increasing time scales. The highest accuracy was obtained for a 12-month time scale and the lowest accuracy for the 3-month time scale of drought conditions in the arid climate.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explores the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalysed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets are statistically analysed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums are examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. But our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

Accurate Quantitative Precipitation Estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The obtained results indicate that merging radar and rain gauge data outperform both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. The study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing evidence for the greater reliability of merging methods

This paper provides a review of the aerodynamic behavior of horizontal axis wind turbines operating in hazardous environmental conditions. Over the past decade, renewable energy use has accelerated due to global warming, depleting fossil fuel reserves, and stricter environmental regulations. Among renewable options, solar and wind energy have shown economic viability and global growth. Horizontal axis wind turbines offer promising solutions for sustainable energy demand. Since wind turbines operate in an open environment, their efficiency depends on environmental conditions. Hazard environmental conditions, such as icing, rainfall, hailstorm, dust or sand, insects’ collisions, increased humidity and sea spray, result in degraded aerodynamic performance. The outcome of the most studies is that lift is degraded and at the same time drag is increased when wind turbines operate under these conditions. The objective of this review is to improve our comprehension of the crucial aspects to take into account when designing wind turbine blades, and it offers suggestions for future research paths. It serves as a valuable resource that can inspire researchers who are dedicated to enhancing the aerodynamic performance of horizontal axis wind turbines.

The effects of global warming combined with the progressive expansion of urbanization have considerably increased exposure to urban flooding and runoff widespread erosion risk, also causing shallow landslides and mud flows, respectively in urbanized areas of lowland and hill/foothill environments. Increasing urban flooding resilience has become a priority at virtually all levels of governance. The analysis of a different hazard scenarios, in which various hydro-meteorological conditions and management alternatives are examined, should act as the basis for the effective design and evaluation of interventions to improve urban flooding resilience. Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west side and a flat or hilly part in the east side. During the warm season, these environmental conditions make the urban area prone to severe atmospheric convection causing frequent hailstorms and rainstorms of high intensity that may impact on urban infrastructures (i.e., drainage system and road network), thus requiring an adequate management as a key factor to reducing risk and losses. The urban areas of TMA are monitored by polarimetric Doppler weather radars and by a dense rain gauges network. Analyzing several case studies of urban flooding, this research work assesses the feasibility of a meteorological radar early warning system based on the identification of rainfall thresholds that characterize urban flooding, occurring in the lowlands, and the runoff erosion phenomena affecting the urbanized hills and foothills.

Trash mulches are very effective in preventing soil erosion; reduce sediment transport rate, runoff rate and increasing infiltration. The study was carried out with the objectives to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions by using rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching in soil loss reduction. The quantity of mulch was taken as, 6 t/ha, 8 t/ha and 10 t/ha, three rainfall intensities viz. 11cm/h, 13cm/h and 14.65cm/h at 0%, 2% and 4% land slopes were selected. The duration of rainfall was fixed (10 minutes) for every mulch treatment. The total runoff volume was found to be varying with different mulch rates for particular rainfall input and land slope. The runoff distribution pattern was observed to be increasing with the increase in land slope. The average sediment concentration (SC) and outflow was found to be increasing with the increasing land slope, but SC and outflow decreased with increasing mulch rate for particular land slope and rainfall intensity. The SOR (SOR) for no mulch treated land was higher as compared to trash mulch treated lands. Mathematical relationships were developed for relating SOR, SC, land slope and rainfall intensity for a particular mulch treatment. It was observed that values of SOR and average SC had a good correlation with rainfall intensity and land slope for each mulch treatment. The correlation coefficients of developed models were found to be more than 90%.

In this study, regression analyses were used to find a relationship between the rain gauge rainfall rate R and radar reflectivity Z for the urban catchment of the Służewiecki Stream in Warsaw, Poland. Rainfall totals for 18 events which were measured at two rainfall stations were used for these analyses. Various methods for determining calculational values of radar reflectivity in reference to specific rainfall cells with 1-km resolution within an event duration were applied. The influence of each of these methods on the Z-R relationship was analyzed. The correction coefficient for data from the SRI (Surface Rainfall Intensity) product was established, in which the values of rainfall rate are calculated based on parameters a and b determined by Marshall and Palmer. Relatively good agreement between measured and estimated rainfall totals for the analyzed events was obtained using the Z-R relationships as well as the correction coefficient determined in this study. Rainfall depths estimated from radar data for two selected events were used to simulate flow hydrographs in the catchment using the SWMM (Storm Water Management Model) hydrodynamic model. Different scenarios were applied to investigate the stream response to changes in rainfall depths, in which the data both for 2 existing as well as 64 virtual rain gauges assigned to appropriate rainfall cells in the catchment were included.

