Assessment of climate change and development of data based prediction models of sediment yields in Upper Indus Basin

Hohe Raten von Sedimentflüssen und ihre Schätzungen in Flusseinzugsgebieten erfordern die Auswahl effizienter Quantifizierungsansätze mit einem besseren Verständnis der dominierten Faktoren, die den Erosionsprozess auf zeitlicher und räumlicher Ebene steuern. Die vorherige Bewertung von Einflussfaktoren wie Abflussvariation, Klima, Landschaft und Fließprozess ist hilfreich, um den geeigneten Modellierungsansatz zur Quantifizierung der Sedimenterträge zu entwickeln. Einer der schwächsten Aspekte bei der Quantifizierung der Sedimentfracht ist die Verwendung traditioneller Beziehung zwischen Strömungsgeschwindigkeit und Bodensatzlöschung (SRC), bei denen die hydrometeorologischen Schwankungen, Abflusserzeugungsprozesse wie Schneedecke, Schneeschmelzen, Eisschmelzen usw. nicht berücksichtigt werden können. In vielen Fällen führt die empirische Q-SSC Beziehung daher zu ungenauen Prognosen. Heute können datenbasierte Modelle mit künstlicher Intelligenz die Sedimentfracht präziser abschätzen. Die datenbasierten Modelle lernen aus den eingespeisten Datensätzen, indem sie bei komplexen Phänomenen wie dem Sedimenttransport die geeignete funktionale Beziehung zwischen dem Output und seinen Input-Variablen herstellen. In diesem Zusammenhang wurden die datenbasierten Modellierungsalgorithmen in der vorliegenden Forschungsarbeit am Lehrstuhl für Wasser- und Flussgebietsmanagement des Karlsruher Instituts für Technologie in Karlsruhe entwickelt, die zur Vorhersage von Sedimenten in oberen unteren Einzugsgebieten des oberen Indusbeckens von Pakistan (UIB) verwendet wurden. Die dieser Arbeit zugrunde liegende Methodik gliedert sich in vier Bearbeitungsschritte: (1) Vergleichende Bewertung der räumlichen Variabilität und der Trends von Abflüssen und Sedimentfrachten unter dem Einfluss des Klimawandels im oberen Indus-Becken (2) Anwendung von Soft-Computing-Modellen mit Eingabevektoren der schneedeckten Fläche zusätzlich zu hydro-klimatischen Daten zur Vorhersage der Sedimentfracht (3) Vorhersage der Sedimentfracht unter Verwendung der NDVI-Datensätze (Hydroclimate and Normalized Difference Vegetation Index) mit Soft-Computing-Modellen (4) Klimasignalisierung bei suspendierten Sedimentausträge aus Gletscher und Schnee dominierten Teileinzugsgebeiten im oberen Indus-Becken (UIB). Diese im UIB durchgeführte Analyse hat es ermöglicht, die dominiertenden Parameter wie Schneedecke und hydrologischen Prozesses besser zu und in eine verbesserte Prognose der Sedimentfrachten einfließen zu lassen. Die Analyse der Bewertung des Klimawandels von Flüssen und Sedimenten in schnee- und gletscherdominierten UIB von 13 Messstationen zeigt, dass sich die jährlichen Flüsse und suspendierten Sedimente am Hauptindus in Besham Qila stromaufwärts des Tarbela-Reservoirs im ausgeglichenen Zustand befinden. Jedoch, die jährlichen Konzentrationen suspendierter Sedimente (SSC) wurden signifikant gesenkt und lagen zwischen 18,56% und 28,20% pro Jahrzehnt in Gilgit an der Alam Bridge (von Schnee und Gletschern dominiertes Becken), Indus in Kachura und Brandu in Daggar (von weniger Niederschlag dominiertes Becken). Während der Sommerperiode war der SSC signifikant reduziert und lag zwischen 18,63% und 27,79% pro Jahrzehnt, zusammen mit den Flüssen in den Regionen Hindukush und West-Karakorum aufgrund von Anomalien des Klimawandels und im unteren Unterbecken mit Regen aufgrund der Niederschlagsreduzierung. Die SSC während der Wintersaison waren jedoch aufgrund der signifikanten Erwärmung der durchschnittlichen Lufttemperatur signifikant erhöht und lagen zwischen 20,08% und 40,72% pro Jahrzehnt. Die datenbasierte Modellierung im schnee und gletscherdominierten Gilgit Teilbecken unter Verwendung eines künstlichen neuronalen Netzwerks (ANN), eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit Gitterpartition (ANFIS-GP) und eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit subtraktivem Clustering (ANFIS) -SC), ein adaptives Neuro-Fuzzy-Logik- Inferenzsystem mit Fuzzy-C-Mittel-Clustering, multiplen adaptiven Regressionssplines (MARS) und Sedimentbewertungskurven (SRC) durchgeführt. Die Ergebnisse von Algorithmen für maschinelles Lernen zeigen, dass die Eingabekombination aus täglichen Abflüssen (Qt), Schneedeckenfläche (SCAt), Temperatur (Tt-1) und Evapotranspiration (Evapt-1) die Leistung der Sedimentvorhersagemodelle verbesserne. Nach dem Vergleich der Gesamtleistung der Modelle schnitt das ANN-Modell besser ab als die übrigen Modelle. Bei der Vorhersage der Sedimentfrachten in Spitzenzeiten lag die Vorhersage der ANN-, ANIS-FCM- und MARS-Modelle näher an den gemessenen Sedimentbelastungen. Das ANIS-FCM-Modell mit einem absoluten Gesamtfehler von 81,31% schnitt bei der Vorhersage der Spitzensedimente besser ab als ANN und MARS mit einem absoluten Gesamtfehler von 80,17% bzw. 80,16%. Die datenbasierte Modellierung der Sedimentfrachten im von Regen dominierten Brandu-Teilbecken wurde unter Verwendung von Datensätzen für Hydroklima und biophysikalische Eingaben durchgeführt, die aus Strömungen, Niederschlag, mittlerer Lufttemperatur und normalisiertem Differenzvegetationsindex (NDVI) bestehen. Die Ergebnisse von vier ANNs (Artificial Neural Networks) und drei ANFIS-Algorithmen (Adaptive Neuro-Fuzzy Logic Inference System) für das Brandu Teilnbecken haben gezeigt, dass der mittels Fernerkundung bestimmte NDVI als biophysikalische Parameter zusätzlich zu den Hydroklima-Parametern die Leistung das Modell nicht verbessert. Der ANFIS-GP schnitt in der Testphase besser ab als andere Modelle mit einer Eingangskombination aus Durchfluss und Niederschlag. ANN, eingebettet in Levenberg-Marquardt (ANN-LM) für den Zeitraum 1981-2010, schnitt jedoch am besten mit Eingabekombinationen aus Strömungen, Niederschlag und mittleren Lufttemperaturen ab. Die Ergebnisgenauigkeit R2 unter Verwendung des ANN-LM-Algorithmus verbesserte sich im Vergleich zur Sedimentbewertungskurve (SRC) um bis zu 28%. Es wurde gezeigt, dass für den unteren Teil der UIB-Flüsse Niederschlag und mittlere Lufttemperatur dominierende Faktoren für die Vorhersage von Sedimenterträgen sind und biophysikalische Parameter (NDVI) eine untergeordnete Rolle spielen. Die Modellierung zur Bewertung der Änderungen des SSC in schnee- und gletschergespeiste Gilgit- und Astore-Teilbecken wurde unter Verwendung des Temp-Index degree day modell durchgeführt. Die Ergebnisse des Mann-Kendall-Trendtests in den Flüssen Gilgit und Astore zeigten, dass der Anstieg des SSC während der Wintersaison auf die Erwärmung der mittleren Lufttemperatur, die Zunahme der Winterniederschläge und die Zunahme der Schneeschmelzen im Winter zurückzuführen ist. Während der Frühjahrssaison haben die Niederschlags- und Schneedeckenanteile im Gilgit-Unterbecken zugenommen, im Gegensatz zu seiner Verringerung im Astore-Unterbecken. Im Gilgit-Unterbecken war der SSC im Sommer aufgrund des kombinierten Effekts der Karakorum-Klimaanomalie und der vergrößerten Schneedecke signifikant reduziert. Die Reduzierung des Sommer-SSC im Gilgit Fluss ist auf die Abkühlung der Sommertemperatur und die Bedeckung der exponierten proglazialen Landschaft zurückzuführen, die auf erhöhten Schnee, verringerte Trümmerflüsse Trümmerflüsse und verringerte Schneeschmelzen von Trümmergletschern zurückzuführen sind. Im Gegensatz zum Gilgit River sind die SSC im Astore River im Sommer erhöht. Der Anstieg des SSC im Astore-Unterbecken ist auf die Verringerung des Frühlingsniederschlags und der Schneedecke, die Erwärmung der mittleren Sommerlufttemperatur und den Anstieg des effektiven Niederschlags zurückzuführen. Die Ergebnisse zeigen ferner eine Verschiebung der Dominanz von Gletscherschmelzen zu Schneeschmelzen im Gilgit-Unterbecken und von Schnee zu Niederschlägen im Astore-Unterbecken bei Sedimenteden Sedimentfrachten in UIB. Die vorliegende Forschungsarbeit zur Bewertung der klimabedingten Veränderungen des SSC und seiner Vorhersage sowohl in den oberen als auch in den unteren Teilbecken des UIB wird nützlich sein, um den Sedimenttransportprozess besser zu verstehen und aufbauen auf dem verbessertenProzessverständnis ein angepasstes Sedimentmanagement und angepasste Planungen der zukünftigen Wasserinfrastrukturen im UIB ableiten zu können

