Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

Combining measurements, remote sensing and numerical modelling to assess multi-scale flow dynamics in groundwater-dependent environmental systems

Groundwater flow modelling provides an important quantitative instrument for addressing issues related to the quantity and quality of groundwater and the connected water resources. Consequently, groundwater flow models have been developed and used ubiquitously in science to deepen the understanding of subsurface processes and their drivers as well as management and planning tools. The present work investigates how numerical models can be linked to field investigations and public databases to quantitatively approach questions in the area of groundwater research. The primary goal is to develop new, efficient ways to overcome limitations of the individual hydrological concepts for solving specific hydrological problems and to increase the understanding of practical applicability of different methods. For this purpose, tailor-made approaches were developed for different study areas covering diverse spatial scales: the hydrology of a small mining lake, the riparian aquifer at the scale of a single meander as well as the aquifer systems of a large-scale river basin in China. The first part of the work deals with the physical and mathematical modelling of water constituents balance in a meromictic mining lake in Lusatia. The capability of using a rather simple mass-balance model based on a sufficient dataset of field data to evaluate lake stratification and lake-groundwater interaction were shown. In the second part, a transient numerical groundwater flow model was developed for the riparian aquifer of a stream meander and was calibrated by three different salt tracer tests. The model was used to proof the reliability of subsurface travel times derived from time series analysis and to give insights in the riparian zone dynamics during changing hydraulic gradients. The third part of the work describes the methodology to conduct risk assessment of groundwater contamination on the large catchment scale of the Songhua River in China. A comprehensive literature study was conducted to get an overview about measurement data on water quality data in China. A three-dimensional numerical flow and mass transport model was applied to access the flow and matter transport dynamics in the aquifer system of a sub-basin considering changing groundwater exploitation scenarios. Consequently, numerical groundwater modelling was combined with processed remote sensing and web mapping service data to overcome field data limitations and to derive groundwater vulnerability, groundwater hazard and groundwater risk maps for the entire Songhua River Basin. Summarizing, this doctoral thesis could develop new methods of combining field measurements, data assimilation and aggregation from various sources and groundwater modelling strategies and successfully apply these methods to find solutions on problems of multiple scales and across water systems.Die Grundwassermodellierung stellt eine wichtige wissenschaftliche Methode zur quantitativen Analyse von Fragestellungen zum Schutz der Menge und Güte der Grundwasserressourcen sowie der angeschlossenen Wasserkörper dar. Dementsprechend werden Grundwassermodelle sowohl für Planungs- und Bewertungszwecke im Wasserressourcenmanagement als auch zur wissenschaftlichen Erforschung der Prozesse im Untergrund entwickelt und angewendet. Die vorliegende Arbeit untersucht in diesem Rahmen, wie numerische Modelle, Feldmessungen und Daten generiert aus Fernerkundungsdaten und Webplattformen systematisch verknüpft werden können, um Fragestellungen im Bereich der Grundwasserforschung quantitativ zu beantworten. Das Ziel der Arbeit ist es neue effiziente Abläufe zu entwickeln, die die Limitierung der einzelnen Methoden überwinden und diese auf deren Anwendbarkeit für die Lösung spezifischer hydrologischer Probleme zu analysieren. Zu diesem Zweck wurden in dieser Doktorarbeit fallspezifische Lösungen für verschiedene Untersuchungsgebiete entwickelt, die sowohl in der räumlichen Skale als auch in den zu untersuchenden hydrologischen Fragestellungen eine große Diversität aufweisen. Im ersten Teil der Arbeit wurde die Massenbilanz von Wasserinhaltsstoffen in einem meromiktischen Tagebaurestsee im Lausitzer Revier durch physikalische und mathematische Modellierungsmethoden untersucht. Dabei konnte gezeigt werden, dass auf Basis einer gewonnenen mehrjährigen Zeitreihe von Messdaten ein einfaches Massenbilanzmodell in der Lage ist, sowohl Seeschichtungs- als auch Grundwasseraustauschdynamiken quantitativ zu beschreiben. Der zweite Teil der Arbeit umfasst die Entwicklung eines transienten numerischen Grundwassermodells für den quartären Uferaquifer im Bereich eines Flussmäanders der Selke welches anhand von Daten aus mehreren Salztracertests kalibriert wurde. Das Modell wurde dafür verwendet die transienten Verweilzeiten in der gesättigten Zone des Mäanderbogens unter dem Einfluss dynamischer hydraulischer Bedingungen zu untersuchen. Die Ergebnisse wurden im Anschluss mit Verweilzeiten verglichen, die aus der Analyse der zeitlichen Verschiebung von gemessenen elektrischen Leitfähigkeitszeitreihen zwischen Fluss und Grundwassermessstellen gewonnen wurden. Durch dieses kombinierte Verfahren konnten sowohl die Beschränkungen der zeitreihenbasierten Verweilzeitberechnung aufgezeigt als auch ein tieferes Systemverständnis für die Interaktionsdynamiken zwischen Grund- und Flusswasser auf der Mäanderskala gewonnen werden. Der dritte Teil der Arbeit beschreibt die Vorgehensweise für die Bewertung des Grundwasserkontaminationsrisikos im Einzugsgebiet des Songhua Flusses in China. Eine umfassende Literaturstudie wurde durchgeführt, um einen Überblick über die Verfügbarkeit von Messdaten zur Belastung der Wasserressourcen Chinas mit organischen Schadstoffen zu erhalten. Danach wurde für ein Teileinzugsgebiet ein dreidimensionales numerisches Grundwassermodell auf Basis der vorhandenen hydrogeologischen Daten aufgebaut. Dieses wurde dazu verwendet die Änderungen im Stofftransports und den Schadstoffkonzentrationen innerhalb des Aquifersystems unter steigenden Entnahmeraten zu analysieren. Basierend auf diesen Studien wurden auf der Skale des Gesamteinzugsgebiets, um die beschränkte Verfügbarkeit von Felddaten auszugleichen, die Ergebnisse der numerischen Grundwassermodellierung mit Fernerkundungsdaten und Webdatenbanken in einem Indexsystem kombiniert mit dem für die oberflächennahen Aquifere Vulnerabilität, Gefährdungspotential und Verschmutzungsrisiko in einer räumlichen Auflösung von 1 km² bestimmt wurden. Zusammenfassend konnten durch die vorliegende Doktorarbeit neue passgenaue Methoden zur effektiven Kombination von in-situ Messungen, der Datenerhebung und Datenintegration aus vielfältigen Datenquellen sowie numerischen Grundwassermodellierungsstrategien entwickelt und zur Lösung der untersuchten hydrologischer Fragestellen auf den verschiedenen Skalen und über die Grenzen der einzelnen hydrologischen Teilsysteme hinaus erfolgreich angewandt werden