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. This study is aimed at improving rainfall prediction using ensemble methods. It first assesses the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janji´c (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members are generated from the three best performing convective schemes (i.e. KF, GF & G3). The performance of three ensemble methods (i.e. ensemble mean (EM); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) is also analyzed using the RMSE and ME. The EM presented a smaller RMSE compared to individual schemes (EM:10.02; KF:23.96; BMJ:26.04; GF:25.85; G3:24.07; NT:29.13 & GD:26.27) and a better bias (EM:-1.28; KF:-1.62; BMJ:-4.04; GF:-3.90; G3:-3.62; NT:-5.41 & GD:-4.07). The EMA and MAEM presented 13 out of 21 stations & 17 out of 21 stations respectively with smaller RMSE compared to EM thus demonstrating additional improvement in predictive performance. The MAEM is a new approach proposed and described in the study.

The inter-tip times (ITTs) of tipping-bucket rain gauges potentially provide the highest-resolution intensity data that can be acquired from this type of gauge. At an intensity of 100 mm h-1, a typical gauge with a sensitivity of 0.2 mm of rainfall would be expected to tip every 7.2 seconds. However, TBRGs are often equipped with syphons, to reduce the dynamic calibration error that results from continued (and unmeasured) inflow to a bucket as it tips. This increases the accuracy of rainfall depth recording, but the time to fill and empty the syphon can reduce the ability of a TBRG to respond to (and for the ITTs to reflect) short-term intensity fluctuations. This ability is already limited by the discretisation arising from the filling and emptying of the buckets themselves. Laboratory tests with controlled water inflow rates were performed using two high-quality TBRGs, one a 'straight-through' design and the other syphon-equipped. These confirmed that at all intensities at which the syphon operates, a regular sequence of fixed-duration ITTs (such as the 7.2 s mentioned above) does not occur. Rather, the ITTs are perturbed by the syphon cycling. The gauges were also co-located in the field, and linked to carefully synchronised event data loggers. Data collected during several rainfall events revealed differences in the ITTs, and again confirm that the ITT sequence of a syphon-equipped TBRG exhibits artefacts related to syphon operation, that are not present in the 'straight-through' data. These artefacts can result in ITT differences of many minutes, depending on the rainfall intensity, and are problematic for the use of ITTs to estimate intensity. Peaks and troughs in the intensity profile also differed between the two gauges. It is recommended that in the application of TBRGs for studies where short-term intensity data are required, 'straight-through' gauges should be used, and syphon-equipped gauges avoided.

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to enhance the 30-day rainfall forecast skill in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900-mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200-mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that multi-scale geospatial interactions occur at the daily and sub-seasonal time windows. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation increases in response to the enhanced simulation of the GP-LLJ and CGT. Finally, the multi-dimensional analysis of covariance reveals that high-precipitation forecasting skill is associated with a better prognostic of GP-LLJ and CGT.

This study investigates the importance of convective systems for extreme rainfall along the northern coast of the Gulf of Guinea (GG) and their relationship with atmospheric and oceanic conditions. Convective system data (MCSs), daily precipitation, sea surface temperature (SST) and moisture flux anomalies are used from June to September 2007-2016. The results show that 2/3 of MCSs crossing Abidjan are produced in June, which is the core of the major rainy season. Likewise, 2/3 of MCSs originate from continental areas, while 1/3 comes from the ocean. Oceanic MCSs are mostly initiated close to the coast, which also corresponds to the Marine Heat Waves region. Continental MCSs are mostly initiated inland. The results also highlight the moisture flux contribution of three zones which have an impact on the onset and the sustaining of MCSs: (i) the seasonal migration of ITCZ, (ii) the GG across the northern coastline, (iii) and finally the continent. These contributions of moisture fluxes coincide with oceanic warming off the Northeast Brazil and the northern coast of the GG both two days before and the day of extreme rainfall events. The ocean contributes to moisten the atmosphere, and therefore to supply and sustain the MCSs during their lifecycle.

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modelling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modelling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modelling and assessment in Japan.

The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020-2050. Historical analysis revealed bimodal annual rainfall pattern for March-May (MAM) and September-November (SON) gradients representing heavier to lighter rainfall events respectively over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann-Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall.

