Geomorphic risk maps for river migration using probabilistic modeling – a framework

Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % &lt; probability &lt; 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.</p

Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework

Exponential wash-off models are the most widely used method to predict sediment wash-off from urban surfaces. In spite of many studies, there is still a lack of knowledge on the effect of external drivers such as rainfall intensity and surface slope on wash-off predictions. In this study, a more physically realistic "structure" is added to the original exponential wash-off model (OEM) by replacing the invariant parameters with functions of rainfall intensity and catchment surface slope, so that the model can better represent catchment and rainfall conditions without the need for lookup tables and interpolation/extrapolation. In the proposed new exponential model (NEM), two such functions are introduced. One function describes the maximum fraction of the initial load that can be washed off by a rainfall event for a given slope and the other function describes the wash-off rate during a rainfall event for a given slope. The parameters of these functions are estimated using data collected from a series of laboratory experiments carried out using an artificial rainfall generator, a 1 m2 bituminous road surface and a continuous wash-off measuring system. These experimental data contain high temporal resolution measurements of wash-off fractions for combinations of five rainfall intensities ranging from 33 to 155 mm/h and three catchment slopes ranging from 2 to 8%. Bayesian inference, which allows the incorporation of prior knowledge, is implemented to estimate parameter values. Explicitly accounting for model bias and measurement errors, a likelihood function representative of the wash-off process is formulated, and the uncertainty in the prediction of the NEM is quantified. The results of this study show: 1) even when the OEM is calibrated for every experimental condition, the NEM's performance, with parameter values defined by functions, is comparable to the OEM. 2) Verification indices for estimates of uncertainty associated with the NEM suggest that the error model used in this study is able to capture the uncertainty well

K-Sign in retrocaecal appendicitis: a case series

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Variations in position of the vermiform appendix considerably changes clinical findings. Retrocaecal appendicitis presents with slightly different clinical features from those of classical appendicitis associated with a normally sited appendix. K-sign looks for the presence of tenderness on posterior abdominal wall in the retrocaecal and paracolic appendicitis. This is the first case report of this kind in the literature. The K-sign has been named, as a mark of respect, after the region of origin of this sign, Kashmir, so called as &quot;Kashmir Sign&quot;. The sign being present in view of inflamed appendix crossing above its non palpable position above iliac crest on the posterior abdominal wall and the tenderness is by irritation of posterior peritoneum Case presentation: The author is reporting a case series of four patients in whom a K-sign, a clinical sign, was elicited and found positive on the posterior abdominal wall for presence of tenderness in a specific area bound by the 12th rib superiorly, spine medially, lateral margin of posterior abdominal wall laterally and iliac crest inferiorly and was found to be present in three retrocaecal and one paracolic appendicitis. Each case had tenderness in this specific area o

Efficient land water management practice and cropping system for increasing water and crop productivity in semi‐arid tropics

In Indian semi-arid tropics (SATs), low water and crop productivity in Vertisols and associated soils are mainly due to poor land management and erratic and low rainfall occurrence. This study was conducted from 2014 to 2016 at the ICRISAT in India to test the effect of broad bed furrows (BBF) as land water management against conventional flatbed planting for improving soil water content (SWC) and water and crop productivity of three cropping systems: sorghum [Sorghum bicolor (L.) Moench]–chickpea (Cicer arientinum L.) and maize (Zea mays)–groundnut (Arachis hypogaea L.) as sequential and pearl millet [Pennisetum glaucum (L.)] + pigeonpea [Cajanus cajan (L.) Millsp.] as intercropping, grown under different nutrients management involving macronutrients (N, P, and K) only and combined application of macro- and micronutrients. The results stated that the SWC in BBF was higher over flatbed by 9.35–10.44% in 0- to 0.3-m, 4.56–9.30% in 0.3- to 0.6-m and 3.85–5.26% in 0.6- to 1.05-m soil depths during the cropping season. Moreover, depletion of the soil water through plant uptake was higher in BBF than in flatbed. Among the cropping systems, sorghum–chickpea was the best in bringing highest system equivalent yield and water productivity with the combined application of macro- and micronutrients. The BBF minimized water stress at critical crop growth stages leading to increase crop yield and water productivity in SATs. Thus, BBF along with the application of macro- and micronutrients could be an adaptation strategy to mitigate erratic rainfall due to climate change in SATs