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Hydrography90m: a new high-resolution global hydrographic dataset

The geographic distribution of streams and rivers drives a multitude of patterns and processes in hydrology, geomorphology, geography, and ecology. Therefore, a hydrographic network that accurately delineates both small streams and large rivers, along with their topographic and topological properties, with equal precision would be indispensable in the earth sciences. Currently, available global hydrographies do not feature small headwater streams in great detail. However, these headwaters are vital because they are estimated to contribute to more than 70 % of overall stream length. We aimed to fill this gap by using the MERIT Hydro digital elevation model at 3 arcsec (∼90 m at the Equator) to derive a globally seamless, standardised hydrographic network, the “Hydrography90m”, with corresponding stream topographic and topological information. A central feature of the network is the minimal upstream contributing area, i.e. flow accumulation, of 0.05 km2 (or 5 ha) to initiate a stream channel, which allowed us to extract headwater stream channels in great detail. By employing a suite of GRASS GIS hydrological modules, we calculated the range-wide upstream flow accumulation and flow direction to delineate a total of 1.6 million drainage basins and extracted globally a total of 726 million unique stream segments with their corresponding sub-catchments. In addition, we computed stream topographic variables comprising stream slope, gradient, length, and curvature attributes as well as stream topological variables to allow for network routing and various stream order classifications. We validated the spatial accuracy and flow accumulation of Hydrography90m against NHDPlus HR, an independent, national high-resolution hydrographic network dataset of the United States. Our validation shows that the newly developed Hydrography90m has the highest spatial precision and contains more headwater stream channels compared to three other global hydrographic datasets. This comprehensive approach provides a vital and long-overdue baseline for assessing actual streamflow in headwaters and opens new research avenues for high-resolution studies of surface water worldwide. Hydrography90m thus offers significant potential to facilitate the assessment of freshwater quantity and quality, inundation risk, biodiversity, conservation, and resource management objectives in a globally comprehensive and standardised manner. The Hydrography90m layers are available at https://doi.org/10.18728/igb-fred-762.1 (Amatulli et al., 2022a), and while they can be used directly in standard GIS applications, we recommend the seamless integration with hydrological modules in open-source QGIS and GRASS GIS software to further customise the data and derive optimal utility from it

Controls on the diurnal streamflow cycles in two subbasins of an alpine headwater catchment

In high-altitude alpine catchments, diurnal streamflow cycles are typically dominated by snowmelt or ice melt. Evapotranspiration-induced diurnal streamflow cycles are less observed in these catchments but might happen simultaneously. During a field campaign in the summer 2012 in an alpine catchment in the Swiss Alps (Val Ferret catchment, 20.4 km2, glaciarized area: 2%), we observed a transition in the early season from a snowmelt to an evapotranspiration-induced diurnal streamflow cycle in one of two monitored subbasins. The two different cycles were of comparable amplitudes and the transition happened within a time span of several days. In the second monitored subbasin, we observed an ice melt-dominated diurnal cycle during the entire season due to the presence of a small glacier. Comparisons between ice melt and evapotranspiration cycles showed that the two processes were happening at the same times of day but with a different sign and a different shape. The amplitude of the ice melt cycle decreased exponentially during the season and was larger than the amplitude of the evapotranspiration cycle which was relatively constant during the season. Our study suggests that an evapotranspiration-dominated diurnal streamflow cycle could damp the ice melt-dominated diurnal streamflow cycle. The two types of diurnal streamflow cycles were separated using a method based on the identification of the active riparian area and measurement of evapotranspiration