It has been an outstanding challenge for global climate models to simulate and predict East Asia (EA) summer monsoon (EASM) rainfall. This study evaluates the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we have modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields significantly improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, seasonal march of the subtropical rain belt, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has notable biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern EA) but misplaced precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well, but the bias in the circulation-rainfall connection caused predicted rainfall errors. The results here suggest that over EA land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and seasonal march of rainfall and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

Maritime Continent (MC) positions in between Asian and Australian summer monsoons zone. Its complex topography and shallow seas around it is a major challenge for the climate researchers to model and understand it. Monsoon in this area is affected by inter-scale ocean-atmospheric interactions like El-Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Madden-Julian Oscillation. Monsoon rainfall in MC (especially in Indonesia and Malaysia) profoundly exhibits its variability dependency on ocean-atmospheric phenomena in this region. This monsoon shift often introduces to dreadful events like biomass burning (BB) in Southeast Asia (SEA) which sometimes leads to severe trans-boundary haze pollution. In this study, the episode of BB in 2015 of SEA is highlighted and discussed. Observational satellite datasets are tested by performing simulations with numerical weather prediction (NWP) model using WRF-ARW (Advanced research WRF). Observed and model datasets are compared to study the sea surface temperature (SST) and precipitation (rainfall) anomalies influenced by ENSO, IOD and MJO. Correlations have been recognised which explains the delayed rainfall of regular monsoon in MC due to the influence of ENSO, IOD and MJO during 2015 BB episode, eventually leading to intensification of fire and severe haze.

The objective of the study was to analyze the climate change experienced in Spain between 1971 and 2022 and to estimate the future climate (2050). The main objectives were: 1) to analyze the temporal evolution of temperature from 1971 to the present, to quantify the warming process experienced in the case study and to evaluate the increase in ex-treme heat events (heatwaves); 2) to study the evolution of the precipitation regime to determine whether there is a statistically representative trend towards a drier climate and an increase in extreme precipitation; 3) to investigate the interaction between annual precipitation and the continuous increase in temperature; and 4) to estimate the future climate scenario for mainland Spain and the Balearic Islands towards 2050, analyzing the trends in land aridity and predicting a possible change from a Mediterranean climate to a warm steppe climate, according to the Köppen classi-fication. In this study, we used the E-OBS dataset provided by the European Climate Assessment and Dataset (ECA&D) project. E-OBS is a high-resolution gridded daily climate dataset. Its advantage over other databases is that it is based on direct climate information from the weather stations included in the Copernicus ECA&D. Furthermore, a detailed interpolation and refinement process allows a complete, uniform view of the studied territory. This dataset has pro-vided daily weather information since 1950 and is presented as a complete dataset of the European region. It is availa-ble on a regular grid of 0.1 and 0.25 degrees for the elements of daily average temperature (TG), daily minimum tem-perature (TN), daily maximum temperature (TX) and the sum of daily precipitation (RR), among other climatic varia-bles. In this study, we obtained daily temperature data, including the average, maximum and minimum temperature, and precipitation from 1971 to 2022, using the 27.0e dataset. We used information from E-OBS from 1 January 1971 to 31 December 2022, with a resolution of 0.25 degrees. The main purpose of this study was to analyze whether there is a temporal evolution of the precipitation regime in Spain towards less precipitation, and to study the relationship between annual precipitation and the tendency to-wards progressive warming. Specifically, the aim was to test the hypothesis that the increase in temperature resulting from the global warming process implies a tendency towards progressive drought. The hypothesis is + T  - RR. Given the extreme annual variability of the climate, in addition to the ordinary least squares methodology, the techniques mainly used in this study were the Mann-Kendall test and the Kendall-Theil-Sen regression. The Mann-Kendall test confirmed the statistical significance of the relationship between RR and TX, with a statistical relia-bility close to 99.9%. The KTS regression predicted a reduction of 38.25 mm for each 1°C increase. If the warming trend experienced in recent years (1973– 2022) continues, it is foreseeable that by 2050 there will be a reduction in precipitation in Spain of between 14% and 23% with respect to the precipitation of the reference period (understood as the average between 1971 and 2000). Spain’s climate is likely to vary from Mediterranean to warm steppe in the Köppen classification system (from “C” to “B”).