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Increased arid and semi-arid areas in India with associated shifts during 1971-2004

Climate change is one of the major challenges in 21st century faced by Agriculture in India, more so in the Semi-Arid Tropics (SAT) of the country. In recent years, natural and anthropogenic factors have impacted climate variability and contributed to a large extent to climate change. Based on one degree gridded data of India Meteorological Department (IMD) for 34 years (1971-2004), climatic water balances are computed for 351 pixels in India and used for classifying in to six climate types following Thornthwaite’s moisture regime classification and areas falling under different climatic zones in India are delineated. Considerable changes in the country’s climate area observed between the two periods; 1971-90 and 1991-2004. Increased semi-arid area by 8.45 M ha in five states viz., Madhya Pradesh, Bihar, Uttar Pradesh, Karnataka and Punjab, and decreased semi-arid area by 5 M ha in eleven states, contributed to overall increase in SAT area of 3.45 M ha in the country.Overall, there has been a net reduction of 10.71 M ha in the dry sub-humid area in the country. Results indicated that dryness and wetness are increasing in different parts of the country in the place of moderate climates existing earlier in these regions. ICRISAT’s Hypothesis of Hope through Integrated Genetic and Natural Resources Management (IGNRM) using climate ready crops and Integrated Watershed Management could be a potential adaptation strategy by bridging the yield gaps for developing climate resilient agriculture in the country

Key and Smart Actions to Alleviate Hunger and Poverty Through Irrigation and Drainage

In the pursuit of information to support policies and actions to alleviate hunger and poverty through irrigation and drainage, this paper attempts to provide correlations between water scarcity, communities and poverty. Many reviews have found strong direct and indirect relationships between irrigation and poverty. One of the main goals of the international community is to eliminate hunger and poverty and in this perspective, through the Millennium Development Goals, much progress has been achieved and evidence obtained. Sustainable Development Goals and various other United Nations initiatives intend to move forward this agenda by making it a part of broader development frameworks. In this paper, the important elements of irrigation and drainage that affect the alleviation of hunger and poverty are discussed. These elements are grouped into governance, rights-based developments, water rights and pricing, management, efficiency improvement, and the role of technology. Both the potential and the need for innovative technology and solutions in irrigation are underlined, which can be used to cater for the challenges in different subsectors. The main focus of these solutions is on maximizing productivity and efficiency, reducing water losses, achieving sustainable intensification and managing demands on water resources and the associated trade-offs

Extreme photometric and polarimetric variability of blazar S4 0954+65 at its maximum optical and γ-ray brightness levels

In 2022 the BL Lac object S4 0954+65 underwent a major variability phase, reaching its historical maximum brightness in the optical and γ -ray bands. We present optical photometric and polarimetric data acquired by the Whole Earth Blazar Telescope (WEBT) Collaboration from 2022 April 6 to July 6. Many episodes of unprecedented fast variability were detected, implying an upper limit to the size of the emitting region as low as 10−4 parsec. The WEBT data show rapid variability in both the degree and angle of polarization. We analyse different models to explain the polarization behaviour in the framework of a twisting jet model, which assumes that the long-term trend of the flux is produced by variations in the emitting region viewing angle. All the models can reproduce the average trend of the polarization degree, and can account for its general anticorrelation with the flux, but the dispersion of the data requires the presence of intrinsic mechanisms, such as turbulence, shocks, or magnetic reconnection. The WEBT optical data are compared to γ -ray data from the Fermi satellite. These are analysed with both fixed and adaptive binning procedures. We show that the strong correlation between optical and γ -ray data without measurable delay assumes different slopes in faint and high brightness states, and this is compatible with a scenario where in faint states we mainly see the imprint of the geometrical effects, while in bright states the synchrotron self-Compton process dominates