Flow and transport modeling in large river networks

textThe work presented in this dissertation discusses large scale flow and transport in river networks and investigates advantages and disadvantages of grid-based and vector-based river networks. This research uses the Mississippi River basin as a continental-case study and the Guadalupe and San Antonio rivers and Seine basin in France as regional-case studies. The first component of this research presents an extension of regional river flow modeling to the continental scale by using high resolution river data from NHDPlus dataset. This research discovers obstacles of flow computations for river a network with hundreds of thousands river segments in continental scales. An upscaling process is developed based on the vector-based river network to decrease the computational effort, and to reduce input file size. This research identifies drainage area as a key factor in the flow simulation, especially in a wet climate. The second component of this research presents an enhanced GIS framework for a steady-state riverine nitrogen transport modeling in the San Antonio and Guadalupe river network. Results show that the GIS framework can be applied to represent a spatial distribution of flow and total nitrogen in a large river network with thousands of connected river segment. However, time features of the GIS environment limit its applicability to large scale time-varied modeling. The third component shows a modeling regional flow and transport with consideration of stream-aquifer interactions at a regional scale at high resolution. The STICS- Eau-Dyssée combined system is implemented for entire seine basin to compute daily nitrate flux in the Seine grid river network. Results show that river-aquifer exchange has a significant impact on river flow and transport modeling in larger river networks.Civil, Architectural, and Environmental Engineerin

Automatic drainage pattern recognition in river networks

In both geographic information system and terrain analysis, drainage systems are important components. Owing to local topography and subsurface geology, a drainage system achieves a particular drainage pattern based on the form and texture of its network of stream channels and tributaries. Although research has been done on the description of drainage patterns in geography and hydrology, automatic drainage pattern recognition in river networks is not well developed. This article introduces a new method for automatic classification of drainage systems in different patterns. The method applies to river networks, and the terrain model is not required in the process. A series of geometric indicators describing each pattern are introduced. Network classification is based on fuzzy set theory. For each pattern, the level of membership of the network is given by the different indicator values. The method was implemented, and the experimental results are presented and discussed

IPH-Hydro Tools : uma ferramenta open source para determinação de informações topológicas em bacias hidrográficas integrada a um ambiente SIG

Watershed delineation, drainage network generation and determination of river hydraulic characteristics are important issues in hydrological sciences. In gene- ral, this information can be obtained from Digital Elevation Models (DEM) processing within GIS commercial softwares, such as ArcGIS and IDRISI. On the other hand, the use of open source GIS tools has increased significantly, and their advantages include free distribution, continuous development by user communities and full customization for specific requirements. Herein, we present the IPH-Hydro Tools, an open source tool coupled to MapWindow GIS software designed for watershed topology acquisition, including preprocessing steps in hydrological models such as MGB-IPH. In addition, several tests were carried out assessing the performance and applicability of the developed tool, given by a comparison with available GIS packages (ArcGIS, IDRISI, WhiteBox) for similar purposes. The IPH-Hydro Tools provided satisfactory results on tested applications, allowing for better drainage network and less processing time for catchment delineation. Regarding its limitations, the developed tool was incompatible with huge terrain data and showed some difficulties to represent drainage networks in extensive flat areas, which can occur in reservoirs and large riversA delimitação de bacias hidrográficas, geração da rede de drenagem e determinação de características hidráulicas de um rio de interesse são partes importantes de estudos na área de hidrologia. Atualmente muitas dessas informações são obtidas com o processamento de modelos digitais de elevação (MDEs) em sof- twares comerciais de SIG, como o ArcGIS e o IDRISI. Por outro lado, pacotes de SIG para uso livre, ou seja, gratuitos e de código aberto, têm aumentado significativamente nos últimos anos, e as vantagens desses pacotes incluem ampla distribuição e customização, desenvolvimento continuado pela comunidade de usuários e atendimento a necessidades específicas. Este trabalho apresenta o pacote livre (open-source) denominado IPH-Hydro Tools, um conjunto de ferramentas acoplado ao software livre MapWindow GIS criado para facilitar a aquisição de informações topológicas em bacias hidrográficas, bem como realização de etapas de pré-processamento em modelos hidrológicos a exemplo do MGB-IPH. Para avaliar a aplicabilidade e o desempenho da ferramenta desenvolvida foram realizados testes específicos, através da comparação dos resultados do IPH-Hydro Tools em relação a outros pacotes de SIG (ArcGIS, IDRISI, WhiteBox) disponíveis para esta finalidade. O IPH-Hydro Tools apresentou qualidade de rede de drenagem geralmente superior aos demais pacotes e menor tempo de processamento necessário para delimitação de bacias, apesar de algumas limitações como incompatibilidade em relação a matrizes muito grandes e dificuldade na representação da rede de drenagem em áreas extensas de mesma cota, a exemplo de reservatórios e rios muito largos