Sri Lanka's vulnerability to climate change-induced natural disasters, particularly floods, under-scores the urgent need for an integrated approach to disaster risk management (DRM) that ad-dresses the interconnected challenges within the water-food-land-energy nexus. The Deduru Oya basin, located in the north-western region, exemplifies this vulnerability, with its predominantly intermediate climatic zone susceptible to flooding. While the focus has traditionally been on flood risk reduction, it is imperative to recognize the broader implications of floods on water, food, land, and energy systems. This study aims to strengthen data acquisition and management efforts to better understand rainfall trends and their impact on the water-food-land-energy nexus in the Deduru Oya basin. By compiling 60 years of rainfall data and conducting time series analysis, we observed a decreasing trend in monthly rainfall, particularly during the South-West Monsoon, Second Inter Monsoon, and North-East Monsoon seasons. These findings have significant implica-tions for water availability, agricultural productivity, land use patterns, and energy generation in the basin. Moreover, the malfunction of stakeholders' responsibilities for local-level flood risk management exacerbates the challenges faced by communities living in flood-prone areas. By in-tegrating insights from this study into flood risk management strategies, stakeholders can develop holistic approaches that not only mitigate flood risk but also enhance resilience across the water-food-land-energy nexus. This interdisciplinary approach will inform policymakers, flood manag-ers, practitioners, and the community in formulating effective strategies to address the multifacet-ed challenges posed by floods in the Deduru Oya basin and beyond.

The objective of the present study is to analyze COVID-19 transmission in specific dampest regions with excess rainfall and recurring fogs. The working hypothesis is that widespread transmission of SARS CoV 2 (leading to COVID-19) and similar viral agents can be explained by specific climate factors having high air humidity. The main case study of the Turkish Black Sea region is investigated. Results reveal that the provinces in region under study have some climate factors and geographical features that foster the accelerated transmission of viral agents, such as SARS-CoV-2, and consequential negative impact on society. In particular, Spearman's Correlation Coefficient shows a statistically significant positive association between the average atmospheric pressure and the spread of the COVID-19 confirmed cases in Samsun province (Spearman’s correlation coefficient rs =0.86, p-value 0.05). A statistically significant positive association between the average precipitation and the spread of COVID-19 confirmed cases in Sinop province (Spearman’s correlation coefficient rs =0.79, p-value 0.05), and finally, a statistically significant negative association between the average sun hour and the spread of the COVID-19 confirmed cases in Samsun province (Spearman’s correlation coefficient rs = 0.89, p-value 0.01). These findings suggest that regions' geographical characteristics, demographic structure, climate and environmental parameters must be considered in the national scale epidemic management plans to design effective anti-pandemic health policies to cope with future waves of the COVID-19 and new airborne diseases and to reduce negative effects on health, social and economic systems.

The present study aims to evaluate the capability of the TRMM-3B42-(V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus basin (UIB) and the analysis of the dependency of the estimates’ accuracies on the time scale. To that avail statistical analyses and comparison of the TMPA- products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the time scale is analysed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA- (TRMM) precipitation estimates for the UIB, as well as high false alarms and miss ratios. The correlation of the TMPA- estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e. time scale. The BIAS is mostly positive for the summer season, while for the winter season it is predominantly negative, thereby showing a slight over-estimation of the precipitation in summer and under-estimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA- estimates and gauge data, the use of the former in hydrological watershed modelling, endeavoured presently by the authors, may be a valuable alternative in data- scarce regions, like the UIB, but still must be taken with a grain of salt.

GR4J (Genie Rural a 4 parame`tres Journalier) is a rainfall-runoff model widely used to modelling rainfall into discharge. In this model, the input data used are daily rainfall and evapotranspiration data, calibrated using daily observed discharge data. This model seeks the optimum parameter value that produces the minimum deviation or error. The Nash-Sutcliffe Coefficient and Relative Volume Error (RVE) are used to estimate the errors that occur. One of the modelling parameters, namely X4 (peak time), has a value that does not match the observed hydrograph peak time (Tp). For this reason, it is necessary to adjust the modelling equation so that the unit hydrograph of the model appropriately in terms of value and shape with observed unit hydrograph. This study examines ten watersheds in Java Island so that it is expected to represent catchment conditions in tropical areas and developing countries. A relationship was obtained between the difference peak time from modelling (X4) and observed Tp. Adjustments Formula can be made to the general GR4J modelling equation for unit hydrograph in Java Island watersheds.