SATELLITE REMOTE SENSING AND HYDROLOGIC MODELING FOR FLOOD MONITORING IN DATA POOR ENVIRONMENTS

Study of hydroclimatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data combined with the recently available satellite remote sensing data, the hydroclimatology of Nzoia basin, one of the contributing sub-catchments of Lake Victoria in the East African highlands is analyzed. The basin, with a semi-arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the primary cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10- year peak discharges, for the entire study period showed that more years since the mid 1990s have had high peak discharges despite having relatively less annual rain.The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June.The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.Implementation of a flood prediction system can potentially help mitigate flood induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in-situ observations (e.g. rain gauges and stream gauges). Recently, satellite remote sensing data has emerged as a viable alternative or supplement to the in-situ observations due to its availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a semi-distributed hydrologic model to characterize the spatial extent of flooding over sparsely-gauged or ungauged basins. A satellite remote sensing based approach is proposed to calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based semi-distributed hydrologic model, CREST, is implemented for the Nzoia basin, a sub-basin of Lake Victoria in Africa. MODIS Terra and ASTER-based raster flood inundation maps were produced over the region and used to benchmark the hydrologic model simulations of inundated areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography and other data products along with space based flood inundation extents as inputs for the hydrologic model. It is concluded that the quantification of flooding spatial extent through optical sensors can help to evaluate hydrologic models and hence potentially improve hydrologic prediction and flood management strategies in ungauged catchments

On the use of global flood forecasts and satellite-derived inundation maps for flood monitoring in data-sparse regions

Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA’s two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: 1) general agreement was found between the GFDS and MODIS flood detection systems, 2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and 3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. Overall, the satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large scale flood monitoring tools.JRC.H.7-Climate Risk Managemen

Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue

Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology

The Nierji Basin, in the north-east of China, is one of the most important basins in the joint operation of the entire Songhua River, containing a major reservoir used for flood control. It is necessary to forecast the flow of the basin during periods of flood accurately and with the maximum lead time possible. This paper presents a flood forecasting system, using the Data Based Mechanistic (DBM) modeling approach and Kalman Filter data assimilation for flood forecasting in the data limited Nierji Reservoir Basin (NIRB). Examples are given of the application of the DBM methodology using both single input (rainfall or upstream flow) and multiple input (rainfalls and upstream flow) to forecast the downstream discharge for different sub-basins. Model identification uses the simplified recursive instrumental variable (SRIV) algorithm, which is robust to noise in the observation data. The application is novel in its use of stochastic optimisation to define rain gauge weights and identify the power law nonlinearity. It is also the first application of the DBM methodology to flood forecasting in China. Using the methodology allows the forecasting with lead times of 1-day, 2-day, 3-day, 4-day, 5-day with 98%, 97%, 96%, 96% and 93% forecast coefficient of determination respectively, which is sufficient for the regulation of the reservoirs in the basin

Simulation of Irrigation Demand and Control in Catchments – A Review of Methods and Case Studies

The world's water resources are continuously facing challenges in fulfilling the needs of increasing agricultural water demand with finite or diminishing resources. Therefore, it is important to quantify the amount of irrigation water required to attain sustainable yield at a local, regional, and global level, especially in arid and semi-arid regions. This is mostly quantified by using agro-hydrological or agricultural models. The advances in simulation models and several options incorporated in them allow catchment/site-specific application of irrigation water to depict the field management practices undertaken by farmers. The objective of the present study is to provide a review of the simulation of irrigation water demand at catchment scale by agro-hydrological and agricultural models. This study discusses the different types of models, their dimensions, and the hydrological and agricultural process models incorporated into them. Additionally, this review provides an overview of how irrigation can be scheduled, how water is applied, and from which sources irrigation water can be extracted by the considered models, taking horizontal hydrological connectivity into consideration. Adding to the model review, seven different fields of innovative case studies are covered. Many agricultural models have been applied in a regional context without simulating horizontal hydrological fluxes, but only a few hydrological catchment models provide full support of both irrigation and plant growth simulation, which are important for the simulation of future crop yield under different climatic and agricultural management scenarios

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Remote sensing based evaluation of uncertainties on modelling of streamflow affected by climate change

Assessment of the impacts of land-use and climate change on streamflow is vital to develop climate adaptation strategies. However, uncertainties in the climate impact study framework could lead to changes on streamflow impact. The aim of this study is to assess the uncertainties on Digital Elevation Model (DEM), Satellite Precipitation Product (SPP) and climate projection on the modelling of streamflow affected by climate changes. These uncertainties are evaluated and reduced independently. The climate projection uncertainty is addressed through the modification of the Quantifying and Understanding the Earth System - Global Scale Impacts (QUEST-GSI) methodology. Twenty-six modified QUEST-GSI climate scenarios were used as climate inputs into the calibrated Soil and Water Assessment Tool (SWAT) model to evaluate the impacts and uncertainties of climate change on streamflow for three future periods (2015-2034, 2045-2064 and 2075-2094). The selected study areas are the Johor River Basin (JRB) and Kelantan River Basin (KRB), Malaysia. The Shuttle Radar Topography Mission (SRTM) version 4.1 (90m resolution) DEM and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) SPP which show a better performance were selected for the SWAT model modification, calibration and validation. The results indicated that the modified SWAT model could simulate the monthly streamflow well for both basins. Land-use and climate changes from 1985 to 2012 reduced annual streamflow of the JRB and KRB by 5% and 4.2%, respectively. In future, the annual precipitation and temperature of the JRB / KRB are projected to increase by -0.4-10.3% / 0.1-11.2% and 0.6-3.2oC / 0.8-3.3oC, respectively, and that this will lead to an increase of annual streamflow by 0.5-13.3% / 4.4-18.5%. This study showed that satellite data play an important role in providing input data to hydrological models