Technical note: Seamless extraction and analysis of river networks in R

Spatially explicit mathematical models are key to a mechanistic understanding of environmental processes in rivers. Such models necessitate extended information on networks' morphology, which is often retrieved from geographic information system (GIS) software, thus hindering the establishment of replicable script-based workflows. Here I present rivnet, an R package for GIS-free extraction and analysis of river networks based on digital elevation models (DEMs). The package exploits TauDEM's flow direction algorithm in user-provided or online accessible DEMs, and allows for computing covariate values and assigning hydraulic variables across any network node. The package is designed so as to require minimal user input while allowing for customization for experienced users. It is specifically intended for application in models of ecohydrological, ecological or biogeochemical processes in rivers. As such, rivnet aims to make river network analysis accessible to users unfamiliar with GIS-based and geomorphological methods and therefore enhance the use of spatially explicit models in rivers.</p

Enhancing Operational Flood Detection Solutions through an Integrated Use of Satellite Earth Observations and Numerical Models

Among natural disasters floods are the most common and widespread hazards worldwide (CRED and UNISDR, 2018). Thus, making communities more resilient to flood is a priority, particularly in large flood-prone areas located in emerging countries, because the effects of extreme events severely setback the development process (Wright, 2013). In this context, operational flood preparedness requires novel modeling approaches for a fast delineation of flooding in riverine environments. Starting from a review of advances in the flood modeling domain and a selection of the more suitable open toolsets available in the literature, a new method for the Rapid Estimation of FLood EXtent (REFLEX) at multiple scales (Arcorace et al., 2019) is proposed. The simplified hydraulic modeling adopted in this method consists of a hydro-geomorphological approach based on the Height Above the Nearest Drainage (HAND) model (Nobre et al., 2015). The hydraulic component of this method employs a simplified version of fluid mechanic equations for natural river channels. The input runoff volume is distributed from channel to hillslope cells of the DEM by using an iterative flood volume optimization based on Manning\u2019s equation. The model also includes a GIS-based method to expand HAND contours across neighbor watersheds in flat areas, particularly useful in flood modeling expansion over coastal zones. REFLEX\u2019s flood modeling has been applied in multiple case studies in both surveyed and ungauged river basins. The development and the implementation of the whole modeling chain have enabled a rapid estimation of flood extent over multiple basins at different scales. When possible, flood modeling results are compared with reference flood hazard maps or with detailed flood simulations. Despite the limitations of the method due to the employed simplified hydraulic modeling approach, obtained results are promising in terms of flood extent and water depth. Given the geomorphological nature of the method, it does not require initial and boundary conditions as it is in traditional 1D/2D hydraulic modeling. Therefore, its usage fits better in data-poor environments or large-scale flood modeling. An extensive employment of this slim method has been adopted by CIMA Research Foundation researchers for flood hazard mapping purposes over multiple African countries. As collateral research, multiple types of Earth observation (EO) data have been employed in the REFLEX modeling chain. Remotely sensed data from the satellites, in fact, are not only a source to obtain input digital terrain models but also to map flooded areas. Thus, in this work, different EO data exploitation methods are used for estimating water extent and surface height. Preliminary results by using Copernicus\u2019s Sentinel-1 SAR and Sentinel-3 radar altimetry data highlighted their potential mainly for model calibration and validation. In conclusion, REFLEX combines the advantages of geomorphological models with the ones of traditional hydraulic modeling to ensure a simplified steady flow computation of flooding in open channels. This work highlights the pros and cons of the method and indicates the way forward for future research in the hydro-geomorphological domain