Through the integration of high-resolution hydrodynamic modeling and comprehensive field measurements, this study elucidates the intricate three-dimensional hydrographic characteristics of the eastern Gulf of Thailand (eGOT). In addition to the prevalent tidal currents dictating alternating flow along the northwestern and southeastern axes, our investigation reveals pronounced seasonal variations in mean currents, water temperature, and salinity within the eGOT, closely linked to the dynamics of the Asian-Australian monsoon system. During the southwest monsoon, mean currents exhibit a southeasterly trend, contrasting with a northwesterly pattern during the northeast monsoon. Lowest water temperatures occur during the latter, while the highest levels are observed during the 1st monsoon transition (April-March). Notably, salinity levels reach their lowest levels during the southwest monsoon and the 2nd monsoon transition (October), coinciding with the seasonal stratification of the water column and the emergence of a distinct stable along-the-shore northwesterly current with the average speed of 15 cm/s, defined here as the “Chathaburi Coastal Current (CCC)”. Model experiments attribute the formation of the CCC to decreased salinity induced by direct rainfall, highlighting the significance of rainfall as a key factor influencing coastal water dynamics in tropical regions or areas experiencing high precipitation.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Disasters related to climate change on our water resources are on the rise in terms of scale and severity. Therefore, predicting groundwater levels (GWL) is a crucial means to aid adaptive capacity towards disasters related to climate change in our water resources. In this study Gradient Boosting (GB) regression modelling approach for GWL prediction as a function of rainfall and antecedent GWL is used. Firstly, we sought to demonstrate the effects of rainfall changes on our groundwater resources through a Mann-Kendall trend analysis. Secondly, we evaluated the relationship between the input and response variables and determined the optimal lag times between the variables using autocorrelations and cross-correlations. Lastly a predictive model was developed for eight groundwater stations in the Upper Crocodile. 50 % of the groundwater stations revealed declining trends, while 25% had no trends and the other 25% showed an increasing trend. Generally low cross-correlation maximum (CCmax) were obtained, with the highest CCmax being 0.299 at an optimal lag of 2-month. While the highest autocorrelation was 0.969 at a 1-month lag. The best groundwater predictive model had R2 and MSE of 0.66 and 0.06, respectively. The stations that generally performed better had both high autocorrelation and cross-correlation coefficients. GB model performed satisfactorily in predicting GWL for most of the stations in the study area. Therefore, GB can be used for GWL prediction in the Upper Crocodile.

In the Czech Republic, the Universal Soil Loss Equation provides the basis for defining the soil protection strategy. Field rainfall simulators were used to define the actual cover-management factor values of the most extensively seeded crops in the Czech Republic. More than 380 simulations between 2016 and 2021 provided data. The methodology focused on multi-seasonal measurements to cover the most important phenological phases. A comparison with the original USDA values for maize showed that it is desirable to redefine the C-factor. 71 fallow plot experiments showed that the rainfall-runoff relation is much easier to replicate than the actual sediment transport. For 30-minute intensive rainfall, the runoff ratio reached 62%, and the coefficient of variation was 25%. On saturated soil, the runoff ratio reached 81% and the coefficient of variation dropped to 12%. Soil protection techniques have a significant effect on runoff reduction. Maize seeded after cover crops and combined with reduced tillage or direct seeding can reduce the runoff ratio to 10-20% for ‘dry’ conditions and to 12-40% for ‘saturated’ conditions. Concerning soil loss, the variations are greater, with the coefficient of variation reaching 42% during fallow plot experiments. The reader should consider associated uncertainties.

Very heavy stratiform precipitation (> 200 mm/per day) occurred in the Hokitika region on the west coast of the South Island of New Zealand on 18 June 2015, under north-westerlies with small CAPE (< 25 J/kg). Analyses of model simulations and observations showed that this heavy rainfall was due to cold front lifting enhanced by orographic lifting over the Southern Alps. At 1.5 km grid-length, the model terrain underestimated the average height of the 103 tallest mountains over the South Island by ~ 800 m. This leads to weaker orographic lifting and mountain blocking, and a faster-moving and stronger cold front in the Hokitika region. As a result, large errors in the heavy rainfall prediction occur. By increasing either the resolved or the sub-grid mountain heights, the simulated rainfall errors were largely reduced through stronger orographic lifting and mountain blocking, and a better simulation of the cold front movement and strength. All the experiments have the same “flow-over” regime with mountain waves and/or wave breaking (Fm ranges 0.61 – 1.21). However, the rainfall amount and distribution on the windward side of mountains altered significantly. Our new findings were that the Southern Alps can have significant indirect effects on heavy rainfall by altering the speed and strength of the cold front, in addition to the well-known direct dynamical effects (i.e., orographic lifting and mountain blocking). A combination of these direct and indirect effects makes the heavy rainfall simulation sensitive to mountain heights even under the same “flow-over” regime.

Soil water infiltration is an important component of the hydrological cycle and it is best evaluated when the raindrop is impacted on the ground surface. For this reason, it is affected by changes in land use and occupation, and by the characteristics and physical-hydric properties of the soil. This study aimed to evaluate soil water infiltration in areas occupied with annual crops (soybean and corn) and pastures, in two watersheds of the Teles Pires River-MT, using simulated rainfall, physical models and principal component analysis. Infiltration rates were evaluated from simulated rainfall with an average intensity of 75 mm h-1, with 4 repetitions per region (upper, middle and lower) of the hydrographic sub-basins of the Caiabi and Renato rivers, and soil use with cover, without cover and disturbed. Soil tillage provided higher water infiltration rates into the soil, especially in pasture areas in the two hydrographic sub-basins. There were significant adjustments of the mathematical models to the infiltration rate data for all land use and land cover conditions. The soil attributes that most interfered with the infiltration rate were microporosity, soil density and total porosity in the crop areas of the middle Caiabi, and microporosity, clay content, total porosity and silt content in the areas farming at the source of the Renato River. The Horton and Philip models presented the best adjustments in the hydrographic sub-basins of the Renato and Caiabi and Renato rivers, being recommended for estimating the rate of water infiltration into the soil in different uses, coverages, and regions.

The study focused on two climatic variables i.e. precipitation and stream flow for analysing change in trend of rainfall for the River Ssezibwa Catchment area and stream flow of River Ssezibwa. This Catchment is found in the districts of Mukono and Buikwe in Uganda. In this area agriculture is majorly dependent on rainfall and irrigation on a small scale. However, rainfall occurrence has become unpredictable over the past few years as result of the changes in patterns of weather. This has caused severe effects on the agricultural cropping system as well as caused negative effects on the natural water resources. Stream flow data of 57 years (1960 – 2017) and rainfall data for 35 years (1982 – 2017) on a daily basis was analysed to find out the trend and detect change point. Trend analysis was done by using the non-parametric analysis while the change point detection was carried out by using the Pettit test (1979).Magnitude of trend for the time series data was carried out using Sen’s Slope estimator and Mann – Kendall test was done to determine the trend. Results from the statistical analysis highlighted that; for stream flow the trend was generally positive and change point detected to be in the year 2000 while for rainfall data analysis indicated that the trend was predominantly negative and change point was in the year 1998.

Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (> 3 years) and short term (< 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

This research examines how rainfall and mining affect the slope damage resulting from the transition from open-pit mining to underground mining. Using unmanned aerial vehicle (UAV), the Huangniu slope of Dexing Copper Mine was fully characterized, and experiments were conducted on rock samples from appropriate sites. First, the mechanical properties of the samples were measured. Then, the parameters of the similarity simulation experiments were derived based on the similarity theory. Subsequently, the rainfall, rock slope, data acquisition and monitoring systems were designed. Finally, the rock mass failure with different slope angles was analyzed, and the deformation and damage patterns under the coupling effect were obtained. The results show that rainfall increases pore water pressure and moisture content. Rainfall and slope-slip water have more impact on the open-pit platform. The pore water pressure values on the upper rock mass rise faster than inside. In the open-pit mining stage, the rock mass shifts slightly to the upper left. In the room mining stage, vertical fractures and goaf sinking occur. The fractures above the mine room form a semi-ellipse. In the pillar mining stage, the overlying rock displacement is evident, and fractures persist. In the continuous pillar mining stage, the overlying rock collapses massively. The 65° slope model is the most damaged, while the 55° slope model is the least damaged. The results also suggest that the UAV guides sample selection.

Due to the increase in the emission of greenhouse gases, the hydrologic cycle is being altered on the daily basis. This has affected the variations in relations of intensity, duration, and frequency of rainfall events. Intensity Duration Frequency (IDF) curves describe the relationship between rainfall intensity, rainfall duration and return period. IDF curves are one of the most often applied implements in water resource engineering, in areas such as for operating, planning and designing of water resource projects, or for numerous engineering projects aimed at controlling floods. In particular, IDF curves for precipitation answer problems of improper drainage systems or conditions and extreme characters of precipitation which are the main cause of floods in Nyabugogo catchment. This study aims to establish Rainfall IDF empirical equations, curves and hydrological discharge (predicted peak rate of runoff (Qlogy)) equations for eight Districts that will be used for designing an appropriate and sustainable hydraulic structures for controlling flood to reduce potential loss of human and aquatic life, degradation of water, air and soil quality and property damage and economic lessen caused by flood in Nyabugogo catchment. However Goodness of Fit tests revealed that Gumbel’s Extreme-Value Distribution method appears to have the most appropriate fit compared with Pearson type III distribution for validating the Intensity-Duration-Frequency curves and equations through the use of daily annual for each meteorological station. The findings of the study show that the intensity of rainfall increases with a decrease in rainfall duration. Additionally, a rainfall of every known duration will have a higher intensity if its return period is high, while the predicted peak rate of runoff (Qlogy) increases also with an increase in the intensity of rainfall.

We have defined M cycles as modified form of Gleissberg cycles to study the quasi-periodic secular changes in sunspot activity. Using direct and proxy observations for more than 1200 years we have provided evidence for the influence of the above M cyles on the monsoon rainfall variability and occurrences of major droughts in India. The solar cycle averages of All Indian summer monsoon rainfall (AISMR) and probability of observation of below or above normal AISMR is found to show correlated variations with changes in amplitude of sunspot cycles during the years 1901-2018 AD. Major droughts in India show a preference to occur during minima and declining phases of the M cycles during the years 650-2018 AD . We could generally find low probability of occurrence of droughts in India during the medieval solar maximum epoch ( 1100-1250 AD) and during most of the prolonged sunspot minima periods including the Maunder minima. The evolution of mean Indian summer monsoon rainfall during 650-1900 AD is studied using proxy data from multiple sources with maxima during the 12^th century and minima during 14^th and 19^th centuries. The association of Indian droughts with El-Nino and the possibilities of long term prediction of Indian summer monsoon rainfall variability will be also discussed

The paper presents a modeling framework to analyze the effect of episodic rainfall supply on groundwater dynamics in the Ionian coastal plain multilayered aquifer. The focus is essentially on the short-term behavior of the shallowest layer, with a specific analysis on episodic rainfall events. In the studied aquifer, groundwater level responds sensitively to rainfall events, highlighting the presence of preferential recharge zones. The hydraulic head peak is a function of groundwater level antecedent to the rainfall event. A kinematic dispersion wave model was used to model infiltration processes via preferential pathways. A one-dimensional and non-linear particle based numerical model was developed. Particles with constant water volume travel according to celerity and hydraulic dispersion to simulate the infiltration rate wave through the vadose zone. The flow rate that reaches the water table represents the input function to determine groundwater level fluctuations along groundwater flow direction and according to the lithological features of the surficial levels of the multilayered aquifer, its storage capacity changes passing from unconfined to confined conditions. The model was validated with high time resolution time series of precipitation and groundwater level coming from Terra Montonata meteo-climatic and groundwater level monitoring station. The developed model represents a basis for evaluating and predicting groundwater discharge of the shallowest layers of the Ionian coastal multilayered aquifer under natural conditions including episodic rainfall.

This study reports the first example of major erosion from hurricanes degrading a rocky coastline anywhere around the Gulf of California, although other sources of evidence are well known regarding the effect of inland erosion due to catastrophic rainfall in the Southern Cape Region of the Baja California peninsula and farther north. The uplifted, 12-meter terrace on the eastern shore of Isla del Carmen is the site of an unconsolidated coastal boulder deposit (CBD) consisting of large limestone blocks and boulders eroded from underlying Pliocene strata. The CBD stretches approximately 1.5 km in length, mostly set back 25 m from the lip of the terrace. The largest blocks of upturned limestone near the terrace edge are estimated to weigh between 5.8 and 28 metric tons. Waves impacting the rocky coast that peeled back slabs of horizontally layered limestone at this spot are calculated to have been between 11.5 and 14 m in height. Analysis of sampled boulders from the CBD set back from the terrace edge by 25 m suggest that the average wave height responsible for moving those boulders was on the order of 4.3 m. Additional localities with exposed limestone shores, as well as other more common rock types of igneous origin, have yet to be surveyed for this phenomenon elsewhere around the Gulf of California.

In India, particularly within its North-Eastern territories, the occurrence of landslides triggered by post-dry period rainfall events is a significant concern, with the phenomena persistently causing extensive damage to both life and infrastructure. Despite the inevitability of such natural disasters, it is understood that the impact they exert can be substantially mitigated through the implementation of pre-emptive measures. Critical to the endeavour of landslide prevention is the application of a spectrum of mitigation strategies, predicated upon a foundational analysis of slope stability to ascertain critical surfaces for intervention. This investigation delves into the dynamics of partially saturated soils, a condition commonly resultant from the prolonged dry periods preceding the monsoonal rains. The study's focal point is an experimental examination conducted on soil samples to assess parameters such as soil matric suction and unconfined compressive strength across varying degrees of saturation and density, alongside performing analyses of slope failure incidents after dry period during the 2017 monsoon in the Lunglei district, Mizoram based on some established data. The Soil-Water Characteristic Curve (SWCC), derived from soil suction measurements employing the ASTM D5298-10 standard, utilizes a contact filter paper method and serves to delineate the moisture transition phases of the slope. To replicate the desiccation of the soil slope after monsoonal rains, a microwave drying technique based on the ASTM standard was employed to prepare unsaturated soil samples for Unconfined Compressive Strength (UCS) testing. The integrity of the slope was evaluated in a temporal context, before and after rainfall events of varied intensities throughout the calendar year, utilizing a blend of numerical modeling and empirical laboratory investigations. The persistence of real onsite slope failures underscores the necessity for a numerical analysis approach to unravel the underlying factors causing slope instability, especially given the absence of prior stability assessments for the landslide in question. An evaluation of SWCC curves, UCS tests on partially saturated soil at different densities and saturation and the utilization of the benchmark data collected in 2017 on the shallow transitional landslide in Lunglei, Mizoram caused by intense monsoonal precipitation, are central key data and information used to carry out this research. Rainfall data for Lunglei, sourced from the Mizoram Meteorological Department, facilitated a real-time monsoonal data-driven numerical analysis. This analysis elucidated the pivotal role of rainfall in inducing slope failure, indicating a marked destabilization of the slope after precipitation events. The implications of these findings are far-reaching, suggesting a foundational basis for the development of an advanced landslide early warning system, thereby enhancing disaster preparedness, and mitigating potential damages.

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1,259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images taken a few days after the events allowed us to map 6,491 coesismic landslides, which occurred in 14 study areas extending for about 400 km2. Four different Multivariate Adaptive Regression Splines (MARS) models were produced by using different covariate sets and landslide inventories, the latter containing the slope failures triggered by an extreme rainfall event of November 2009 and those induced by the earthquakes of 2001. Moreover, two validation scenarios were employed, including the 25% and 95% of the mapped landslides, respectively. The results of our experiment revealed that: (i) MARS algorithm provides reliable predictions of coesismic landslides; (ii) models calibrated with rainfall-induced landslides predict with acceptable accuracy landslides caused by deep earthquakes and distributed over vast areas; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) a small portion of the landslides produced by an earthquake can be used to calibrate MARS predictive models that help to identify slopes where yet unreported landslides may have occurred.

Currently, Korea is experiencing localized extreme rainfall, which accounts for more than 80% of flood-related disasters, and is increasing in small river basins, where more than 60% of flood-related casualties occur. These events are caused by climate change and geological factors and their impact is becoming more severe. As a result, an effective measurement system is required to mitigate their impact, particularly in small stream basins that are especially vulnerable due to their steep slopes, small catchment areas, and lack of maintenance and management capacity. In addition, a Flood Early Warning Framework (FEWF) that forecasts discharge and depth during flood events is crucial for reducing casualties. Therefore, this research is focused on developing the FEWF using the nomograph and rating curve methods, which are established by the robust constrained nonlinear equation solver and are suitable for small streams. The FEWF is evaluated using real-time data observed over 7-years period from the Closed-circuit Television-based Automatic Discharge Measurement Technology (CADMT), and the results show that the FEWF is effective in forecasting discharge and depth during flood events. The use of CADMT technology for real-time data can develop an accurate and reliable FEWF, which can help mitigate the impacts of extreme rainfall events and reduce the number of flood-related casualties in small stream basins.

Several cities are experiencing an increase in flood hazard and exposure, and consequentially in flood risk, attributable to the coupled effect of climate change and urbanization. Non-structural protection strategies demonstrate significant potential in the short term for mitigating hydraulic risk in urban areas, particularly where the implementation of structural measures is impeded by a complex urban environment. This work proposes an Early Warning System (EWS) for fluvial floods developed for the city of Palermo (Italy). It is based on the preliminary definition of po-tential Flood Event Scenarios (FESs) as a function of typical potential precursors. Each FES pre-dicts first points of flooding, flood areas and water depths, also providing specific hazard maps for people, vehicles, and buildings. The EWS uses rainfall depth-duration thresholds to assess the expected hydrograph as a function of rainfall forecasts and the water stage observed in real time at an upstream section of the river crossing the city. This last also allows for estimating the ante-cedent wetness conditions. Based on this information, the EWS retrieves an expected FES from a pre-built library with a preannouncement time up to 36 h. The EWS is here successfully tested on a historical flood event.

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.